Journal: Nature biotechnology

Article Title: A multi-center study benchmarking single-cell RNA sequencing technologies using reference samples

doi: 10.1038/s41587-020-00748-9

Figure Lengend Snippet: (a) Schematic overview of the study design (see detailed descriptions and notations in the Methods). Two reference cell lines (Sample A, HCC1395; and Sample B, HCC1395BL) were used to generate scRNA-seq data across four platforms (10X Genomics, Fluidigm C1, Fluidigm C1 HT, and Takara Bio ICELL8), four testing sites (LLU, NCI, FDA, and TBU). At the LLU and NCI sites (10X), mixed single-cell captures and library constructions were also prepared with either 10% or 5% cancer cells spiked into the B lymphocytes. At the NCI site, single-cell captures and library constructions were also performed with methanol-fixed cell mixtures (5% cancer cells spiked into B lymphocytes, Fixed 1 & 2). One set of 10X scRNA libraries from NCI was also sequenced using a shorter modified sequencing method. Bulk cell RNA-seq was also obtained from these cell lines, each in triplicate. See Methods for details about study design. (b) For both the breast cancer cell line (Sample A) and the B lymphocyte line (Sample B) across 14 pair-wise datasets, percentage of reads mapped to the exonic region (blue), non-exonic region (orange), or not mapped to the human genome (gray). For unique molecular identifier (UMI) methods (10X), dark blue indicates the exonic reads with UMIs. (c) Median number of genes detected per cell at different sequencing read depths.

Article Snippet: The 10X Genomics scRNA datasets were preprocessed using CellRanger 3.1.

Techniques: Modification, Sequencing, RNA Sequencing

Journal: Nature biotechnology

Article Title: A multi-center study benchmarking single-cell RNA sequencing technologies using reference samples

doi: 10.1038/s41587-020-00748-9

Figure Lengend Snippet: The violin plot shows the number of genes detected in each cell across 20 scRNA-seq datasets. The plot was generated using Seurat (version 3.1). Each dot represents a single cell. The violin shapes summarize the data distributions, which are colored in the background to signify each of the 20 different scRNA seq datasets. Each scRNA-seq dataset is plotted on the X-axis; the Y-axis shows the corresponding number of genes detected in a cell (nGene) for that dataset. The average number of genes detected in each cell was about 4000 and most of the cells had 2500–7500 genes, except for samples C1_LLU_A and C1_LLU_B. The 10X Genomics scRNA datasets were preprocessed using CellRanger 3.1.

Article Snippet: The 10X Genomics scRNA datasets were preprocessed using CellRanger 3.1.

Techniques: Generated

Journal: Nature biotechnology

Article Title: A multi-center study benchmarking single-cell RNA sequencing technologies using reference samples

doi: 10.1038/s41587-020-00748-9

Figure Lengend Snippet: (a–c) Evaluation of the UMI-based (10X) data with Cell Ranger, UMI-Tools, or zUMIs. (d–e) Evaluation of data from non-UMI based technologies C1 full-length transcript, C1 HT, and ICELL8 full-length transcript using FeatureCounts, Kallisto, or RSEM. (a) Bar plot showing the number of cells captured with UMI-based technology; (b) and (d) Box plot showing the number of genes detected per cell in UMI-based and non-UMI based technologies, respectively; (c) and (e) Violin plots showing the gene expression correlation and consensus genes [represented by IoU (Intersection over Union)] per cell between any two pipelines in UMI-based and non-UMI based technologies, respectively. The sample sizes (n) used to derive statistics in (b) and (d) were: (b) 10X_LLU_A, n= 3045 cells; 10X_NCI_A, n=6425 cells; 10X_NCI_M_A, n=6483 cells; 10X_LLU_B, n=1439 cells; 10X_NCI_B, n=3296 cells; 10X_NCI_M_B, n=3273 cells; (d) C1_LLU_A, n=80 cells; C1_FDA_HT_A, n=203 cells; ICELL8_SE_A, n=600 cells; ICELL8_PE_A, n=598 cells; C1_LLU_B, n=66 cells; C1_FDA_B, n=241 cells; ICELL8_SE_B, n=600 cells; ICELL8_PE_B, n=596 cells. For detailed statistics regarding minima, maxima, centre, bounds of box and whiskers and percentile related to the figure, please refer to Supplementary Table 5.

Article Snippet: The 10X Genomics scRNA datasets were preprocessed using CellRanger 3.1.

Techniques: Gene Expression

Journal: Nature biotechnology

Article Title: A multi-center study benchmarking single-cell RNA sequencing technologies using reference samples

doi: 10.1038/s41587-020-00748-9

Figure Lengend Snippet: (a) Batch-effect correction in Scenario #1, where all 20 scRNA-seq datasets were combined, including mixed and non-mixed, with large proportions of two dissimilar types of cells (Sample A, breast cancer cell line HCC1395; and Sample B, B-lymphocyte line HCC1395BL). Datasets from 10X were down-sampled to 1200 cells per dataset. (b) Batch-effect correction in Scenario #2, where five scRNA-seq datasets (10X_LLU_A, 10X_NCI_A, C1_FDA_HT_A, C1_LLU_A, and ICELL8_SE_A) from the breast cancer cells were generated separately at the four centers (LLU, NCI, FDA, and TBU) on four platforms (10X, Fluidigm C1, Fluidigm C1_HT, and TBU ICELL8); (c) Batch-effect correction in Scenario #3, where five scRNA-seq datasets (10X_LLU_B, 10X_NCI_B, C1_FDA_HT_B, C1_LLU_B, and ICELL8_SE_B) from the B lymphocytes were generated separately at the four centers on the same four platforms; (d) Batch-effect correction in Scenario #4, where four datasets (10X_LLU_Mix10, 10X_NCI_M_Mix5, 10X_NCI_M_Mix5_F, 10X_NCI_M_Mix5_F2) were generated from 5% or 10% breast cancer cells spiked into B lymphocytes, and analyzed with the 10X Genomics platform at two centers in four different batches. Each dataset is indicated by a unique color in panels (a) to (d). Idealized projection of cells for the four different scenarios is presented on the left. *Note for BBKNN, only UMAP is available and shown. Silhouette width score quantifying the clusterability for (e) Scenario #1 or (f) Scenario #4, corresponding to panels (a) and (d), respectively. (g) kBET acceptance score quantifying the mixability, calculated using the cross-platform/center scRNA-seq data acquired either from breast cancer cells only or from B-lymphocytes only for all four scenarios (a-d, also labeled as Scenarios #1–#4).

