cell ranger arc Search Results


cell ranger arc count v 2 0 2  (10X Genomics)
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10X Genomics cell ranger arc count v 2 0 2
Cell Ranger Arc Count V 2 0 2, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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cell ranger arcv2 0 0  (10X Genomics)
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10X Genomics cell ranger arcv2 0 0
Cell Ranger Arcv2 0 0, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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cell ranger arc 1 0 1 atac genome  (10X Genomics)
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10X Genomics cell ranger arc 1 0 1 atac genome
a : Overview of the computational pipeline implemented for analysis of multimodal data. b - c : UMAP (uniform manifold approximation and projection) scatterplot of multimodal snATAC-seq data from donors P13, P14 and Hrv43 (n = 52,798 nuclei) coloured by cell state (b), donor, sample (10X library) or unbiased clustering labels (c). Data is annotated based on the corresponding single-nuclei RNA sequencing (snRNA-seq) cell state assignment. d : UMAP scatterplot of integrated multimodal single-nuclei <t>ATAC</t> sequencing (snATAC-seq) data for trophoblast only from donors P13, P14 and Hrv43 (n = 7449 nuclei) coloured by cell state, donor and sample (10X library). e : UMAP scatterplot of multiome (snRNA-snATACseq) data of invading trophoblast cells from donor P13 (n = 1605 nuclei) coloured by cell state. The manifold is calculated based on dimensionality reduction performed by MEFISTO (model with n = 9 factors). f : (Left) UMAP scatterplot of multiome (snRNA-snATACseq) data of invading trophoblast cells from donor P13 (n = 1605 nuclei) coloured by sample. The manifold is calculated based on dimensionality reduction performed by MEFISTO (model with n = 9 factors). (Right) Scatterplot of UMAP coordinates obtained from the RNA expression data that were used as covariates for MEFISTO, coloured by cell state. g : Heatmap representing percentage of variance explained by each MEFISTO factor in each data modality. h : Smoothness along differentiation estimated with MEFISTO. i : UMAP scatterplot of multiome (snRNA-snATACseq) data of invading trophoblast cells from donor P13 (n = 1605 nuclei) coloured by cell cycle phase and MEFISTO factor values for important selected factors. j : Spearman’s rank correlation coefficients of each latent factor learned with MEFISTO and the number of genes per counts in snATAC-seq data (multiome). k : Gene set (RNA) enrichment analysis overview of MEFISTO factor 2 using two-sided parametric t-test, FDR is used to rank gene sets. l : Peak set (ATAC) enrichment analysis overview of MEFISTO factor 10 using two-sided parametric t-test, FDR is used to rank peak sets. Villous cytotrophoblast (VCT), cytotrophoblast cell column (CCC), proliferative (p), extravillous trophoblast (EVT), interstitial EVTs (iEVTs), giant cells (GC), endovascular EVT (eEVT), dendritic cells (DC), lymphatic (l), maternal (m), fetal (f) Hofbauer cells (HOFB), innate lymphocytes (ILC), macrophages (M), monocytes (MO), natural killer (NK), perivascular (PV), decidual (d), epithelial (epi), stromal (S), fibroblasts (F), uterine smooth muscle cells (uSMC).
Cell Ranger Arc 1 0 1 Atac Genome, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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cell ranger arc count pipeline  (10X Genomics)
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10X Genomics cell ranger arc count pipeline
a : Overview of the computational pipeline implemented for analysis of multimodal data. b - c : UMAP (uniform manifold approximation and projection) scatterplot of multimodal snATAC-seq data from donors P13, P14 and Hrv43 (n = 52,798 nuclei) coloured by cell state (b), donor, sample (10X library) or unbiased clustering labels (c). Data is annotated based on the corresponding single-nuclei RNA sequencing (snRNA-seq) cell state assignment. d : UMAP scatterplot of integrated multimodal single-nuclei <t>ATAC</t> sequencing (snATAC-seq) data for trophoblast only from donors P13, P14 and Hrv43 (n = 7449 nuclei) coloured by cell state, donor and sample (10X library). e : UMAP scatterplot of multiome (snRNA-snATACseq) data of invading trophoblast cells from donor P13 (n = 1605 nuclei) coloured by cell state. The manifold is calculated based on dimensionality reduction performed by MEFISTO (model with n = 9 factors). f : (Left) UMAP scatterplot of multiome (snRNA-snATACseq) data of invading trophoblast cells from donor P13 (n = 1605 nuclei) coloured by sample. The manifold is calculated based on dimensionality reduction performed by MEFISTO (model with n = 9 factors). (Right) Scatterplot of UMAP coordinates obtained from the RNA expression data that were used as covariates for MEFISTO, coloured by cell state. g : Heatmap representing percentage of variance explained by each MEFISTO factor in each data modality. h : Smoothness along differentiation estimated with MEFISTO. i : UMAP scatterplot of multiome (snRNA-snATACseq) data of invading trophoblast cells from donor P13 (n = 1605 nuclei) coloured by cell cycle phase and MEFISTO factor values for important selected factors. j : Spearman’s rank correlation coefficients of each latent factor learned with MEFISTO and the number of genes per counts in snATAC-seq data (multiome). k : Gene set (RNA) enrichment analysis overview of MEFISTO factor 2 using two-sided parametric t-test, FDR is used to rank gene sets. l : Peak set (ATAC) enrichment analysis overview of MEFISTO factor 10 using two-sided parametric t-test, FDR is used to rank peak sets. Villous cytotrophoblast (VCT), cytotrophoblast cell column (CCC), proliferative (p), extravillous trophoblast (EVT), interstitial EVTs (iEVTs), giant cells (GC), endovascular EVT (eEVT), dendritic cells (DC), lymphatic (l), maternal (m), fetal (f) Hofbauer cells (HOFB), innate lymphocytes (ILC), macrophages (M), monocytes (MO), natural killer (NK), perivascular (PV), decidual (d), epithelial (epi), stromal (S), fibroblasts (F), uterine smooth muscle cells (uSMC).
