CellScope: high-performance cell atlas workflow with tree-structured representation

Domain: neuroscience
Published: 2025-12-30
Editorial outcome: major_revision

Abstract
Single-cell sequencing enables comprehensive profiling of individual cells, revealing cellular heterogeneity and function with unprecedented resolution. However, current analysis frameworks lack the ability to simultaneously explore and visualize cellular hierarchies at multiple biological levels. To address these limitations, we present CellScope, a promising framework for constructing high-resolution cell atlases at multiple clustering levels. CellScope employs a two-stage manifold fitting process for gene selection and noise reduction, followed by agglomerative clustering, and integrates UMAP visualization with hierarchical clustering to intuitively represent cellular relationships simultaneously at multiple levels—such as cell lineage, cell type, and cell subtype levels. Compared to established pipelines such as Seurat and Scanpy, CellScope comprehensively improves clustering performance, visualization clarity, computational efficiency, and algorithm interpretability, while reducing dependence on hyperparameters across a multitude of single-cell datasets. Most importantly, it can reveal biological insights that other contemporary methods are unable to detect, thereby deepening our understanding of cellular heterogeneity and function, and potentially informing disease research.

Introduction
The advent of single-cell sequencing has fundamentally changed our understanding of biology by providing an unprecedented look into the heterogeneity of biological systems at the individual cell level. Over the past decade, the increasing accessibility of single-cell technologies has led to a rise in the generation of large, comprehensive single-cell datasets—collectively known as cell atlases. By providing comprehensive high-resolution maps that identify, characterize, and spatially locate every cell type within an organism or tissue, cell atlases offer invaluable insights into cellular heterogeneity, interactions, and functions1. These detailed maps have the potential to revolutionize our understanding of normal development, aging, and disease pathogenesis, paving the way for new diagnostic, prognostic, and therapeutic strategies2,3. Consequently, many specialized atlases have emerged, including those focusing on neurodegenerative diseases-mapping cellular changes in Alzheimer’s and Parkinson’s disease tissues4–6. Others have focused on developmental biology, creating time-resolved atlases that track cellular differentiation during organ formation7–9. Cancer-specific atlases have also gained prominence, helping to delineate tumor micro-environments and identify new therapeutic targets10,11. With this increasing availability of cell atlases, the ability to extract biologically meaningful information from these datasets is paramount to the progression of our knowledge of system-specific cellular dynamics and disease mechanisms.

Despite remarkable progress in the single-cell field, existing computational methodologies face several limitations that hinder their ability to fully capture the complexity of single-cell data. Commonly used pipelines, such as Seurat12, Scanpy13, and SnapATAC14, rely on conventional unsupervised learning techniques that may not adequately handle the high-dimensionality, sparsity, and noise inherent in single-cell datasets. The highly variable genes (HVG) selection methods employed by both Seurat and Scanpy, as unsupervised approaches based on variance-to-mean ratio calculations, rely purely on statistical dispersion without considering the biological relevance of gene expression patterns across different cell types, and thus cannot consistently identify genes that are truly informative for characterizing cellular heterogeneity15. Similarly, clustering algorithms like Louvain16 and Leiden17, which optimize community detection through modularity maximization, lack hierarchical structure to capture nested relationships between cell types and cannot effectively use resolution changes to control the merging and further subdivision of cell types18. Moreover, popular visualization techniques like t-SNE19 and UMAP20 have limitations in representing the global structure and hierarchical organization of cells, often emphasizing local similarities at the expense of preserving the overall topology21.

Recent advances in single-cell genomics have led to growing recognition of the low-dimensional nature of single-cell data22,23. This characteristic can be understood from two perspectives. Firstly, despite the vast number of genes measured in single-cell experiments, only a small subset is typically informative for distinguishing cell types. The majority of genes are housekeeping genes, which maintain basic cellular functions and exhibit relatively constant expression across cell types. Studies such as15 have demonstrated that focusing on fewer, highly informative genes can lead to improved visualization and analysis outcomes24. Secondly, due to the interconnected nature of genes, single-cell data tends to occupy a low-dimensional manifold within the high-dimensional gene expression space25. As such, many state-of-the-art single-cell clustering frameworks have begun incorporating this concept of manifolds26,27. In particular, manifold fitting28,29 represents a conceptual framework that aims to preserve data structure while offering high interpretability and theoretical backing, thus emerging as an innovative approach for dimensionality reduction. Building on their previous work29, Yao et al. developed scAMF30—the first framework to implement this manifold fitting concept for single-cell analysis.

Here, we introduce CellScope, a promising method for constructing multi-level, high-resolution cellular atlases. By leveraging manifold fitting and neighborhood graph-based aggregative clustering, CellScope addresses three key challenges in single-cell analysis: (1) inadequate gene selection and single-level marker gene characterization that fails to capture genes dynamically across cell type subdivisions; (2) limited clustering resolution and hierarchical structure that cannot support nested organization; (3) inability to generate tree-structured visualizations that integrate cellular trees with cellular atlases.

To overcome these limitations, CellScope integrates four core innovations: intelligent gene selection that separates signal from noise spaces, precise delineation of similar cell subpopulations through manifold-based denoising, dynamic characterization of cellular landscapes at multiple resolutions, and multi-level functional analysis through dynamic “molecular identity” classification.

We conducted extensive validation across 36 datasets covering various species, organs, and sequencing modalities, demonstrating CellScope’s exceptional performance. Notable achievements include the identification of Oligodendrocyte subpopulations and the simultaneous characterization of cell types and health status in COVID-19 patients—discoveries that existing methods like Seurat and Scanpy were unable to detect. Importantly, CellScope achieves these results with exceptional speed, parameter-free operation, and high interpretability, thereby opening new avenues for understanding cellular diversity in complex biological systems.

Results
Overview of cellScope workflow
CellScope uses manifold fitting to model single-cell data, addressing intrinsic complexity and noise to derive crucial biological insights. CellScope assumes that the true biological structure of single-cell data lies on a low-dimensional manifold31. This manifold represents the intrinsic, lower-dimensional structure of gene expression that captures the genuine relationships between cells, including cellular states and subtypes. However, the observed single-cell data does not directly reflect this manifold due to two types of noise. The first type of noise refers to the expression of housekeeping genes, which are crucial for basic cellular functions but, due to their ubiquitous expression, do not contribute to distinguishing cell populations. We thus define “noise space” to denote the space of housekeeping genes and “signal space” to represent the remaining gene expression profiles that reflect cell type differences. The second type of noise lies in this signal space and represents technical noise due to mRNA loss, inefficient molecular capture, and sequencing errors32. This stochastic noise may distort the true expression patterns of the marker genes that are key to distinguishing between cell types and states. Together, these two types of noise combine with the underlying biologically meaningful manifold to constitute the observable single-cell gene expression matrix (Fig. 1a).

To recover the essential low-dimensional manifold and improve the quality of downstream analyses, CellScope employs a two-stage manifold fitting process (Fig. 1b). The first stage in our manifold fitting approach aims to mitigate the noise introduced by ubiquitous housekeeping genes, which are irrelevant to cell classification, while preserving critical genes for further analysis (Methods A). We base this process on a widely accepted assumption in manifold learning and translate it to a biological context: low-dimensional representations of individual cells that belong to different cell types lie on distinct submanifolds33. These cell type submanifolds are characterized by a high density of cells and are separated from one another by regions of low cell density34.

Leveraging this principle, CellScope selects multiple sets of distant high-density cells, termed “manifold seeds”, along with their neighboring cells, designated as “highly reliable cliques” (Fig. 1c). These cliques originate from multiple separate cell types and help distinguish between noise and signal spaces. Features in the signal space exhibit low variance within the same clique but high variance between different cliques, while features in the noise space lack this property. By exploiting this distinction, CellScope filters out most noise while preserving key genes for determining cell identity.

The second stage ensures proper stratification of different cell types by assigning cells residing in low-density regions, which may represent transitional cell states or have higher levels of technical noise, to the nearest cell type submanifold (Methods B). This denoising stage refines the representation of each cell type, emphasizing genuine biological signals over technical artifacts, and better reflects the underlying cellular heterogeneity.

After manifold fitting, CellScope constructs a cell-to-cell neighborhood similarity graph, where cells with more similar gene expression profiles are assigned higher similarity. Based on this graph, CellScope then performs agglomerative clustering (Methods C). Starting with each cell as a cluster, the algorithm iteratively merges the most similar clusters until no two cluster exhibits significant similarity, yielding precise and biologically meaningful classifications.

An innovative aspect of CellScope is its ability to generate an informative tree-structured visualization (Methods D) that integrates UMAP20 and hierarchical clustering. In addition to an initial UMAP visualization of the manifold-fitted data that provides an intuitive representation of complex cellular relationships, CellScope provides the tree-structured visualization that depicts the hierarchical relationships between cell types, illustrating how different populations emerge, branch, and specialize. By annotating the gene expression differences that drive the emergence of each cell cluster, researchers can gain insight into key regulatory genes and pathways involved in cell fate decisions, development, and functional specialization. Based on the tree-structured visualization, CellScope introduces an innovative multilevel gene identity system, referred to as dynamic “molecular identity”. By analyzing the expression differences of genes among different cell clusters within the hierarchical levels of clustering, CellScope classifies genes into distinct identities, including housekeeping genes, moderately cell-type-related genes, and strongly cell-type-related genes (Fig. 1d and Methods E). By evaluating changes in gene identities across these clustering hierarchies, CellScope transcends the traditional binary classification of genes as either marker or non-marker genes.

CellScope differs fundamentally from existing frameworks through its comprehensive mathematical approach. While Seurat employs variance-mean relationship models for gene selection, linear PCA for dimensionality reduction, and flat Louvain clustering, and Scanpy uses standardized variance-based HVG selection with similar linear PCA and Leiden clustering, CellScope introduces a manifold-based framework with three key innovations. First, it adaptively identifies signal genes through manifold seeds detection rather than parameter-dependent selection. Second, it preserves both local and global cellular relationships via manifold fitting instead of linear subspace assumptions. Third, it constructs multi-level hierarchical structures through agglomerative clustering rather than single-level partitioning. Detailed mathematical comparisons between CellScope, Seurat, and Scanpy are provided in Supplementary Note 1.1 and Supplementary Table 1. While CellScope and scAMF30 share the conceptual foundation of manifold fitting, their specific mathematical implementations and biological applications are fundamentally distinct. scAMF focuses on general technical noise reduction through a uniform manifold hypothesis, whereas CellScope introduces a biology-driven dual-noise model. Specifically, CellScope explicitly distinguishes housekeeping gene noise from technical noise, implements cell-type-aware submanifold detection using composite metrics, and incorporates ANOVA-based statistical gene selection from biologically reliable cliques. These innovations transform manifold fitting from a general clustering enhancement technique into a specialized biological discovery platform capable of hierarchical validation and dynamic molecular characterization across multiple levels of cellular organization. Detailed comparisons between CellScope and scAMF are provided in Supplementary Note 1.2.

CellScope demonstrates superior performance in cell clustering and gene selection
We evaluated CellScope’s performance in cell clustering using 36 distinct scRNA-seq datasets with known cell types (Methods G and Supplementary Tables 2–3). These datasets cover various human and mouse tissues—including brain, pancreas, embryos, and immune cells—and range widely in size (90 to 265,767 cells) and complexity (3 to 20 cell types). Each dataset includes gold-standard cell type labels determined through methods like cell morphology and marker gene expression. We compared CellScope against two widely used single-cell analysis methods (Seurat35 and Scanpy13) and three recent methods (scLEGA36, scDCCA37, and CellBRF38) with details in Methods F. True cell type labels were used only for post-hoc evaluation. All clustering results shown below are based on analyses performed on the Google Colab platform (44-core CPU, 150 GB RAM) to ensure consistency (Methods H).

CellScope achieves the best cell clustering performance across all datasets regarding accuracy, robustness, and computational efficiency. To quantify clustering performance, we used multiple clustering evaluation metrics: adjusted rand index (ARI)39, the clustering accuracy (ACC)40, the normalized mutual information (NMI)41, and jaccard index (JI)42, where higher values indicate better clustering performance (Definitions of these metrics are provided in the Supplementary Note 1.3). Among the six methods tested, CellScope, Seurat, and Scanpy successfully completed clustering across all 36 datasets. In contrast, due to computational limitations, scLEGA, scDCCA, and CellBRF could only be executed on datasets containing up to 50,000, 75,000, and 25,000 cells, respectively. As shown in Fig. 2a, CellScope achieved the highest overall average ARI of 0.88 with the lowest standard deviation, ranking first on 32 out of the 36 datasets and second on 3 others, significantly outperforming all other methods (Fig. 2b). Seurat and Scanpy attained lower average ARIs of 0.65 and 0.68, respectively. Among the recent methods, scLEGA, scDCCA, and CellBRF achieved average ARIs of 0.54, 0.74, and 0.49 on datasets where they could be successfully applied. Wilcoxon signed-rank tests further confirmed CellScope’s statistically significant superiority, with all p-values against the other five methods being less than 10−5. Similar advantages were observed across other clustering evaluation metrics (see Supplementary Fig. 7 and Tables 4–7). Beyond its accuracy, CellScope also demonstrated competitive computational efficiency across three dataset-size regimes (Fig. 2c, Supplementary Table 8). Among the three methods that could handle all dataset sizes (CellScope, Scanpy, and Seurat), CellScope consistently achieved the fastest or among the fastest runtimes across small (<3000 cells), medium (3000–50,000 cells), and large (>50,000 cells) datasets. The other three methods (scLEGA, scDCCA, and CellBRF) showed computational limitations, with scLEGA and CellBRF failing on large datasets and scDCCA exhibiting prohibitively long runtimes.

To assess CellScope’s stability across different computing environments, we conducted additional tests on two memory-constrained personal computers—a MacBook (8-core CPU, 16 GB RAM) and an iMac (10-core CPU, 16 GB RAM) (Methods H). Since other methods could not complete all datasets on these platforms, we compared only CellScope and Scanpy. Results show that CellScope typically achieved faster runtimes than Scanpy, demonstrated memory advantages on memory-constrained platforms while performing comparably on high-memory platforms (never exceeding twice Scanpy’s memory usage), and exhibited significantly more stable clustering performance than Scanpy (Supplementary Fig. 15 and Tables 23–25).

Gene selection is crucial for clustering results. CellScope exhibits superior performance in this area compared to other methods, including Disp (Seurat)43, VST (Seurat)35, HVG (Scanpy)13, mixHVG44, FEAST45, and HRG46. We used multiple evaluation metrics to assess the effectiveness of gene selection methods, including Average Silhouette Width (ASW)47, Variance Ratio, Cell-type Local Inverse Simpson Index48, KNN Classification Accuracy, and Neighborhood Purity (definitions provided in Supplementary Note 1.5). CellScope consistently achieved higher ASW values on the majority of datasets (Fig. 2d), while other metrics further confirmed its superiority in gene selection effectiveness (Supplementary Tables 9–14). One-sided Wilcoxon signed-rank tests on these gene selection metrics validated CellScope’s superiority in gene selection with all p-values <0.01 (Supplementary Table 15).

To further validate the effectiveness of CellScope’s gene selection strategy beyond direct metric comparisons and statistical tests, we combined the gene selection outputs from Seurat and Scanpy with CellScope’s graph-based clustering module and compared the results to those of the CellScope pipeline. The complete CellScope workflow achieved the highest average ARI of 0.88, outperforming the hybrid versions using Scanpy (average ARI = 0.75) and Seurat (average ARI = 0.77) gene selection, which themselves performed better than the original Scanpy (average ARI = 0.68) and Seurat (average ARI = 0.65) pipelines (Supplementary Fig. 8a, b). These results confirm that CellScope’s gene selection strategy contributes significantly to its superior clustering performance.

Figure 2f compares the visualizations produced by CellScope and Seurat using human pancreatic cells from Wang et al.49. Seurat’s results incorrectly suggest that the three cell types—Alpha, Duct, and Beta—are interconnected and indistinguishable, whereas CellScope accurately achieves clear separation among the cell types. This difference is largely attributed to CellScope’s manifold fitting-based gene selection strategy. As shown in Fig. 2e, only 125 genes overlap between Seurat’s 2000 and CellScope’s 500 selected genes, with 63% of these shared genes exhibiting significant variation (Variance Ratio >1, defined as the ratio between a gene’s inter-cell-type and intra-cell-type expression variance, indicating its power to distinguish cell types). More notably, 30% of CellScope-specific genes showed strong differential expression (Variance Ratio >1) between cell classes, whereas only 3% of Seurat-specific genes exhibited such pronounced differences. This indicates that CellScope captures high-quality marker genes and discovers meaningful high-variance genes that Seurat overlooked. For instance, the expression patterns of Seurat-selected genes (e.g., SST, REG1A) show minimal cell type specificity, resulting in poor cluster separation. In contrast, CellScope uniquely identifies genes (e.g., CRH) that are not selected by Seurat and exhibit highly specific expression patterns, facilitating more accurate cell type differentiation (Fig. 2g, h). A comparison between CellScope and Scanpy gene selection can be seen in Supplementary Fig. 8c–f, and results for all other benchmark datasets are available in Supplementary Tables 20–22.

CellScope enhances the ability to distinguish similar cell types, detect rare cell types, and perform multi-level clustering
CellScope demonstrates superior performance in distinguishing similar cell types and detecting rare cell populations. This was exemplified using the human brain cell dataset from NHGRI50 and the mouse pancreatic cell dataset from Keller (P)51. CellScope’s visualization results show clearer separation of cell types compared to popular methods such as Scanpy and Seurat (Fig. 3a, b). For instance, in the NHGRI dataset, Scanpy and Seurat could only distinguish the Cerebellum from other brain regions, while the remaining cell types exhibited mixed clustering without clear boundaries. In contrast, CellScope’s results successfully separated Basal Ganglia, Cerebellum, and Cortex from other cell types. Furthermore, Amygdala and Hippocampus clustered together as one cluster, and Hypothalamus, spinal cord, and Substantia nigra formed another, with clear boundaries maintained between each cell type within these clusters. Quantitative analysis revealed that CellScope achieved significantly higher Silhouette Scores for nearly all cell types in the NHGRI dataset compared to Scanpy or Seurat (Supplementary Fig. 9a). The analysis of the Keller (P) dataset further demonstrated CellScope’s robust ability to recognize both non-rare and rare cell types. For instance, non-rare cell types such as type B pancreatic cells, comprising approximately 40% of the total population, formed distinct clusters. Meanwhile, rare cell types, including leukocytes, pancreatic PP cells, and pancreatic stellate cells, representing 3.6%, 2.1%, and 1.4% of the population, respectively, were distinctly separated (Supplementary Fig. 9b).

Through systematic analysis of all 36 datasets, CellScope demonstrated significant superiority over Scanpy for visualization Silhouette Scores (Fig. 3c), with the difference statistically confirmed by Wilcoxon signed-rank test (p = 9.6 × 10−8). To further quantify its effectiveness in identifying both non-rare and rare cell types, we defined rare cell types as those comprising less than 5% of the total population and non-rare types as those comprising 5% or more. We then calculated their respective visualization Silhouette Scores (see Supplementary Note 1.4 for detailed calculation). The analysis revealed that CellScope achieved high and stable visualization Silhouette Scores for rare cell types (Fig. 3d, p = 4.26 × 10−5), while demonstrating greater advantages for non-rare cell types (Fig. 3e, p = 8.82 × 10−6). To validate the robustness of these findings, we performed comprehensive sensitivity analyses using multiple thresholds (2%, 10%, 15%). Across all threshold conditions, CellScope maintained consistently superior performance in identifying rare cell populations, while performing as well as or better than Scanpy for non-rare cell identification (Supplementary Fig. 10). These results collectively highlight CellScope’s unique capability to resolve low-abundance and high-abundance cell populations with high efficacy.

Additionally, CellScope demonstrated advanced multi-level clustering capabilities by analyzing the mouse lumbar cells from Usoskin dataset52, which contains four major cell types and a total of eight subtypes. Using our hierarchical clustering, we first mapped major cell types at the second clustering level (Fig. 3f, h), successfully separating tyrosine hydroxylase containing (TH), neurofilament containing (NF), peptidergic nociceptors (PEP), and non-peptidergic nociceptors (NP) cell types. For subtypes without clear boundaries, we extended the clustering process (Fig. 3g, i) to accurately identify NP and NF subtypes. This superior performance can be attributed to CellScope’s gene selection strategy, which identifies genes with subtype-specific expression patterns (Fig. 3j). For instance, CellScope selects Tac1 and Th as distinctive markers based on their specific expression in PEP1 and TH cells, respectively. Additionally, CellScope identified Agtr1a based on its preferential expression in NP1 neurons. This gene selection approach, combined with multi-level clustering, enables accurate identification of cell types and subtypes while preserving the biological relationships between cell populations. This level of precision could not be achieved by Seurat and Scanpy (Supplementary Fig. 11).

CellScope’s tree-structured visualizations refine characterization of brain cell atlases
CellScope’s tree-structured visualization effectively displays the hierarchical relationships between cell types and their subtypes. To demonstrate, we implemented CellScope with a dataset from the red nucleus within the human midbrain53, designated Siletti-1. First, CellScope’s tree-structured visualization identifies the majority of cells previously annotated cell types in the Siletti-1 dataset. Specifically, CellScope categorized cells into nine distinct classes and identified nearly all superclusters previously reported in ref. 53. Notably, CellScope successfully identified Fibroblasts comprising only 26 cells (Fig. 4a).

Second, our analysis revealed that Oligodendrocytes (OLs) were further differentiated into two subtypes, designated as Oligodendrocyte 1 (OL1) and Oligodendrocyte 2 (OL2), containing 541 and 1592 cells, respectively. To elucidate the hierarchical expression patterns of key marker genes distinguishing these two subtypes, Fig. 4b, c highlights the expression distributions of five differentially expressed genes across three clustering levels: Cluster, SubCluster, and SubSubCluster. Specifically, the high expression of the OL1 marker gene RBFOX1 indicates that these cells have reached a terminally differentiated53. In contrast, the high expression of the OL2 marker gene OPALIN reflects active myelination, suggesting that OL2 cells are undergoing active differentiation. Meanwhile, the low expression of OPALIN in OL1 further supports the notion that OL1 cells have reached terminal differentiation54. Additionally, CTNND255 plays a critical role in cell adhesion and synapse formation, while Laminin-2, encoded by LAMA256, regulates the spreading of OLs and myelination in the central nervous system via the integrin signaling pathway. Therefore, the cell population with high expression of these genes (OL2) exhibits enhanced interaction with axons, further indicating active myelination processes57.