Article Snippet: The 10X Genomics scRNA datasets were preprocessed using CellRanger 3.1.

Techniques: Generated, Labeling

Journal: Nature biotechnology

Article Title: A multi-center study benchmarking single-cell RNA sequencing technologies using reference samples

doi: 10.1038/s41587-020-00748-9

Figure Lengend Snippet: Batch-effect corrections were performed for the following four scenarios: (a) Scenario 1, where all 20 scRNA-seq datasets were combined, including mixed and non-mixed, with large proportions of two dissimilar types of cells (Sample A, breast cancer cell line HCC1395 and Sample B, B-lymphocyte line HCC1395BL); Datasets from 10X were down-sampled to 1200 cells per dataset. (b) Scenario 2, where five datasets (10X_LLU_A, 10X_NCI_A, C1_FDA_HT_A, C1_LLU_A, and ICELL8_SE_A) from the breast cancer cells (Sample A, HCC1395) were generated separately at four centers (LLU, NCI, FDA, and TBU) on four platforms (10X, Fluidigm C1, Fluidigm C1_HT, and TBU ICELL8); (c) Scenario 3, where five datasets (10X_LLU_B, 10X_NCI_B, C1_FDA_HT_B, C1_LLU_B, and ICELL8_SE_B) from B-lymphocytes (Sample B, HCC1395BL) were generated separately at four centers (LLU, NCI, FDA, and TBU) on four platforms (10X, Fluidigm C1, Fluidigm C1_HT, and TBU ICELL8); and (d) Scenario 4, where four datasets (10X_LLU_Mix10, 10X_NCI_M_Mix5, 10X_NCI_M_Mix5_F, 10X_NCI_M_Mix5_F2) were generated from 5% or 10% of breast cancer cells (Sample A, HCC1395) spiked into the B-lymphocytes (Sample B, HCC1395BL) and analyzed with the 10X Genomics platform at two centers (LLU and NCI) in four different batches. *For BBKNN, only UMAPs were available and shown in (a-d). The HCC1395 breast cancer cells (Sample A) were labeled in red and the HCC1395BL B lymphocytes (Sample B) were labeled in blue. Batch correction methods included Seurat v3.1, fastMNN (SeuratWrappers v0.1.0), Scanorama V1.4, BBKNN V1.3.5, Harmony V0.99.9, limma V3.40.4, and Combat (sva V3.32.1). The top 2000 HVGs were used as the gene set for batch correction. All the 10X data were preprocessed using CellRanger 3.1.

Article Snippet: The 10X Genomics scRNA datasets were preprocessed using CellRanger 3.1.

Techniques: Generated, Labeling

Journal: Nature biotechnology

Article Title: A multi-center study benchmarking single-cell RNA sequencing technologies using reference samples

doi: 10.1038/s41587-020-00748-9

Figure Lengend Snippet: Boxplot of silhouette values stratified by eight normalization methods across 14 datasets, including (a) 10X_LLU, (b) 10X_NCI, (c) 10X_NCI_M, (d) C1_FDA_HT, (e) C1_LLU, (f) ICELL8_PE, and (g) ICELL8_SE in breast cancer cells (HCC1395; Sample A) and B lymphocytes (HCC1395BL; Sample B). Eight normalization methods included SCTransform, Scran Deconvolution, CPM, LogCPM, TMM, DESeq, Quantile, and Linnorm. For each dataset, reads of each cell were down-sampled to two different read depths (10K and 100K per cell) before calculating the silhouette width values. LogCPM normalization performed fairly well and was used as the default normalization for our subsequent batch-effect correction analyses. Two normalization methods developed for bulk cell RNA-seq (TMM and Quantile) had the lowest scores. The sample sizes (n) used to derive statistics were: 10X_LLU_A, n= 3560 cells, 10X_LLU_B, n=1770 cells; 10X_NCI_A, n=4284 cells, 10X_NCI_B, n=4136 cells; 10X_NCI_M_A, n=1372 cells, 10X_NCI_M_B, n=2082 cells; C1_LLU_A, n=160 cells, C1_LLU_B, n=132 cells; C1_FDA_HT_A, n=318 cells, C1_FDA_HT_B, n=374 cells; ICELL8_SE_A, n=1134 cells, ICELL8_SE_B, n=1078 cells; ICELL8_PE_A, n=980 cells, ICELL8_PE_B, n=954 cells). For detailed statistics regarding minima, maxima, centre, bounds of box and whiskers and percentile related to the figure, please refer to Supplementary Table 6.

Article Snippet: The 10X Genomics scRNA datasets were preprocessed using CellRanger 3.1.

Techniques: RNA Sequencing

Journal: Nature biotechnology

Article Title: A multi-center study benchmarking single-cell RNA sequencing technologies using reference samples

doi: 10.1038/s41587-020-00748-9

Figure Lengend Snippet: Five different batches of scRNA-seq data (10X_LLU_A, 10X_LLU_B, 10X_NCI_A, 10X_NCI_B, and 10X_NCI_Mix5) generated at two sites (LLU and NCI) are shown either as t-SNE plots (panels a-d) or as UMAPs (panels e-h). (a) LogNormalized, scaled data with no regression; (b) LogNormalized, scaled data filtered with mitochondrial (Mito) gene regression >5% and UMI normalization by Seurat v3; (c) ScTransform with no regression; (d) SCTransform with mitochondrial gene regression and UMI normalization; (e) LogNormalized, scaled data with no regression; (f) scaled data with mitochondrial gene regression and UMI normalization; (g) SCTransform with no regression; and (h) SCTransform with mitochondrial gene regression and UMI normalization.

Article Snippet: The 10X Genomics scRNA datasets were preprocessed using CellRanger 3.1.

