scatac seq dataset  (10X Genomics)

Journal: Nature Communications

Article Title: Cell neighborhood topology directs rare cell population identification

doi: 10.1038/s41467-026-71180-x

Figure Lengend Snippet: a Benchmarking RareQ against MarsGT in the paired Sim-PBMC 1, 2, and 3 datasets via F 1 score. RareQ was applied to each modality independently (RareQ_RNA for RNA data, RareQ_ATAC for ATAC data, RareQ_WNN for WNN-integrated data), while MarsGT integrates both modalities. b Comparative results of RareQ against MarsGT on the four human PBMC paired scRNA-seq datasets (PBMC-bench-1, 2, 3 and 4) with scATAC-seq data via F 1 scores. c Magnified view of Supplementary Fig. showing the progenitor cell types identified by RareQ on ATAC modality. d Heatmaps of cell-type-specific markers of identified cell subsets in expression (left) and accessibility (right). Benchmarking RareQ against MarsGT under optimal parameter settings using 10 paired scRNA-seq and scATAC-seq datasets on ( e ) rare cell detection via F 1 score, precision and recall, and ( f ) global clustering via NMI. Boxes extend from the first to the third quartile (Q1 – Q3) with a line in the middle denoting the median. Whisker lines extending from both ends of the box indicate variability outside Q1 and Q3, whose minimum/maximum values are calculated as Q1 − 1.5 × IQR and Q3 + 1.5 × IQR. Source data are provided as a file.

Article Snippet: The paired B lymphoma scRNA-seq and scATAC-seq dataset, Xenium spatial datasets of mouse brain and human RCC were obtained from the 10X Genomics website.

Techniques: Expressing, Whisker Assay

Journal: Cell Genomics

Article Title: Capturing cell-type-specific activities of cis -regulatory elements from peak-based single-cell ATAC-seq

doi: 10.1016/j.xgen.2025.100806

Figure Lengend Snippet: Characterization of peak-based analysis in PBMC 10× Multiome dataset for CD14 + monocytes and CD8 + naive T cells (A) Comparison of library sizes between scATAC-seq and scRNA-seq. (B) Proportions of zero counts plotted against mean fragment per peak (ATAC) and mean UMI per gene (RNA), with an assumed Poisson distribution curve shown as a dashed line. (C) Relationship between mean fragment count and peak width. (D) Density plots comparing sizes of peaks and CREs annotated by ENCODE, including the percentage of peaks covering multiple CREs genome wide and within ±1 kb of TSSs. (E) Pie charts depicting the distribution of Tn5 cleavage sites across MACS2 peaks. (F) Distribution of Tn5 cleavage site-to-peak ratios and the proportion of CRE-associated Tn5 cleavages across MACS2 peaks. (G) An illustration of a peak near the SFT2D2 gene exhibiting distinct regulatory patterns between CD14 + and CD8 + cells. (H) Violin plots of fragment counts, gene activities, and RNA UMI counts across cells in SFT2D2 .

Article Snippet: We downloaded the PBMC Chromium X2 scATAC-seq dataset, which includes peak and fragment files, from 10x Genomics ( https://www.10xgenomics.com/datasets/10k-human-pbmcs-atac-v2-chromium-x-2-standard ).

Techniques: Comparison, Genome Wide

Journal: Cell Genomics

Article Title: Capturing cell-type-specific activities of cis -regulatory elements from peak-based single-cell ATAC-seq

doi: 10.1016/j.xgen.2025.100806

Figure Lengend Snippet: The CREscendo framework can uncover distinct regulatory patterns within scATAC-seq peaks (A) Schematic representation of the CREscendo analysis highlighting differential usage of CREs. (B) Scatterplot comparing chi-squared statistics from the CREscendo analysis with log2 fold change from Signac’s DA analysis. Peaks with chi-squared statistics greater than 2,000 are highlighted in red text. (C) Detailed view of a peak near the CD248 gene compared with ENCODE data annotations. (D) Detailed view of a peak near the NFKBIZ gene compared with data from the PBMC Chromium X2 dataset.

Article Snippet: We downloaded the PBMC Chromium X2 scATAC-seq dataset, which includes peak and fragment files, from 10x Genomics ( https://www.10xgenomics.com/datasets/10k-human-pbmcs-atac-v2-chromium-x-2-standard ).