Third, CellScope’s multi-layer cell clustering provides an innovative perspective to study the dynamic role of marker genes. In other words, CellScope assigns each gene a new dynamic “molecular identity” depending on whether it is solely unique to one layer of clustering or continues to function as a marker across all layers of clustering. In Siletti-1, CellScope implements a three-level hierarchical clustering system, referred to as Clusters, Subclusters, and SubSubclusters, which progressively identify homogeneous cell groups with increasing resolution (Fig. 4a). By categorizing genes into three dynamic identities—housekeeping genes (HG), moderately cell-type-related genes (MCTRG), and strongly cell-type-related genes (SCTRG)—based on their significance across clustering levels (see Methods E for details), we visualized the relationships between these gene identities using a Sankey diagram (Fig. 4d). Additionally, the overlaps and transitions of these gene identities across the three clustering levels were analyzed (Fig. 4e). As the clustering resolution increases, the number of housekeeping genes gradually increases, while the number of SCTRGs and MCTRGs gradually decreases. This phenomenon reflects the changing roles of genes at different clustering levels. Specifically, as the resolution increases, many genes transition from SCTRGs or MCTRGs to housekeeping genes. During cell differentiation, SCTRGs and MCTRGs are typically involved in establishing cell-specific functions or characteristics, especially during the early and middle stages of development. As cells progress into maturity, more housekeeping genes are activated, indicating that the cells have entered a phase focused on maintaining stable functions, such as protein synthesis and metabolism, which are essential for basic cell maintenance and biological processes.

We used three flow patterns as examples (Flow 6 HG-SCTRG-SCTRG, Flow 4 HG-MCTRG-HG, and Flow 19 SCTRG-HG-HG) and identified genes RBFOX1 in Flow 6, PPM1H in Flow 4, and PRANCR in Flow 19, which serve as marker genes exclusively at the SubSubCluster, SubCluster, and Cluster levels, respectively (see Fig. 4f, g). Specifically, in Flow 6, RBFOX1 is widely expressed in the nervous system and regulates various alternative splicing events related to neural development and maturation, including transcription factors, splicing factors, and synaptic proteins58. RBFOX1 shows high expression levels in both Oligodendrocytes (OLs) and Splatter-type cells, while showing minimal expression differences between Oligodendrocyte precursor cells (OPCs) and Oligodendrocytes. This is likely because OPCs are the direct precursor cells of OLs, and during this developmental transition, RBFOX1 primarily supports basic cell development, differentiation, and gene regulation59, maintaining developmental continuity. As mentioned earlier, there is a significant expression difference between the two OL subtypes, OL1 and OL2, due to their distinct differentiation states.

In Flow 4, gene enrichment analysis using ClusterProfiler60 reveals that PPM1H is enriched in functions related to dephosphorylation and protein regulation. During OL differentiation, PPM1H regulates specific protein dephosphorylation events, contributing to myelin protein synthesis. Interestingly, the expression differences of PPM1H between different OL subtypes are minimal, likely because mature OLs need to maintain a stable signaling environment for myelin formation, which results in consistent expression of PPM1H across different OL subtypes. In Flow 19, PRANCR, a long non-coding RNA known to regulate keratinocyte proliferation and cell cycle progression61, shows low expression in Cluster 5, particularly in OPCs and OLs. This is consistent with its established role in cell proliferation rather than differentiation. Since myelinating cells are specialized and post-mitotic, the low expression of PRANCR in these cells aligns with their specialized function in myelination62.

To further validate CellScope’s capability in identifying cellular subtypes within brain cell datasets, we applied it to two distinct datasets: a human thalamic dataset from the same study as Siletti-153 and a mouse primary motor cortex dataset63. For the human thalamic dataset, CellScope not only successfully resolved nearly all superclusters but also precisely identified two distinct subtypes of Oligodendrocytes. Notably, these subtypes exhibited marker genes similar to those of Oligodendrocyte populations in the midbrain red nucleus, suggesting conserved maturation pathways across different brain regions. For the mouse primary motor cortex dataset, in addition to successfully identifying nine canonical cell types, our analysis revealed two L5 IT neuronal subtypes (designated L5 IT1 and L5 IT2). Marker gene profiling demonstrated that L5 IT2 is involved in long-range information transmission, while L5 IT1 primarily contributes to local circuit modulation. Detailed analyses are provided in the Supplementary Note 1.8.

CellScope improves analysis of disease-control cell atlases
Disease-control cell atlases are invaluable in modern medical research, offering critical insights into disease mechanisms, potential therapeutic targets, and innovative diagnostic approaches by comparing the cellular composition and functional states of healthy and diseased individuals. In complex diseases like COVID-19, these atlases provide opportunities to help uncover cellular changes during disease progression, track immune responses, and identify cell subtypes associated with disease severity. While existing analysis pipelines64 have made significant contributions, there remains room for improvement in detecting such signals, particularly in distinguishing cell populations associated with disease states.

To better analyze the disease-control atlas, we developed a CellScope-based analytical pipeline (Supplementary Fig. 12) and applied it to peripheral blood mononuclear cell (PBMC) data from healthy individuals and COVID-19 patients with varying disease severities64. Focusing on the monocyte-dendritic cell system, we successfully identified and isolated this system in step 10 of the CellScope analysis (Fig. 5a and Supplementary Fig. 13). CellScope clearly distinguished classical monocytes, non-classical monocytes, and conventional dendritic cells (middle, left, and right clusters), outperforming traditional UMAP (Fig. 5b). It also revealed a continuous differentiation trajectory from classical monocytes to conventional dendritic cells and non-classical monocytes.

Moreover, compared to Seurat, CellScope provides a clearer distinction between the three disease states—severe, moderate, and healthy. Specifically, in step 11, CellScope further refined the separation of COVID-19-associated cells, demonstrating its exceptional capability in identifying disease states. Figure 5c illustrates the expression of eight marker genes in Cluster 1 (mostly COVID-19) and Cluster 2 (mostly healthy). Among them, seven marker genes—IFIT1, OAS2, OAS3, RNASE2, SIGLEC1, IFI44, and IFI27—exhibit significantly higher expression in Cluster 1, while HLA-DRB5 shows elevated expression in Cluster 2, providing key molecular markers for distinguishing disease states. The expression levels of these genes in the monocyte-dendritic cell system increase progressively with disease severity (Fig. 5d), highlighting the marked differential expression between COVID-19 and healthy states. Notably, such differences were not observed in other cell types (Fig. 5c). This likely underscores the critical role of the monocyte-dendritic cell system in viral recognition, antiviral responses, and immune regulation. The upregulation of Cluster 1 genes highlights a robust immune defense against SARS-CoV-2, particularly interferon-mediated antiviral responses, which are crucial components of the innate immune system’s defense against viral pathogens65. This gene expression pattern not only indicates active viral infection but also reveals the complex interactions between the virus and the host immune system.

SARS-CoV-2 appears to impair dendritic cell function by downregulating HLA-DRB5 expression, leading to a loss of antigen-presentation capacity in infected monocytes and dendritic cells, facilitating viral evasion of T cell-mediated immune responses66. Additionally, ClusterProfiler60 enrichment analysis of the seven genes highly expressed in Cluster 1 (Fig. 5e) revealed their key roles in antiviral immune responses. IFIT1 and IFI27 are strongly linked to interferon responses, while OAS2 and SIGLEC1 activate interferon signaling, triggering antiviral defenses and inhibiting viral replication. These genes also regulate viral RNA replication, halting virus proliferation. The enrichment analysis further uncovered their roles in multiple stages of the viral life-cycle, from recognition to response and clearance, highlighting their importance in antiviral immunity.

CellScope demonstrates interpretability and robustness
CellScope demonstrates significant advantages in algorithm interpretability, robustness, and user-friendliness. It efficiently identifies biologically meaningful key genes, adapts seamlessly to diverse datasets, and offers intuitive and informative visualization. In contrast, existing tools such as Seurat and Scanpy primarily focus on HVG for gene selection, which have limitations in distinguishing between housekeeping genes and key genes. These tools also often require careful parameter adjustment to achieve optimal results.

First, CellScope enhances algorithm interpretability through its innovative gene selection approach. It selects samples with high density and large distances from each other as “manifold seeds” to effectively distinguish between noise and meaningful signals. This approach is grounded in a fundamental principle of unsupervised learning and cluster analysis—the relationship between local density and distance on manifolds67. The intuition behind this strategy is that high-density points have a greater probability of residing at the centers of manifold clusters, where their local neighborhoods typically exhibit higher class purity. Analysis of the Mouse Cerebral Cortex cells from the Zeisel dataset68 revealed a strong negative correlation between local density and distance from true cluster centers (Fig. 6a), where high-density points consistently exhibited improved neighborhood purity (Fig. 6b), supporting our density-distance based manifold seeds identification method. We also compared the differences between selected and unselected genes within and between clusters (Fig. 6c, d). Relative to the unselected genes, the genes selected by CellScope exhibit significantly larger inter-class differences and smaller intra-class differences (p = 3.8 × 10−38). In contrast, the unselected genes show that intra-class variance is significantly greater than inter-class variance. This enhanced distinction facilitates better cluster separation, demonstrating that our gene selection process effectively captures the most informative genes for distinguishing different cell types.

Second, CellScope demonstrates exceptional robustness and adaptability by minimizing manual parameter tuning and maintaining consistent performance across various datasets. Unlike existing algorithms that often require meticulous manual parameter tuning, CellScope achieves robust performance with minimal user intervention. We performed comprehensive robustness analyses to evaluate key parameters in our gene selection process, including the number of PCA dimensions, the number of selected manifold seeds, and the number of selected genes. First, we assessed how varying the number of PCA dimensions affects clustering performance. The ARI remained stable across different numbers of PCA dimensions (Fig. 6e and Supplementary Table 18), with optimal performance observed at around 100 dimensions. Second, we compared our adaptive method for manifold seeds selection with fixed-number approaches. Our adaptive method consistently outperformed fixed-number methods across different dataset sizes (Fig. 6f and Supplementary Table 17). This adaptability allows CellScope to automatically adjust to the characteristics of each dataset without manual intervention. Third, we investigated the impact of the number of genes selected during gene selection. The clustering performance remained consistent over a wide range of gene counts (Fig. 6g and Supplementary Table 16), with optimal results achieved around 500 genes. However, selecting too few genes (e.g., 50 genes) led to insufficient clustering information due to the omission of key genes that determine cell identity, resulting in poor cluster separation. Conversely, selecting too many genes (e.g., 10,000 genes) introduced redundant information, which masked cell type-specific signals and hindered effective separation of different cell types. This phenomenon was evident in the analysis of human oral cavity cells from the Tirosh dataset69 (Fig. 6h). These results emphasize the importance of judicious gene selection in single-cell analysis and highlight CellScope’s robustness and adaptability in handling this critical parameter.

To evaluate the importance and necessity of each design component, we conducted a comprehensive evaluation combining targeted component analysis and systematic ablation studies, with all quantitative results provided in Supplementary Table 19. Following the CellScope analysis pipeline, we first validated the necessity of the normalization step through ablation experiments. Normalization helps mitigate technical variability and standardizes data scales across cells, thereby enhancing the reliability of clustering. The results showed that applying normalization improved clustering performance, with an average ARI increase of 0.09. Next, we evaluated the importance of the initial dimensionality reduction via PCA, which serves as a critical step in the first stage of manifold fitting. This step extracts the most informative features from the high-dimensional expression matrix and enables the identification of more reliable manifold seeds. The ablation results demonstrated that applying PCA prior to manifold fitting improved clustering accuracy by approximately 0.16 in ARI, compared to skipping this step. We further investigated the impact of the manifold fitting process on CellScope’s performance. Figure 6i (left) illustrates that, in 31 out of 36 datasets, the application of manifold fitting yielded superior results compared to those without manifold fitting.

We then compared the performance of the graph-based hierarchical clustering method adopted in CellScope with traditional distance-based hierarchical clustering. The ARI values showed that our graph-based clustering achieved equal or better performance than distance-based clustering in 33 out of 36 datasets (Fig. 6i (right)). This can be attributed to the ability of graph-based algorithms to more effectively capture local structures and nonlinear relationships in the data by constructing nodes and edges. This is particularly beneficial in high-dimensional datasets and clusters with complex morphologies, where traditional distance-based algorithms may struggle due to their inability to capture intricate structural properties. To further optimize CellScope, we implemented an adaptive distance metric based on dataset size: Euclidean distance for smaller datasets and Jaccard distance for larger datasets. The results (Supplementary Table 19) confirm the reliability of this adaptive strategy. The effectiveness of this approach stems from the fact that Euclidean distance can introduce dimensional artifacts in large datasets, a phenomenon known as the “curse of dimensionality”70. In contrast, the Jaccard distance metric effectively alleviates this issue by focusing on the presence or absence of features rather than their magnitude, making it more suitable for high-dimensional data.

Methods
CellScope aims to analyze single-cell data through manifold fitting, enabling precise identification of differences between cell types and subtypes. By identifying highly reliable cliques, the method effectively distinguishes between noise and signal spaces. CellScope then constructs a cell-to-cell similarity graph and performs agglomerative clustering based on this graph to generate a hierarchical structure of cells. A tree-structured visualization intuitively represents the hierarchical relationships and reveals differentiation pathways among cells. Furthermore, CellScope analyzes gene expression changes along differentiation pathways and expression differences within the same hierarchy, providing functional insights into genes.

The CellScope workflow consists of five main steps, as explained in detail below: (A) Manifold fitting stage 1, (B) Manifold fitting stage 2, (C) Graph-based agglomerative clustering, (D) Tree-structured visualization, and (E) Characterization of genes from different categories.

The pseudocode and flowchart of the algorithm can be found in Supplementary Note 1.7 and the tutorial website https://cellscope.readthedocs.io/en/latest/, respectively.

We summarize the main notations used in the description of the method as follows: suppose we have a expression counts matrix X={x_i}_{i=1}^N, where each vector x_i corresponds to the expression values [x^{(1)},...,x^{(D)}] of the i-th cell across D genes. We also let G={g_i}_{i=1}^D, where G represents the collection of genes related to X. After manifold fitting stage 1, we obtain Y={y_i}_{i=1}^N, where Y represents the scRNA-seq data after manifold fitting stage 1. Each vector y_i corresponds to the expression values [y^{(1)},...,y^{(D_1)}] of the i-th cell across D_1 selected genes, where D_1 << D. The set of selected genes is denoted as G_1={g_b}_{b=1}^{D_1}, where G_1 \subseteq G. Subsequently, after manifold fitting stage 2, we obtain Z={z_i}_{i=1}^N, where Z represents the fitted scRNA-seq data for subsequent downstream analysis.

Manifold fitting stage 1
Data preprocessing. We applied consistent preprocessing to all single-cell RNA sequencing datasets. First, we normalized each cell’s expression profile, which is a standard procedure prior to downstream analyses. This stage is essential for eliminating differences in total expression levels between cells, which may arise from technical or biological factors. It ensures that highly expressed genes in cells with elevated overall expression do not dominate the dimensionality reduction process, thereby preventing bias in subsequent analyses. Let X_P={x_i}_{i=1}^N denote the preprocessed data, where each vector x_i=[x_i^{(1)},...,x_i^{(D)}] represents the expression values of the i-th cell after preprocessing. The set of genes after preprocessing is denoted as G_e={g_i}_{i=1}^D.

Find highly reliable cliques. We begin this process with Principal Component Analysis (PCA)73, aiming to reduce noise and complexity in the data while retaining the primary sources of variation, thereby clarifying the manifold structure of the data and providing a solid foundation for subsequent manifold exploration. In PCA, we set the target dimensionality to n_1 (defaulting to 100) and apply it to the preprocessed data X_P. This results in a collection of cells represented in a low-dimensional space, denoted as P={p_i}_{i=1}^N, where p_i \in R^{n_1}. We aim to identify highly reliable cliques associated with distinct submanifolds, beginning with the identification of the centers of these submanifolds, referred to as manifold seeds. Inspired by ref. 67, local density reflects the compactness of a cell’s surrounding distribution, while relative distance measures the separation of a cell from other cells with higher density. Manifold seeds tend to exhibit significantly higher values in both metrics. Therefore, we evaluate the potential of each cell p_i to serve as a manifold seed by calculating its local density \rho(p_i) and relative distance \delta(p_i), defined as:

\rho(p_i) = \sum_{j \in N_i} d(p_i, p_j), \delta(p_i) = \min_{j \in N_i, \rho(p_j) > \rho(p_i)} d(p_i, p_j), (1)

where N_i is the set of k nearest neighbors of cell p_i (with k=20 by default), and d(\cdot, \cdot) denotes the distance between two cells, defaulting to Euclidean distance. Next, we compute the composite metric:

\gamma(p_i) = \rho(p_i) \delta(p_i), (2)

and select the cells with the highest \gamma(p_i) values as manifold seeds. Specifically, we choose the top m cells with the largest \gamma(p_i) values as manifold seeds, with m being a predefined hyperparameter which refers to the number of submanifolds hypothesized in advance.

However, since m is related to the number of cell types, an intelligent approach is required to determine the number of manifold seeds when the number of cell types is unknown. We first address the issue of scale differences between \rho and \delta in the calculation of \gamma. Both \rho and \delta are normalized as

\rho'(p_i) = \frac{\rho(p_i) - \min(\rho)}{\max(\rho) - \min(\rho)}, \delta'(p_i) = \frac{\delta(p_i) - \min(\delta)}{\max(\delta) - \min(\delta)}, (3)

where \rho={\rho(p_i)}_{i=1}^N and \delta={\delta(p_i)}_{i=1}^N. Next, we compute a scale-less index by multiplying the two normalized metrics:

\gamma'(p_i) = \delta'(p_i) \cdot \rho'(p_i). (4)

We then sort \gamma'={\gamma'(p_i)}_{i=1}^N in descending order, with \gamma''_j representing the j-th value in the sorted list. To further refine the selection of manifold seeds, we introduce the relative rate of change in \gamma'':

R_j = \begin{cases} \frac{\gamma''_{j+1} - \gamma''_j}{\gamma''_j - \gamma''_{j-1}} & \text{if } 2 \le j \le N-1, \\ \frac{\gamma''_2 - \gamma''_1}{\gamma''_1} & \text{if } j=1, \\ 0 & \text{if } j=N. \end{cases} (5)

Finally, we select the manifold seeds that satisfy the following conditions:

C = {p_i | \rho'(p_i) > \bar{\rho'}, \delta'(p_i) > \bar{\delta'}, \gamma'(p_i) > \bar{\gamma'}, R_{I(p_i)} < \bar{R}}, (6)

where \bar{\cdot} denotes the mean value of the respective set and R={R_i}_{i=1}^N. The index I(p_i) refers to the position of \gamma'(p_i) in \gamma'' after sorting \gamma'. The combined metric-based strategy ensures that the selected manifold seeds possess both high local density and relative distance. The introduction of the relative rate of change further optimizes this selection process, intelligently selecting all high-confidence manifold seeds. Ultimately, we denote the set of selected seeds as C={c_1, \dots, c_m}, where m represents the number of seeds selected.

Given that the identified manifold seeds are highly likely to reside at the centers of their respective submanifolds, the cells in the immediate neighborhood of each seed are assumed to belong to the same class as the seed. Therefore, for each seed c_i, we select its k_1 nearest neighboring cells (with k_1=5 by default), denoted as N_{c_i}. To ensure a high-confidence classification of the sets {N_{c_1}, \dots, N_{c_m}}, we combine the concept of connected components in graphs, and define the following partition:

\begin{cases} N_{c_i} \text{ and } N_{c_j} \text{ belong to different groups; if } N_{c_i} \cap N_{c_j} = \phi, \\ N_{c_i} \text{ and } N_{c_j} \text{ belong to the same group; otherwise,} \end{cases} (7)

for 1 \le i \neq j \le m. The resulting partition of high reliable cliques {N_{c_1}, \dots, N_{c_m}} is recorded as \ell_1.

Signal space identification. Due to the significant variance differences of genes in the signal space, both within and between cell clusters, our goal was to identify genes that exhibit notable expression differences, particularly between different cell types, within highly reliable cliques. To do this, we leveraged the high-confidence labels \ell_1 obtained from these reliable cliques and performed a one-way analysis of variance (ANOVA74) on each preprocessed gene g_i \in G_e. For each gene, the corresponding p-value was computed based on its expression across different cell clusters. We then selected the top D_1 genes with the lowest p-values (default: D_1=500), denoted as G_1, as the result of gene selection. Furthermore, we retained the genes from the signal space in X_P, denoted as Y.

Manifold fitting stage 2
To further highlight the true biological signals and better reflect the underlying cellular heterogeneity, we project cells located between submanifolds or near manifold boundaries closer to the centers of their respective submanifolds. This process ensures that the boundaries between submanifolds become clearer after projection. We assume that the density of data points decreases as the distance from the manifold center increases. Therefore, our fitting process focuses on low-density points, as they are more likely to be influenced by noise.

First, we calculate the local density \rho(y_i) for each cell y_i to assess its position within the manifold, using the following formula:

\rho(y_i) = \frac{1}{\sum_{y_j \in N_k(y_i)} d(y_i, y_j)}. (8)

We then select the 5% of cells with the lowest densities to form the set of manifold outliers O. We assume that the closest high-density point to each outlier belong to the same class and is closer to the center of its respective manifold. To achieve this, we adopt the projection estimation method defined by the previous work30: for each outlier y_i \in O, the nearest high-density point is y_b = \text{argmin}_{y_b \in Y} d(y_b, y_i), with \rho(y_b) > \rho(y_i), \forall y_i \in O. (9)

Subsequently, while preserving the relative position between the outlier y_i and y_b, we project y_i closer to the center of the manifold, as given by:

z_i = t y_b + (1-t) y_i, (10)

where t is 0.9 by default. For regular points not identified as outliers, we define their projection as z_i = y_i, if y_i \notin O. The final dataset after the second stage of manifold fitting is represented as Z={z_i}_{i=1}^N.