Techniques: Generated

Journal: Nature biotechnology

Article Title: A multi-center study benchmarking single-cell RNA sequencing technologies using reference samples

doi: 10.1038/s41587-020-00748-9

Figure Lengend Snippet: Batch-effect corrections were performed for the following four scenarios: (a) Scenario 1, where all 20 scRNA-seq datasets were combined, including mixed and non-mixed, with large proportions of two dissimilar types of cells (Sample A, breast cancer cell line HCC1395 and Sample B, B-lymphocyte line HCC1395BL); Datasets from 10X were down-sampled to 1200 cells per dataset. (b) Scenario 2, where five datasets (10X_LLU_A, 10X_NCI_A, C1_FDA_HT_A, C1_LLU_A, and ICELL8_SE_A) from the breast cancer cells (Sample A, HCC1395) were generated separately at four centers (LLU, NCI, FDA, and TBU) on four platforms (10X, Fluidigm C1, Fluidigm C1_HT, and TBU ICELL8); (c) Scenario 3, where five datasets (10X_LLU_B, 10X_NCI_B, C1_FDA_HT_B, C1_LLU_B, and ICELL8_SE_B) from B-lymphocytes (Sample B, HCC1395BL) were generated separately at four centers (LLU, NCI, FDA, and TBU) on four platforms (10X, Fluidigm C1, Fluidigm C1_HT, and TBU ICELL8); and (d) Scenario 4, where four datasets (10X_LLU_Mix10, 10X_NCI_M_Mix5, 10X_NCI_M_Mix5_F, and 10X_NCI_M_Mix5_F2) were generated from 5% or 10% of breast cancer cells (Sample A, HCC1395), spiked into the B-lymphocytes (Sample B, HCC1395BL), and analyzed with the 10X Genomics platform at two centers (LLU and NCI) in four different batches. Batch correction methods included Seurat v3.1, fastMNN (SeuratWrappers v0.1.0), Scanorama V1.4, BBKNN V1.3.5, Harmony V0.99.9, limma V3.40.4, and Combat (sva V3.32.1). The top 2000 highly variable genes (HVGs) of these datasets were used as the gene set for batch correction. All the 10X data were preprocessed using CellRanger 3.1.

Article Snippet: The 10X Genomics scRNA datasets were preprocessed using CellRanger 3.1.

Techniques: Generated

Journal: Nature biotechnology

Article Title: A multi-center study benchmarking single-cell RNA sequencing technologies using reference samples

doi: 10.1038/s41587-020-00748-9

Figure Lengend Snippet: t-SNE plots and UMAPs showing the batch-effect corrections performed by seven methods using 20 scRNA-seq datasets across different platforms. Datasets from 10X were down-sampled to 1200 cells per dataset. *Note, for BBKNN, only UMAP was available and shown. The scRNA-seq datasets are colored to identify the four different platforms: 10X 3´ scRNA-seq platform (red), C1 3´ HT scRNA-seq platform (yellow), C1 full-length scRNA-seq platform (light blue), and ICELL8 full-length scRNA-seq platform (dark blue). Batch correction methods included: Seurat v3.1, fastMNN (SeuratWrappers v0.1.0), Scanorama V1.4, BBKNN V1.3.5, Harmony V0.99.9, limma V3.40.4, and Combat (sva V3.32.1). Scanorama failed to separate two cell types into discrete clusters when non-10X platforms were included in the analysis. The top 2000 HVGs across all datasets were used as the gene set for batch correction. All the 10X data were preprocessed using CellRanger 3.1.

Article Snippet: The 10X Genomics scRNA datasets were preprocessed using CellRanger 3.1.

Techniques:

Journal: Nature biotechnology

Article Title: A multi-center study benchmarking single-cell RNA sequencing technologies using reference samples

doi: 10.1038/s41587-020-00748-9

Figure Lengend Snippet: (a) t-SNE plot and (b) UMAP showing batch-effect corrections using twelve 10X Genomics scRNA-seq datasets consisting of both mixed and non-mixed samples from two sites (LLU and NCI) in different batches after Scanorama (version 1.4.) batch correction. (c) t-SNE plot and (d) UMAP showing projections of batch-effect corrections using six 10X scRNA-seq datasets consisting of only non-mixed samples from two sites (LLU and NCI) in different batches after Scanorama (version 1.4.) batch correction. Different colors represent different datasets. All the datasets were down-sampled to 1200 cells per dataset. After the batch correction, cells from the same cell line type clustered together and mixed adequately within the same cell types. All the data were preprocessed using CellRanger 3.1.

Article Snippet: The 10X Genomics scRNA datasets were preprocessed using CellRanger 3.1.

Techniques:

Journal: Nature biotechnology

Article Title: A multi-center study benchmarking single-cell RNA sequencing technologies using reference samples

doi: 10.1038/s41587-020-00748-9

Figure Lengend Snippet: t-SNE plots and UMAPs showing batch-effect corrections performed by seven methods using 14 non-mixture scRNA-seq datasets across different platforms and sites. Six spiked-in mixture scRNA-seq datasets (10X_LLU_Mix10, 10X_NCI_Mix5, 10X_NCI_Mix5_F, 10X_NCI_M_Mix5, 10X_NCI_M_Mix5_F, and 10X_NCI_M_Mix5_F2) were removed from the 20 datasets in Scenario 1 for batch-effect correction evaluation. The fourteen non-mixture scRNA-seq datasets are from both breast cancer cells (10X_LLU_A, 10X_NCI_A, 10X_NCI_M_A, C1_FDA_HT_A, C1_LLU_A, ICELL8_SE_A, and ICELL8_PE_A) and B-lymphocytes (10X_LLU_B, 10X_NCI_B, 10X_NCI_M_B, C1_FDA_HT_B, C1_LLU_B, ICELL8_SE_B, and ICELL8_PE_B). Datasets from 10X were down-sampled to 1200 cells per dataset. *Note, for BBKNN, only UMAP was available and shown. Batch correction methods included Seurat v3.1, fastMNN (SeuratWrappers v0.1.0), Scanorama V1.4, BBKNN V1.3.5, Harmony V0.99.9, limma V3.40.4, and Combat (sva V3.32.1). All the 10X data were preprocessed using CellRanger 3.1.

Article Snippet: The 10X Genomics scRNA datasets were preprocessed using CellRanger 3.1.