Techniques:

Journal: Cell Genomics

Article Title: Capturing cell-type-specific activities of cis -regulatory elements from peak-based single-cell ATAC-seq

doi: 10.1016/j.xgen.2025.100806

Figure Lengend Snippet:

Article Snippet: We downloaded the PBMC Chromium X2 scATAC-seq dataset, which includes peak and fragment files, from 10x Genomics ( https://www.10xgenomics.com/datasets/10k-human-pbmcs-atac-v2-chromium-x-2-standard ).

Techniques: Software

Journal: Nature Communications

Article Title: MATES: a deep learning-based model for locus-specific quantification of transposable elements in single cell

doi: 10.1038/s41467-024-53114-7

Figure Lengend Snippet: This assessment highlights MATES's efficiency in cell clustering, evaluated through the Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI), using different TE quantification strategies. a , b The impact of various TE quantification methods on cell clustering is compared within the 10x scRNA Dataset of Chemical Reprogramming and Smart-Seq2 Dataset of Glioblastoma. These methods include scTE (gene expression combined with scTE-quantified TE), SoloTE (gene expression combined with SoloTE-quantified TE), and MATES (gene expression integrated with MATES-quantified TE). MATES outperforms both scTE and SoloTE by enhancing gene expression with TE data (left). The middle panel compares clustering based solely on TE quantification methods-unique TEs, scTE, SoloTE, and MATES-with 'unique TEs' representing unique-mapping reads TE expression, highlighting MATES's consistently improved performance. The right panel confirms MATES's advantage over scTE and SoloTE when considering only multi-mapping TE reads. Note: SoloTE's incompatibility with Smart-Seq2 data results in a blank section. Panel ( a ) uses ARI for evaluation, while panel ( b ) utilizes NMI. c The 10x scATAC Dataset of the Adult Mouse Brain is analyzed to contrast peak and TE quantification using scTE and MATES against peak-only datasets (left). TE mapping reads from scTE and MATES are also compared against unique TE mapping reads (right). SoloTE's incompatibility with scATAC data leads to its exclusion from this part of the analysis. The boxes represent the interquartile ranges (IQRs), and the solid lines indicate the medians. The whiskers extend to points within 1.5 IQRs of the lower and upper quartiles. The experiments run with N = 10 different seeds. The p -values were calculated using a one-sided Student’s t-test. Source data are provided as a Source Data file.

Article Snippet: The scATAC-seq 10x 5k adult mouse brain cell dataset is available at https://support.10xgenomics.com/single-cell-atac/datasets/1.2.0/atac_v1_adult_brain_fresh_5k , and we generated the cell type labels following the workflow provided in the Signac tutorial ( https://stuartlab.org/signac/articles/pbmc_vignette ).

Techniques: Gene Expression, Expressing

Journal: Nature Communications

Article Title: MATES: a deep learning-based model for locus-specific quantification of transposable elements in single cell

doi: 10.1038/s41467-024-53114-7

Figure Lengend Snippet: a – c Analyze the correlation between TE expression quantified by ( a ) MATES, ( b ) scTE, and ( c ) SoloTE from real 10x short-read data and the nanopore long-read sequencing for the same set of cells. This comparison is at the subfamily level. d The distribution of lengths of Alu family TE regions. e Correlation between locus-level TE expression quantified by MATES and the simulated ground truth for the Alu repeat simulated data. f Comparison of quantified results by MATES and scTE to the simulated ground truth for the Alu repeat simulated data. The blue bar and orange bar represent the percentage of simulated reads captured by MATES and scTE, respectively. Among the 6 simulated Alu families, MATES on average captured 96.14% simulated reads while scTE recaptured 92.57%. The p -values of R 2 (Coefficient of determination) were calculated using the one-sided F-test. Source data are provided as a Source Data file.

Article Snippet: The scATAC-seq 10x 5k adult mouse brain cell dataset is available at https://support.10xgenomics.com/single-cell-atac/datasets/1.2.0/atac_v1_adult_brain_fresh_5k , and we generated the cell type labels following the workflow provided in the Signac tutorial ( https://stuartlab.org/signac/articles/pbmc_vignette ).

Techniques: Expressing, Sequencing, Comparison