dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, integration methods that can efficiently and accurately harmonize a wide range of data sources are essential for accelerating life sciences research  [11]. Although integration methods for single-cell transcriptomics analysis have evolved along with single-cell sequencing technologies, the rapid accumulation of new and diverse single-cell datasets has introduced three major challenges to the integration task. First, as the sample size of each single-cell dataset grows dramatically, numerous extensive datasets with hundreds of thousands or even millions of cells have been produced  [8, 9, 12]. The emergence of large-scale datasets requires integration methods to be fast, memory-efficient, and scalable to millions of cells. Second, technology now allows effective, comprehensive characterization of complex organs, containing rare subpopulations of cells that can now be captured, albeit in small numbers, thanks to the scale of profiling that is now possible  [7, 13].
Investigation into highlevel heterogeneity among cell populations is essential for understanding the mechanism of complex biological systems. Hence, the ideal integration method needs to carefully preserve finegrained cell populations from each atlas-level dataset. Third, the biological origins of datasets has expanded in diversity, with data now spanning across not only different technological platforms and data types, different individual donors, but even across different species, which can be especially interesting for evolutionary studies  [14, 15, 16]. Integrative analysis of such diverse datasets would allow researchers to unify resources to address a wider range of biological questions. Recent single-cell atlasing efforts are a primary example of these challenges -various human tissue atlases  [12, 17] , mouse multi-tissue atlases  [7, 18] , and non-human primate atlases  [19, 20]  have been generated, culminating in data from millions of single cells and single nuclei.
Both within and across atlas comparisons are of interest. To perform integrative and comparative analyses based on such diverse data sources, there is an urgent need for methods that can flexibly account for heterogeneous dataset-specific effects, while maintaining a high level of integration accuracy. Many methods have been developed to align single-cell datasets  [10] , including Harmony  [21] , Seurat  [22] , online iNMF  [23] , VIPCCA  [24] , scVI  [25] , fastMNN  [26] , Scanorama  [27]  and BBKNN  [28]. Several of these methods that were designed for large datasets at the time of publication are now less attractive in terms of scalability in the face of atlas-level dataset sizes. For instance, a representative category of methods leverages the mutual nearest neighbors (MNN) to perform data alignment.
These MNN-based methods, such as Seurat, fastMNN and Scanorama, require identification of MNN pairs across datasets, thus the time and memory costs quickly become unbearably high when the dataset exceeds one million cells. Another limitation of existing methods is that they are mainly targeted towards integrating datasets of less complex tissues, utilizing strategies such as MNN, matrix factorization, and soft-clustering to capture major biological variations. With these strategies, inaccurate mixing of different cell types can be avoided when clear clustering patterns are present; but when dealing with more complex tissues, they tend to overcorrect fine-grained cell subpopulations, resulting in the loss of power in revealing interesting biological variations  [29, 30]. Lastly, most existing methods are designed to correct batch effects caused by technical artifacts.
To this end, a number of methods, like BBKNN and fastMNN, assume that the biological variation is much larger than the variation of batch effects. This assumption may not be true when applied across data types and species. To simultaneously address the above three challenges, we created Portal, a machine learningbased algorithm for aligning atlas-level single-cell datasets with high efficiency, flexibility, and accuracy. Viewing datasets from different studies as distinct domains with domain-specific effects (including technical variation and other sources of unwanted variation), Portal achieves extraordinary data alignment performance through a unified framework of domain translation networks that incorporates an adversarial learning mechanism  [31]. To find the correspondence between two domains, our domain translation network utilizes an encoder to embed cells from one domain into a latent space where domain-specific effects are removed, and then uses a generator to map latent codes to another domain.