Techniques:

Journal: Nature biotechnology

Article Title: A multi-center study benchmarking single-cell RNA sequencing technologies using reference samples

doi: 10.1038/s41587-020-00748-9

Figure Lengend Snippet: Panels (a-c) show results obtained using fastMNN when the spiked-in (mixed) datasets (i.e., 10X_LLU_Mix10, 10X_NCI_Mix5, 10X_NCI_Mix5_F, 10X_NCI_M_Mix5, 10X_NCI_M_Mix5_F, and 10X_NCI_M_Mix5_F2) were imported into the pipeline before other non-mixed scRNA-seq datasets from the 20 scRNA-seq datasets of Scenario 1. (a) t-SNE vs. UMAP with color-coding by dataset; (b) tSNE vs. UMAP, colored by cell types (HCC1395, red; HCC1395BL, blue); and (c) A silhouette score = 0.52 showing that fastMNN correctly separated the two cell types into two clusters representing breast cancer cells and B lymphocytes. Panels (d-f) show results obtained using fastMNN when the non-mixed datasets were imported into the pipeline before the mixture datasets. (d) tSNE vs. UMAP with color-coding by datasets or (e) tSNE vs. UMAP colored by cell types; and (f) A low silhouette score of 0.22 showing that fastMNN had difficulty correctly separating the two cell types in this case. Batch-effect corrections were performed using fastMNN (SeuratWrappers v0.1.0) and silhouette width scores were calculated using the silhouette function from the R package cluster (v.2.0.8). Datasets from 10X were down-sampled to 1200 cells per dataset. The order of dataset input is shown on the top of the Figures (a, b, c or d, e, f).

Article Snippet: The 10X Genomics scRNA datasets were preprocessed using CellRanger 3.1.

Techniques:

Journal: Nature biotechnology

Article Title: A multi-center study benchmarking single-cell RNA sequencing technologies using reference samples

doi: 10.1038/s41587-020-00748-9

Figure Lengend Snippet: Scatter plots displaying the gene expression profile correlations between each of seven scRNA-seq datasets (10X_LLU, 10X_NCI, 10X_NCI_M, C1_FDA, C1_LLU, ICELL8_SE, and ICELL8_PE) vs. their corresponding bulk RNA-seq dataset (BK_RNA-seq) for either (a) breast cancer cells or (b) B lymphocytes. The commonly detected transcripts [(log(CPM +1) normalized] across all datasets were used (15,553 genes for breast cancer cells and 15,201 genes for B lymphocytes) to generate the scatter plots. Each dot represents each gene as a point in each scatterplot; x,y values represent the gene expression variation in a pair of compared datasets. The middle diagonal bar charts display the distribution of the most abundant or rare genes in each dataset and also provide the labels for the respective datasets. The Pearson correlation coefficient R between each of the datasets compared is shown to display the consistency of the different RNA-seq datasets.

Article Snippet: The 10X Genomics scRNA datasets were preprocessed using CellRanger 3.1.

Techniques: Gene Expression, RNA Sequencing

Journal: Nature biotechnology

Article Title: A multi-center study benchmarking single-cell RNA sequencing technologies using reference samples

doi: 10.1038/s41587-020-00748-9

Figure Lengend Snippet: Feature plots generated across 20 scRNA-seq datasets using the top 10 DEGs specific for (a) breast cancer cells before batch-effect correction; (b) breast cancer cells after fastMNN batch-effect correction; (c) B lymphocytes before batch correction; and (d) B lymphocytes after fastMNN batch-effect correction. Datasets from 10X were down-sampled to 1200 cells per dataset. In feature plots, genes with relatively high expression in each cell are highlighted in brick red (corresponding to breast cancer cells; Sample A) or blue (corresponding to B cells; Sample B).

Article Snippet: The 10X Genomics scRNA datasets were preprocessed using CellRanger 3.1.

Techniques: Generated, Expressing

Journal: Nature biotechnology

Article Title: A multi-center study benchmarking single-cell RNA sequencing technologies using reference samples

doi: 10.1038/s41587-020-00748-9

Figure Lengend Snippet: (a) Gene detection sensitivity measured separately for each of the three classes of scRNA-seq protocol: 10X-, non-10X-based 3´ tagging, and full-length. (b) Normalization methods ranked by their clusterability as measured by Z-scores (either the median or the variance of the silhouette width across the 14 datasets). (c) Batch-correction methods ranked by their clusterability as measured by Z-score from the harmonic mean of the silhouette scores (Scenarios #1 and #4). (d) Batch-correction methods ranked by their mixability as measured by Z-score from the harmonic mean of kBET acceptance scores (Scenarios #1–#4). Z-scores are plotted as circles with their size and color shade scaled to the Z-score value from large to small, and dark blue to light blue. Note that larger Z-score values imply better performance, except for clusterability variance, where a smaller value is preferred: *Larger is better; **Smaller is better. (e) Best practice recommendations for single-cell RNA-seq analysis. #The current version of Scanorama did not correct batch effects for data from multiple platforms; however, it worked well when only 10X Genomics data were analyzed. ##Seurat v.3 was suitable for biologically similar samples, but over-corrected batch effects and misclassified cell types if large fractions of distinct cell types were present in different batches.

Article Snippet: The 10X Genomics scRNA datasets were preprocessed using CellRanger 3.1.

Techniques: RNA Sequencing

Journal: Nature biotechnology

Article Title: A multi-center study benchmarking single-cell RNA sequencing technologies using reference samples

doi: 10.1038/s41587-020-00748-9

Figure Lengend Snippet: (a, un-corrected) UMAP of 10 datasets (10X: PBMCs 68K, PBMCs 3K, CD19+ B cells, CD14+ monocytes, CD4+ helper T cells, CD56+ NK cells, CD8+ cytotoxic T cells, CD4+CD45RO+ memory T cells, CD4+CD25+ regulatory T cells; Drop-seq: PBMCs) out of 26 datasets from Hie et al.8 before batch correction by Scanorama. (b, corrected-based on dataset) UMAP of 10 different datasets shown in (a) from Hie et al. after batch correction by Scanorama, colored to identify the datasets. (c, corrected-based on platform) UMAP of 10 different datasets shown in (a) from Hie et al. colored to identify the two different platforms used (10X Genomics and Drop-seq); note poor results using Drop-seq. (d, un-corrected) UMAP of 8 datasets (breast cancer cells: C1_FDA_HT_A, C1_LLU_A, ICELL8_SE_A, and ICELL8_PE_A; and B lymphocytes: C1_FDA_HT_B, C1_LLU_B, ICELL8_SE_B, and ICELL8_PE_B) out of 20 datasets in our study analyzed using three different non-10X sequencing platforms before batch correction by Scanorama. (e, corrected-based on dataset) UMAP of 8 datasets shown in (d) after batch correction by Scanorama, colored to identify the datasets. Note lack of discrimination between different cell types. (f, corrected-based on platform) UMAP of 8 datasets shown in (d) after batch correction by Scanorama, colored to identify the platforms (C1_FDA_HT, blue; C1, purple; ICELL8, pink). The PBMC datasets were downloaded from http://scanorama.csail.mit.edu/data_light.tar.gz. Our eight datasets were preprocessed using the featureCounts pipeline and batch-effect correction was performed using Scanorama V1.4.