Cell Ranger Arc Count Pipeline, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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single nuclei multiome atac gene expression data cell ranger arc  (10X Genomics)
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10X Genomics single nuclei multiome atac gene expression data cell ranger arc
a : Overview of the computational pipeline implemented for analysis of multimodal data. b - c : UMAP (uniform manifold approximation and projection) scatterplot of multimodal snATAC-seq data from donors P13, P14 and Hrv43 (n = 52,798 nuclei) coloured by cell state (b), donor, sample (10X library) or unbiased clustering labels (c). Data is annotated based on the corresponding single-nuclei RNA sequencing (snRNA-seq) cell state assignment. d : UMAP scatterplot of integrated multimodal single-nuclei <t>ATAC</t> sequencing (snATAC-seq) data for trophoblast only from donors P13, P14 and Hrv43 (n = 7449 nuclei) coloured by cell state, donor and sample (10X library). e : UMAP scatterplot of multiome (snRNA-snATACseq) data of invading trophoblast cells from donor P13 (n = 1605 nuclei) coloured by cell state. The manifold is calculated based on dimensionality reduction performed by MEFISTO (model with n = 9 factors). f : (Left) UMAP scatterplot of multiome (snRNA-snATACseq) data of invading trophoblast cells from donor P13 (n = 1605 nuclei) coloured by sample. The manifold is calculated based on dimensionality reduction performed by MEFISTO (model with n = 9 factors). (Right) Scatterplot of UMAP coordinates obtained from the RNA expression data that were used as covariates for MEFISTO, coloured by cell state. g : Heatmap representing percentage of variance explained by each MEFISTO factor in each data modality. h : Smoothness along differentiation estimated with MEFISTO. i : UMAP scatterplot of multiome (snRNA-snATACseq) data of invading trophoblast cells from donor P13 (n = 1605 nuclei) coloured by cell cycle phase and MEFISTO factor values for important selected factors. j : Spearman’s rank correlation coefficients of each latent factor learned with MEFISTO and the number of genes per counts in snATAC-seq data (multiome). k : Gene set (RNA) enrichment analysis overview of MEFISTO factor 2 using two-sided parametric t-test, FDR is used to rank gene sets. l : Peak set (ATAC) enrichment analysis overview of MEFISTO factor 10 using two-sided parametric t-test, FDR is used to rank peak sets. Villous cytotrophoblast (VCT), cytotrophoblast cell column (CCC), proliferative (p), extravillous trophoblast (EVT), interstitial EVTs (iEVTs), giant cells (GC), endovascular EVT (eEVT), dendritic cells (DC), lymphatic (l), maternal (m), fetal (f) Hofbauer cells (HOFB), innate lymphocytes (ILC), macrophages (M), monocytes (MO), natural killer (NK), perivascular (PV), decidual (d), epithelial (epi), stromal (S), fibroblasts (F), uterine smooth muscle cells (uSMC).
Single Nuclei Multiome Atac Gene Expression Data Cell Ranger Arc, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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v2 0 10x genomics  (10X Genomics)
86
10X Genomics v2 0 10x genomics
Figure 1. EAC primary and metastatic samples show a diverse landscape of TME and malignant cells in transcriptomic and epigenetic data (A) Schematic representation of the study workflow. Biopsies from 10 patients in our discovery cohort, including normal adjacent tissue (NAT), primary tissue, and metastatic samples, were subjected to single-nuclei RNA and ATAC sequencing using <t>10X</t> Chromium technology. For a subset of these patients as well as three additional patients, matched primary and metastatic samples were profiled with 10X Visium and 10X Xenium spatial transcriptomics (ST) technologies. For single- nuclei data, cells were annotated by cell type and categorized into malignant and TME components. TME subtypes were linked to metastasis, with validation against an external pan-cancer fibroblast atlas. 16 The malignant cell components underwent analysis using consensus non-negative matrix factorization (cNMF) to uncover malignant programs, which were further characterized for transcriptional and epigenetic heterogeneity at a single-cell and spatial level and candidate master transcription factors. External validation was performed in two single-cell validation cohorts, 9,10 and associations with clinical and molecular charac- teristics, as well as survival, were assessed in three bulk validation cohorts. 7,10,17
V2 0 10x Genomics, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Image Search Results