Second, without using any cell type label information, three regularizers of Portal can guide domain translation networks to find correct correspondence between domains, account for domain-specific effects, and retain biological variation in the latent space. Third, through a tailored design of lightweight neural networks and mini-batch optimization accelerated by graphics processing units (GPUs), Portal can scale up to datasets containing millions of cells in minutes with nearly constant memory usage. With the above innovations in model and algorithm designs, Portal enables fast and accurate integration of atlas-level datasets across samples, technological platforms, data modalities, and species. Through a comprehensive benchmarking study, where integration of heterogeneous collections of atlas-level single-cell RNA sequencing (scRNA-seq) data are included, Portal shows its superiority over state-of-the-art alignment algorithms in terms of both computational efficiency and accuracy.
We then show that Portal can accurately align cells from complex tissues profiled by scRNA-seq and single-nucleus RNA sequencing (snRNA-seq) as well as align scRNA-seq data and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) data, even in the presence of highly unbalanced cell type compositions. We also apply Portal to the integration of cells in differentiation processes, especially the alignment of the gradient of cells in the spermatogenesis process across multiple species (mouse, macaque, and human). Using these diverse and challenging experiments, we demonstrate Portal's versatility and power for a broad range of applications. Comprehensive analyses of real, expert annotated data confirm that integrated cell embeddings provided by Portal can be reliably used for identification of rare cell populations via clustering or label transfer, studies of differentiation trajectories, and transfer learning across data types and across species.
Results Method Overview: Portal learns a harmonized representation of different datasets with adversarial domain translation. Expression measurements from different datasets fall into different domains due to the existence of domain-specific effects, including technical variation and other sources of unwanted variation (Fig. 1a ), causing difficulty when performing joint analyses. Without loss of generality, here we consider two domains, X and Y. We assume that domain X and domain Y can be connected through a low-dimensional shared latent space Z, which captures the biological variation and is not affected by the domain-specific effects. By taking the measurements of cells from X and Y as inputs, we aim to learn a harmonized representation of cells in latent space Z to obtain data alignment between X and Y.
More detailed explanation can be found in the Methods section. Accurate integration of atlas-level datasets within minutes and requiring lower memory consumption compared to other methods. The rapid accumulation of large-scale single-cell datasets requires integration algorithms to efficiently handle datasets containing millions of cells without loss of accuracy. For a comprehensive comparison, we first benchmarked Portal and existing representative methods, including Harmony  [21] , Seurat v3  [22] , online iNMF  [23] , VIPCCA  [24] , scVI  [25] , fastMNN  [26] , Scanorama  [27]  and BBKNN  [28] , in terms of integration performance following a recent benchmarking study  [30]. Using a number of scRNA-seq datasets from diverse tissue types with curated cell cluster annotations, including mouse spleen, marrow, and bladder  [7] , we quantitatively evaluated the integration performance of each method.
We first evaluated alignment performance, which can sometimes be interpreted as batch correction performance, of all compared methods. The score for batch correction was computed by leveraging a collection of batch correction metrics designed in existing studies, including k-nearest neighbor batch-effect test (kBET)  [35] , principal component regression of the batch covariate (PCR batch)  [35] , average silhouette width across batches (batch ASW)  [35] , graph integration local inverse Simpson's Index (graph iLISI)  [30, 21]  and graph connectivity  [30]. The higher the batch  correction score, the higher the degree of mixing across datasets. We also assessed the score for conservation of biological variation using different metrics, including adjusted rand index (ARI)  [36] , normalized mutual information (NMI)  [37] , cell type ASW, graph cell type local inverse Simpson's Index (graph cLISI)  [30, 21] , isolated label F1  [30] , isolated label silhouette  [30]  and cell cycle conservation  [30].
S6, S8 and S10). Although VIPCCA showed reasonable performance in terms of removing batch effects, incorrect mixing of distinct cell types was often observed in VIPCCA's integration results (Fig. S6 ). Therefore, its overall scores are relatively low due to the loss of biological variation (Figs. S5, S7 ). Among those methods with high user popularity, Harmony, Seurat, and online iNMF also showed the best overall integration performance results (Fig. 2a , the first three columns; Figs. S6, S8 and S10). To offer precise and robust integration performance, Seurat  [22]  utilizes the detection of mutual nearest neighbors (MNN) to build correspondence between datasets in the shared embedding space obtained by applying canonical correlation analysis (CCA). Harmony  [21]  learns a simple linear correction for dataset-specific effects by running an iterative soft clustering algorithm, enabling fast computation on large datasets.