Article Snippet: The 10X Genomics scRNA datasets were preprocessed using CellRanger 3.1.

Techniques: Sequencing

Journal: Computational and Structural Biotechnology Journal

Article Title: A comprehensive overview of graph neural network-based approaches to clustering for spatial transcriptomics

doi: 10.1016/j.csbj.2023.11.055

Figure Lengend Snippet: This review considers the task of spatial clustering on eight spatially resolved transcriptomics datasets. A. The human DLPFC dataset consisted of 12 neuronal samples from three subjects, each with four sample sections. The displayed sample ID is 151510. B. The mouse brain dataset was obtained from 10X Visium, and each spot contained more than one cell. The entire dataset and its cropped section are available in the Squidpy package. C. The human breast cancer dataset was downloaded from 10X Visium, with manual annotations acquired from the SEDR package. D. The dataset of the olfactory bulbs of mouse was derived from Slide-seqV2 technology, and the particular section Puck_200127_15 was used. E. The Stereo-seq-based dataset of the olfactory bulbs of mouse was obtained from the original paper, and the special section was obtained from the results of SEDR analysis. F. The mouse liver dataset obtained from the Vizgen MERFISH Mouse Liver Map. The special slice is the Liver1Slice1 that measures 347 genes across over > 300,000 liver cells in a single mouse liver slice. G. The pre-processed subset of seqFISH data acquired from Squidpy package. H. The 10x Xenium dataset from the Xenium breast cancer tumor microenvironment Datbase. The related pre-processed data can be gained from Squidpy as well.

Article Snippet: Finally, the latent representations are obtained in an end-to-end manner and are refined by using a deep clustering method to ensure their compactness. conST has been evaluated on the mouse hypothalamus (MERFISH), mouse visual cortex (seqFISH), human DLPFC (10x Visium), human breast cancer (10x genomics), and datasets of the olfactory bulbs of mice (Stereo-seq). conST has also been compared with benchmarking methods of clustering, including Seurat, Giotto, stLearn, SpaGCN, SEDR, and BayesSpace.

Techniques: Derivative Assay

Journal: Computational and Structural Biotechnology Journal

Article Title: A comprehensive overview of graph neural network-based approaches to clustering for spatial transcriptomics

doi: 10.1016/j.csbj.2023.11.055

Figure Lengend Snippet: Clustering performance of eight benchmarking methods on the human DLPFC dataset. A. The mean adjusted Rand index (ARI) of each clustering method was determined (Upper). The variance of each clustering method. GraphST had the highest average ARI and the lowest variance (Middle). The ARI of each spatial clustering algorithm was calculated on 12 samples of the human DLPFC. The eight methods are ranked from 1 to 8 in order from the highest to the lowest ARI (Lower). B. The Normalized Mutual Information (NMI) of each clustering method was determined (Upper). The variance of each clustering method. Spatial-MGCN had the highest average NMI and the lowest variance (Middle). The NMI of each spatial clustering algorithm was calculated on 12 samples of the human DLPFC. The eight methods are ranked from 1 to 8 in order from the highest to the lowest NMI (Lower).

Article Snippet: Finally, the latent representations are obtained in an end-to-end manner and are refined by using a deep clustering method to ensure their compactness. conST has been evaluated on the mouse hypothalamus (MERFISH), mouse visual cortex (seqFISH), human DLPFC (10x Visium), human breast cancer (10x genomics), and datasets of the olfactory bulbs of mice (Stereo-seq). conST has also been compared with benchmarking methods of clustering, including Seurat, Giotto, stLearn, SpaGCN, SEDR, and BayesSpace.

Techniques:

Journal: Computational and Structural Biotechnology Journal

Article Title: A comprehensive overview of graph neural network-based approaches to clustering for spatial transcriptomics

doi: 10.1016/j.csbj.2023.11.055

Figure Lengend Snippet: The ARI and NMI scores of eight spatial clustering methods on three selected DLPFC samples. A, B, C. Three samples were obtained from three different subjects participating in the human DLPFC project. Each subject provided four sections of human brain tissues, and we selected one section from each to compare their ARI values and NMI. Samples 151510 and 151676 were annotated manually into seven clusters, while sample 151672 contained only five groups in the ground truth. The black segment visually represents the standard deviation.

Article Snippet: Finally, the latent representations are obtained in an end-to-end manner and are refined by using a deep clustering method to ensure their compactness. conST has been evaluated on the mouse hypothalamus (MERFISH), mouse visual cortex (seqFISH), human DLPFC (10x Visium), human breast cancer (10x genomics), and datasets of the olfactory bulbs of mice (Stereo-seq). conST has also been compared with benchmarking methods of clustering, including Seurat, Giotto, stLearn, SpaGCN, SEDR, and BayesSpace.

Techniques: Standard Deviation

Journal: Computational and Structural Biotechnology Journal

Article Title: A comprehensive overview of graph neural network-based approaches to clustering for spatial transcriptomics

doi: 10.1016/j.csbj.2023.11.055

Figure Lengend Snippet: The identified spatial domains of eight compared methods on three selected slices. A, B, C. The cell-type clustering task was performed on three samples (151570, 151672, and 151676) by using eight spatial clustering methods: SpaGCN, conST, DeepST, CCST, STAGATE, GraphST, SpaceFlow and Spatial-MGCN. These samples corresponded to the three subjects in the human DLPFC dataset. A comparison was made with the ground truth to assess the clustering performance of each baseline.

Article Snippet: Finally, the latent representations are obtained in an end-to-end manner and are refined by using a deep clustering method to ensure their compactness. conST has been evaluated on the mouse hypothalamus (MERFISH), mouse visual cortex (seqFISH), human DLPFC (10x Visium), human breast cancer (10x genomics), and datasets of the olfactory bulbs of mice (Stereo-seq). conST has also been compared with benchmarking methods of clustering, including Seurat, Giotto, stLearn, SpaGCN, SEDR, and BayesSpace.