Graph-based agglomerative clustering
In the field of unsupervised learning, distance matrix-based agglomerative clustering methods have been extensively studied and applied75,76, whereas graph-based clustering aggregation techniques remain relatively under-explored. A similarity graph can capture the local neighborhood relationships between points within a submanifold, reflecting the manifold’s local geometric properties. At the same time, through the construction of neighborhood relationships, the graph can represent the connectivity between different submanifolds. Therefore, after obtaining a clear manifold structure, we begin by using the Uniform Manifold Approximation and Projection (UMAP) algorithm20 to construct the similarity matrix S={s_{ij}}_{i,j=1}^N. Specifically, for each cell z_i, we determine its k=d\log_2(N)e nearest neighboring cells. We first set the similarity between the cell and cells outside its k nearest neighbors to 0. Next, using a Gaussian kernel function, we compute the local similarity s_{ij} between two cells z_i and z_j:

s_{ij} = \exp \left( - \frac{||z_i - z_j||^2}{\sigma_i^2} \right), (11)

where z_j is in the k nearest neighbors of z_i and ||z_i - z_j|| denotes the Euclidean distance, and \sigma_i is a locally adaptive scale parameter satisfying:

\sum_{j=1}^k \exp \left( - \frac{||z_i - z_j||^2}{\sigma_i^2} \right) = \log_2(k). (12)

To construct a symmetric similarity graph, we define the edge similarity w_{ij} as:

w_{ij} = s_{ij} + s_{ji} - s_{ij} \cdot s_{ji}. (13)

The corresponding symmetric similarity matrix is denoted as W={w_{ij}}_{i,j=1}^N. Finally, using the average linkage method based on pairwise similarities, we systematically merge data points starting with each cell as a separate cluster, and denote the clustering results after K steps as T_K. T_{K+1} merges the two clusters with the highest average similarity, i.e., clusters A_\ell and B_\ell that maximize:

[A_\ell, B_\ell] = \text{argmax}_{A,B \in T_K} \frac{1}{|A||B|} \sum_{a \in A} \sum_{b \in B} w_{ab}. (14)

For exceptionally large datasets (with over 30,000 data points), we employ the following optimization strategy: First, we randomly select a representative subset of 30,000 points from the complete dataset to form subset S_1, while the remaining cells constitute subset S_2. After performing hierarchical clustering on S_1 using the average linkage method to obtain clustering result T_K^0, we analyze the distribution of clusters within the k nearest neighbors (default k=5) of each cell z \in S_2 in S_1 and assign z to the cluster with the highest cell count in its k nearest neighbors.

Tree-structured visualization
To comprehensively and systematically explore cell types and their subtypes, we developed a tree-structured visualization method. First, we applied the UMAP algorithm20 to visualize all cells as the root node. Then, each cluster from T_{N-1} was visualized individually to generate the first layer of child nodes. By comparing the clustering results of T_{N-2} and T_{N-1}, we identified two new subtypes within the cells of the first-layer child nodes and visualized them as the second layer of child nodes. This iterative process was repeated in the same manner, advancing layer by layer.

To clearly illustrate the distribution of each pair of subtypes within their parent nodes, we applied a color-coding scheme to the tree-structured visualization. Specifically, each branch’s outermost nodes were colored first, and the colors of the child nodes were then propagated back to their respective parent nodes. This core visualization method effectively reveals the hierarchical relationships between cell types, illustrating how different populations emerge, differentiate, and specialize over time. The tree-structured visualization automatically generated by CellScope is shown in Supplementary Note 1.9 and the tutorial at https://cellscope.readthedocs.io/en/latest/.

Characterization of genes from different categories
After generating the Tree-structured visualization, we quantified the gene expression differences between sibling nodes (sharing the same direct parent node) using the Wasserstein distance. This metric measures the minimal transport cost required to transform one probability distribution into another, providing a robust comparison between gene expression profiles from sibling nodes.

For gene g_i, assume its expression in two sibling nodes is represented by P={p_1, \dots, p_n} and Q={q_1, \dots, q_m}, respectively. First, both expression vectors are sorted in ascending order, yielding the ordered vectors P^0={p_1^0, \dots, p_n^0} and Q^0={q_1^0, \dots, q_m^0}. We then compute the cumulative distribution functions (CDFs) for the sorted vectors P^0 and Q^0, as defined by the following equations

F_P(p_i^0) = \frac{i}{n}, i=1,2,...,n, (15)

F_Q(q_j^0) = \frac{j}{m}, j=1,2,...,m. (16)

We then use linear interpolation to align the two CDFs F_P and F_Q onto the same probability space. For a given probability value x \in [0,1], if F_P(p_i^0) < x < F_P(p_{i+1}^0), then F_P^{-1}(x) is calculated as

F_P^{-1}(x) = p_i^0 + \frac{x - F_P(p_i^0)}{F_P(p_{i+1}^0) - F_P(p_i^0)} \cdot (p_{i+1}^0 - p_i^0). (17)

Similarly, F_Q^{-1}(x) is computed in the same manner. Finally, the Wasserstein distance77 is defined as the integral of the absolute difference between the two inverse CDFs across the probability space [0,1]

W_1(P,Q) = \int_0^1 |F_P^{-1}(x) - F_Q^{-1}(x)| dx. (18)

Finally, based on the Wasserstein distance calculated from the expression differences of genes between sibling nodes, we classified the genes into three categories

\begin{cases} \text{Housekeeping gene} & \text{if } W_1(P,Q) < 0.5, \\ \text{Moderately cell-type-related gene} & \text{if } 0.5 \le W_1(P,Q) < 1, \\ \text{Strongly cell-type-related gene} & \text{if } 1 \le W_1(P,Q). \end{cases} (19)