The datasets were sampled from two mouse brain atlas datasets (n = 100, 000, 250, 000, 500, 000, and 1, 100, 167). Seurat required 24. 52 GB on the dataset with 100, 000 cells, which was not comparable to the other three benchmarked methods in terms of the peak running memory usage. c. Batch correction and biological variation conservation evaluated using three shared tissues from two mouse brain atlases (profiled by Drop-seq and 10X), including cerebellum, hippocampus, and thalamus. Biological variation conservation performance was assessed based on fine-grained annotations provided by the original publications  [8, 9]. of 160,678 cells. These two mouse brain atlases have data from three shared brain regions: cerebellum, hippocampus, and thalamus. There are many small clusters of neuron subtypes in these datasets, where gene expressions between subclusters could have a relatively small difference.
Thus, these datasets are more challenging to integrate compared to data with clear clustering patterns. First, Portal has superior integration accuracy even when handling datasets which contain many subclusters with small difference. The score of biological variation conservation shows that Portal outperforms other state-of-the-art methods in cluster identity preservation, as the scores were assessed based on fine-grained cell type and subtype annotations. In particular, for all three brain regions tested, Portal has the highest ARI and NMI scores among the compared methods (Fig. 2c ). Second, Portal also outperforms the other three methods on scalability, in terms of time and memory consumption. For this benchmarking test, we obtained datasets from the original full-sized datasets by combining the two atlases and subsampling proportionally from each atlas, with each dataset having increasing sample size ranging from 100,000 to 1,100,167 (full dataset).
The running time and the peak running memory of all methods were recorded using these datasets on the same GPU server. The results show that Portal's running time and peak running memory remained almost constant even when the sample size increased dramatically (Fig. 2b ). Compared to the other three methods, the running time required by Portal was also substantially less. On the dataset containing 500,000 cells, Portal's running time was 80 seconds; when number of cells grew to 1,100,167, Portal's running time only increased to 120 seconds. In comparison, Harmony and online iNMF both needed more than 40 minutes to integrate 500,000 cells and more than 100 minutes to complete the integration of the full dataset. The running time of Seurat increased most rapidly among the compared methods. It took as much as 511 minutes (over 8.
5 hours) to integrate the 500,000-cell dataset. The computational efficiency of Portal is owing to two important factors in its design: 1) its algorithm takes advantage of GPU-accelerated stochastic optimization, such that Portal reads data in mini-batches from the disk rather than having to load the entire dataset at once, which enables fast integration of large single-cell datasets using small amounts of memory; and 2) lightweight neural networks are adopted in Portal to further improve computational efficiency. As such, Portal is also the most memory-efficient approach among the benchmarked methods (Fig. 2b ). Peak running memory required by Portal ranged from 0. 29 GB on 500,000-cell dataset to 0. 57 GB on the full million-cell dataset. Notably, Portal's lightweight networks and mini-batch stochastic optimization algorithm enable us to control the GPU peak running memory usage at a constant level of 0.
06 GB. Among compared methods, online iNMF used less memory than Harmony and Seurat when the sample size became larger than 500,000, because it is also trained in mini-batches. However, its peak running memory was 2. 10 GB on the million-cell dataset, which is 2. 7 times more than Portal's. Seurat required remarkably more memory usage than the other three methods. For clarity of visualization, we did not display the peak running memory required by Seurat as it ranged from 24. 52 GB on the 100,000-cell dataset to 276. 41 GB on the 500,000-cell dataset. Finally, and importantly, Portal's high performance in speed and memory consumption does not compromise its ability to align cell type clusters. The batch correction score shows that Portal's alignment ability is comparable to, if not better than, the other state-of-the-art methods, indicating that Portal is capable to effectively remove domain-specific effects.
