dataesr/scientific-paragraphs-categorization huggingface dataset:
Actin order parameter analysis This analysis was performed with the ImageJ (National Institutes of Health) plugin OrientationJ, which calculates the local orientation in actin images using the structure tensor  (40). The program first smooths the original image using a Gaussian filter. Then, based on the intensity level, the region in the cell is segmented. For each pixel in the cell, the structure tensor J (which has three elements: J 11 , J 12 , and J 22 ) is computed in a local neighborhood that is also Gaussian. The orientation angle, the coherency, and a measure of local gradient (gray level is constant or it changes) are computed from the elements of the structure tensor (l i are the eigenvalues of J): tanð2qÞ ¼ 2J 12 J 22 À J 11 coherency ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðJ 22 À J 11 Þ 2 þ 4J 2 12 q J 11 þ J 22 ¼ l 1 À l 2 l 1 þ l 2 gradient ¼ J 11 þ J 22 ¼ l 1 þ l 2 : The average orientation and order parameter S will be computed by averaging over all pixels for which the coherency is above a threshold value, which can be changed.
Motivated by the experimental observation that, after optogenetic activation, cells return to their baseline stress (Fig. 1  g), we assume that the material law for the cell cannot be purely viscous and must contain a strong elastic element. We therefore model the cell as a thin viscoelastic layer of the Kelvin-Voigt type, which describes a solid in parallel with a viscous element (Fig. 2  a and supplement). Optogenetic activation is modeled by an increase of the active tension acting in parallel to the elastic and viscous elements. As alternatives to this material law, we also considered active versions of the Maxwell model, which describes a fluid with an elastic element in series, as well as of purely elastic material (supporting material). Using finite element calculations in the open software package FEniCS, we then implemented these material laws for thin contractile sheets that are attached to an elastic foundation with the geometry of interest and locally have a polarized actin cytoskeleton (supporting material).
ACKNOWLEDGMENTS M. B. acknowledges financial support from the  French Agence Nationale de la Recherche  ( ANR ) for the  PlatForMechanics grant   ANR-18-CE14-0037-02 , the  Inter-s-cal  grant  ANR-21-CE13-0042-02  and the  MechanoSwitch  grant  ANR-17-CE30-0032-01. U. S. S. acknowledges funding through the  DFG (Deutsche Forschungsgemeinschaft ) grant MechanoSwitch  SCHW 834/2-1. T. B. acknowledges funding through  CNRS   grants (Actions Interdisciplinaires 2017, DEFI Instrumentation aux limites 2017, Tremplin@INP 2021, PEPS CNRS-INSIS 2021, Lumiere Visible et Vie 2022 ) and the  Center of Excellence of Multifunctional Architectured Materials "CEMAM " (no. AN-10-LABX-44-01 ). We would like to thank  Laurent Blanchoin ,  Manuel Thery ,  Matthieu Coppey ,  Simon de Beco ,  Olivier Destaing , and the whole MicroTiss team for useful discussions and feedback.
Two other popular scoring approaches, UCell  12  , which is an extension of AUCell  14  , and JASMINE (Jointly Assessing Signature Mean and Inferring Enrichment), use rank statistics to increase the stability of results in case of strong technical variation and batch effects. UCell is solely based on Mann-Whitney U statistics. It is expected to be robust against technical variation as it only uses per-cell gene rank information. JASMINE computes scores by averaging the mean of signature gene ranks and an enrichment value  7. The enrichment values can correspond to odds ratios or the likelihood, referred to as OR and LH in our benchmarks. Although Noureen et al. 7  investigated the robustness of several signature scoring methods for bulk and single-cell RNA-seq, assessing the methods' sensitivity and specificity on in silico datasets, the study did not assess the comparability of score ranges and score variability linked to differences in the discriminatory potential of genes constituting a signature.