a : Overview of the computational pipeline implemented for analysis of multimodal data. b - c : UMAP (uniform manifold approximation and projection) scatterplot of multimodal snATAC-seq data from donors P13, P14 and Hrv43 (n = 52,798 nuclei) coloured by cell state (b), donor, sample (10X library) or unbiased clustering labels (c). Data is annotated based on the corresponding single-nuclei RNA sequencing (snRNA-seq) cell state assignment. d : UMAP scatterplot of integrated multimodal single-nuclei ATAC sequencing (snATAC-seq) data for trophoblast only from donors P13, P14 and Hrv43 (n = 7449 nuclei) coloured by cell state, donor and sample (10X library). e : UMAP scatterplot of multiome (snRNA-snATACseq) data of invading trophoblast cells from donor P13 (n = 1605 nuclei) coloured by cell state. The manifold is calculated based on dimensionality reduction performed by MEFISTO (model with n = 9 factors). f : (Left) UMAP scatterplot of multiome (snRNA-snATACseq) data of invading trophoblast cells from donor P13 (n = 1605 nuclei) coloured by sample. The manifold is calculated based on dimensionality reduction performed by MEFISTO (model with n = 9 factors). (Right) Scatterplot of UMAP coordinates obtained from the RNA expression data that were used as covariates for MEFISTO, coloured by cell state. g : Heatmap representing percentage of variance explained by each MEFISTO factor in each data modality. h : Smoothness along differentiation estimated with MEFISTO. i : UMAP scatterplot of multiome (snRNA-snATACseq) data of invading trophoblast cells from donor P13 (n = 1605 nuclei) coloured by cell cycle phase and MEFISTO factor values for important selected factors. j : Spearman’s rank correlation coefficients of each latent factor learned with MEFISTO and the number of genes per counts in snATAC-seq data (multiome). k : Gene set (RNA) enrichment analysis overview of MEFISTO factor 2 using two-sided parametric t-test, FDR is used to rank gene sets. l : Peak set (ATAC) enrichment analysis overview of MEFISTO factor 10 using two-sided parametric t-test, FDR is used to rank peak sets. Villous cytotrophoblast (VCT), cytotrophoblast cell column (CCC), proliferative (p), extravillous trophoblast (EVT), interstitial EVTs (iEVTs), giant cells (GC), endovascular EVT (eEVT), dendritic cells (DC), lymphatic (l), maternal (m), fetal (f) Hofbauer cells (HOFB), innate lymphocytes (ILC), macrophages (M), monocytes (MO), natural killer (NK), perivascular (PV), decidual (d), epithelial (epi), stromal (S), fibroblasts (F), uterine smooth muscle cells (uSMC).