Both mouse brain atlas datasets contain extensive data for this brain region (Fig. 3 ), and both studies identified a wide range of transcriptionally distinct cell subpopulations, including a variety of neuron subtypes, whose nuanced transcriptional differences should ideally be preserved by integration methods. After applying Portal and the other three benchmarked methods to integrate the data, we used the integrated cell representations to perform clustering. Using the Louvain method  [40]  with default resolution, we obtained 29 (Portal), 29 (Harmony), 25 (Seurat) and 30 (online iNMF) clusters, respectively (Fig. S19 ). Particularly, we focused on one region where the cell proportions between two datasets were highly unbalanced, as marked in Fig. 3a. Only a few of cells in this region are from the 10X dataset, making it challenging to build alignment a c b Figure  3 : Preservation of fine-grained neuron subpopulations in the integration of hippocampus datasets.
a. We visualized integration results from Portal, Harmony, Seurat and online iNMF of hippocampus datasets profiled by Drop-seq and 10X with UMAP  [39]. Top panels are UMAP plots colored by profiling methods. Middle and bottom panels are UMAP plots of cells from the 10X dataset, the Drop-seq dataset after integration respectively, colored by fine-grained annotations (c). b. We marked a region containing three distinct neuron subpopulations. Results from Louvain clustering algorithm were presented for a comparison of cluster identity preservation performance. c. Cell type annotations and proportions of the two datasets from their original publications  [8, 9]. The comparison among proportions of subpopulations was visualized by the sizes of corresponding dots. between datasets while preserving subpopulations from the Drop-seq dataset.
In the original publication  [8] , cells from the Drop-seq dataset within the marked region were all annotated as neurons but further classified into three transcriptionally distinct subpopulations, namely: Cbln1+/Grp88+ medial entorhinal cortex neurons; Slc17a6+ neurons; and Cbln4+ neurons. Among the benchmarked methods, Portal was the only method that clearly clustered these cells into three coherent groups in the integrated embedding space. Specifically, clusters 4, 13, and 26 identified by the Louvain method recovered the Slc17a6+ neuron; Cbln1+/Grp88+ medial entorhinal cortex neuron; and the Cbln4+ neuron subpopulations, respectively (Fig. 3b ). Each cluster was confirmed by the high expression level of the annotated marker genes (Fig. S20a ). Notably, these three groups only accounted for 4.
Cluster 26, meanwhile, showed moderate levels of expression of all eight genes. In the marked region, cells from the 10X dataset were mostly concentrated in clusters 4 and 13. The alignment by Portal was confirmed by the consistent gene expression levels in clusters 4 and 13 between the two datasets (Fig. S20b ). Besides the eight differentially expressed genes, we also examined a larger set of genes, and computed the cross correlation of these genes pairwise between cells from all three groups. This analysis showed that cells within each cluster had higher similarity in gene expression than cells from other clusters, further showing the biological difference between these three clusters that should not be mixed after integration (Fig. S20d ). The above results highlight Portal's power to preserve rare cell types.
The integrative analysis on the hippocampus tissue demonstrates Portal's ability to maintain nuanced transcriptional differences for small subpopulations. This means that Portal can also be used to "call out" rare subpopulations in one dataset based on integration with another dataset via label transfer. To illustrate this feature, we take 10X and SMART-seq2 (SS2) data generated for a mouse lung scRNA-seq atlas  [7]  as an example: the typically larger sample size of the 10X dataset facilitates powerful clustering analyses for identification of cell types; while the greater sequencing depth and sensitivity of SS2 enables deeper investigation into cell biology  [41]. To leverage the different strengths of the two technologies, we used Portal to perform integrated analysis on 1,676 SS2 cells and 5,404 10X cells (Fig.