Further, a novel benchmark is needed to interrogate the robustness of gene signature scoring methods under the variation in sample cell composition and batch effects; such evaluation would ensure the methods' trustworthiness and usefulness in downstream analysis. In this work, we benchmark and analyze the stability of the most popular cell scoring methods, Scanpy, Seurat, UCell, and JASMINE. We present an improvement of Tirosh's scoring method more resistant to single-cell technology biases than the original approach, which we term Adjusted Neighbourhood Scoring (ANS). We show that ANS is robust with regard to most influencing factors and returns comparable scores for multiple signatures. Specifically, although all scoring methods demonstrate resilience against variations in data composition and variability in signature qualities, they do not exhibit, except for ANS, comparable score ranges for gene signatures designed to discriminate similar cell types, such as memory and naive B cells.
While Seurat_AG selects all genes from an expression bin as control genes, Seurat_LVG chooses genes with the smallest dispersion. Conversely, ANS avoids binning and selects control genes in an average-expression neighborhood closest to the signature genes' average expression values (Methods). We implemented all methods originally developed for the R language in Python (Figure  1a ) and showed their identical behavior (Figure  1b , Methods). The Python package complementing this work provides access to all implemented methods. We benchmarked the methods in six different scenarios (Figure  1a , Table  1 ). The initial and final scenarios involved a preprocessed dataset of peripheral blood mononuclear cells  15  (PBMC), while the remaining four scenarios were carried out on preprocessed datasets of colorectal carcinoma  16  (CRC) and squamous cell carcinoma of the esophagus 1 (ESCC) (Methods).
In the first scenario, we showed the implication of control gene selection on gene signature scoring. In the second and third, we investigated the influence of the data composition and batch effects on scoring. In the fourth and fifth scenarios, we analyzed the impact of the variability in the gene signature length on the resulting scores. In the final scenario, we examined the scores produced by different methods when scoring multiple gene signatures and evaluated the performance of cell score-based classifiers to annotate cell types or cell states. We also evaluated the time requirements for the eight methods as a function of the increasing number of cells and as a function of the increasing number of genes in the signature (Suppl. Figure  S2 ). All methods exhibited linear scalability with dataset size.
For short signatures (up to 10 genes), Seurat, Scanpy, and ANS performed the fastest (<10 sec for 150,000 cells), while Seurat and Scanpy remained the quickest for longer signatures (1,000 genes). In contrast, UCell and Jasmine took approximately seven times longer, while ANS took about three times longer in this case. Nevertheless, these methods consistently operated within seconds to a few minutes, even with large signatures and datasets. Table  1. Qualitative comparison of signature scoring methods based on their properties, influencing factors, and score comparability. ✓ means a scoring method fulfills property or resists an influencing factor. ✕ means the opposite. NA stands for a property or an influencing factor that cannot be evaluated for a scoring method. These results are discussed in detail in Results.
All scoring methods based on Tirosh et al. 13  , i. e. , all methods except JASMINE and UCell, use control genes for gene signature scoring. Typically, genes whose average expressions are similar to the average expression of the signature gene are used as control genes. Consequently, these control genes exhibit a mean average expression, referred to as the mean control value, that is the closest approximation to the average expression of the signature gene itself. The selection of an optimal control set is crucial, as inappropriate selection can introduce a bias and lead to unreliable scores for all cells. We compared strategies for the control gene selection of the methods ANS, Seurat, Seurat_AG, and Seurat_LVG across diverse preprocessed subsets of cell subtypes within the PBMC dataset for the top expressed genes where the effects of the control gene selection were expected to be the most pronounced; we chose the 8% of genes with the highest average expression values corresponding to the last two bins of the Tirosh-based methods (Figure  1c , Suppl.
The influence of cell type proportions and batch effects on gene signature scoring The mean control values and, thus, the choice of control genes depend on the dataset composition of the tissue or sample analyzed. Consequently, when utilizing methods that employ control genes, we anticipated greater variability in gene signature scoring values when scoring was conducted on a per-sample basis compared to a scenario when all cells from all samples were assessed collectively. To highlight this effect, we calculated scores of 100-gene signatures associated with the malignant cell phenotype of CRC and ESCC (Methods). We compared the distributions of the mean scores per sample and cell type under two scenarios: when scoring was conducted on a per-sample basis and on all samples together (Figure  1d , Suppl.