The threshold selection for Wasserstein distance is based on Supplementary Fig. 14, using SubCluster 1 and SubCluster 2 in Siletti-1 (Fig. 4) as an example to demonstrate its rationale. When the Wasserstein distance is less than 0.5, the gene expression distributions are similar. For distances between 0.5 and 1, there are notable differences in means, though some overlap remains, indicating moderate differences in gene expression. When the distance exceeds 1, the first quartile for the cluster with higher mean expression surpasses the third quartile of the other, indicating significant differences in gene expression. In addition, we systematically reviewed three traditional differential gene analysis methods in Supplementary Note 1.6.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The author presents CellScope, an innovative framework for constructing high-resolution cell atlases at multiple clustering levels. Overall, the paper is innovative, with a reasonable methodology and sufficient experimental design. The work is promising, as it has the potential to be widely adopted by researchers, leading to a highly cited paper. However, Seurat and Scanpy are currently well-established analytical frameworks. If the author aims for CellScope to achieve similar recognition and impact, the following issues may need to be addressed.\n--Major points\n1. Although the author effectively introduces the manifold fitting method in the introduction, it lacks a detailed comparative analysis of its mathematical models against existing frameworks, such as Seurat and Scanpy, particularly in terms of discussing the advantages and limitations of manifold fitting.\n2. The paper mentions several modules of CellScope but does not conduct a systematic ablation analysis to quantify the contribution of each module to the final performance. It is recommended that the author perform ablation experiments to clarify the role and contribution of each module in the overall method.\n3. In the model performance testing section 1:\n(1) The comparison of Running Time Ratio in Figure 2b is inconsistent with the results in Supplementary Table S4.\n(2) Among the five metrics used to evaluate gene selection effectiveness, the calculation result of Neighborhood Purity is missing.\n(3) Supplementary Figure S4 is not used to evaluate the effectiveness of gene selection. Please carefully check the correspondence between the supplementary files and the main text.\n(4) Gene selection is a crucial component of the analysis framework, as it enables the identification of a rich set of genes. Although the author has demonstrated the effectiveness of gene selection in CellScope, to surpass Seurat and Scanpy, it is recommended to incorporate statistical tests to further validate its reliability.\n4. In the model performance testing section 2:\n(1) In Figure 3, panels d and e, regarding the F1 Score of Non-Rare Type and F1 Score of Rare Type, it is recommended to include an explanation of how the F1 Score is calculated for better clarity.\n(2) There is a lack of theoretical background or explanation for Non-Rare Type (>20%) and Rare Type (<5%).\n5. In the model performance testing section 3:\nAlthough the author demonstrates the application of CellScope in brain cell atlases, the diversity and complexity of the experimental data are somewhat limited. It is recommended to further validate the performance of CellScope’s tree visualization method on a broader range of brain datasets, such as those from different species or brain regions, to enhance its generalizability.\n6. In the model performance testing section 4:\nThe subdivision between Step 10 and Step 11 in the CellScope analysis is unclear, as Supplementary Figure S5 does not adequately illustrate these steps.\n7. In the model performance testing section 5:\n(1) The \"num_pca=100\" used in the code is inconsistent with the optimal \"num_pca=500\" shown in Figure 6g of the paper.\n(2) Figure 6 only presents data with fewer than 50,000 cells. However, given that Seurat and Scanpy are popular frameworks known for efficiently handling large-scale datasets, data with fewer than 50,000 cells may not be sufficient to fully demonstrate their capabilities. It is recommended to include performance testing on the remaining datasets with more than 50,000 cells in the supplementary figures.\n8. To facilitate broader adoption of CellScope by other researchers, the availability of the code (https://cellscope.readthedocs.io/en/latest/) should be improved. At a minimum, it should ensure that all analysis framework images are provided by CellScope, rather than requiring users to visualize the results after generating them.\n9. The paper lacks a detailed discussion on how CellScope addresses the challenges faced by Seurat and Scanpy in a dialectical manner, particularly the features that Seurat and Scanpy possess but CellScope currently lacks. Additionally, the author should clearly outline at the end of the manuscript how CellScope has the potential to become the next leading framework for single-cell sequencing data analysis.\n--Minor points\n1. There are grammatical issues in the manuscript's figure legends, including the use of parentheses, spacing, and so on.\n2. The cell numbers of Oligodendrocyte 1 and Oligodendrocyte 2 on page 7 are inconsistent with those in Figure 4a.\n3. Are the numbers for Microglia (157) and Vascular (11) in Figure 4a correct?\n4. The values for Y_initial and layer in the link (https://cellscope.readthedocs.io/en/latest/Gene Analysis Through Different Layer.html) are unclear, which prevented the target figure from being generated.\n5. The link (https://cellscope.readthedocs.io/en/latest/Tutorials.html) lacks a comparison of quantitative metrics, and it is recommended that the \"CellScope.cs.Visualization\" generate the visualization figures automatically, eliminating the need for users to draw them manually. The same should apply to other Visualization sections.\n6. I tried running the code from https://cellscope.readthedocs.io/en/latest/Visualization on Marker Gene.html, but I found that the results do not match the expected ones.\n(Remarks on code availability)\nThe results of the paper are reproducible, and the code provides detailed resources for the community. It also includes sufficient instructions for installation and running the application. I was able to install and run the code successfully; however, there were some discrepancies in the reproduced results, which I have commented on and shared with the author.", "reviewer_2": "In this manuscript，the authors establish a pipeline, CellScope, to construct high-resolution cell atlases at multiple clustering levels. The pipeline first employs a two-step manifold fitting process to filter out technical noise and housekeeping gene noise from the raw data, followed by agglomerative clustering for precise hierarchical classification. The experiments are detailed, particularly in providing the sources and access links for all benchmark datasets in the Supplementary File, which provides enough detail for reproducing the work. However, there are still some shortcomings that need to be addressed. I have the following questions for the authors:\n[comment-1]The manifold fitting method has been proposed and applied to single-cell analysis, as demonstrated in the paper titled \"Single-Cell Analysis via Manifold Fitting: A Framework for RNA Clustering and Beyond\", published in Proceedings of the National Academy of Sciences of the United States of America (PNAS). What are the differences between the method proposed in this paper and existing methods? What are the innovations? If the manifold fitting method used in proposed method is the same with scAMF, the reference should be clearly marked where the manifold fitting method appears. Also, the difference and relationship should be clearly stated in the paper. Furthermore, the steps of the manifold fitting method should not occupy a significant portion of the text, as this method has already been proposed. Readers can refer to the original text for specific details. The authors should focus on introducing the innovative steps of the proposed method.\n[comment-2] In the cell clustering task, the authors compared their method with Scanpy and Seurat. Although Scanpy (2018) and Seurat (2019) are widely used pipelines, there are also many new methods specifically for clustering tasks in recent years. The authors should conduct comparative experiments with these new methods and analyze the results.\n[comment-3] In the Runtime Comparison experiments, CellScope demonstrated superior computational efficiency compared to Scanpy and Seurat. However, how does its memory usage compare to these baselines? In single-cell analyses, memory consumption is equally as critical as time efficiency, as it directly impacts the ability of a server to process larger datasets. It is recommended that the authors add an additional row of data in the Table S4, which compares the memory usage of the baselines methods when running on the same dataset.\n[comment-4] In the Supplementary File, the background colors of the experimental data in the tables presenting are quite confusing. For instance, in Table S7, the result of 1.00 for Yan of CellScope is highlighted in green, whereas the same value of 1.00 for Siletti-1 of CellScope is marked in light green. Additionally, the result of 1.00 for Siletti-1 of Scanpy is indicated in red. It appears that the intensity of the background colors does not correspond to the values of the experimental results. Therefore, it is recommended that the authors clarify the significance of the color gradients in the table captions.\n[comment-5] There is a limited number of references from the past three years. It is recommended to include more recent references to enhance the timeliness and relevance of the manuscript.\n[comment-6] The challenges addressed by this work should be explained in Introduction Section in details with examples.\n[comment-7] To our knowledge, agglomerative clustering has a high computational complexity and is not suitable for large- scale data. The authors should analyze the complexity of hierarchical clustering and conduct experiments on large-scale datasets to analyze its performance.\n[comment-8] Is the agglomerative clustering method used in the proposed approach original or pre-existing? If it is pre- existing, the authors should clearly indicate the references and provide an explanation.\n(Remarks on code availability)\nThe code is OK."}, "author_response": "Response to Reviewer # 1\nThe author presents CellScope, an innovative framework for constructing high-resolution cell atlases\nat multiple clustering levels. Overall, the paper is innovative, with a reasonable methodology and\nsufficient experimental design. The work is promising, as it has the potential to be widely adopted\nby researchers, leading to a highly cited paper. However, Seurat and Scanpy are currently well-\nestablished analytical frameworks. If the author aims for CellScope to achieve similar recognition\nand impact, the following issues may need to be addressed.\nMajor Points\n• Q1. Although the author effectively introduces the manifold fitting method in the introduction,\nit lacks a detailed comparative analysis of its mathematical models against existing frameworks,\nsuch as Seurat and Scanpy, particularly in terms of discussing the advantages and limitations\nof manifold fitting.\nResponse: We thank the reviewer for this insightful comment. We have addressed this\nconcern through two complementary approaches:\n– At the ending of subsection \"Overview of CellScope workflow\", we have added a concise\ncomparison highlighting the mathematical distinctions between CellScope and existing\nframeworks (Page 5, Line 121–129). Our addition states: \"CellScope differs funda-\nmentally from existing frameworks through its comprehensive mathematical\napproach. While Seurat employs variance-mean relationship models for gene\nselection, linear PCA for dimensionality reduction, and flat Louvain cluster-\ning, and Scanpy uses standardized variance-based HVG selection with similar\nlinear PCA and Leiden clustering, CellScope introduces a manifold-based\nframework that: (1) adaptively identifies signal genes through manifold seed\ndetection rather than parameter-dependent top-k selection, (2) preserves\nboth local and global cellular relationships via manifold fitting instead of\nlinear subspace assumptions, and (3) constructs multi-level hierarchical struc-\ntures through agglomerative clustering rather than single-level partitioning.\"\n– Due to space constraints in the manuscript, we provide a comprehensive mathe-\nmatical framework comparison to address your specific concerns in the supplemen-\ntary (Supplementary Note 1.1). Seurat and Scanpy employ variance-based statistical\napproaches rooted in linear assumptions: Seurat uses variance-mean relationship mod-\neling with loess regression for gene selection, followed by standard PCA and Louvain\n1\nmodularity optimization for flat clustering. Scanpy similarly relies on standardized\nvariance-based gene selection and PCA, but employs the improved Leiden algorithm for\ncommunity detection. In contrast, CellScope introduces a manifold-based framework\nconsisting of three synergistic stages: (1) geometric identification of manifold seeds\nand highly reliable cliques for signal-noise separation, (2) two-stage manifold fitting\nthat projects low-density outliers toward high-density regions to reduce technical noise,\nand (3) agglomerative hierarchical clustering using locally adaptive similarity graphs.\nThe key advantages of manifold fitting include: geometric gene selection for enhanced\nbiological relevance, preservation of non-linear cellular relationships, natural hierarchical\norganization, adaptive parameter selection, and integrated molecular identity classifi-\ncation. The primary limitations involve stronger manifold structure assumptions and\nhigher computational complexity, though the latter is effectively addressed through\nsubsampling strategies for large datasets. We also provide Table R1 (correspond-\ning to Supplementary Table S1) that systematically compares the mathematical\nframeworks, methodological approaches, and advantages/limitations across all three\nmethods.\nAspect Seurat Scanpy CellScope\nGene Selection Loess regression-based vari- Normalized dispersion ra- Geometric manifold seeds\nApproach ance modeling. Top-k tios with percentile cutoffs. with signal-noise separa-\nhighly variable genes selec- Statistical dispersion rank- tion. Parameter-free adap-\ntion with manual threshold ing with threshold parame- tive selection via reliable\ntuning. ters. cliques.\nDimensionality Linear PCA using SVD. Standard PCA with k-NN Two-stage manifold fitting:\nReduction Projects data onto princi- graph construction. Lin- structure identification and\npal components maximiz- earassumptionswithgraph projection denoising. Pre-\ning global variance. topology. serves local and global rela-\ntionships.\nClustering Louvain community detec- Leiden algorithm with con- Hierarchicalclusteringwith\nMethod tiononk-NNgraph. Modu- nectivity constraints. Im- adaptive similarity graphs.\nlarityoptimizationwithflat proved community quality Average linkage generates\nclustering output. over Louvain. tree structure.\nKey Well-established with Python integration with Biologically relevant gene\nAdvantages broad adoption. Computa- preprocessing tools. Better selection. Non-linear\ntionally efficient. Good for clustering quality. Good preservation. Hierarchical\nwell-separated cell types. scalability. organization. Parameter-\nfree operation.\nPrimary Linear assumptions miss Similar linear limitations. Strong manifold assump-\nLimitations non-linear relationships. May miss relevant genes. tions. Higher computa-\nManual parameter tuning Single-levelclusteringstruc- tionalcomplexity. Requires\nrequired. Flat clustering ture. geometric interpretation.\nonly.\n3\n• Q2. The paper mentions several modules of CellScope but does not conduct a systematic\nablation analysis to quantify the contribution of each module to the final performance. It is\nrecommended that the author perform ablation experiments to clarify the role and contribution\nof each module in the overall method.\nResponse: Thank you for this suggestion. We have conducted a systematic ablation study to\nquantifythecontributionofeachkeycomponentinCellScope, including data normalization,\nPCA preprocessing, manifold fitting, and graph-based clustering.\nAs summarized in Table R2 (corresponding to Figure 6 in the revised manuscript, and\nSupplementary Table S19), the complete CellScope pipeline achieves the highest average\nARI of 0.88 across 36 benchmark scRNA-seq datasets, outperforming all ablated variants:\n0.79 without normalization, 0.76 without PCA, 0.74 without manifold fitting, and 0.80\nwithout graph-based clustering. These results indicate that each component contributes\nsignificantly and complementarily to the overall performance. Wilcoxon signed-rank tests\nfurther support this, with all p-values for ablated variants being less than 0.01, confirming\nthe statistical significance of each module’s contribution.\nBenchmark scRNA-seq Datasets (Maximum in bold, second maximum underlined)\nWithout\nData Name CellScope\nNormalization PCA Manifold Fitting Graph-based Clustering\nYan 0.83 0.80 0.83 0.77 0.83\nGoolam 0.99 0.91 0.54 0.77 0.95\nPollen 0.90 0.93 0.91 0.80 0.89\nWang 0.90 0.46 0.65 0.83 0.85\nDarmanis 0.75 0.69 0.66 0.71 0.75\nUsoskin 0.96 0.39 0.15 0.72 0.66\nXin 0.90 0.24 0.10 0.82 0.74\nKeller(I) 0.75 0.79 0.75 0.75 0.50\nMuraro 0.95 0.68 0.87 0.87 0.74\nNHGRI 0.93 0.91 0.93 0.89 0.86\nKlein 0.97 0.83 0.95 0.92 0.80\nZeisel 0.82 0.81 0.76 0.66 0.52\nLake 0.81 0.86 0.22 0.39 0.71\nKeller(P) 0.88 0.79 0.62 0.76 0.55\nHan(B) 0.89 0.50 0.90 0.72 0.76\nSiletti-1 0.93 0.93 1.00 0.93 1.00\nTirosh 0.90 0.53 0.51 0.62 0.86\nSiletti-2 0.98 0.87 0.98 0.88 0.97\nBaron(H) 0.95 0.93 0.89 0.84 0.86\nSiletti-3 0.90 0.99 0.91 0.95 0.84\nSiletti-4 0.98 0.87 0.96 0.77 0.99\nSiletti-5 0.93 0.92 0.74 0.93 0.73\nSiletti-6 0.99 0.99 0.88 0.79 0.87\nUlrich(H) 0.80 0.79 0.66 0.63 0.78\nLi 0.84 0.88 0.86 0.61 0.78\nLi(H) 0.95 0.95 0.94 0.17 0.95\nQiu(L) 0.64 0.57 0.59 0.31 0.67\nUlrich 0.83 0.85 0.71 0.59 0.75\nQiu(E) 0.73 0.71 0.78 0.64 0.73\nAyhan 0.83 0.82 0.83 0.66 0.83\nPosner 0.81 0.82 0.68 0.85 0.86\nLi(M) 0.79 0.74 0.78 0.56 0.80\nYu 0.80 0.84 0.87 0.82 0.76\nHoo 0.95 0.97 0.94 0.78 0.92\nYao 1.00 0.97 0.99 0.96 0.76\nLi(HA) 0.91 0.89 0.86 0.82 0.94\nAverage 0.88 0.79 0.76 0.74 0.80\n5\n• Q3. In the model performance testing section 1:\n– (1). The comparison of Running Time Ratio in Figure 2b is inconsistent with the results\nin Supplementary Table S4.\n– (2). Among the five metrics used to evaluate gene selection effectiveness, the calculation\nresult of Neighborhood Purity is missing.\n– (3). Supplementary Figure S4 is not used to evaluate the effectiveness of gene selection.\nPlease carefully check the correspondence between the supplementary files and the main\ntext.\n– (4). Gene selection is a crucial component of the analysis framework, as it enables the\nidentification of a rich set of genes. Although the author has demonstrated the effective-\nness of gene selection in CellScope, to surpass Seurat and Scanpy, it is recommended to\nincorporate statistical tests to further validate its reliability.\nResponse:\n– (1). We appreciate the reviewer’s careful examination of our runtime analysis. The\ninconsistency between the original Figure 2b and Supplementary Table S4 stemmed\nfrom uncontrolled variations in computational environments during preliminary testing.\nTo resolve this rigorously, we implemented three major improvements:\n1. All experiments were re-executed on a unified Google Colab platform (44-core\nCPU, 150 GB RAM) with fixed random seeds. This eliminates hardware/software\nvariability while maintaining reproducibility for readers.\n2. WereplacedshowingtheRunningtimeratiowithdirectvisualizingtimedistributions\nin Figure R1 (corresponding to Figure 2b in the revised manuscript), stratified by\ndataset scales:\n∗ Small (<3,000 cells)\n∗ Medium (3,000–50,000 cells)\n∗ Large (>50,000 cells)\nThis tripartite division better reveals algorithmic scalability across real-world usage\nscenarios. Meanwhile, quantitative comparison showed that CellScope achieved the\nfastest runtime on 26 datasets and ranked second on 9 others, demonstrating both\nconsistency and superior efficiency across various scales.\n3. To evaluate practical usability, we additionally compared CellScope and Scanpy on\ntwo widely accessible personal computers:\n∗ MacBook (8-core CPU, 16GB RAM)\n6\n∗ iMac (10-core CPU, 16GB RAM)\nResults demonstrate that CellScope maintains significant time efficiency advantages\nover competing methods even on hardware with limited memory and processing\npower, see Table R3 (corresponding to revised Supplementary Table S24).\nThe revised analyses collectively demonstrate that CellScope achieves both technical\nrigor and practitioner-friendly performance.\nLess than 3,000 cells 3,000 to 50,000 cells Larger than 50,000 cells\nTime (s)\nTTiimmee ((ss)) Time (s)\n1000 10000 10000\n100 1000 3000\n100\n10 1000\n10\n1 300\nCellScope Scanpy Seurat scLEGA scDCCA CellBRF CellScope Scanpy Seurat scLEGA scDCCA CellBRF CellScope Scanpy Seurat scLEGA scDCCA CellBRF\nFigure R1: Execution time comparison across benchmark datasets of varying sizes. Boxplots\ndisplay the runtime (in seconds) for six methods across datasets stratified by cell number: less than\n3,000 cells, 3,000 to 50,000 cells, and more than 50,000 cells. Each point represents the runtime on\na specific dataset. CellScope consistently demonstrates lower computational time across all dataset\nsizes compared to other methods. Absent boxes indicate method failure or timeout.\n– (2). Thank you for pointing out the omission. We have now updated the results to\ninclude Neighborhood Purity, which We define as the average fraction of each cell’s k\nnearest neighbors N (x ) = {x ,x ,...,x } that share its true label y :\nk i i1 i2 i\nk\ni\nPurity(x ) = 1 X k I (cid:16) y = y (cid:17) ,\ni\nk\nij i\nj=1\nwhere y is the true label of x and I(·) is the indicator function that equals 1 if\nij ij\nthe condition is true and 0 otherwise. Intuitively, Neighborhood Purity measures how\nconsistently a cell is surrounded by others of the same type in feature space. We set\nk = ⌊N/20⌋, where N is the number of cells. The overall Neighborhood Purity is the\nmean of Purity (x ) across all cells. Definitions and results of Neighborhood Purity are\ni\nreported alongside the other four metrics in the revised manuscript and detailed in the\nSupplementary Note 1.5 and Supplementary Table 13.\n– (3).Thank you for pointing this out. We did not specify which sub-panels of Supple-\nmentary Figure S4 (now S7) were being referenced. Supplementary Figure S7a\npresents a unique ablation study designed to demonstrate the advantages of CellScope’s\nand MacBook (Scanpy/CellScope indicates the runtime ratio between Scanpy and CellScope, the\nfastest runtime on the same device is in bold)\niMac MacBook\nData Name #Cells\nCellScope Scanpy Scanpy/CellScope CellScope Scanpy Scanpy/CellScope\nYan 90 2.11 1.49 0.70 4.42 2.44 0.55\nGoolam 124 1.75 0.95 0.54 3.79 1.05 0.28\nPollen 249 3.86 1.38 0.36 2.11 1.51 0.71\nWang 457 4.02 2.32 0.58 3.96 2.83 0.72\nDarmanis 466 4.27 1.99 0.47 3.00 2.61 0.87\nUsoskin 622 4.47 2.33 0.52 3.52 3.19 0.91\nXin 1600 8.37 6.37 0.76 7.26 11.57 1.59\nKeller(I) 1887 7.08 6.69 0.95 4.92 12.46 2.53\nMuraro 2126 7.63 6.06 0.79 5.24 11.00 2.10\nNHGRI 2642 8.80 8.04 0.91 6.59 15.13 2.30\nKlein 2717 9.43 10.73 1.14 6.97 18.64 2.67\nZeisel 3005 9.47 9.46 1.00 8.06 17.54 2.18\nLake 3042 9.90 11.02 1.11 8.14 19.76 2.43\nKeller(P) 3384 10.28 11.42 1.11 8.06 20.47 2.54\nHan(B) 4038 10.48 12.34 1.18 7.23 22.94 3.17\nSiletti-1 4713 17.57 22.41 1.28 17.00 33.91 1.99\nTirosh 5902 13.40 21.74 1.62 13.19 31.90 2.42\nSiletti-2 6877 12.48 60.24 4.83 50.45 55.01 1.09\nBaron(H) 8569 16.31 31.92 1.96 25.15 59.49 2.36\nwang 23349 71.06 101.83 1.43 214.88 205.57 0.96\nSiletti-4 27111 71.67 122.85 1.71 163.35 227.78 1.39\nSiletti-5 28724 99.24 125.73 1.27 226.77 216.81 0.96\nSiletti-6 47416 128.43 254.22 1.98 297.60 522.63 1.76\nUlrich(H) 59738 204.93 257.11 1.25 657.77 522.64 0.79\nLi 67996 206.72 623.48 3.02 570.39 1084.52 1.90\nLi(H) 72788 160.05 362.35 2.26 402.52 697.40 1.73\nQiu(L) 76732 252.96 708.88 2.80 813.22 1320.67 1.62\nUlrich 77536 305.10 379.26 1.24 1080.03 686.92 0.64\nQiu(E) 93695 247.87 1091.89 4.41 718.80 1810.65 2.52\nAyhan 129905 388.96 1197.86 3.08 901.67 2465.14 2.73\nPosner 130908 377.77 989.77 2.62 1009.96 1933.85 1.91\nLi(M) 147523 435.98 885.89 2.03 1094.80 1588.03 1.45\nYu 155232 948.94 1550.88 1.63 2594.01 3476.82 1.34\nHoo 158978 757.43 1277.41 1.69 2249.08 3106.02 1.38\nYao 159738 934.07 1552.77 1.66 2581.86 2890.68 1.12\nLi(HA) 265767 1440.68 2820.06 1.96 3470.15 5070.66 1.46\n8\ngene selection method. In this figure, we systematically replaced CellScope’s gene selec-\ntion component with alternative approaches (Seurat VST and Scanpy HVG) while keep-\ning the graph-based clustering algorithm intact of CellScope. The results show that when\nCellScope’s gene selection is substituted with these alternatives (SeuratVST.CellScope\nand ScanpyHVG.CellScope), the clustering performance (ARI) decreases compared\nto the full CellScope method, although it still outperforms the baseline comparison\nmethods (Seurat and Scanpy). This ablation analysis directly validates the effectiveness\nand necessity of our gene selection approach. We have revised the manuscript by adding\na paragraph (Page 7, line 183–189) to describe this ablation study in detail to improve\nclarity and readability. Supplementary Figure S7 c–f correspond to Figure 2 d–g\nof the main manuscript, illustrating a direct comparison between CellScope and Scanpy\ngene selection, which were already correctly referenced in the original manuscript.