S21a ). Specifically, we defined the 10X dataset annotations from the original publication  [7]  as reference labels (Fig. S21b ), then made use of the Portal's integration results to identify cell types for the SS2 dataset based on these reference labels. After integration, for each SS2 cell, label transfer was performed by detecting its nearest neighbors among 10X cells. From this analysis, we identified four subpopulations of myeloid cells for the SS2 dataset, namely alveolar macrophages, dendritic cell and interstitial macrophages, classical monocytes, and non-classical monocytes (Fig. S21d ). Transferred labels of these four subpopulations were validated by known marker gene expression levels  [42]. For example, compared to classical monocytes, non-classical monocytes showed lower expression of Ccr2 and higher expressions of Treml4 (Fig.
S22 ). Consistent with the gene expression pattern of alveolar macrophages in the 10X dataset, alveolar macrophages annotated by Portal in the SS2 dataset had high expression levels of marker genes Mrc1 and Siglec5. Notably, in the SS2 dataset, the alveolar macrophage subpopulation only accounted for 0. 78% of total sample size, and could not be distinguished from the other SS2-profiled macrophages in the original publication  [7]. Based on the original labels, alveolar macrophages were unidentified as they were labeled in a more general group named "dendritic cell, alveolar macrophage, and interstitial macrophage" (Fig. S21c ). Making good use of the larger 10X dataset, Portal successfully identified extremely rare subpopulations within the SS2 dataset. We then used the mouse lemur bladder scRNA-seq datasets from the Tabula Microcebus Consortium  [43]  as another example to demonstrate Portal's ability for discovering rare subpopulations via label transfer.
In this example, mouse lemur bladder tissue was also profiled by both SS2 and 10X. When we integrated these datasets and transferred labels from the 10X dataset to the SS2 dataset using Portal, we were able to distinguish a very small myofibroblast subpopulation of just 11 cells in the SS2 dataset from the rest of the fibroblasts (Fig. S23a ). We verified their myofibroblast identity based on their high expressions of known marker genes ACTA2, MYH11, TAGLN  [44]  (Fig. S23b ). Integration of comprehensive whole-organism cell atlases. So far, Portal has shown impressive performance in aligning tissue-level atlases where nuanced transcriptional differences among subpopulations can be maintained after integration. We next assess Portal's capabilities under another challenging scenario: integrating two atlases across an entire organism, where one of the atlases includes many more organs and tissue types than the other.
This can be very challenging for some integration algorithms due to having "missing cell types" in one of the datasets  [10]. In contrast to these approaches, Portal uses discriminators with tailored design in the adversarial domain translation framework to distinguish domain-specific cell types from cell types shared across domains automatically, and is thus robust to non-overlapped tissue samples. To build a foundation for extensive study of cell populations across the whole organism, the Tabula Muris Consortium  [7]  profiled cells from 20 tissues using a combination of SS2 (44,779 cells) and 10X (54,865 cells) (Fig. 4 ). Notably, seven of these 20 tissues were only profiled by SS2 but not 10X: brain (myeloid and non-myeloid), diaphragm, fat, large intestine, pancreas and skin. We used Portal to build a comprehensive integrated mouse atlas that merges all the cells, and we found Portal to show extraordinary accuracy in aligning cells of the same cell type from the two datasets profiled by different platforms, not only in the shared latent space but also in both domains (Figs.
By looking into the domain of 10X data (10X space), the discriminator in the 10X domain was found inactive for tissue-specific cell types that were only in the SS2 dataset (Fig. 4b ). For these cells, Portal ensured that their identities were preserved by making the adversarial learning objective inactive on them automatically. Portal's ability to conserve information of cell populations indicates its reliability for integrating atlas-level single-cell datasets across entire organisms. Besides the alignment between datasets, Portal's integration result could characterize the similarities and differences among cell types. For example, immune cells such as B cells, T cells, natural killer cells (NK cells), monocytes and macrophages were profiled by both platforms and contained in multiple tissues including brain (myeloid), diaphragm, fat, kidney, limb muscle, liver, lung, mammary gland, marrow, spleen, and thymus.