Therefore, our results suggest that scoring should be performed on all cells in the dataset simultaneously for all methods using control gene sets, including ANS. The influence of the variability in the gene signature length on gene signature scoring We evaluated the effects of the signature length on gene signature scoring. We assessed the robustness of scoring methods to small signatures on CRC and ESCC. We compared the scoring methods based on the number of required signature genes associated with the malignant cell phenotype to perfectly classify, in terms of area under the receiver-operator curve (AUCROC), malignant and non-malignant cells based on scores (Methods). Classifiers based on the scores provided by all methods achieved perfect classification accuracy for ESCC using maximally twelve of the most differentially expressed genes (lowest adjusted p-value and highest log 2 FC) (Suppl.
e. , to the presence of irrelevant genes in the signature, for all scoring methods except Seurat_LVG. Discrepancies in Seurat scores between R (AddModuleScore of Seurat) and Python are attributed to randomization in the scoring method. c, Control gene selection bias of the scoring methods for the top 8% of highly expressed genes in B memory kappa cells within the PBMC dataset. The x-axis illustrates genes sorted by their average expression levels, while the y-axis depicts the mean score across all cells for a single-gene signature. Vertical dashed lines indicate the expression bin boundary and the top 50 highly expressed genes. The bias of a scoring method is indicated by how far the mean score of a gene deviates from zero. d, The influence of dataset composition and batch effect on scoring CRC cells using a 100-gene signature associated with malignant cells.
Comparability of score ranges when scoring for multiple signatures One of the applications of gene signature scoring is to measure the activation of different transcriptional states and use the scores to make statements about the association of cells to these states. However, interpreting cell scores and/or assignment of cells to states requires scores with high information quantity and comparable score ranges for signatures describing exclusive cell states or programs. To assess the information quantity and the comparability of score ranges produced by ANS and other scoring methods, we investigated the usage of scores for two classification tasks on the PBMC dataset  15. The first task, termed the easy task, aimed to score cells with non-overlapping gene signatures associated with three clearly distinguishable cell types, B cells, monocytes, and NK cells, and differentiate between these cell types based on the signature scores.
Specifically, we applied ANS to address the problem of the lack of malignant cell-specific EMT signature applicable across cancer types. The difficulty in constructing such a gene signature comes from the high similarity of transcriptional states of malignant EMT-exhibiting cells with malignant non-EMT cells, on the one hand, and with non-malignant mesenchymal cells, such as cancer-associated fibroblasts (CAFs), on the other hand. Building a cancer cell-specific signature for EMT would allow scoring bulk RNA-seq tumor datasets to quantify the degree of EMT transformation in human tumors while minimizing the bias induced by the presence of non-malignant cells of mesenchymal origin in the tumors. Indeed, when we explored the behavior of previously published EMT signatures using the ANS scoring method on single cells from the ESCC 1 , lung adenocarcinoma  17  (LUAD), CRC  16  and breast carcinoma  18  tumors, we observed that the pan-cancer EMT signatures, such as the Hallmark EMT  19  , the pEMT signature  20  , and five other EMT signatures  [21] [22] [23] [24] [25]  , resulted in extremely high scores in CAFs in addition to the mesenchymal-like (MES-like) malignant cells (Figure  3a  and Suppl.
The devised cancer cell-specific EMT signature included many known EMT-related genes. We found 8 of the 135 signature genes intersecting with the Hallmark EMT and the pEMT signatures (Figure  3b ). Moreover, 43 out of 135 genes appeared to be in at least one of the previously considered pan-cancer EMT signatures (Suppl. Table  S3 ). By construction, the devised signature scored highly for the MES-like malignant cells in these two cancer types used for training (Figure  3a ). We observed its strong discriminative power on the validation datasets CRC and BREAST (Figure  3a , Suppl. Table  S4 ). The accuracy of our gene signature discriminating MES-like malignant cells versus all remaining cells measured with the area under the precision-recall curve (AUCPRC) on CRC and breast carcinoma datasets was 0.