Techniques: Comparison

Journal: bioRxiv

Article Title: TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment

doi: 10.1101/2020.08.15.251959

Figure Lengend Snippet: TISCH automatically parsed and curated tumor single-cell RNA-seq datasets from GEO or Array Express databases. All datasets were then uniformly processed with a standardized workflow, including quality control, batch effect removal, cell clustering, differential expression analysis, cell type annotation at multiple levels. Each dataset in TISCH is displayed with relevant study information, including species, treatment, the number of patients and cells, technology platform, stage, and related study. In the Dataset module, TISCH provides two functions: single-dataset exploration and multiple-dataset comparison. In the Gene module, TISCH allows single gene expression visualization across multiple datasets and cell types. TISCH also supports downloading of expression matrices, DE gene tables and meta-information for each dataset.

Article Snippet: To expand the utility of TISCH, we also included the scRNA-seq datasets of mouse treated with immunotherapy and three scRNA-seq datasets of human peripheral blood mononuclear cells (PBMC) from 10X Genomics.

Techniques: RNA Sequencing Assay, Expressing

Journal: Cell

Article Title: Maturation and persistence of the anti-SARS-CoV-2 memory B cell response

doi: 10.1016/j.cell.2021.01.050

Figure Lengend Snippet: Quantification and functional assessment of the anti-SARS-CoV-2 humoral immune response in COVID-19 convalescent patients, Related to Figure 1 (A) Percentage of in vitro neutralization shown by individual sera from S-CoV (n = 10) and M-CoV (n = 12) patients at M6 at increasing dilutions. (B) Representative wells for the neutralization assay. Blue spots represent SARS-CoV-2 positive cells. (C) Representative results of an anti-SARS-CoV-2 S IgG ELISA on supernatants from sorted SARS-CoV-2 S-specific MBCs (dark dots) and non spike-specific MBCs (white dots), as validation of FACS SARS-CoV-2 S-staining. Lines indicate median value. Dashed line indicates the positivity threshold (≥3 x blank). (D–H) Flow cytometric gating strategies for the analysis and sorting of major B cell populations from PBMCs of convalescent COVID-19 patients. (D) Gating strategy to analyze SARS-CoV-2 S-specific B cell population. Lymphocytes were first gated based on morphology, before exclusion of doublets, dead cells and CD3/CD14 cells. CD19 + cells were next gated before exclusion of CD38 hi plasma cells. CD38 int/- cells were then divided in four quadrants using CD27 and IgD. Upper left quadrant defines memory B cells (MBCs), lower left quadrant double-negative (DN), upper right quadrant CD27 + IgD + cells (MZB) and lower right quadrant naive B cells (excluding CD38 hi transitional). SARS-CoV-2 S-specific B cells were then analyzed within the B cell population of interest using a double-staining strategy with anti-His antibodies of a His-tagged SARS-CoV-2 S protein. (E) Gating strategy to separate activated and classical switched B cells using CD71, within the IgD - CD27 + gate (F) Gating strategy to analyze CD27 hi CD38 hi plasma cells in CD3 - CD14 - live cells. (G) Gating strategy for sorting of CD19 + IgD - cells from PBMCs for 10X Genomics single cell experiment. Lymphocytes were gated before exclusion of doublets and of CD14/CD15/CD56/IgD + cells, before sorting of CD19 + cells. (H) Gating strategy for single-cell sorting of SARS-CoV-2 S-specific B cells for single cell culture at M3 and M6. Lymphocytes were gated before double exclusion of doublets, dead cells and CD3/CD14 cells. CD19 + cells were then gated before exclusion of CD38 hi plasma cells. CD38 int/- cells were further divided into four quadrants using CD27 and IgD. Double stained SARS-CoV-2 S specific cells were sorted from all three non-naive quadrants (not IgD + CD27 - ).

Article Snippet: Right: bar plot showing the proportion of RBD specificity and cross-reactive specificities among S-specific cells for each patient. (B) Average percentage of RBD-specific cells among SARS-CoV-2 S-specific sorted cells from indicated S-CoV and M-CoV patient at M3 and M6. (C) Violin plot showing the number of mutations in the Ig V H segment of cells in the original M0 10X Genomics scRNA-seq VDJ dataset found to be in clonal relationship with M3 or M6 SARS-CoV-2 S-specific sorted cells showing specificity against RBD (RBD + clones), seasonal betacoronavirus S proteins (HKU1-CoV or OC43-CoV) or against SARS-CoV-2 S (Spike + clones). (D and E) Histograms showing the relative distribution of mutation numbers in the Ig V H segment from all SARS-CoV-2 S- (D) or RBD- (E) specific clone members (10X Genomics scRNA-seq dataset and single-cell heavy-chain sequencing data) at M0, M3, and M6 as well as from sequences from the literature ( ; ; ; ; ; ; ), mainly determined between M0 and M3. (F) Plot showing the percentage of neutralization, normalized to the IgG concentration, for single-cell culture supernatants from identified SARS-CoV-2 RBD-specific B cells at M6.

Techniques: Functional Assay, In Vitro, Neutralization, Enzyme-linked Immunosorbent Assay, Biomarker Discovery, Staining, Clinical Proteomics, Double Staining, FACS, Cell Culture

Journal: Cell

Article Title: Maturation and persistence of the anti-SARS-CoV-2 memory B cell response