\n– (4). To rigorously assess the overall effectiveness of CellScope’s gene selection method,\nwe employed a comprehensive statistical framework based on multiple evaluation metrics,\nincluding average silhouette width (ASW), variance ratio, cell-type local inverse Simpson\nindex, k-NN classification accuracy and Neighborhood Purity. For each metric, we\ncomputed the performance scores across all benchmark datasets for CellScope and the\ncomparison methods (e.g., Seurat, Scanpy, etc.). We then applied paired Wilcoxon\nsigned-rank tests to compare CellScope’s scores with those of other methods under the\nfollowing hypotheses.\nH : There is no significant difference in performance across datasets (1)\nselection methods, with Wilcoxon signed-rank tests consistently yielding p-values less\nthan 0.05, indicating strong statistical evidence of superiority. For example, in the ASW\nmetric,CellScopeshowedsignificantlybetterperformancethanScanpy(p-values=1.89×\n10−7 by Wilcoxon test), as well as than other classical methods such as Disp and VST.\nEven when compared against recently proposed methods, CellScope maintained a clear\nadvantage. For instance, in Neighborhood Purity, CellScope significantly outperformed\nMixhvg (p-values = 5.40×10−9 by Wilcoxon test), and in variance ratio, CellScope\nwas also significantly superior to HRG (p-values = 1.67×10−6 by Wilcoxon test). In\naddition, we use the win ratio—the proportion of datasets in which CellScope achieves a\nbetter score than the comparison method—to more intuitively demonstrate the excellent\nperformance of CellScope. As shown in Table R4, the win ratio exceeds 0.8 in the vast\nmajority of comparisons, indicating that CellScope outperforms existing gene selection\n9\nmethods across a wide range of datasets and evaluation criteria. The consistently\nsignificant Wilcoxon p-values and high win ratios underscore the robustness, reliability,\neffectiveness, and generalizability of CellScope’s gene selection strategy. Full statistical\ntest results are provided in Table R4 and Supplementary Table S15.\nevaluation metrics. Each cell reports the Wilcoxon signed-rank p-value and the performance win\nratio across datasets.\nMetric Method Wilcoxon p Win Ratio\nScanpy 1.89×10−7 0.944\nDisp 1.67×10−5 0.806\nVST 8.44×10−5 0.694\nAverage Silhouette Width\nMixhvg 1.52×10−7 0.944\nFEAST 0.00286 0.857\nHRG 5.24×10−5 0.957\nScanpy 6.01×10−7 0.861\nDisp 7.35×10−4 0.667\nVST 5.57×10−6 0.806\nKNN Classification Accuracy\nMixhvg 4.28×10−4 0.722\nFEAST 0.00153 0.857\nHRG 0.00048 0.739\nScanpy 2.15×10−7 0.889\nDisp 0.00451 0.639\nVST 0.0119 0.639\nCell-type Local Inverse Simpson Index\nMixhvg 5.12×10−2 0.833\nFEAST 0.0001831 0.929\nHRG 0.000149 0.783\nScanpy 1.46×10−11 1.000\nDisp 2.16×10−8 0.972\nVST 6.50×10−9 0.972\nVariance Ratio\nMixhvg 1.46×10−11 1.000\nFEAST 0.00201 0.857\nHRG 1.67×10−6 0.870\nScanpy 2.96×10−8 0.889\nDisp 4.02×10−8 0.889\nVST 0.00125 0.806\nNeighbor Purity\nMixhvg 5.40×10−9 0.889\nFEAST 0.0006104 0.857\nHRG 6.56×10−6 0.913\n11\n• Q4. In the model performance testing section 2:\n– (1). In Figure 3, panels d and e, regarding the F1 Score of Non-Rare Type and F1\nScore of Rare Type, it is recommended to include an explanation of how the F1 Score is\ncalculated for better clarity.\n– (2). There is a lack of theoretical background or explanation for Non-Rare Type (> 20%)\nand Rare Type (< 5%).\nResponse:\n(1). WeacknowledgethatF1Scorehasmultiplevariants(macro, micro, weighted)whichmaycause\nconfusion in multi-class scenarios. More importantly, F1-score is not the most appropriate\nmetric for evaluating the quality of CellScope’s 2D visualization results in distinguishing rare\nand non-rare cell populations. Therefore, we have replaced F1 Score with Silhouette Score,\nwhich directly measures the separation quality of different cell types in the 2D embedding\nspace. Specifically, we calculate the average Silhouette Score for rare and non-rare cell\ntypes separately using their true labels, providing a more intuitive assessment of CellScope’s\nperformanceinenhancingcellularclustercompactnessandseparability. Thedetaileddefinition\nof the Silhouette Score is provided below or can be found in the Supplementary Note\n1.4. The Silhouette Score [2] is an important metric for evaluating the consistency between\ntwo-dimensional visualizations and true labels, which quantifies the reliability of visualizations\nby measuring the degree of sample clustering in the visualization space. For a given sample\nx , its Silhouette Score is defined as\ni\nb(i)−a(i)\ns(i) = ,\nmaxa(i),b(i)\nwhere a(i) = 1 P d(i,j) represents the average distance between the sample and\n|Ci|−1 j∈Ci,j̸=i\nother samples in the same cluster, reflecting its cohesion with samples of the same cluster;\nb(i) = min 1 P d(i,j) denotes the average distance from the sample to the nearest\nC k ̸=Ci |C k | j∈C k\nneighboring cluster, reflecting its separation from samples of different classes. The range of\nthe Silhouette Score is [−1,1]. When s(i) approaches 1, it indicates ideal clustering of the\nsample; when it is close to 0, it suggests the sample may lie near cluster boundaries; and\nwhen it is less than 0, it implies potential misclassification of the sample.\nFor the evaluation of the entire dataset, the Silhouette Score S is defined as the arithmetic\nmean of the Silhouette Scores of all samples:\nX\nS = s(i),\nN\ni=1\n12\nwhere N is the total number of samples. This global metric reflects the overall quality\nof the visualization results, with higher values indicating better consistency between the\nvisualization and the true labels.\nFor the evaluation of a specific cluster C, the Silhouette Score is calculated by labeling\nsamples in the target cluster as 1 and other samples as 0. Specifically, a new binary label\nvector y is constructed, where y = 1 if and only if x ∈ C, otherwise y = 0. The mean\ni i i\nSilhouette Score of all samples based on this binary labeling is then computed to obtain the\nSilhouette Score of cluster C:\nX\nS = s(i).\nC\nN\ni=1\nThis definition maintains consistency with the standard Silhouette Score calculation method\nwhile enabling specialized evaluation of the target cluster’s aggregation in the visualization\nspace. It directly quantifies the cluster’s cohesion within the visualization results and its\nseparation from other clusters.\n2% 5% 10% 20%\n0.75 0.75 0.4\n0.6\n0.50 0.50 0.3\n0.4 0.2\n0.25 0.25 0.2 0.1\n0.00 0.00 0.0 0.0\n−0.25 −0.1\n−0.25\nCellScope Scanpy CellScope Scanpy CellScope Scanpy CellScope Scanpy\n0.5 0.6 0.6 0.6\n0.4\n0.3 0.4 0.4 0.4\n0.2\n0.2 0.2\n0.1 0.2\n0.0 0.0\nCellScope Scanpy CellScope Scanpy CellScope Scanpy CellScope Scanpy\nsepyt\nllec\nerar\nfo\nIS\nsepyt\nllec\nerar-non\nfo\nIS\np=0.0268 p=0.036 p=0.00286 p=0.00847\np=0.0192 p=0.00984 p=0.422 p=0.0559\nFigure R2: Silhouette Score of rare cell types and non-rare cell types.\n(2). Our choice of 5% as the rare cell types threshold is motivated by statistical significance\nprinciples commonly used in hypothesis testing (α = 0.05), and this threshold has been widely\nadopted in single-cell literature for defining rare cell populations [1]. Non-rare cell types are re-\ndefined as all cell populations that do not meet the rare cell criteria (i.e., comprising ≥ 5% of\nthe total cells), which is different from the way in the original manuscript. To demonstrate the\nrobustness of our approach, we conducted comprehensive sensitivity analysis using multiple\nrare cell thresholds: 2%, 5%, 10%, 20%. For each threshold, rare cell types are defined as\npopulations below the threshold, while non-rare cell types comprise all remaining populations.\nRemarkably, across all threshold conditions, CellScope demonstrated significantly superior\n13\nperformance over Scanpy in both rare and non-rare cell types identification, as quantified by\nSilhouette Scores shown in Figure R2. Wilcoxon signed-rank tests revealed maximum p-\nvalues of merely 0.036 for rare cell categories, indicating statistically significant improvements.\nTheperformanceadvantagewasparticularlypronouncedatthe5%threshold, whereCellScope\nachieved a p-value of 0.00984 for non-rare cell populations, further substantiating its robust\nperformance advantage.\n14\n• Q5. In the model performance testing section 3: Although the author demonstrates the appli-\ncation of CellScope in brain cell atlases, the diversity and complexity of the experimental data\nare somewhat limited. It is recommended to further validate the performance of CellScope’s\ntree visualization method on a broader range of brain datasets, such as those from different\nspecies or brain regions, to enhance its generalizability.\nResponse: We appreciate the reviewer’s valuable suggestion regarding the validation of\nCellScope’s generalizability across diverse brain datasets. To address this concern, we have\nconducted additional experiments on datasets from different brain regions and species.\n– Human Brain Data Expansion: Beyond our original analysis of the red nucleus\nregion within the midbrain from the Siletti-1 dataset , we extended our validation to\nthe thalamic region of the same dataset [3]. This cross-brain-region analysis revealed\nconsistent patterns in oligodendrocyte classification, where CellScope successfully identi\nfied two distinct subtypes (OL1 and OL2) with differential expression of five key marker\ngenes (LINC01608, CTNND2, LAMA2, OPALIN, and RBFOX1). Notably, CellScope\nachieved superior performance in the thalamic region with an ARI of 0.998, significantly\noutperforming Scanpy’s 0.698, and perfectly replicated the Oligodendrocyte subtype\ndiscovery pattern observed in the red nucleus region.\n– Cross-Species Validation: To demonstrate cross-species applicability, we applied\nCellScope to a mouse primary motor cortex dataset [4]. Our method achieved an ARI\nof 0.837 when comparing to original subclass labels, successfully distinguishing major\nneuronal subclasses. Furthermore, we identified two novel L5 IT neuron subtypes (L5 IT1\nand L5 IT2) with distinct functional implications: L5 IT2 appears to mediate long-range\ninformation transfer, while L5 IT1 is more involved in local circuit modulation, providing\nnew cellular-level insights into cortical information processing.\nWe have incorporated a new paragraph in the revised manuscript (Page 11, Line 302–311)\ndescribing these validation results and their biological implications. Furthermore, we have\nprovidedcomprehensiveadditionalanalyzesintheSupplementaryMaterials(Supplementary\nNote 2), including detailed performance metrics and marker gene analyzes. These expanded\nvalidations demonstrate that CellScope’s tree-structured visualization method maintains\nits effectiveness and biological relevance across different brain regions and species, thereby\nenhancing its generalization as suggested by the reviewer.\n15\n• Q6. In the model performance testing section 4: The subdivision between Step 10 and Step\n11 in the CellScope analysis is unclear, as Supplementary Figure S5 does not adequately\nillustrate these steps.\nResponse: To present the analytical results more clearly, we have explicitly labeled the key\nsteps (step 10 and step 11) at appropriate positions in the tree-structured visualization shown\nin Figure 5a of the main text, with detailed explanations provided in the accompanying\ntext. Furthermore, to highlight CellScope’s core capability in distinguishing diseased cells\nfrom healthy ones, the tree-structured visualization in Figure 5a was pruned (represented by\nellipses indicating the pruned sections), which explains why the visualization in the main text\ndisplays fewer than 10 steps. The complete, unpruned visualization is presented in Figure\nR3, where the red dashed box clearly marks the specific sections that were pruned in Figure\n5a. Specifically, the roles of step 10 and step 11 are as follows:\n– Step 10: At this stage, CellScope successfully isolated the monocyte-dendritic cell\nsystem (right node of Step 10 in Figure R3), which specifically comprises classical\nmonocytes, non-classical monocytes, and conventional dendritic cells.\n– Step 11: CellScope further subdivided the monocyte-dendritic cell system into Cluster\n1 (predominantly containing diseased cells) and Cluster 2 (predominantly contain-\ning healthy cells), thereby fully demonstrating CellScope’s exceptional capability in\nidentifying disease states.\nCluster 1 Cluster 2\nStep 11\nStep 10\nNon-Classical Monocyte\nConventional Dendritic Cell\nClassical Monocyte\nFigure R3: Tree-structured visualization generated by CellScope.\n16\n• Q7. In the model performance testing section 5:\n– (1). The \"num_pca=100\" used in the code is inconsistent with the optimal\n\"num_pca=500\" shown in Figure 6g of the paper.\n– (2). Figure 6 only presents data with fewer than 50,000 cells. However, given that\nSeurat and Scanpy are popular frameworks known for efficiently handling large-scale\ndatasets, data with fewer than 50,000 cells may not be sufficient to fully demonstrate\ntheir capabilities. It is recommended to include performance testing on the remaining\ndatasets with more than 50,000 cells in the supplementary figures.\nResponse:\n(1). We thank the reviewer for pointing out this inconsistency. We clarify that the optimal\nPCA dimension is indeed num_pca=100, which matches the parameter used in our code.\nThe original Figure 6g in our draft contained an error and now has been corrected in\nthe revised manuscript.\nTo provide comprehensive evidence for parameter selection, we have conducted a\nsystematic evaluation of clustering performance across multiple PCA dimensions (50,\n100, 200, 300, 400, 500) for all datasets. The results demonstrate that num_pca=100\nachieves optimal performance, while also showing that CellScope maintains stable\nperformance across different PCA dimensions, indicating the robustness of our method.\nThe detailed clustering performance (measured by ARI) for each dataset at each PCA\ndimension are presented in Supplementary Table S18 in the revised manuscript.\n(2). We thank the reviewer for this valuable suggestion regarding the evaluation on large-\nscale datasets. In the revised version, we have addressed this concern by including\ncomprehensive testing on datasets with more than 50,000 cells.\nSpecifically, we have incorporated 13 datasets with cell counts exceeding 50,000, with\nthe largest dataset containing over 260,000 cells. Figure 6h of the revised manuscript\nnow presents the ablation study results of CellScope on these large-scale datasets, with\nthe cell counts for each dataset displayed on the right side of the figure. In addition\nto Figure 6h, comprehensive ablation experiment comparisons on these large-scale\ndatasets are presented in Supplementary Table S19 of the revised manuscript.\n17\n• Q8. To facilitate broader adoption of CellScope by other researchers, the availability of\nthe code (https://cellscope.readthedocs.io/en/latest/) should be improved. At a minimum,\nit should ensure that all analysis framework images are provided by CellScope, rather than\nrequiring users to visualize the results after generating them.\nResponse: We sincerely appreciate the reviewers’ valuable suggestions regarding the usability\nand visualization design of the CellScope tool. Based on user feedback, we have identified\ntwo primary visualization needs:\n– Direct visualization for smaller datasets: Users analyzing smaller datasets require\nstraightforward, complete visualization outputs directly generated by CellScope.\n– Focused visualization for larger datasets: Users working with larger datasets prefer to\nfocus on specific clustering states rather than viewing the complete overview, allowing\nfor more detailed examination of particular regions of interest.\nTo address these diverse requirements, we now provide the following four standardized\nvisualization schemes. We demonstrate CellScope’s visualization outputs using the human\nbrain dataset Siletti-1 [3] as an exemplar case.\n– 1. Complete tree-structured visualization: For smaller datasets requiring direct visualiza-\ntion, we have developed two novel functionalities—\"tree_structure_visualization_static\"\n(static tree visualization) and \"tree_structure_visualization_dynamic\" (dynamic tree\nvisualization)—which generate static and dynamic hierarchical tree visualizations, re-\nspectively. As illustrated in Figure R4, the static visualization features a dual-panel\nlayout: the left panel displays the complete tree nomenclature system, including the root\nnode (Initial), first-level nodes (Cluster 0 to Cluster 4), second-level nodes (e.g., Cluster\n3-1, Cluster 3-2), and third-level nodes (e.g., Cluster 3-2-1, Cluster 3-2-2), while the right\npanel presents the UMAP projection of samples corresponding to each node, with color\ncoding based on CellScope’s clustering results. The dynamic visualization results have\nbeen incorporated into the supplementary materials, demonstrating the complete tempo-\nral evolution of node generation. For implementation details, please refer to the tutorial\nat https://cellscope.readthedocs.io/en/latest/Tree%20Structure%20Construction.html.\n– 2. Branches of tree-structured visualization: For larger datasets requiring focused\nvisualization, we have developed the \"tree_structure_visualization_step\" function for\ngenerating modular diagrams. As shown in Figure R5, Figure R5a displays the root\nnode and first-level nodes, which are automatically saved as \"Initial.pdf\". Additionally,\nthe visualization results for each first-level node are provided with corresponding names,\nsuch as Figure R5b labeled as \"Cluster4.pdf\". Figure R5c shows the two second-level\nCluster 0\nCluster 2\nInitial\nCluster4-2-2\nCluster 4 Cluster4-2\nCluster 3\nCluster3-1 Cluster4-2-1\nCluster4-1\nCluster3-2\nCluster4-1-2\nCluster4-1-1\nCluster3-2-1\nCluster3-2-2\nFigure R4: The tree-structured visualization of human brain cell atlas automatically generated by\nCellScope.\nnodesconnectedtothefirst-levelnodecorrespondingtoCluster4,whichareautomatically\nsaved by CellScope as \"Cluster4-1&2\". All generated files are in high-resolution vector\nformat and can be easily assembled using standard PDF editing software such as Adobe\nIllustrator. Based on our testing, a complete visualization can be accomplished within\n10 minutes.\n– 3. Styles matching the manuscript: For users requiring publication-ready visualiza-\ntions, we specifically provide standardized framework PDF files that ensure complete\nconsistency with those in our manuscript. As illustrated in Figure R6, Figure R6a\nconsists of the root node and first-level nodes, while Figure R6b comprises the two\nsecond-level nodes connected to the first-level nodes. Furthermore, through CellScope’s\n\"generate_tree_structured\" and \"visualize_tree_structured\" functions, you can obtain\nindependent visualization PDF files for each node. These files are in vector format and\ninclude pre-designed frameworks that require only simple assembly operations to create\npublication-quality figures.\n– 4. Differential gene expression visualization: For comprehensive biological interpretation,\nwe have also organized differential gene expression results into high-resolution PDF\nfiles. Users can seamlessly integrate these gene expression visualizations into their\ntree-structured figures according to their specific analytical needs and presentation\nrequirements. This modular approach allows for flexible combination of clustering\nresults with corresponding biological insights.\nIt is important to note that due to the inherent complexity of tree structures and the\n19\na b c\nFigure R5: Step-by-step tree structure visualization of the human brain cell atlas automatically\ngenerated by CellScope. a The tree-structured visualization of zeroth-level and first-level nodes. b\nA node called cluster 4 at the first level. c Second-level node connected to first-level node \"Cluster4,:\nnamed as \"Cluster4-1&2\".\na b\nFigure R6: Tree structure node asset library for the human brain cell atlas. a The tree-structured\ndiagram of zeroth-level and first-level nodes. b Framework diagram of connections between second-\nlevel and first-level nodes.\n20\ndiverse perspectives of different users, it would be impractical to automatically generate the\noptimal visualization scheme for all datasets that perfectly matches every user’s preferences.\nTherefore, we have adopted a hybrid approach that combines automatic generation with\nmanual assembly, providing users with the flexibility to create visualizations that best suit\ntheir specific research objectives and aesthetic preferences. We believe this balanced strategy\nwill significantly enhance the user experience with CellScope while maintaining the high\nquality and customizability that researchers require.\n21\n• Q9. The paper lacks a detailed discussion on how CellScope addresses the challenges faced by\nSeurat and Scanpy in a dialectical manner, particularly the features that Seurat and Scanpy\npossess but CellScope currently lacks. Additionally, the author should clearly outline at the\nend of the manuscript how CellScope has the potential to become the next leading framework\nfor single-cell sequencing data analysis.\nResponse (Bingjie): We have addressed this important question by adding a comprehensive\ndiscussion paragraph at the end of discussion section in our revised manuscript. Here we\nquote:\n\"While CellScope addresses key limitations of established frameworks like Seurat\nand Scanpy through its manifold-based gene selection and hierarchical cluster\ning approach, we acknowledge that these established methods possess valuable\nfeatures including mature ecosystems, extensive documentation, and broad data\ntype support that currently exceed CellScope’s scope. Seurat’s modular R-based\narchitecture and Scanpy’s Python integration offer computational accessibility\nand workflow flexibility that have made them community standards. However,\nCellScope’s theoretical foundations—particularly its ability to distinguish bio\nlogical signal from noise and capture cellular hierarchies—position it to become\nthe next leading framework for single-cell analysis. As datasets grow larger\nand more complex, CellScope’s parameter-free operation, superior clustering\nperformance, and discovery-oriented design address critical needs that existing\nmethods struggle to meet. Moving forward, we plan to continuously maintain\nand enhance the CellScope package, including expanding its compatibility with\nemerging data modalities such as spatial transcriptomics and multimodal omics\nintegration. Additionally, we envision systematic reanalysis of large-scale public\ndatabases using CellScope’s framework, which could reveal previously undetected\ncellular subtypes and biological insights across diverse tissues and disease contexts.\nThrough sustained development, community engagement, and comprehensive\nreanalysis efforts, CellScope has the potential to establish itself as the preferred\nframework for researchers seeking to push the boundaries of cellular biology\nunderstanding beyond traditional cell type identification toward comprehensive\nfunctional characterization and novel biological discovery.