701 and 0. 511, respectively; it was slightly outperformed on CRC only by the gene signature proposed by Hollern et al. 25  (AUCPRC 0. 707) (Suppl. Table  S4 ). However, this signature performed sub-optimally in our ESCC and LUAD training datasets (AUCPRC 0. 028 versus 0. 592 of our gene signature in ESCC, and 0. 244 versus 0. 513 in LUAD). To investigate the clinical significance of the proposed cancer cell-specific EMT signature, we used bulk RNA-seq and clinical information from The Cancer Genome Atlas (TCGA) datasets. Specifically, we were interested in analyzing the link between the extent of cancer cells expressing EMT and the molecular subtypes of analyzed samples. Calculation of cancer cell-specific phenotypes using bulk RNA-seq data poses a significant advantage in cases when corresponding single-cell data is not available for a number of reasons.
First, since EMT plays a crucial role as one of the main drivers of tumor plasticity and invasiveness, accurately measuring its activity in both TME and cancer cells is essential for designing successful targeted therapies. Second, an association between the cancer cell-specific EMT activity and histological subtypes may highlight the role of the cancer cell EMT phenotype in promoting, likely more invasive, molecular subtypes, and expand our understanding thereof. We selected 24 TCGA datasets for which more than one histological subtype was available, scored the cancer cell-specific EMT signature, and associated the scores with histological subtypes (independently in each cancer type). We found our signature to be significantly associated with the histological subtype of 10 cancer datasets (Figure  3c , Supp.
Discussion In the present work, we developed a deterministic and robust gene signature scoring method, Adjusted Neighborhood Scoring (ANS), which improves the approach introduced by Tirosh et al. 13. In contrast to the Tirosh approach, ANS avoids binning genes by mean gene expression values and, by construction, chooses the optimal control gene sets to normalize gene expression values for technical effects. We demonstrated that the issue of sub-optimal stability of ANS and all methods based on the Tirosh approach, such as Scanpy and Seurat when scoring gene signatures on samples with variable proportions of different cell types, can be solved by scoring all cells simultaneously from all samples in the dataset. We compared ANS with other scoring methods developed for single-cell data to demonstrate the robustness and stability of the proposed scoring procedure.
Nonetheless, it is worth noting that the underlying assumption of exclusive state activation within individual cells could be limiting in certain situations. Furthermore, we have included all single-cell RNA-seq signature scoring techniques within a signature scoring package, enabling the utilization of methods originally implemented in R in Python. We showcased the use of the ANS scoring method to derive a gene signature specific to MES-like malignant cells that we validated in CRC and breast carcinoma. Building a cancer cell-specific signature for EMT could allow scoring bulk RNA-seq tumor datasets to quantify the degree of EMT transformation in human tumors while minimizing the bias induced by the presence of non-malignant cells of mesenchymal origin in the tumors. Our ESCC-and LUAD-specific cancer EMT signature outperformed others in distinguishing MES-like malignant cells from CAFs across all datasets.
Lastly, our analysis has revealed that the identified cancer cell-specific EMT signature corresponds to histological subtypes in several TCGA datasets, highlighting the potential role of EMT-expressing cancer cells in their molecular characteristics. This finding highlights the biological significance of the proposed signature; however, future work is needed to verify its clinical applicability. We acknowledge the limitations inherent in our study. The evaluation of scoring methods in the easy task scenario was conducted based on cell-type classifications. However, due to the exclusive assignment of each cell to a specific cell type and the absence of annotations for intermediate or transitioning states, our benchmarking did not encompass the assessment of performance in scenarios involving cells undergoing such intermediate states.
Also, when applying our method to establish the cancer-specific EMT signature in the three datasets, LUAD, ESCC, and CRC, we relied on the selection of the cancer cells expressing previously published EMT-related genes. In contrast, in the basal-like breast cancer dataset, we relied on the original annotation of cell states and used the GM3 state strongly associated with EMT to label the EMT-presenting malignant cells. The selection procedure might have biased the downstream analysis. In summary, this work presents an improved version of the scoring method proposed by Tirosh  13  , called Adjusted Neighborhood Scoring (ANS). ANS is a deterministic and robust scoring method that outputs comparable scores for multiple gene expression signatures. The ANS method is implemented in a user-friendly way to be integrated into R and Python single-cell analysis pipelines.