doi: 10.1016/j.cell.2021.01.050

Figure Lengend Snippet: Maturation of the SARS-CoV-2 S-specific repertoire up to 6 months post-infection in M- and S-CoV patients (A) Left: pie charts showing the average percentage of RBD + and cross-reactive specificities (RBD + OC43 + , RBD + HKU1 + OC43 + , RBD + HKU1 + , OC43 + , HKU1 + , and OC43 + HKU1 + ) among single-cell cultured S-specific B cells as determined by ELISA. Three and four S-CoV patients, all previously included in our scRNA-seq dataset, were analyzed at M3 and M6, respectively. Right: bar plot showing the proportion of RBD specificity and cross-reactive specificities among S-specific cells for each patient. (B) Average percentage of RBD-specific cells among SARS-CoV-2 S-specific sorted cells from indicated S-CoV and M-CoV patient at M3 and M6. (C) Violin plot showing the number of mutations in the Ig V H segment of cells in the original M0 10X Genomics scRNA-seq VDJ dataset found to be in clonal relationship with M3 or M6 SARS-CoV-2 S-specific sorted cells showing specificity against RBD (RBD + clones), seasonal betacoronavirus S proteins (HKU1-CoV or OC43-CoV) or against SARS-CoV-2 S (Spike + clones). (D and E) Histograms showing the relative distribution of mutation numbers in the Ig V H segment from all SARS-CoV-2 S- (D) or RBD- (E) specific clone members (10X Genomics scRNA-seq dataset and single-cell heavy-chain sequencing data) at M0, M3, and M6 as well as from sequences from the literature ( Brouwer et al., 2020 ; Kreer et al., 2020 ; Liu et al., 2020 ; Robbiani et al., 2020 ; Shi et al., 2020 ; Wec et al., 2020 ; Zost et al., 2020 ), mainly determined between M0 and M3. (F) Plot showing the percentage of neutralization, normalized to the IgG concentration, for single-cell culture supernatants from identified SARS-CoV-2 RBD-specific B cells at M6. Two dilutions (1/2 and 1/20) were assayed for each supernatant tested. Dashed lines indicate 80% and 50% neutralization. (G) Violin plot representing the number of mutations in the Ig V H sequence in strong neutralizing antibodies at M6 (neutralization >80% at 1/2 dilution) versus the number of mutations in the Ig V H sequence of all cells from anti-RBD clones at M6. (H) Evolutionary tree of an RBD-specific and neutralizing clone, built on sequences from 10X Genomics scRNA-seq, cell culture, and literature. Each circle represents a unique sequence from that clone. Circle color indicates time point of origin, and the number inside indicates the calculated number of mutations from an inferred unmutated common ancestor (“germline”). Grey indicates a theoretically inferred common precursor. ∗ indicates that the antibody associated with that sequence has been validated as neutralizing in vitro . CDR3 from all sequences in the tree are represented as a frequency plot logo (top left) as well as below the tree, where each amino acid in red indicates a change compared to the top listed CDR3. ANOVA and two-tailed Mann-Whitney tests (B and C). ∗∗∗ p < 0.01, ∗ p < 0.05. See also Figure S4 and .

Article Snippet: Right: bar plot showing the proportion of RBD specificity and cross-reactive specificities among S-specific cells for each patient. (B) Average percentage of RBD-specific cells among SARS-CoV-2 S-specific sorted cells from indicated S-CoV and M-CoV patient at M3 and M6. (C) Violin plot showing the number of mutations in the Ig V H segment of cells in the original M0 10X Genomics scRNA-seq VDJ dataset found to be in clonal relationship with M3 or M6 SARS-CoV-2 S-specific sorted cells showing specificity against RBD (RBD + clones), seasonal betacoronavirus S proteins (HKU1-CoV or OC43-CoV) or against SARS-CoV-2 S (Spike + clones). (D and E) Histograms showing the relative distribution of mutation numbers in the Ig V H segment from all SARS-CoV-2 S- (D) or RBD- (E) specific clone members (10X Genomics scRNA-seq dataset and single-cell heavy-chain sequencing data) at M0, M3, and M6 as well as from sequences from the literature ( ; ; ; ; ; ; ), mainly determined between M0 and M3. (F) Plot showing the percentage of neutralization, normalized to the IgG concentration, for single-cell culture supernatants from identified SARS-CoV-2 RBD-specific B cells at M6.

Techniques: Infection, Cell Culture, Enzyme-linked Immunosorbent Assay, Clone Assay, Mutagenesis, Sequencing, Neutralization, Concentration Assay, In Vitro, Two Tailed Test, MANN-WHITNEY

Journal: Cell

Article Title: Maturation and persistence of the anti-SARS-CoV-2 memory B cell response

doi: 10.1016/j.cell.2021.01.050

Figure Lengend Snippet: Cross-reactivity, convergence, and accumulation of somatic mutations in anti-SARS-CoV-2 S-specific memory B cells from convalescent COVID-19 patients, Related to Figure 4 (A) Heatmaps showing the RBD, OC43 and HKU1 ELISA blank ratio for all tested SARS-CoV-2-spike-specific single cell culture supernatants at M3 and M6. Each line represents one tested supernatant. Light red lines indicate that no value is available for that supernatant. (B) FACS plots representing index sorting data of spike-specific cells according to their specificity for SARS-CoV-2 spike or RBD. (C) Repartition of the sorted SARS-CoV-2 spike- and RBD-specific cells at M3 and M6 between the MBCs, DN or IgD + CD27 + compartments. (D) Pie chart representing clone size in all the sequences generated via single cell culture for each of the 4 patients previously included in our scRNaseq analysis. Total number of sequenced cells is indicated in the middle of the pie. (E) Circus plot showing SARS-CoV-2 spike- (gray) or RBD- (light blue) specific clones shared between patients in our dataset or with sequences from the literature. (F) Histogram showing log-normalized counts for both barcoded his-tagged SARS-CoV-2 S proteins in all cells from the M6 time point of our 10X Genomics scRNA-seq dataset (white) and cells in clonal relationship with sequence from single cell culture (dark gray) (G and H) Histograms showing the distribution of mutations in Ig V H for SARS-CoV-2 spike- (G) and RBD- (H) specific clones at M0, M3 and M6 according to the assay of origin. (I) Plot showing the raw percentage of neutralization for single cell culture supernatants from identified SARS-CoV-2 RBD-specific B cells at M6. Two dilutions (1/2 and 1/20) were assayed for each supernatant tested. Dashed lines indicate 80% and 50% neutralization (see Figure 4 F). (J) Evolutionary tree of a convergent RBD-specific and neutralizing clone, built on sequences from 10X Genomics scRNA-seq and cell culture from two patients and from the literature. Each circle represents a unique sequence from that clone. Circle color indicates time-point of origin and the number inside indicates the calculated number of mutations from an inferred unmutated common ancestor (germline). Grey indicates a theoretically inferred common progenitor. CDR3 from all sequences in the tree are represented as a frequency plot logo (top left) as well as below the tree, where each amino-acid in red indicates a change compared to the first listed CDR3 sequence.

Article Snippet: Right: bar plot showing the proportion of RBD specificity and cross-reactive specificities among S-specific cells for each patient. (B) Average percentage of RBD-specific cells among SARS-CoV-2 S-specific sorted cells from indicated S-CoV and M-CoV patient at M3 and M6. (C) Violin plot showing the number of mutations in the Ig V H segment of cells in the original M0 10X Genomics scRNA-seq VDJ dataset found to be in clonal relationship with M3 or M6 SARS-CoV-2 S-specific sorted cells showing specificity against RBD (RBD + clones), seasonal betacoronavirus S proteins (HKU1-CoV or OC43-CoV) or against SARS-CoV-2 S (Spike + clones). (D and E) Histograms showing the relative distribution of mutation numbers in the Ig V H segment from all SARS-CoV-2 S- (D) or RBD- (E) specific clone members (10X Genomics scRNA-seq dataset and single-cell heavy-chain sequencing data) at M0, M3, and M6 as well as from sequences from the literature ( ; ; ; ; ; ; ), mainly determined between M0 and M3. (F) Plot showing the percentage of neutralization, normalized to the IgG concentration, for single-cell culture supernatants from identified SARS-CoV-2 RBD-specific B cells at M6.