\"\nWe acknowledge that established frameworks like Seurat and Scanpy possess significant\nadvantages that currently exceed CellScope’s scope, including:\n– Mature ecosystems with extensive third-party extensions\n– Comprehensive documentation and community support\n22\n– Broad data type compatibility (multimodal, spatial, etc.)\n– Established computational accessibility and workflow flexibility\nHowever, CellScope addresses several key limitations of these frameworks through its unique\ntheoretical foundations:\n– Manifold-based gene selection that better distinguishes biological signal from noise\n– Hierarchical clustering approach that captures cellular hierarchies more effectively\n– Parameter-free operation that reduces user bias and improves reproducibility\n– Superior performance on large-scale datasets as demonstrated in our evaluations\nRather than claiming to be the definitive next-generation framework, we position CellScope\nas a complementary approach that could potentially fill important gaps in current single-cell\nanalysis workflows. As datasets become increasingly complex, CellScope’s discovery-oriented\ndesign may offer valuable advantages for researchers seeking to identify novel cellular subtypes\nand biological insights.\nOur development roadmap includes: (1) expanding compatibility with emerging data modali\nties, (2) systematic reanalysis of public databases to validate broader applicability, and (3)\nsustained community engagement to gather feedback and improve functionality. Through\nthese efforts, CellScope aims to contribute meaningfully to the single-cell analysis ecosystem\nrather than replace existing successful frameworks.\n• Q1. There are grammatical issues in the manuscript’s figure legends, including the use of\nparentheses, spacing, and so on.\nResponse: Wethank the reviewer for pointingout the grammatical issuesin the manuscript’s\nfigure legends. We have conducted a thorough review of all figure legends and have corrected\nthe grammatical errors, including proper use of parentheses, spacing, and other formatting\nissues. The revised manuscript now presents consistent and grammatically correct figure\nlegends throughout.\n• Q2. The cell numbers of Oligodendrocyte 1 and Oligodendrocyte 2 on page 7 are inconsistent\nwith those in Figure 4a.\nResponse: We thank the reviewer for pointing out this inconsistency. This was due to a typo\nin the manuscript text. We have re-confirmed our results, and the correct oligodendrocyte\nsubtype counts are OL1=541 and OL2=1592. The corrected numbers have been updated in\nthe revised manuscript text, while Figure 4a remains unchanged as it already displayed the\naccurate data.\n• Q3. Are the numbers for Microglia (157) and Vascular (11) in Figure 4a correct?\nResponse: We thank the reviewer for this important verification question. We have carefully\nre-examined our data analysis and can confirm that the cell counts for Microglia (n = 157)\nand Vascular (n = 11) in Figure 4a are accurate. The relatively low number of Vascular cells\nis consistent with their typical sparse distribution in brain tissue datasets.\n• Q4. The values for Y_initial and layer in the link https://cellscope.readthedocs.io/en/latest-\n/Gene Analysis Through Different Layer.html are unclear, which prevented the target figure\nfrom being generated.\nResponse: Thank you for pointing it out. The error originated from a naming inconsistency\nin the function input variables, which we have now corrected in the tutorial code to ensure\nproper execution.\n• Q5. The link (https://cellscope.readthedocs.io/en/latest/Tutorials.html) lacks a comparison\nof quantitative metrics, and it is recommended that the \"CellScope.cs.Visualization\" generate\nthe visualization figures automatically, eliminating the need for users to draw them manually.\nThe same should apply to other Visualization sections.\nResponse: We sincerely appreciate your valuable suggestions for improving our computa\ntional methodology. Regarding the comparative analysis of quantitative metrics, CellScope\n24\nnow provides five specific clustering accuracy indices: ARI (Adjusted Rand Index),\nNMI (Normalized Mutual Information), ACC (Accuracy), Jaccard index, and F1-Score. Addi\ntionally, we have made available comparative algorithm implementations for Scanpy in the\nCellScope GitHub repository (https://github.com/zhigang-yao/CellScope), facilitating direct\nbenchmarking tests of CellScope’s performance.\nRegarding automatic visualization generation, the comprehensive automation solutions for\nour core hierarchical structure visualization have been detailed in our response to Q8. We\nhave designed both automatic and manual approaches to address two distinct user needs,\nenabling users to achieve visualization results relatively quickly and easily. It is important to\nemphasize that compared to Scanpy and Seurat, CellScope’s visualization outputs provide\nsignificantly richer information content, which inherently makes the visualization process\nmore complex and difficult. We will continue to update and enhance the tool’s usability in\nfuture developments.\nFor other visualization sections, it should be noted that some plotting code is not directly\nrelated to CellScope’s core functionality but rather serves the presentation needs of the\nmanuscript. Therefore, we have not included these codes in the tutorial documentation but\nhave made them available on our GitHub repository for users who wish to replicate the\nexact figures shown in the paper. The complete automated workflow for core CellScope\nfunctionalities is available at https://cellscope.readthedocs.io/en/latest/Tutorials.html, while\nadditional manuscript-specific visualization codes can be found in our GitHub repository\n(https://github.com/zhigang-yao/CellScope).\n• Q6. I tried running the code from https://cellscope.readthedocs.io/en/latest/Visualization on\nMarker Gene.html, but I found that the results do not match the expected ones.\nResponse: We thank you for pointing this out. We identified that the tutorial’s code\nexecution issues stemmed from the omission of numpy package imports and standardized\nmarker gene nomenclature references. We have now comprehensively updated the tutorial\ncontent to include these critical components, ensuring the code runs as intended.\nRemarks on code Availability\nThe results of the paper are reproducible, and the code provides detailed resources for the community.\nIt also includes sufficient instructions for installation and running the application. I was able to\ninstall and run the code successfully; however, there were some discrepancies in the reproduced\nresults, which I have commented on and shared with the author.\nis precisely this kind of responsible evaluation that enables CellScope to become a better method\nand more reliable tool for the community.\nWhile our point-by-point responses above have addressed the specific code modifications based\non the reviewer’s comments, we are pleased to describe some additional testing and improvements\nwe have conducted beyond those mentioned:\n• Cross-platform Performance Testing: We have extensively tested CellScope on two\npersonal computers with limited memory and one cloud servers: one iMac (10 cores, 16 GB\nRAM), one MacBook (8 cores, 16 GB RAM), and Google Colab Pro cloud servers (44 cores,\nsingle-cell datasets (with the largest exceeding 260,000 cells) across all platforms, achieving\nfaster runtime performance compared to Scanpy, see Table R5, or Supplementary Table\nS24.\n• Cross-device Stability Analysis: Algorithm stability across different devices is important\nfor users who want to reproduce results. We compared clustering performance differences\nacross three different computing environments and found CellScope to be remarkably stable.\nIn contrast, Scanpy showed less consistent performance across platforms. Figure R7\nillustrates the distribution of ARI differences in pairwise comparisons between the three\ndevices, clearly demonstrating CellScope’s superior stability. Detailed stability can be found\nin Table R6 here, or Table S23 in the revised Supplementary.\nWe believe these additional validations, combined with the specific improvements made in\nresponse to the reviewer’s feedback, ensure that CellScope provides a truly reproducible and stable\nsolution for the single-cell analysis community.\nDatasets Across Three Devices (iMac, MacBook, Google Colab, the fastest runtime on the same\ndevice is in bold)\niMac Macbook Google Colab\nData #Cells\nCellScope Scanpy CellScope Scanpy CellScope Scanpy\nYan 90 2.11 1.49 4.42 2.44 2.99 3.13\nGoolam 124 1.75 0.95 3.79 1.05 2.17 0.83\nPollen 249 3.86 1.38 2.11 1.51 2.59 1.17\nWang 457 4.02 2.32 3.96 2.83 2.95 2.12\nDarmanis 466 4.27 1.99 3.00 2.61 3.58 1.92\nUsoskin 622 4.47 2.33 3.52 3.19 3.39 2.43\nXin 1600 8.37 6.37 7.26 11.57 4.62 7.48\nKeller(I) 1887 7.08 6.69 4.92 12.46 4.26 8.46\nMuraro 2126 7.63 6.06 5.24 11.00 4.33 7.91\nNHGRI 2642 8.80 8.04 6.59 15.13 4.92 10.89\nKlein 2717 9.43 10.73 6.97 18.64 5.00 13.24\nZeisel 3005 9.47 9.46 8.06 17.54 4.97 13.55\nLake 3042 9.90 11.02 8.14 19.76 5.79 14.13\nKeller(P) 3384 10.28 11.42 8.06 20.47 5.29 14.29\nHan(B) 4038 10.48 12.34 7.23 22.94 5.06 17.36\nSiletti-1 4713 17.57 22.41 17.00 33.91 8.34 27.36\nTirosh 5902 13.40 21.74 13.19 31.90 7.85 27.20\nSiletti-2 6877 12.48 60.24 50.45 55.01 10.04 80.23\nBaron(H) 8569 16.31 31.92 25.15 59.49 10.25 58.85\nwang 23349 71.06 101.83 214.88 205.57 85.45 162.48\nSiletti-4 27111 71.67 122.85 163.35 227.78 114.85 198.68\nSiletti-5 28724 99.24 125.73 226.77 216.81 143.48 173.71\nSiletti-6 47416 128.43 254.22 297.60 522.63 235.50 371.28\nUlrich(H) 59738 204.93 257.11 657.77 522.64 320.43 426.60\nLi 67996 206.72 623.48 570.39 1084.52 389.24 1060.97\nLi(H) 72788 160.05 362.35 402.52 697.40 366.82 558.83\nQiu(L) 76732 252.96 708.88 813.22 1320.67 428.69 1114.44\nUlrich 77536 305.10 379.26 1080.03 686.92 498.66 595.32\nQiu(E) 93695 247.87 1091.89 718.80 1810.65 564.12 1665.68\nAyhan 129905 388.96 1197.86 901.67 2465.14 980.35 2370.86\nPosner 130908 377.77 989.77 1009.96 1933.85 1023.44 1615.46\nLi(M) 147523 435.98 885.89 1094.80 1588.03 1281.53 1601.71\nYu 155232 948.94 1550.88 2594.01 3476.82 1542.89 2424.87\nHoo 158978 757.43 1277.41 2249.08 3106.02 1907.47 2940.18\nYao 159738 934.07 1552.77 2581.86 2890.68 1741.57 2246.72\nLi(HA) 265767 1440.68 2820.06 3470.15 5070.66 3997.76 4496.55\nacross Three Devices (iMac, MacBook, and Google Colab), Measured by ARI (bold indicates\nbetter performance under same device)\nCellScope Scanpy\nData Name\niMac Macbook Colab iMac Macbook Google Colab\nYan 0.8301 0.8301 0.8301 0.8576 0.8576 0.8575\nGoolam 0.9896 0.9896 0.9896 0.8602 0.8602 0.8602\nPollen 0.8950 0.8950 0.8950 0.8909 0.8909 0.8909\nWang 0.9027 0.9027 0.9027 0.8211 0.8211 0.8211\nDarmanis 0.7531 0.7531 0.7531 0.6931 0.6931 0.6931\nUsoskin 0.9641 0.9641 0.9641 0.7948 0.7948 0.7948\nXin 0.8995 0.8995 0.8995 0.9404 0.9404 0.9404\nKeller(I) 0.7537 0.7537 0.7537 0.7882 0.6961 0.6961\nMuraro 0.9507 0.9507 0.9507 0.9219 0.9219 0.9219\nNHGRI 0.9285 0.9285 0.9285 0.8217 0.8217 0.8217\nKlein 0.9685 0.9685 0.9685 0.8984 0.8984 0.9014\nZeisel 0.8188 0.8188 0.8188 0.7130 0.7130 0.7499\nLake 0.8134 0.8134 0.8134 0.7561 0.7555 0.7713\nKeller(P) 0.8759 0.8759 0.8759 0.7900 0.7900 0.7900\nHan(B) 0.8792 0.8792 0.8861 0.1165 0.1211 0.1657\nSiletti-1 0.9272 0.9272 0.9272 0.9273 0.9273 0.9273\nTirosh 0.8981 0.8981 0.8981 0.5023 0.5023 0.4907\nSiletti-2 0.9865 0.9865 0.9839 0.8709 0.8709 0.8701\nBaron(H) 0.9345 0.9345 0.9453 0.9452 0.9461 0.9462\nWang 0.9040 0.9040 0.9044 0.5150 0.5459 0.5151\nSiletti-4 0.9826 0.9826 0.9826 0.3932 0.3932 0.3921\nSiletti-5 0.9273 0.9273 0.9273 0.1588 0.1587 0.1606\nSiletti-6 0.9864 0.9864 0.9864 0.3525 0.3517 0.3519\nUlrich(H) 0.8055 0.8055 0.8039 0.6277 0.6260 0.6253\nLi 0.8819 0.8819 0.8394 0.3002 0.4386 0.3214\nLi(H) 0.9480 0.9480 0.9530 0.7649 0.7850 0.7027\nQiu(L) 0.6155 0.6155 0.6427 0.5963 0.7020 0.6591\nUlrich 0.7697 0.7697 0.8258 0.6264 0.6249 0.6233\nQiu(E) 0.7269 0.7269 0.7347 0.7410 0.7318 0.7310\nAyhan 0.8363 0.8363 0.8347 0.7896 0.7994 0.8172\nPosner 0.7961 0.7961 0.8137 0.7500 0.7305 0.7580\nLi(M) 0.7705 0.7705 0.7878 0.6716 0.6701 0.6670\nYu 0.9728 0.9728 0.9484 0.9375 0.9406 0.9405\nHoo 0.8576 0.8576 0.7999 0.3892 0.3454 0.3447\nYao 0.9381 0.9381 0.9975 0.3452 0.3451 0.3446\nLi(HA) 0.8817 0.8817 0.9057 0.5747 0.5732 0.5726\nAverage 0.8769 0.8769 0.8798 0.6790 0.6829 0.6788\n0.075\n0.050\n0.025\n0.000\nCellScope Scanpy\nFigure R7: ARI Difference Comparison Between CellScope and Scanpy on 36 Benchmark scRNA-\nseq Datasets, measured by mean absolute ARI difference on three devices\n[1] Aashi Jindal, Prashant Gupta, Jayadeva, and Debarka Sengupta. Discovery of rare cells from\nvoluminous single cell expression data. Nature communications, 9(1):4719, 2018.\n[2] Peter J Rousseeuw. Silhouettes: a graphical aid to the interpretation and validation of cluster\nanalysis. Journal of computational and applied mathematics, 20:53–65, 1987.\n[3] Kimberly Siletti, Rebecca Hodge, Alejandro Mossi Albiach, Ka Wai Lee, Song-Lin Ding, Lijuan\nHu, Peter Lönnerberg, Trygve Bakken, Tamara Casper, Michael Clark, et al. Transcriptomic\ndiversity of cell types across the adult human brain. Science, 382(6667):eadd7046, 2023.\n[4] Zizhen Yao, Hanqing Liu, Fangming Xie, Stephan Fischer, Ricky S Adkins, Andrew I Aldridge,\nSeth A Ament, Anna Bartlett, M Margarita Behrens, Koen Van den Berge, et al. A transcrip-\ntomic and epigenomic cell atlas of the mouse primary motor cortex. Nature, 598(7879):103–110,\n2021.\nThe author presents CellScope, an innovative framework for constructing high-resolution cell atlases\nat multiple clustering levels. Overall, the paper is innovative, with a reasonable methodology and\nsufficient experimental design. The work is promising, as it has the potential to be widely adopted\nby researchers, leading to a highly cited paper. However, Seurat and Scanpy are currently well-\nestablished analytical frameworks. If the author aims for CellScope to achieve similar recognition\nand impact, the following issues may need to be addressed.\nMajor Points\n• Q1. The manifold fitting method has been proposed and applied to single-cell analysis, as\ndemonstrated in the paper titled “Single-Cell Analysis via Manifold Fitting: A Framework\nfor RNA Clustering and Beyond”, published in Proceedings of the National Academy of\nSciences of the United States of America (PNAS). What are the differences between the\nmethod proposed in this paper and existing methods? What are the innovations? If the\nmanifold fitting method used in the proposed method is the same as scAMF, the reference\nshould be clearly marked where the manifold fitting method appears. Also, the difference and\nrelationship should be clearly stated in the paper. Furthermore, the steps of the manifold\nfitting method should not occupy a significant portion of the text, as this method has already\nbeen proposed. Readers can refer to the original text for specific details. The authors should\nfocus on introducing the innovative steps of the proposed method.\nResponse: We thank the reviewer for this important question regarding the relationship\nbetween our proposed CellScope method and existing manifold fitting approaches, particularly\nscAMF.Intherevisedmanuscript,wehaveaddedclearreferencestoscAMFwherevermanifold\nfitting is mentioned, and explicitly stated the conceptual relationship and practical differences\nbetween the two methods (Page 5, line 129–137 in the manuscript, and supplementary\nNote 1.2), and focused our methodology description on CellScope’s innovative components\nrather than general manifold fitting principles.\nManifold fitting represents a broad conceptual framework or theoretical paradigm for dimen-\nsionality reduction that aims to preserve data structure while offering high interpretability.\nBased on this conceptual foundation, multiple distinct and operationally different models can\nbe independently developed to achieve specific analytical goals. Both scAMF and CellScope\nare concrete implementations that draw from this manifold fitting concept but represent\nfundamentally different mathematical models designed independently to address distinct\n1\nbiological challenges.\nAs detailed in our revised manuscript and supplementary, and summarized in Table R1,\nwhile CellScope and scAMF share the conceptual foundation of manifold fitting, their specific\nimplementations are fundamentally distinct across multiple key technical aspects:\n1. Theoretical Foundation: scAMF employs a general manifold hypothesis for noise\nreduction, whereas CellScope introduces a biology-driven dual-noise model that explicitly\ndistinguishes housekeeping gene noise from technical noise with explicit biological noise\ncharacterization.\n2. Manifold Identification: CellScope implements two-step biological manifold fitting\nwith cell-type-aware submanifold detection, contrasting with scAMF’s single adaptive\nmanifold fitting approach.\n3. Manifold Seeds Selection: CellScopeemployscompositemetrics(ρ×δ)withbiological\nvalidation for cell-type center identification, while scAMF lacks this dedicated module.\n4. Projection Strategy: CellScope implements two-stage projection comprising signal\nspace identification and boundary cell refinement through sequential biological and\ntechnical denoising, compared to scAMF’s maximum density point along projection line\napproach.\n5. Gene Selection: CellScope incorporates ANOVA-based statistical gene selection from\nbiologically reliable cliques using statistically-driven biological gene prioritization, a\nfeature entirely absent in scAMF.\n6. Clustering Methodology: CellScope uses graph-based agglomerative clustering with\nbiological similarity through biology-aware similarity construction, while scAMF employs\nmultiple basic clustering methods with UVI selection.\nThese comprehensive innovations transform CellScope from a general clustering tool (as\nscAMF functions) into a specialized biological discovery platform capable of hierarchical vali\ndation and dynamic molecular characterization across multiple scales of cellular organization.\nAspect scAMF CellScope\nTheoretical Foundation General manifold hypothesis for Biology-driven dual-noise model\nnoise reduction (housekeeping + technical noise)\nwith explicit biological noise char\nacterization\nManifold Identification Single adaptive manifold fitting Two-step biological manifold fitting\nwith cell-type-aware submanifold de\ntection\nManifold Seeds Selection Density-based outlier detection Manifold seeds via composite metric\n(ρ×δ) with biological validation for\ncell-type center identification\nProjection Strategy Maximum density point along pro- Two-stage projection: (1) Signal\njection line spaceidentification(2)Boundarycell\nrefinementthroughsequentialbiolog\nical and technical denoising\nGene Selection Method No gene selection ANOVA-based signal gene selec\ntion from reliable cliques using\nstatistically-driven biological gene\nprioritization\nClustering Approach Multiple basic clustering methods Graph-based agglomerative cluster\nwith UVI selection ingwithbiologicalsimilaritythrough\nbiology-awaresimilarityconstruction\nApplication Scope General clustering tool for dimen- Specialized biological discovery plat\nsional reduction and cell grouping form capable of hierarchical valida\ntion and dynamic molecular charac\nterization across multiple scales of\ncellular organization\n3\n• Q2. In the cell clustering task, the authors compared their method with Scanpy and Seurat.\nAlthough Scanpy (2018) and Seurat (2019) are widely used pipelines, there are also many\nnew methods specifically for clustering tasks in recent years. The authors should conduct\ncomparative experiments with these new methods and analyze the results.\nResponse: We thank you for this valuable suggestion. Following your recommendation,\nwe have added experiments with three additional clustering methods for scRNA-seq data:\nscLEGA (2024 [2]), scDCCA (2023 [8]), and CellBRF (2023 [10] ). All methods were\nimplemented using the authors’ recommended parameter settings to ensure fair comparison.\nComprehensiveperformancecomparisonsareprovidedinthesupplementarymaterials(Tables\nS4–7). Figure 2a has been updated to reflect these new results. Here we briefly introduce\nthese methods and how we implement them following the original authors’ recommendations.\n– scLEGA: Combines denoising autoencoder with graph autoencoder using multi-head\nattention to fuse expression and topological information. Following the authors’ recom\nmended settings, we used learning rate 0.001, latent dimension 16, 8 attention heads,\n2,500 highly variable genes, and 200 training epochs.\n– scDCCA: Deep contrastive clustering method that integrates denoising auto-encoder\nwith dual contrastive learning. We followed the authors’ recommended configuration:\n2,000 highly variable genes, z_dim=32, encoder layers [256, 64], and ran 70 pretraining\nepochs followed by 100 clustering epochs.\n– CellBRF: Random forest-based method for cell type identification with class balancing\nstrategy. We used the authors’ recommended parameters: k=15 nearest neighbors,\npcs=50 principal components, and enabled redundancy removal with correlation\nthreshold 0.8.\nComprehensive benchmarking across all 36 datasets shows the following ARI performance:\nAmong the three newly added methods, scDCCA achieved the highest average ARI of 0.74,\nfollowed by scLEGA at 0.54, and CellBRF at 0.49. scDCCA performed particularly well\non datasets such as Siletti-1 (ARI=0.99), Zeisel (ARI=0.87), and Muraro (ARI=0.94), but\nshowed weaker performance on some datasets including Li (ARI=0.30) and Li(H) (ARI=0.52).\nscLEGA demonstrated consistent performance across most datasets but encountered diffi\nculties with certain datasets like Darmanis (ARI=0.15) and Keller(P) (ARI=0.00). Despite\nscDCCA being the best-performing among the new methods, CellScope achieved a signif\nicantly higher overall average ARI of 0.88, demonstrating a substantial performance gap.\nCellScope outperformed all compared methods on 20 out of 36 datasets and maintained\nrobust performance even on challenging datasets where other methods failed. The complete\ncomparison results are detailed in Table R2 and Tables S4–7 of the Supplementary.\nand Scanpy, Measured by ARI (maximum in bold, second maximum underlined)\nARI\nData Name\nCellScope Scanpy Seurat scLEGA scDCCA CellBRF\nYan 0.83 0.86 0.85 0.66 0.90 0.38\nGoolam 0.99 0.86 0.63 0.60 0.96 0.26\nPollen 0.90 0.89 0.86 0.87 0.85 0.77\nWang 0.90 0.82 0.62 0.43 0.82 0.30\nDarmanis 0.75 0.69 0.46 0.15 0.76 0.44\nUsoskin 0.96 0.79 0.74 0.30 0.79 0.31\nXin 0.90 0.94 0.95 0.78 0.93 0.24\nKeller(I) 0.75 0.70 0.78 0.18 0.63 0.20\nMuraro 0.95 0.92 0.49 0.94 0.94 0.50\nNHGRI 0.93 0.82 0.77 0.41 0.72 0.60\nKlein 0.97 0.90 0.91 0.79 0.87 0.46\nZeisel 0.82 0.75 0.69 0.64 0.87 0.51\nLake 0.81 0.77 0.64 0.22 0.53 0.72\nKeller(P) 0.88 0.79 0.09 0.00 0.56 0.59\nHan(B) 0.89 0.17 0.03 - - -\nSiletti-1 0.93 0.93 0.77 0.93 0.99 0.43\nTirosh 0.90 0.49 0.56 0.53 0.80 0.56\nSiletti-2 0.98 0.87 0.87 0.72 0.99 0.87\nBaron(H) 0.95 0.95 0.91 0.40 0.92 0.54\nSiletti-3 0.90 0.52 0.55 0.84 0.60 0.54\nSiletti-4 0.98 0.39 0.39 0.64 0.59 -M\nSiletti-5 0.93 0.16 0.20 0.32 0.36 -M\nSiletti-6 0.99 0.35 0.62 -M 0.40 -M\nUlrich(H) 0.80 0.63 0.77 -M 0.86 -M\nLi 0.84 0.32 0.35 -M 0.30 -M\nLi(H) 0.95 0.70 0.90 -M 0.52 -M\nQiu(L) 0.64 0.66 0.68 -M -T -M\nUlrich 0.83 0.62 0.77 -M -T -M\nQiu(E) 0.73 0.73 0.73 -M -T -M\nAyhan 0.83 0.82 0.69 -M -T -M\nPosner 0.81 0.76 0.69 -M -T -M\nLi(M) 0.79 0.67 0.79 -M -T -M\nYu 0.80 0.34 0.72 -M -T -M\nHoo 0.95 0.94 0.83 -M -T -M\nYao 1.00 0.34 0.39 -M -T -M\nLi(HA) 0.91 0.57 0.80 -M -T -M\nAverage 0.88 0.68 0.65 0.54 0.74 0.49\n5\n• Q3. In the Runtime Comparison experiments, CellScope demonstrated superior computational\nefficiency compared to Scanpy and Seurat. However, how does its memory usage compare\nto these baselines? In single-cell analyses, memory consumption is equally as critical as\ntime efficiency, as it directly impacts the ability of a server to process larger datasets. It is\nrecommended that the authors add an additional row of data in the Table S4, which compares\nthe memory usage of the baselines methods when running on the same dataset.