Dataset and Preprocessing We used five different datasets in this work: four cancer datasets and one peripheral blood mononuclear cell (PBMC) dataset with cell type annotation supported by multimodal analysis  15. The scRNA-seq datasets for cancer included colorectal carcinoma  16  (CRC) with 60 samples, squamous cell carcinoma of the esophagus 1 (ESCC) with 54 samples, lung adenocarcinoma  17  (LUAD) with 19 samples and breast carcinoma  18  with 7 basal-like samples (Suppl. Figure  S8 ). The PBMC datasets consisted of data from three time points for each of the eight patients, i. e. , 24 samples. Preprocessing scRNA-seq data is crucial to address inherent biases and enable downstream analysis. Cancer datasets and the PBMC dataset were preprocessed differently. For the cancer datasets, we used the preprocessing module of CanSig 55 with the default parameter as the first preprocessing step (https://github.
com/BoevaLab/CanSig). In brief, we applied standard quality control metrics and removed potential misannotated cells using CNV inference; the preprocessing steps are extensively described in the original paper. Additionally, we filtered genes based on a minimum expression in 1% of cells, and normalized cell reads using a shift logarithm log 2 (x+1) with the mean total raw count per cell as the target sum, as described and implemented by Ahlmann-Eltze and Huber  56. Regarding the PBMC dataset, we created several sub-datasets based on cell type annotations. The first sub-dataset consisted of B-cells, monocytes, and natural killer cells, while the second subdataset contained B-cell subtypes (B-naive, B-intermediate, and B-memory cells). The remaining four were single-cell-type subsets with the following cell subtypes: B memory kappa, CD8 TM2, CD14 Mono, and NK 3.
All sub-datasets were preprocessed as follows: Initially, we filtered low-quality reads according to the guidelines provided in chapter 6. 3 of the book "Single-cell Best Practices" by Heumos et al. 57. A threshold of 5 median absolute deviations (MADs) was selected for filtering covariates, including total counts, number of genes with positive counts in a cell, and the percentage of mitochondrial counts. Similar to the cancer datasets, we filtered genes based on a minimum expression in 1% of cells and normalized the cell reads using the shift logarithm with the mean total raw count per cell as the target sum.
Control genes selection in Tirosh-based gene signature scoring and ANS The methods implemented in the scRNA-seq analysis packages Scanpy 8 (in Python) and Seurat 10 (in R) were built upon the procedure first described by Tirosh et al. 13  Here, we recall this procedure in a formal manner. Let be the preprocessed expression matrix for cells and genes, generally representing log-transformed normalized read counts. Let be a given gene signature, i. e. , a set of genes. Let be the average expression vector for each gene over all cells ordered by increasing average expression (i. e. , for any ). In Tirosh-based gene signature scoring methods, is split into 25 equally sized bins, called expression bins. For each signature gene , we sample (control size) genes from 's expression bin. Let be the set of control genes for the signature gene.
The score for a cell is then computed as follows: in Python. Additionally, we provided the ANS method in both Python and R. We showed that the scoring methods yield identical scores for both Python and R. We selected a 100-gene signature for malignant cells based on the genes with the lowest adjusted p-value and the highest log 2 FC (Supplementary Methods). We then scored this signature on preprocessed datasets of two cancer types: CRC and ESCC. To ensure consistency, we converted these datasets into SingleCellExperiments  60  and employed the original R versions of the scoring methods. Notably, the R implementation of the Seurat method based on the original version of Trisoh et al. , known as AddModuleScore  61  , is included in the scRNA-seq package Seurat 15. To evaluate the concordance between the R and Python scores, we computed the Pearson correlation coefficient using the scipy package 62 (version 1.
Benchmark experiments In the first part of the study, we compared the properties and resistance to influencing factors of eight gene signature scoring methods: JASMINE 7 using the likelihood or odds-ratio (Jasmine_LH and Jasmine_OR), UCell  12  , the original method by Tirosh et al. 13  implemented in Seurat  10, 11  (Seurat), the method implemented in Scanpy 8 (Scanpy), Seurat using all genes as control genes (Seurat_AG), Seurat using the least variable genes as control genes (Seurat_LVG), and our proposed method Adjusted Neighbourhood Scoring (ANS). Unless stated differently, we chose signatures of desired lengths associated with the malignant cell phenotype (Supplementary Methods) for the cancer datasets CRC and ESCC. This selection was based on identifying genes with the smallest adjusted p-value and the largest log 2 FC.