Techniques: Enzyme-linked Immunosorbent Assay, Cell Culture, Generated, Clone Assay, Sequencing, Neutralization

Journal: Science Advances

Article Title: A universal sequencing read interpreter

doi: 10.1126/sciadv.add2793

Figure Lengend Snippet: ( A ) Read structures of sci-ATAC-seq and 10x scATAC-seq. ID, identifier. ( B ) Two-dimensional uniform manifold approximation and projection (UMAP) embeddings of sci-ATAC-seq data processed by its original pipeline for Drosophila embryo 6 to 8 hours after egg laying and that obtained by Cell Ranger ATAC with the read translation using INTERSTELLAR. Cell state annotations obtained by the original pipeline were applied to both embeddings. ( C ) Correlation in distance of two cells between the high-dimensional genomic accessibility count space of the original sci-ATAC-seq data and that by Cell Ranger ATAC. For each dataset, Euclidean distances in a high-dimensional latent semantic indexing (LSI) space were measured for the same 50,000 randomly sampled cell pairs. The inset sina plot represents rank difference distribution in the Euclidean distance of the same cell pairs before and after translation. The crossbar represents the median. *** P < 2.2 × 10 −16 by the two-sided Wilcoxon rank sum test. R , correlation coefficient.

Article Snippet: We simulated a sequencing read pool of a highly complex hypothetical read structure from a total of four 10x Chromium V3 scRNA-seq read datasets (two heart and two neural samples) obtained from the 10x Genomics website.

Techniques:

Journal: Science Advances

Article Title: A universal sequencing read interpreter

doi: 10.1126/sciadv.add2793

Figure Lengend Snippet: ( A ) Read structures of different single-cell RNA sequencing (scRNA-seq) methods. bp, base pair. ( B ) Two-dimensional UMAP embeddings of scRNA-seq datasets processed by their original pipelines and those analyzed using 10x Cell Ranger and dropseq-tools by read translation using INTERSTELLAR with the unique molecular ID (UMI) reassignment strategy. Cell state annotations obtained by the original pipelines were applied to the translated results. ( C ) Correlation in distance of two cells between the high-dimensional transcriptome spaces of the original datasets and those translated for Cell Ranger and dropseq-tools with the UMI reassignment and UMI bequeathing strategies. For each dataset, Euclidean distances in the gene expression count matrix were measured for 50,000 randomly sampled cell pairs. The bottom-right inset sina plot of each panel represents rank difference distribution in the Euclidean distance of the same cell pairs before and after translation. The crossbar represents the median. ( D ) Two-dimensional UMAP embeddings of 10x Chromium and SPLiT-seq datasets processed by their original pipelines and those analyzed by dropseq-tools using INTERSTELLAR without value space optimizations. ( E ) Correlation in distance of two cells between the high-dimensional transcriptome spaces of the original datasets and those translated for dropseq-tools without value space optimizations. ( F ) UMI loss rate per cell with and without value space optimizations. ( G ) Two-dimensional UMAP embedding of the Drop-seq dataset self-translated for dropseq-tools with no cell ID error correction. ( H ) Correlation in distance of two cells between the high-dimensional transcriptome spaces of the original and self-translated Drop-seq datasets. *** P < 2.2 × 10 −16 by the two-sided Wilcoxon rank sum test.

Article Snippet: We simulated a sequencing read pool of a highly complex hypothetical read structure from a total of four 10x Chromium V3 scRNA-seq read datasets (two heart and two neural samples) obtained from the 10x Genomics website.

Techniques: RNA Sequencing, Gene Expression

Journal: Science Advances

Article Title: A universal sequencing read interpreter

doi: 10.1126/sciadv.add2793

Figure Lengend Snippet: ( A ) Read structures of Slide-seq and 10x Visium. ( B ) Strategy to associate Slide-seq positional barcodes to those of multiple 10x Visium slides. Multiple Visium slides are first tiled across an enlarged Slide-seq field with a given scaling factor. Slide-seq positional barcodes are then associated to the closest Visium positional barcodes. ( C ) Relative frequency distributions in number of Slide-seq positional barcodes assigned per Visium positional barcode with scaling factors of ×1, ×5, and ×10. Error bar indicates mean ± SEs. ( D ) Original Slide-seq datasets and those analyzed by 10x Space Ranger with ×10 scaling. Each grid represents a tiled Visium slide. The spatial data points are color coded according to their gene expression profile clusters identified independently in the analysis of each sample. ( E ) Correlation in Euclidean distance of two positional transcriptome profiles (UMI count matrices) between the original Slide-seq datasets and those translated and analyzed using Space Ranger with the read translation. Randomly sampled 50,000 positional barcode pairs with unique correspondences between the original and translated datasets were analyzed for each tissue sample. The inset sina plot represents rank difference distribution in the Euclidean distance of the same cell pairs before and after translation. The crossbar represents the median. *** P < 2.2 × 10 −16 by the two-sided Wilcoxon rank sum test.

Article Snippet: We simulated a sequencing read pool of a highly complex hypothetical read structure from a total of four 10x Chromium V3 scRNA-seq read datasets (two heart and two neural samples) obtained from the 10x Genomics website.

Techniques: Gene Expression

Journal: Science Advances

Article Title: A universal sequencing read interpreter

doi: 10.1126/sciadv.add2793

Figure Lengend Snippet: ( A ) Read structures of sci-Space. ( B ) Two-dimensional UMAP embeddings of cells and the spatial distributions of cell states for the original sci-Space data (top) and the translated data analyzed by 10x Cell Ranger (bottom). The cell state clusters are color coded according to their gene expression profile clusters identified independently in each analysis.

Article Snippet: We simulated a sequencing read pool of a highly complex hypothetical read structure from a total of four 10x Chromium V3 scRNA-seq read datasets (two heart and two neural samples) obtained from the 10x Genomics website.

Techniques: Gene Expression