\nResponse: To address this concern, we conducted extensive experiments to comprehensively\nevaluate the memory and runtime performance of CellScope under diverse and realistic\ncomputational conditions. Our benchmarking setup included not only a standard laptop\n(MacBook, 8-core CPU, 16 GB RAM), but also a desktop (iMac, 10-core CPU, 16 GB\nRAM) and a cloud server (Google Colab, 44-core CPU, 150 GB RAM). This configuration\nwas designed to simulate both typical low-cost research environments and cloud computing\nresources, allowing us to assess the method’s robustness across varying hardware capacities.\nFor CellScope, all 36 benchmark datasets were successfully processed on all three platforms,\nincluding datasets with over 260,000 cells, demonstrating its scalability and reliability.\nWe compared our method against Scanpy—one of the fastest, most widely adopted, and\nthoroughly optimized Python frameworks—as a baseline. Our results (Table R3) show\nthat for datasets with more than 3,000 cells, CellScope consistently outperformed Scanpy in\nruntime across all three devices. While it was marginally slower on smaller datasets with\nfewer than 3,000 cells, the total runtime remained under 10 seconds in all cases, indicating\nnegligible overhead in practical usage. In terms of memory (Table R4), we focused on\ndatasets with more than 1,000 cells, as smaller datasets pose minimal memory challenges.\nOn the iMac, CellScope used memory comparable to or slightly less than Scanpy. On the\nMacBook and Google Colab, CellScope demonstrated clear memory advantages for datasets\nranging from 1,000 to 50,000 cells, and remained competitive for larger datasets over 50,000\ncells, with memory usage differences consistently below a factor of two, and an average\nCellScope-to-Scanpy ratio of approximately 1.4 across larger datasets over 50,000 cells. These\nresults highlight CellScope’s efficiency, scalability, and practical usability, particularly in\nmemory-constrained environments. The complete results are detailed in Tables S24–S25 of\nthe supplementary materials.\nThree Devices (iMac, MacBook, and Google Colab, Scanpy/CellScope indicates the runtime ratio\nbetween Scanpy and CellScope, the fastest runtime is in bold)\niMac MacBook GoogleColab\nDataName #Cells\nCellScope Scanpy Scanpy/CellScope CellScope Scanpy Scanpy/CellScope CellScope Scanpy Scanpy/CellScope\nYan 90 2.11 1.49 0.70 4.42 2.44 0.55 2.99 3.13 1.05\nGoolam 124 1.75 0.95 0.54 3.79 1.05 0.28 2.17 0.83 0.38\nPollen 249 3.86 1.38 0.36 2.11 1.51 0.71 2.59 1.17 0.45\nWang 457 4.02 2.32 0.58 3.96 2.83 0.72 2.95 2.12 0.72\nDarmanis 466 4.27 1.99 0.47 3.00 2.61 0.87 3.58 1.92 0.54\nUsoskin 622 4.47 2.33 0.52 3.52 3.19 0.91 3.39 2.43 0.72\nXin 1600 8.37 6.37 0.76 7.26 11.57 1.59 4.62 7.48 1.62\nKeller(I) 1887 7.08 6.69 0.95 4.92 12.46 2.53 4.26 8.46 1.98\nMuraro 2126 7.63 6.06 0.79 5.24 11.00 2.10 4.33 7.91 1.82\nNHGRI 2642 8.80 8.04 0.91 6.59 15.13 2.30 4.92 10.89 2.21\nKlein 2717 9.43 10.73 1.14 6.97 18.64 2.67 5.00 13.24 2.65\nZeisel 3005 9.47 9.46 1.00 8.06 17.54 2.18 4.97 13.55 2.73\nLake 3042 9.90 11.02 1.11 8.14 19.76 2.43 5.79 14.13 2.44\nKeller(P) 3384 10.28 11.42 1.11 8.06 20.47 2.54 5.29 14.29 2.70\nHan(B) 4038 10.48 12.34 1.18 7.23 22.94 3.17 5.06 17.36 3.43\nSiletti-1 4713 17.57 22.41 1.28 17.00 33.91 1.99 8.34 27.36 3.28\nTirosh 5902 13.40 21.74 1.62 13.19 31.90 2.42 7.85 27.20 3.46\nSiletti-2 6877 12.48 60.24 4.83 50.45 55.01 1.09 10.04 80.23 7.99\nBaron(H) 8569 16.31 31.92 1.96 25.15 59.49 2.36 10.25 58.85 5.74\nwang 23349 71.06 101.83 1.43 214.88 205.57 0.96 85.45 162.48 1.90\nSiletti-4 27111 71.67 122.85 1.71 163.35 227.78 1.39 114.85 198.68 1.73\nSiletti-5 28724 99.24 125.73 1.27 226.77 216.81 0.96 143.48 173.71 1.21\nSiletti-6 47416 128.43 254.22 1.98 297.60 522.63 1.76 235.50 371.28 1.58\nUlrich(H) 59738 204.93 257.11 1.25 657.77 522.64 0.79 320.43 426.60 1.33\nLi 67996 206.72 623.48 3.02 570.39 1084.52 1.90 389.24 1060.97 2.73\nLi(H) 72788 160.05 362.35 2.26 402.52 697.40 1.73 366.82 558.83 1.52\nQiu(L) 76732 252.96 708.88 2.80 813.22 1320.67 1.62 428.69 1114.44 2.60\nUlrich 77536 305.10 379.26 1.24 1080.03 686.92 0.64 498.66 595.32 1.19\nQiu(E) 93695 247.87 1091.89 4.41 718.80 1810.65 2.52 564.12 1665.68 2.95\nAyhan 129905 388.96 1197.86 3.08 901.67 2465.14 2.73 980.35 2370.86 2.42\nPosner 130908 377.77 989.77 2.62 1009.96 1933.85 1.91 1023.44 1615.46 1.58\nLi(M) 147523 435.98 885.89 2.03 1094.80 1588.03 1.45 1281.53 1601.71 1.25\nYu 155232 948.94 1550.88 1.63 2594.01 3476.82 1.34 1542.89 2424.87 1.57\nHoo 158978 757.43 1277.41 1.69 2249.08 3106.02 1.38 1907.47 2940.18 1.54\nYao 159738 934.07 1552.77 1.66 2581.86 2890.68 1.12 1741.57 2246.72 1.29\nLi(HA) 265767 1440.68 2820.06 1.96 3470.15 5070.66 1.46 3997.76 4496.55 1.12\nover 1,000 Cells across Three Devices (iMac, MacBook, and Google Colab, Scanpy/CellScope\nindicates the memory ratio between Scanpy and CellScope, minimum in bold)\niMac MacBook GoogleColab\nDataName #Cells\nCellScope Scanpy Scanpy/CellScope CellScope Scanpy Scanpy/CellScope CellScope Scanpy Scanpy/CellScope\nXin 1600 300.77 361.61 1.20 296.61 361.23 1.22 309.71 484.56 1.56\nKeller(I) 1887 141.23 180.75 1.28 155.42 375.75 2.42 129.64 89.90 0.69\nMuraro 2126 155.63 285.13 1.83 155.59 427.65 2.75 155.16 284.88 1.84\nNHGRI 2642 154.61 296.44 1.92 181.16 423.99 2.34 175.82 296.50 1.69\nKlein 2717 252.30 497.97 1.97 278.88 663.75 2.38 271.06 563.59 2.08\nZeisel 3005 229.02 432.84 1.89 233.84 432.83 1.85 268.14 432.57 1.61\nLake 3042 291.36 872.11 2.99 290.88 765.45 2.63 337.21 581.41 1.72\nKeller(P) 3384 271.98 452.25 1.66 277.02 662.56 2.39 326.18 451.99 1.39\nHan(B) 4038 275.02 468.75 1.70 273.64 468.75 1.71 322.84 468.49 1.45\nSiletti-1 4713 826.42 1070.17 1.29 1105.44 1296.60 1.17 1099.75 1542.39 1.40\nTirosh 5902 722.00 1453.50 2.01 666.18 1342.85 2.02 752.07 1453.30 1.93\nSiletti-2 6877 635.39 1563.75 2.46 575.90 1193.19 2.07 1763.85 2486.30 1.41\nBaron(H) 8569 1136.13 1244.61 1.10 1086.41 1604.24 1.48 946.39 1604.07 1.69\nwang 23349 3545.92 2558.27 0.72 3652.97 1256.70 0.34 6935.41 2575.99 0.37\nSiletti-4 27111 3727.80 4107.55 1.10 3825.13 4237.83 1.11 6908.63 4299.46 0.62\nSiletti-5 28724 4951.16 4758.50 0.96 4187.39 4213.51 1.01 7928.87 4391.77 0.55\nSiletti-6 47416 5055.73 5404.34 1.07 6729.28 4986.30 0.74 6807.79 8340.93 1.23\nUlrich(H) 59738 6489.45 4443.64 0.68 8855.63 5067.92 0.57 5586.25 5114.77 0.92\nLi 67996 6005.39 5687.55 0.95 8926.21 5786.13 0.65 9241.61 5295.92 0.57\nLi(H) 72788 7068.11 4478.83 0.63 8521.48 4909.73 0.58 10392.89 8306.05 0.80\nQiu(L) 76732 6435.38 4550.95 0.71 8679.13 5519.75 0.64 9590.09 7894.35 0.82\nUlrich 77536 7830.33 5508.13 0.70 7541.92 5756.67 0.76 9814.93 6527.91 0.67\nQiu(E) 93695 8096.19 5332.00 0.66 9012.20 4584.84 0.51 17328.70 7364.07 0.42\nAyhan 129905 6781.95 6909.11 1.02 8751.86 4639.56 0.53 19427.28 11121.26 0.57\nPosner 130908 8318.06 6421.77 0.77 10084.61 5190.77 0.51 17626.38 13289.79 0.75\nLi(M) 147523 9264.05 6215.11 0.67 11778.68 6072.32 0.52 20300.17 9440.31 0.47\nYu 155232 6637.56 7467.64 1.13 10050.16 6461.95 0.64 12951.52 9670.48 0.75\nHoo 158978 8429.72 7047.11 0.84 11443.43 5365.63 0.47 24060.06 22012.23 0.91\nYao 159738 7808.69 6944.88 0.89 10680.73 6420.27 0.60 22155.85 18961.21 0.86\nLi(HA) 265767 6237.13 9857.25 1.58 9221.30 5782.18 0.63 60225.24 35108.84 0.58\n8\n• Q4. In the Supplementary File, the background colors of the experimental data in the tables\npresenting are quite confusing. For instance, in Table S7, the result of 1.00 for Yan of\nCellScope is highlighted in green, whereas the same value of 1.00 for Siletti-1 of CellScope is\nmarked in light green. Additionally, the result of 1.00 for Siletti-1 of Scanpy is indicated in\nred. It appears that the intensity of the background colors does not correspond to the values of\nthe experimental results. Therefore, it is recommended that the authors clarify the significance\nof the color gradients in the table captions.\nResponse: Thank you for your careful review. To address this issue, we have revised\nthe coloring scheme for all tables in the Supplementary File. In the revised version, we\nhave adopted a more straightforward approach: only the best value has been highlighted in\nbold, while the second-best value is marked with an underline. All remaining values are left\nunmarked. The meaning of these markings has been clearly explained in the corresponding\ntable captions. Note that Supplementary Tables S1–S3, S15, and S20–S22 present\ndataset descriptions, additional gene selection information, and other results that are not\nintended for direct quantitative comparison. Therefore, these tables do not include the\naforementioned markings.\n9\n• Q5. There is a limited number of references from the past three years. It is recommended to\ninclude more recent references to enhance the timeliness and relevance of the manuscript.\nResponse: Inresponsetoyourrecommendation, wehavesignificantlyenhancedourliterature\nreview by adding recent publications from the past three years. These additions include both\nmethodological papers that present comparative approaches to our work and application\npapers that demonstrate the practical implementation of related techniques in current\nresearch.\nSpecifically, in addition to the three methodological papers published after 2023 mentioned\nin our response to Q2, we have incorporated the following recent application literature:\n- [6] from Nature, which discusses the Human Cell Atlas transition from a cell census to\na unified foundation model, providing important context for large-scale single-cell data\nintegration approaches.\n- [9] from Cell, presenting automatic cell-type harmonization and integration methods across\nHuman Cell Atlas datasets, which is directly relevant to our methodological framework.\n- [3] from Cell, offering a single-cell atlas that reveals correlates of cognitive function and\nAlzheimer’s disease pathology, demonstrating the application of single-cell analysis in neuro\nscience research.\n- [1] from Nature, providing a prenatal skin atlas that reveals immune regulation mechanisms,\nshowcasing the utility of atlas-based approaches in developmental biology.\n10\n• Q6. The challenges addressed by this work should be explained in Introduction Section in\ndetails with examples.\nResponse: We thank the reviewer for this constructive suggestion. Following the reviewer’s\nrecommendation, we have added a comprehensive description of three key challenges that\nCellScope addresses in single-cell analysis:\n1. Gene Selection and Characterization Limitations: Current methods suffer from\ninadequate and inaccurate gene selection coupled with single-level marker gene char\nacterization that fails to dynamically capture genes along cell type subdivisions. This\nlimitation prevents researchers from identifying cell-type-specific markers that change\nacross different levels of cellular hierarchy.\n2. Clustering Resolution and Structure Deficiencies: Existing approaches provide\nlimited clustering resolution and hierarchical structure that cannot achieve nested\norganization, making it difficult to capture the full spectrum of cellular diversity and\nrelationships.\n3. Visualization Integration Challenges: Current methods lack the ability to generate\ntree-structured visualizations that integrate cellular trees with cellular atlases, limiting\ncomprehensive understanding of cellular organization.\nTo address these challenges, we have clearly outlined CellScope’s four core innovations:\n– Intelligent gene selection that separates signal from noise spaces\n– Precise delineation of similar cell subpopulations through manifold-based denoising\n– Dynamic characterization of cellular landscapes at multiple resolutions\n– Multi-level functional analysis through dynamic molecular identity classification\nWe have also included specific examples of CellScope’s achievements that demonstrate these\ncapabilities:\n– Identification of novel oligodendrocyte subpopulations that existing methods failed to\ndetect\n– Simultaneous detection of cell types and health status in COVID-19 patients\n– Superior performance across 36 diverse datasets covering various species, organs, and\nsequencing modalities\n11\n• Q7. To our knowledge, agglomerative clustering has a high computational complexity and is\nnot suitable for large-scale data. The authors should analyze the complexity of hierarchical\nclustering and conduct experiments on large-scale datasets to analyze its performance.\nResponse: While the naive agglomerative clustering algorithm exhibits a worst-case time\ncomplexity of O(N3), we have implemented two key optimizations to make it tractable for\nlarge-scale single-cell datasets:\n– Optimized linkage via fastcluster: By leveraging the highly efficient Python package\nfastcluster [4], we reduce the clustering step to the theoretically optimal O(N2) bound.\n– Subsampling with efficient assignment: We randomly select a fixed subset of 30,000\ncells and build the full adjacency matrix only for this subset—thereby bounding the\nworst-case complexity to O(30,0002)—and then assign the remaining cells to clusters\nusing nearest neighbor assignment in linear time. The subset size of 30,000 cells is\nadjustable based on available computational memory and time requirements, providing\nflexibility for different hardware configurations.\nTo address concerns about representativeness, our extensive validation on large-scale datasets\ndemonstrates that 30,000 cells provide sufficient representation of the entire dataset structure.\nThe un-sampled cells are assigned to clusters through nearest neighbor identification to the\nsampled cells, ensuring comprehensive coverage while maintaining computational efficiency.\nOur clustering and visualization results on large-scale datasets confirm that this subsampling\nstrategy achieves excellent clustering accuracy and enables meaningful biological discoveries\nwithout compromising the detection of rare cell types or subtle cellular relationships.\n12\n• Q8. Is the agglomerative clustering method used in the proposed approach original or pre\nexisting? If it is pre-existing, the authors should clearly indicate the references and provide\nan explanation.\nResponse: Distance matrix-based agglomerative clustering methods have been extensively\nstudied and applied in numerous works [5, 7]. These existing classical methods have been\nreferenced in the manuscript. CellScope innovatively incorporates UMAP graph structures\ninto the agglomerative clustering process, which is a new method. This unique design enables\nthe algorithm to simultaneously achieve precise modeling of local manifold structures and\npreservation of global topological properties, thereby effectively capturing cellular neigh\nborhood relationships within submanifolds while accurately identifying spatial correlations\nbetween different submanifolds. Specifically, our agglomerative clustering module integrates\nthe following core components:\n– Novel similarity graph construction: We introduce a new approach to build the similarity\nmatrix S by capturing local neighborhood relationships within each learned submani\nfold. This construction encodes both the manifold’s intrinsic geometric structure and\nthe connectivity across submanifolds. Specifically, we employ the Uniform Manifold\nApproximation and Projection (UMAP) algorithm to compute pairwise affinities and\ngenerate S. Full algorithmic details are provided in the Methods section. As shown in\nFigure 6, our graph-based approach outperforms traditional distance-based matrices in\nclustering performance.\n– Classical agglomerative clustering: After constructing the similarity matrix S, we\nperform agglomerative clustering through an iterative merging strategy: sequentially\ncombining the two clusters with the highest average similarity at each step until only\ntwo cluster structures remain.\nReferences\n[1] Nusayhah Hudaa Gopee, Elena Winheim, Bayanne Olabi, Chloe Admane, April Rose Foster,\nNi Huang, Rachel A Botting, Fereshteh Torabi, Dinithi Sumanaweera, Anh Phuong Le, et al.\nA prenatal skin atlas reveals immune regulation of human skin morphogenesis. Nature, pages\n1–11, 2024.\n[2] Zhenze Liu, Yingjian Liang, Guohua Wang, and Tianjiao Zhang. sclega: an attention-based\ndeep clustering method with a tendency for low expression of genes on single-cell rna-seq data.\nBriefings in Bioinformatics, 25(5):bbae371, 2024.\n13\n[3] Hansruedi Mathys, Zhuyu Peng, Carles A Boix, Matheus B Victor, Noelle Leary, Sudhagar\nBabu, Ghada Abdelhady, Xueqiao Jiang, Ayesha P Ng, Kimia Ghafari, et al. Single-cell atlas\nreveals correlates of high cognitive function, dementia, and resilience to alzheimer’s disease\npathology. Cell, 186(20):4365–4385, 2023.\n[4] Daniel Müllner. fastcluster: Fast hierarchical, agglomerative clustering routines for r and\npython. Journal of Statistical Software, 53:1–18, 2013.\n[5] Fionn Murtagh and Pedro Contreras. Algorithms for hierarchical clustering: an overview.\nWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(1):86–97, 2012.\n[6] Jennifer E Rood, Samantha Wynne, Lucia Robson, Anna Hupalowska, John Randell, Sarah A\nTeichmann, and Aviv Regev. The human cell atlas from a cell census to a unified foundation\nmodel. Nature, 637(8048):1065–1071, 2025.\n[7] GaborJSzekely, MariaLRizzo, etal. Hierarchicalclusteringviajointbetween-withindistances:\nExtending ward’s minimum variance method. Journal of classification, 22(2):151–184, 2005.\n[8] Jing Wang, Junfeng Xia, Haiyun Wang, Yansen Su, and Chun-Hou Zheng. scdcca: deep\ncontrastive clustering for single-cell rna-seq data based on auto-encoder network. Briefings in\nBioinformatics, 24(1):bbac625, 2023.\n[9] Chuan Xu, Martin Prete, Simone Webb, Laura Jardine, Benjamin J Stewart, Regina Hoo,\nPeng He, Kerstin B Meyer, and Sarah A Teichmann. Automatic cell-type harmonization and\nintegration across human cell atlas datasets. Cell, 186(26):5876–5891, 2023.\n[10] Yunpei Xu, Hong-Dong Li, Cui-Xiang Lin, Ruiqing Zheng, Yaohang Li, Jinhui Xu, and Jianxin\nWang. Cellbrf: a feature selection method for single-cell clustering using cell balance and\nrandom forest. Bioinformatics, 39(Supplement_1):i368–i376, 2023.\nThe authors have undertaken a thorough and substantive revision of the manuscript, and I am highly\nsatisfied. The revised version addresses all of my previous concerns in a direct and satisfactory\nmanner. In particular, the newly added ablation studies provide clear and convincing evidence. I\nhave no further concerns at this stage and fully support the acceptance of the manuscript.\nResponse: We thank the reviewer for the supportive comments and are glad that the added\nablation studies convincingly addressed the reviewer’s previous concerns.\nThe authors appear to have addressed the previously raised concerns. I have the following additional\nsuggestions for further improvement:\n• Q1. In Table R2, the results for the three newly added comparison methods on some datasets\nare missing. The authors should provide an explanation for the absence of these values in\ncertain datasets.\nResponse: We thank the reviewer for pointing out the missing results in Table R2. We\nexplain the missing values.\n1. The missing values arise from computational limitations when running the newly added\nmethods on certain datasets. In the revised version, we use a notation system to categorize\nthese limitations:\n– “-M”: Out-of-memory errors\n– “-T”: Runtime timeout (exceeding 5 hours)\n– “-C”: Program crashes or technical failures\nWe added notes to all Supplementary Tables with missing values, explaining the reasons for\neach absence. Table R1 shows an example of how these notations are applied in our results.\nby ARI (Maximum in bold, second maximum underlined; “-M” out-of-memory, “-T” out-of-time\n(> 5h), “-C” crashed)\nARI\nData Name\nCellScope Scanpy Seurat scLEGA scDCCA CellBRF\nYan 0.83 0.86 0.85 0.66 0.90 0.38\nGoolam 0.99 0.86 0.63 0.60 0.96 0.26\nPollen 0.90 0.89 0.86 0.87 0.85 0.77\nWang 0.90 0.82 0.62 0.43 0.82 0.30\nDarmanis 0.75 0.69 0.46 0.15 0.76 0.44\nUsoskin 0.96 0.79 0.74 0.30 0.79 0.31\nXin 0.90 0.94 0.95 0.78 0.93 0.24\nKeller(I) 0.75 0.70 0.78 0.18 0.63 0.20\nMuraro 0.95 0.92 0.49 0.94 0.94 0.50\nNHGRI 0.93 0.82 0.77 0.41 0.72 0.60\nKlein 0.97 0.90 0.91 0.79 0.87 0.46\nZeisel 0.82 0.75 0.69 0.64 0.87 0.51\nLake 0.81 0.77 0.64 0.22 0.53 0.72\nKeller(P) 0.88 0.79 0.09 0.00 0.56 0.59\nHan(B) 0.89 0.17 0.03 -C -C -C\nSiletti-1 0.93 0.93 0.77 0.93 0.99 0.43\nTirosh 0.90 0.49 0.56 0.53 0.80 0.56\nSiletti-2 0.98 0.87 0.87 0.72 0.99 0.87\nBaron(H) 0.95 0.95 0.91 0.40 0.92 0.54\nSiletti-3 0.90 0.52 0.55 0.84 0.60 0.54\nSiletti-4 0.98 0.39 0.39 0.64 0.59 -M\nSiletti-5 0.93 0.16 0.20 0.32 0.36 -M\nSiletti-6 0.99 0.35 0.62 -M 0.40 -M\nUlrich(H) 0.80 0.63 0.77 -M 0.86 -M\nLi 0.84 0.32 0.35 -M 0.30 -M\nLi(H) 0.95 0.70 0.90 -M 0.52 -M\nQiu(L) 0.64 0.66 0.68 -M -T -M\nUlrich 0.83 0.62 0.77 -M -T -M\nQiu(E) 0.73 0.73 0.73 -M -T -M\nAyhan 0.83 0.82 0.69 -M -T -M\nPosner 0.81 0.76 0.69 -M -T -M\nLi(M) 0.79 0.67 0.79 -M -T -M\nYu 0.80 0.34 0.72 -M -T -M\nHoo 0.95 0.94 0.83 -M -T -M\nYao 1.00 0.34 0.39 -M -T -M\nLi(HA) 0.91 0.57 0.80 -M -T -M\nAverage 0.88 0.68 0.65 0.54 0.74 0.49\n2\n2. The Han(B) dataset contained missing values that caused scLEGA, scDCCA, and\nCellBRF to fail during execution (marked as “-C” in Table R1). To faithfully reflect the\noriginal computational capabilities of each algorithm, we present the actual running results\nin our main tables and appendices. Out of curiosity and to address potential concerns, we\ntested two imputation strategies for the missing values, and obtained the following results\n(Table R2):\nARI\nImputation Method\nscLEGA scDCCA CellBRF\nMean imputation 0.31 0.39 0.13\nZero imputation 0.12 0.38 0.15\nTable R2: ARI values under two imputation methods for the Han(B) dataset.\nEven when considering the imputed results, the overall average ARI performance across\nall tested datasets remains: scLEGA (0.53), scDCCA (0.72), and CellBRF (0.47). The\nperformance ranking among methods stays unchanged, and even the best-performing scDCCA\n(0.72) shows a substantial gap compared to CellScope as shown in Table R1.\n3\n• Q2. All equations in the manuscript should be numbered for easier reference.\nResponse:Thank you for the reviewer’s suggestion. We have followed the advice and\nnumbered all equations in the main text and supplementary materials.\n4\n• Q3. I suggest including pseudo-code and flowcharts to further improve the readability and\nclarity of the proposed method.\nResponse: Thank you for your suggestions aimed at improving the readability and clarity of\nour method. In response, we have provided a detailed algorithmic flowchart of the CellScope\nworkflow as Algorithm 1 (corresponded to Supplementary Algorithm 1), which clearly\noutlines the five core computational steps—Manifold Fitting Stage 1, Manifold Fitting Stage\n2, Hierarchical Clustering, Tree-Structured Visualization, and Gene Characterization—along\nwith the default hyperparameters used at each stage. Additionally, we have included a compre\nhensive textual description of the workflow as Figure R1(corresponded to Supplementary\nFigure S1) to further enhance reproducibility and clarity.\nThe overall computational procedure consists of the following key stages:\n1. Manifold Fitting Stage 1: The raw gene expression data is first log-transformed and\nrow-normalized. Dimensionality is then reduced to 100 via PCA. Based on the reduced\ndimensions, the local density and relative distance of cells are computed. The top 500\ngenes with the smallest p-values are selected as high-confidence seed genes. Feature\ngenes are further refined through ANOVA-based differential analysis across pre-labeled\ncell cliques.\n2. Manifold Fitting Stage 2: Cells within the lowest 5% of density values are identified\nas outliers. Their positions are adjusted by 90% toward their nearest neighboring cells\nto enhance the continuity and topological stability of the underlying manifold. The\noutcome of this stage is the fitted data.\n3. Similarity Graph Construction and Clustering: A cell-specific similarity graph is built\nfollowing the UMAP algorithm. Hierarchical clustering is then applied to this graph to\nidentify distinct cell subpopulations.\n4. Downstream Analysis: This final phase encompasses three main tasks: (a) constructing\ntree-structured visualizations to illustrate developmental trajectories; (b) detecting\nmarker genes for each identified cell cluster; and (c) categorizing genes based on their\nexpression dynamics using Wasserstein distance.\nFor user convenience, we have also provided a detailed text-based workflow diagram in the\nonline tutorial, available at https://cellscope.readthedocs.io/en/latest/, to facilitate\nbetter learning and understanding. We believe these supplementary materials effectively\nenhance the transparency of the methodology and make the research outcomes easier to\nreproduce.\nRequire: Single-cell expression matrix X ∈ RN×D, gene set G\nEnsure: Hierarchical cell clusters, tree-structured visualization and categorized gene groups\n1: Stage A: Manifold fitting stage 1\n2: A1: Preprocess data: Apply log transform and row normalization to X\n2\n3: A2: Identify reliable cliques:\n4: Reduce dimensions to 100 via PCA\n5: Compute local density ρ and relative distance δ for each cell as Eq. (1)\n6: Select manifold seeds using adaptive thresholds on γ′ = ρ′·δ′ and change rate R as Eqs. (2)-(6)\n7: Form cliques using 5-nearest neighbors of seeds\n8: A3: Detect signal space:\n9: Perform ANOVA using clique labels\n10: Select top 500 genes with smallest p-values\n11: Stage B: Manifold fitting stage 2\n12: Calculate cell density according to Eq. (8), and identify cells with the lowest 5% density values as\noutliers\n13: for each outlier y do\ni\n14: Find nearest high-density point yˆ\ni\n15: Project: z ← 0.9yˆ +0.1y\ni i i\n16: end for\n17: Non-outliers remain unchanged as Z = {z }N\ni i=1\n18: Stage C: Hierarchical Clustering\n19: Build the similarity matrix as Eqs. (11)-(13)\n20: Initialize each cell as singleton cluster\n21: for K = 1 to N −1 do\n22: Merge clusters with highest average similarity\n23: end for\n24: Stage D: Tree-Structured Visualization\n25: Generate UMAP layout for root node (all cells)\n26: for each hierarchy level do\n27: Visualize child clusters\n28: Propagate colors from leaves to root\n29: end for\n30: Stage E: Gene Characterization\n31: for each gene g ∈ G do\n32: Compute Wasserstein distance W between sibling nodes\n1\n33: Classify based on thresholds:\n34: end for\nRaw data\nrow-normalize\nManifold fitting stage 1\nReduce Compute local Select manifold\ndimensions to density ρ and seeds based on\n100 via PCA relativedistance γ ρ and γ\nSelect thetop5 ANOVA-based\nExtract cliques\n00genes with gene selection\nfrom seed 5-\nthesmallest p- across labeled\nneighbors\nvalues cliques\nManifold fitting stage 2\nIdentify the\nShift outliersby\nlowest 5%\n90% toward Fitted data\ndensity cells as\nnearest cell\noutliers\nBuild the similarity graph following UMAP\nHierarchical clustering viasimilarity graph\nDownstream analysis\nCategorize genes\nTree-structured Marker genes based on\nvisualization detection Wasserstein\ndistance\nFigure R1: CellScope workflow: (A) Data preprocessing, (B) Two-stage manifold fitting, (C)\nGraph-based clustering, (D) Tree visualization, (E) Gene characterization.\n7\n• Q4. I suggest adding comparative analysis with the following method, \"scCRT: a contrastive-\nbased dimensionality reduction model for scRNA-seq trajectory inference\".\nResponse: We appreciate the reviewer’s suggestion regarding scCRT [1]. scCRT is a\ndimensionality reduction framework specifically designed for trajectory inference that does\nnot include a clustering pipeline, unlike CellScope, Seurat, or Scanpy. Additionally, scCRT\npartially utilizes label information during training, which would make direct unsupervised\nclustering comparisons less fair. Therefore, we did not directly compare scCRT’s clustering\nperformance with other algorithms in our main analysis. Instead, we added a comparative\ndescription of this framework in our discussion, highlighting its capability in applying\ndimensionalityreductionfortrajectoryinference: \"RecentmethodssuchasscCRT[1]represent\nanother crucial approach to single-cell analysis, which innovatively combines cell-level pairwise\nmodules with cluster-level contrastive learning to preserve cellular relationships for trajectory\ninference applications. While both scCRT and CellScope learn highly informative low-\ndimensional representations and leverage clustering information, they differ in their design\nfocus: scCRT emphasizes continuous processes and trajectory analysis, whereas CellScope is\noptimized for discrete clustering tasks and multi-resolution cellular characterization.\"\nBased on our curiosity, we conducted a special experiment using scCRT features as input\nto CellScope and Scanpy. As shown in Table R3, CellScope achieves superior clustering\naccuracy on most datasets with an average ARI of 0.88, compared to 0.81 for scCRT\nfeatures combined with CellScope clustering. For Scanpy, scCRT features provide moderate\nimprovements, increasing the average ARI from 0.68 (Scanpy alone) to 0.74 (scCRT features\n+ Scanpy clustering) across 17 datasets. However, the scCRT-enhanced Scanpy still performs\nless effectively than CellScope for clustering tasks. These results demonstrate that scCRT\nembeddings can enhance existing clustering methods, while CellScope remains more effective\nfor discrete clustering applications.\n(Better in bold; “-M” out-of-memory, “-C” crashed)\nARI ARI\nData Set\nCellScope scCRT+ CellScope Scanpy scCRT+ Scanpy\nYan 0.83 0.82 0.86 1.00\nGoolam 0.99 1.00 0.86 0.99\nPollen 0.90 0.99 0.89 0.93\nWang 0.90 0.96 0.82 0.98\nDarmanis 0.75 0.85 0.69 0.94\nUsoskin 0.96 0.91 0.79 0.91\nXin 0.90 1.00 0.94 1.00\nKeller(I) 0.75 0.31 0.70 0.24\nMuraro 0.95 -C 0.92 -C\nNHGRI 0.93 0.83 0.82 0.88\nKlein 0.97 0.91 0.90 0.77\nZeisel 0.82 0.73 0.75 0.75\nLake 0.81 0.53 0.77 0.58\nKeller(P) 0.88 0.58 0.79 0.57\nHan(B) 0.89 0.77 0.17 0.31\nSiletti-1 0.93 -C 0.93 -C\nTirosh 0.90 0.94 0.49 0.55\nSiletti-2 0.98 -C 0.87 -C\nBaron(H) 0.95 0.95 0.95 0.95\nSiletti-3 0.90 0.84 0.52 0.84\nSiletti-4 0.98 0.99 0.39 0.58\nSiletti-5 0.93 -C 0.16 -C\nSiletti-6 0.99 -C 0.35 -C\nUlrich(H) 0.80 0.75 0.63 0.80\nLi 0.84 0.42 0.32 0.21\nLi(H) 0.95 0.98 0.70 0.98\nQiu(L) 0.64 0.79 0.66 0.56\nUlrich 0.83 0.83 0.62 0.82\nQiu(E) 0.73 0.80 0.73 0.60\nAyhan 0.83 -M 0.82 -M\nPosner 0.81 -M 0.76 -M\nLi(M) 0.79 -M 0.67 -M\nHoo 0.95 -M 0.94 -M\nYu 0.80 -M 0.34 -M\nYao 1.00 -M 0.34 -M\nLi(HA) 0.91 -M 0.57 -M\nAverage 0.88 0.81 0.68 0.74\n9\n• Q5. Remarks on code availability. The authors need to provide detailed information about\nthe hardware and software configurations used for running the code.\nResponse: Thank you for your suggestion. We have added detailed information about the\nhardware configuration and software environment in the Computational Environments\nsection of the Methods section in the manuscript, as well as in the supplementary materials.\nSpecifically, we included a reference in the main text to the software configuration table in\nthe supplementary materials and noted the versions of the comparative algorithms used,\nas follows: CellScope was executed in a Python environment, with its Python version and\nrequired library dependencies detailed in Supplementary Table S26. All comparative algorithms\nutilized the latest publicly available versions, with Scanpy version 1.10.3 and Seurat version\n5.2.0.\nIn Table R4 (corresponded to Supplementary Table S26), we listed the Python environ\nment and the versions of the relevant dependency libraries used to run CellScope on three\ndifferent devices, to facilitate the reproduction of experimental results by readers and method\nusers.\nLibrary iMac Macbook Google Colab\npython 3.10.16 3.10.16 3.10.16\nanndata 0.11.4 0.11.4 0.12.2\nfastcluster 1.2.6 1.2.6 1.3.0\njoblib 1.4.2 1.4.2 1.5.1\nkeleido 0.2.1 0.2.1 0.2.1\nmatplotlib 3.10.1 3.10.1 3.10.0\nnbformat 5.10.4 5.10.4 5.10.4\nnetworkx 3.4.2 3.4.2 3.5\nnumba 0.61.2 0.61.2 0.61.2\nnumpy 1.26.4 1.26.4 2.0.2\npandas 2.2.3 2.2.3 2.2.2\nplotly 6.0.1 6.0.1 5.24.1\nrequests 2.32.3 2.32.3 2.32.3\nscikit-learn 1.6.1 1.6.1 1.6.1\nscipy 1.13.1 1.13.1 1.15.3\nseaborn 0.13.2 0.13.2 0.13.2\nstatsmodels 0.14.4 0.14.4 0.14.5\nthreadpoolctl 3.6.0 3.6.0 3.6.0\numap-learn 0.5.7 0.5.7 0.5.7\nReferences\n[1] Yuchen Shi, Jian Wan, Xin Zhang, Tingting Liang, and Yuyu Yin. sccrt: a contrastive-based\ndimensionality reduction model for scrna-seq trajectory inference. Briefings in Bioinformatics,\n25(3):bbae204, 2024.\n11"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have undertaken a thorough and substantive revision of the manuscript, and I am highly satisfied with the\nquality and depth of these changes.\nThe revised version addresses all of my previous concerns in a direct and satisfactory manner. In particular, the newly\nadded ablation studies provide clear and quantitative insights into the individual contributions of each module. The\nexpanded parameter sensitivity analysis further reinforces the methodological rigor of the work. Moreover, the incorporation\nof statistical tests for the selection of information-rich genes substantially strengthens the robustness and scientific\nsoundness of the approach. These enhancements collectively improve the reliability, stability, and generalizability of\nCellScope.\nI have no further concerns at this stage and fully support the acceptance of the manuscript.", "reviewer_2": "The authors appear to have addressed the previously raised concerns. I have the following additional suggestions for further\nimprovement:\n1.In Table R2, the results for the three newly added comparison methods on some datasets are missing. The authors should\nprovide an explanation for the absence of these values in certain datasets.\n2.All equations in the manuscript should be numbered for easier reference.\n3.I suggest including pseudocode and flowcharts to further improve the readability and clarity of the proposed method.\n4.I suggest adding comparative analysis with the following method, \"scCRT: a contrastive-based dimensionality reduction\nmodel for scRNA-seq trajectory inference\".\n(Remarks on code availability)\nThe authors need to provide detailed information about the hardware and software configurations used for running the code."}, "author_response": "Response to Reviewer # 1\nThe authors have undertaken a thorough and substantive revision of the manuscript, and I am highly\nsatisfied. The revised version addresses all of my previous concerns in a direct and satisfactory\nmanner. In particular, the newly added ablation studies provide clear and convincing evidence. I\nhave no further concerns at this stage and fully support the acceptance of the manuscript.\nResponse: We thank the reviewer for the supportive comments and are glad that the added\nablation studies convincingly addressed the reviewer’s previous concerns.\nThe authors appear to have addressed the previously raised concerns. I have the following additional\nsuggestions for further improvement:\n• Q1. In Table R2, the results for the three newly added comparison methods on some datasets\nare missing. The authors should provide an explanation for the absence of these values in\ncertain datasets.\nResponse: We thank the reviewer for pointing out the missing results in Table R2. We\nexplain the missing values.\n1. The missing values arise from computational limitations when running the newly added\nmethods on certain datasets. In the revised version, we use a notation system to categorize\nthese limitations:\n– “-M”: Out-of-memory errors\n– “-T”: Runtime timeout (exceeding 5 hours)\n– “-C”: Program crashes or technical failures\nWe added notes to all Supplementary Tables with missing values, explaining the reasons for\neach absence. Table R1 shows an example of how these notations are applied in our results.\nby ARI (Maximum in bold, second maximum underlined; “-M” out-of-memory, “-T” out-of-time\n(> 5h), “-C” crashed)\nARI\nData Name\nCellScope Scanpy Seurat scLEGA scDCCA CellBRF\nYan 0.83 0.86 0.85 0.66 0.90 0.38\nGoolam 0.99 0.86 0.63 0.60 0.96 0.26\nPollen 0.90 0.89 0.86 0.87 0.85 0.77\nWang 0.90 0.82 0.62 0.43 0.82 0.30\nDarmanis 0.75 0.69 0.46 0.15 0.76 0.44\nUsoskin 0.96 0.79 0.74 0.30 0.79 0.31\nXin 0.90 0.94 0.95 0.78 0.93 0.24\nKeller(I) 0.75 0.70 0.78 0.18 0.63 0.20\nMuraro 0.95 0.92 0.49 0.94 0.94 0.50\nNHGRI 0.93 0.82 0.77 0.41 0.72 0.60\nKlein 0.97 0.90 0.91 0.79 0.87 0.46\nZeisel 0.82 0.75 0.69 0.64 0.87 0.51\nLake 0.81 0.77 0.64 0.22 0.53 0.72\nKeller(P) 0.88 0.79 0.09 0.00 0.56 0.59\nHan(B) 0.89 0.17 0.03 -C -C -C\nSiletti-1 0.93 0.93 0.77 0.93 0.99 0.43\nTirosh 0.90 0.49 0.56 0.53 0.80 0.56\nSiletti-2 0.98 0.87 0.87 0.72 0.99 0.87\nBaron(H) 0.95 0.95 0.91 0.40 0.92 0.54\nSiletti-3 0.90 0.52 0.55 0.84 0.60 0.54\nSiletti-4 0.98 0.39 0.39 0.64 0.59 -M\nSiletti-5 0.93 0.16 0.20 0.32 0.36 -M\nSiletti-6 0.99 0.35 0.62 -M 0.40 -M\nUlrich(H) 0.80 0.63 0.77 -M 0.86 -M\nLi 0.84 0.32 0.35 -M 0.30 -M\nLi(H) 0.95 0.70 0.90 -M 0.52 -M\nQiu(L) 0.64 0.66 0.68 -M -T -M\nUlrich 0.83 0.62 0.77 -M -T -M\nQiu(E) 0.73 0.73 0.73 -M -T -M\nAyhan 0.83 0.82 0.69 -M -T -M\nPosner 0.81 0.76 0.69 -M -T -M\nLi(M) 0.79 0.67 0.79 -M -T -M\nYu 0.80 0.34 0.72 -M -T -M\nHoo 0.95 0.94 0.83 -M -T -M\nYao 1.00 0.34 0.39 -M -T -M\nLi(HA) 0.91 0.57 0.80 -M -T -M\nAverage 0.88 0.68 0.65 0.54 0.74 0.49\n2\n2. The Han(B) dataset contained missing values that caused scLEGA, scDCCA, and\nCellBRF to fail during execution (marked as “-C” in Table R1). To faithfully reflect the\noriginal computational capabilities of each algorithm, we present the actual running results\nin our main tables and appendices. Out of curiosity and to address potential concerns, we\ntested two imputation strategies for the missing values, and obtained the following results\n(Table R2):\nARI\nImputation Method\nscLEGA scDCCA CellBRF\nMean imputation 0.31 0.39 0.13\nZero imputation 0.12 0.38 0.15\nTable R2: ARI values under two imputation methods for the Han(B) dataset.\nEven when considering the imputed results, the overall average ARI performance across\nall tested datasets remains: scLEGA (0.53), scDCCA (0.72), and CellBRF (0.47). The\nperformance ranking among methods stays unchanged, and even the best-performing scDCCA\n(0.72) shows a substantial gap compared to CellScope as shown in Table R1.\n3\n• Q2. All equations in the manuscript should be numbered for easier reference.\nResponse:Thank you for the reviewer’s suggestion. We have followed the advice and\nnumbered all equations in the main text and supplementary materials.\n4\n• Q3. I suggest including pseudo-code and flowcharts to further improve the readability and\nclarity of the proposed method.\nResponse: Thank you for your suggestions aimed at improving the readability and clarity of\nour method. In response, we have provided a detailed algorithmic flowchart of the CellScope\nworkflow as Algorithm 1 (corresponded to Supplementary Algorithm 1), which clearly\noutlines the five core computational steps—Manifold Fitting Stage 1, Manifold Fitting Stage\n2, Hierarchical Clustering, Tree-Structured Visualization, and Gene Characterization—along\nwith the default hyperparameters used at each stage. Additionally, we have included a compre\nhensive textual description of the workflow as Figure R1(corresponded to Supplementary\nFigure S1) to further enhance reproducibility and clarity.\nThe overall computational procedure consists of the following key stages:\n1. Manifold Fitting Stage 1: The raw gene expression data is first log-transformed and\nrow-normalized. Dimensionality is then reduced to 100 via PCA. Based on the reduced\ndimensions, the local density and relative distance of cells are computed. The top 500\ngenes with the smallest p-values are selected as high-confidence seed genes. Feature\ngenes are further refined through ANOVA-based differential analysis across pre-labeled\ncell cliques.\n2. Manifold Fitting Stage 2: Cells within the lowest 5% of density values are identified\nas outliers. Their positions are adjusted by 90% toward their nearest neighboring cells\nto enhance the continuity and topological stability of the underlying manifold. The\noutcome of this stage is the fitted data.\n3. Similarity Graph Construction and Clustering: A cell-specific similarity graph is built\nfollowing the UMAP algorithm. Hierarchical clustering is then applied to this graph to\nidentify distinct cell subpopulations.\n4. Downstream Analysis: This final phase encompasses three main tasks: (a) constructing\ntree-structured visualizations to illustrate developmental trajectories; (b) detecting\nmarker genes for each identified cell cluster; and (c) categorizing genes based on their\nexpression dynamics using Wasserstein distance.\nFor user convenience, we have also provided a detailed text-based workflow diagram in the\nonline tutorial, available at https://cellscope.readthedocs.io/en/latest/, to facilitate\nbetter learning and understanding. We believe these supplementary materials effectively\nenhance the transparency of the methodology and make the research outcomes easier to\nreproduce.\nRequire: Single-cell expression matrix X ∈ RN×D, gene set G\nEnsure: Hierarchical cell clusters, tree-structured visualization and categorized gene groups\n1: Stage A: Manifold fitting stage 1\n2: A1: Preprocess data: Apply log transform and row normalization to X\n2\n3: A2: Identify reliable cliques:\n4: Reduce dimensions to 100 via PCA\n5: Compute local density ρ and relative distance δ for each cell as Eq. (1)\n6: Select manifold seeds using adaptive thresholds on γ′ = ρ′·δ′ and change rate R as Eqs. (2)-(6)\n7: Form cliques using 5-nearest neighbors of seeds\n8: A3: Detect signal space:\n9: Perform ANOVA using clique labels\n10: Select top 500 genes with smallest p-values\n11: Stage B: Manifold fitting stage 2\n12: Calculate cell density according to Eq. (8), and identify cells with the lowest 5% density values as\noutliers\n13: for each outlier y do\ni\n14: Find nearest high-density point yˆ\ni\n15: Project: z ← 0.9yˆ +0.1y\ni i i\n16: end for\n17: Non-outliers remain unchanged as Z = {z }N\ni i=1\n18: Stage C: Hierarchical Clustering\n19: Build the similarity matrix as Eqs. (11)-(13)\n20: Initialize each cell as singleton cluster\n21: for K = 1 to N −1 do\n22: Merge clusters with highest average similarity\n23: end for\n24: Stage D: Tree-Structured Visualization\n25: Generate UMAP layout for root node (all cells)\n26: for each hierarchy level do\n27: Visualize child clusters\n28: Propagate colors from leaves to root\n29: end for\n30: Stage E: Gene Characterization\n31: for each gene g ∈ G do\n32: Compute Wasserstein distance W between sibling nodes\n1\n33: Classify based on thresholds:\n34: end for\nRaw data\nrow-normalize\nManifold fitting stage 1\nReduce Compute local Select manifold\ndimensions to density ρ and seeds based on\n100 via PCA relativedistance γ ρ and γ\nSelect thetop5 ANOVA-based\nExtract cliques\n00genes with gene selection\nfrom seed 5-\nthesmallest p- across labeled\nneighbors\nvalues cliques\nManifold fitting stage 2\nIdentify the\nShift outliersby\nlowest 5%\n90% toward Fitted data\ndensity cells as\nnearest cell\noutliers\nBuild the similarity graph following UMAP\nHierarchical clustering viasimilarity graph\nDownstream analysis\nCategorize genes\nTree-structured Marker genes based on\nvisualization detection Wasserstein\ndistance\nFigure R1: CellScope workflow: (A) Data preprocessing, (B) Two-stage manifold fitting, (C)\nGraph-based clustering, (D) Tree visualization, (E) Gene characterization.\n7\n• Q4. I suggest adding comparative analysis with the following method, \"scCRT: a contrastive-\nbased dimensionality reduction model for scRNA-seq trajectory inference\".\nResponse: We appreciate the reviewer’s suggestion regarding scCRT [1]. scCRT is a\ndimensionality reduction framework specifically designed for trajectory inference that does\nnot include a clustering pipeline, unlike CellScope, Seurat, or Scanpy. Additionally, scCRT\npartially utilizes label information during training, which would make direct unsupervised\nclustering comparisons less fair. Therefore, we did not directly compare scCRT’s clustering\nperformance with other algorithms in our main analysis. Instead, we added a comparative\ndescription of this framework in our discussion, highlighting its capability in applying\ndimensionalityreductionfortrajectoryinference: \"RecentmethodssuchasscCRT[1]represent\nanother crucial approach to single-cell analysis, which innovatively combines cell-level pairwise\nmodules with cluster-level contrastive learning to preserve cellular relationships for trajectory\ninference applications. While both scCRT and CellScope learn highly informative low-\ndimensional representations and leverage clustering information, they differ in their design\nfocus: scCRT emphasizes continuous processes and trajectory analysis, whereas CellScope is\noptimized for discrete clustering tasks and multi-resolution cellular characterization.\"\nBased on our curiosity, we conducted a special experiment using scCRT features as input\nto CellScope and Scanpy. As shown in Table R3, CellScope achieves superior clustering\naccuracy on most datasets with an average ARI of 0.88, compared to 0.81 for scCRT\nfeatures combined with CellScope clustering. For Scanpy, scCRT features provide moderate\nimprovements, increasing the average ARI from 0.68 (Scanpy alone) to 0.74 (scCRT features\n+ Scanpy clustering) across 17 datasets. However, the scCRT-enhanced Scanpy still performs\nless effectively than CellScope for clustering tasks. These results demonstrate that scCRT\nembeddings can enhance existing clustering methods, while CellScope remains more effective\nfor discrete clustering applications.\n(Better in bold; “-M” out-of-memory, “-C” crashed)\nARI ARI\nData Set\nCellScope scCRT+ CellScope Scanpy scCRT+ Scanpy\nYan 0.83 0.82 0.86 1.00\nGoolam 0.99 1.00 0.86 0.99\nPollen 0.90 0.99 0.89 0.93\nWang 0.90 0.96 0.82 0.98\nDarmanis 0.75 0.85 0.69 0.94\nUsoskin 0.96 0.91 0.79 0.91\nXin 0.90 1.00 0.94 1.00\nKeller(I) 0.75 0.31 0.70 0.24\nMuraro 0.95 -C 0.92 -C\nNHGRI 0.93 0.83 0.82 0.88\nKlein 0.97 0.91 0.90 0.77\nZeisel 0.82 0.73 0.75 0.75\nLake 0.81 0.53 0.77 0.58\nKeller(P) 0.88 0.58 0.79 0.57\nHan(B) 0.89 0.77 0.17 0.31\nSiletti-1 0.93 -C 0.93 -C\nTirosh 0.90 0.94 0.49 0.55\nSiletti-2 0.98 -C 0.87 -C\nBaron(H) 0.95 0.95 0.95 0.95\nSiletti-3 0.90 0.84 0.52 0.84\nSiletti-4 0.98 0.99 0.39 0.58\nSiletti-5 0.93 -C 0.16 -C\nSiletti-6 0.99 -C 0.35 -C\nUlrich(H) 0.80 0.75 0.63 0.80\nLi 0.84 0.42 0.32 0.21\nLi(H) 0.95 0.98 0.70 0.98\nQiu(L) 0.64 0.79 0.66 0.56\nUlrich 0.83 0.83 0.62 0.82\nQiu(E) 0.73 0.80 0.73 0.60\nAyhan 0.83 -M 0.82 -M\nPosner 0.81 -M 0.76 -M\nLi(M) 0.79 -M 0.67 -M\nHoo 0.95 -M 0.94 -M\nYu 0.80 -M 0.34 -M\nYao 1.00 -M 0.34 -M\nLi(HA) 0.91 -M 0.57 -M\nAverage 0.88 0.81 0.68 0.74\n9\n• Q5. Remarks on code availability. The authors need to provide detailed information about\nthe hardware and software configurations used for running the code.\nResponse: Thank you for your suggestion. We have added detailed information about the\nhardware configuration and software environment in the Computational Environments\nsection of the Methods section in the manuscript, as well as in the supplementary materials.\nSpecifically, we included a reference in the main text to the software configuration table in\nthe supplementary materials and noted the versions of the comparative algorithms used,\nas follows: CellScope was executed in a Python environment, with its Python version and\nrequired library dependencies detailed in Supplementary Table S26. All comparative algorithms\nutilized the latest publicly available versions, with Scanpy version 1.10.3 and Seurat version\n5.2.0.\nIn Table R4 (corresponded to Supplementary Table S26), we listed the Python environ\nment and the versions of the relevant dependency libraries used to run CellScope on three\ndifferent devices, to facilitate the reproduction of experimental results by readers and method\nusers.\nLibrary iMac Macbook Google Colab\npython 3.10.16 3.10.16 3.10.16\nanndata 0.11.4 0.11.4 0.12.2\nfastcluster 1.2.6 1.2.6 1.3.0\njoblib 1.4.2 1.4.2 1.5.1\nkeleido 0.2.1 0.2.1 0.2.1\nmatplotlib 3.10.1 3.10.1 3.10.0\nnbformat 5.10.4 5.10.4 5.10.4\nnetworkx 3.4.2 3.4.2 3.5\nnumba 0.61.2 0.61.2 0.61.2\nnumpy 1.26.4 1.26.4 2.0.2\npandas 2.2.3 2.2.3 2.2.2\nplotly 6.0.1 6.0.1 5.24.1\nrequests 2.32.3 2.32.3 2.32.3\nscikit-learn 1.6.1 1.6.1 1.6.1\nscipy 1.13.1 1.13.1 1.15.3\nseaborn 0.13.2 0.13.2 0.13.2\nstatsmodels 0.14.4 0.14.4 0.14.5\nthreadpoolctl 3.6.0 3.6.0 3.6.0\numap-learn 0.5.7 0.5.7 0.5.7\nReferences\n[1] Yuchen Shi, Jian Wan, Xin Zhang, Tingting Liang, and Yuyu Yin. sccrt: a contrastive-based\ndimensionality reduction model for scrna-seq trajectory inference. Briefings in Bioinformatics,\n25(3):bbae204, 2024.\n11"}, {"round": 3, "reviewer_comments": {"reviewer_2": "The authors have undertaken a thorough and substantive revision of the manuscript. The revised version addresses all of\nmy previous concerns in a direct and satisfactory manner."}, "author_response": ""}]

Decision letter