Journal: Nature

Article Title: Spatial multiomics map of trophoblast development in early pregnancy

doi: 10.1038/s41586-023-05869-0

Figure Lengend Snippet: a : Overview of the computational pipeline implemented for analysis of multimodal data. b - c : UMAP (uniform manifold approximation and projection) scatterplot of multimodal snATAC-seq data from donors P13, P14 and Hrv43 (n = 52,798 nuclei) coloured by cell state (b), donor, sample (10X library) or unbiased clustering labels (c). Data is annotated based on the corresponding single-nuclei RNA sequencing (snRNA-seq) cell state assignment. d : UMAP scatterplot of integrated multimodal single-nuclei ATAC sequencing (snATAC-seq) data for trophoblast only from donors P13, P14 and Hrv43 (n = 7449 nuclei) coloured by cell state, donor and sample (10X library). e : UMAP scatterplot of multiome (snRNA-snATACseq) data of invading trophoblast cells from donor P13 (n = 1605 nuclei) coloured by cell state. The manifold is calculated based on dimensionality reduction performed by MEFISTO (model with n = 9 factors). f : (Left) UMAP scatterplot of multiome (snRNA-snATACseq) data of invading trophoblast cells from donor P13 (n = 1605 nuclei) coloured by sample. The manifold is calculated based on dimensionality reduction performed by MEFISTO (model with n = 9 factors). (Right) Scatterplot of UMAP coordinates obtained from the RNA expression data that were used as covariates for MEFISTO, coloured by cell state. g : Heatmap representing percentage of variance explained by each MEFISTO factor in each data modality. h : Smoothness along differentiation estimated with MEFISTO. i : UMAP scatterplot of multiome (snRNA-snATACseq) data of invading trophoblast cells from donor P13 (n = 1605 nuclei) coloured by cell cycle phase and MEFISTO factor values for important selected factors. j : Spearman’s rank correlation coefficients of each latent factor learned with MEFISTO and the number of genes per counts in snATAC-seq data (multiome). k : Gene set (RNA) enrichment analysis overview of MEFISTO factor 2 using two-sided parametric t-test, FDR is used to rank gene sets. l : Peak set (ATAC) enrichment analysis overview of MEFISTO factor 10 using two-sided parametric t-test, FDR is used to rank peak sets. Villous cytotrophoblast (VCT), cytotrophoblast cell column (CCC), proliferative (p), extravillous trophoblast (EVT), interstitial EVTs (iEVTs), giant cells (GC), endovascular EVT (eEVT), dendritic cells (DC), lymphatic (l), maternal (m), fetal (f) Hofbauer cells (HOFB), innate lymphocytes (ILC), macrophages (M), monocytes (MO), natural killer (NK), perivascular (PV), decidual (d), epithelial (epi), stromal (S), fibroblasts (F), uterine smooth muscle cells (uSMC).

Article Snippet: For each sequenced snRNA-seq and ATAC–seq (multiome) library, we performed read alignment to custom made genome consisting of 10X Genomics’ GRCh38 3.0.0 pre-mRNA human reference genome and 10X Genomics Cell Ranger-Arc 1.0.1 ATAC genome, created following instructions from 10X Genomics: https://support.10xgenomics.com/single-cell-multiome-atac-gex/software/pipelines/latest/advanced/references .

Techniques: RNA Sequencing, Sequencing, RNA Expression

Figure 1. EAC primary and metastatic samples show a diverse landscape of TME and malignant cells in transcriptomic and epigenetic data (A) Schematic representation of the study workflow. Biopsies from 10 patients in our discovery cohort, including normal adjacent tissue (NAT), primary tissue, and metastatic samples, were subjected to single-nuclei RNA and ATAC sequencing using 10X Chromium technology. For a subset of these patients as well as three additional patients, matched primary and metastatic samples were profiled with 10X Visium and 10X Xenium spatial transcriptomics (ST) technologies. For single- nuclei data, cells were annotated by cell type and categorized into malignant and TME components. TME subtypes were linked to metastasis, with validation against an external pan-cancer fibroblast atlas. 16 The malignant cell components underwent analysis using consensus non-negative matrix factorization (cNMF) to uncover malignant programs, which were further characterized for transcriptional and epigenetic heterogeneity at a single-cell and spatial level and candidate master transcription factors. External validation was performed in two single-cell validation cohorts, 9,10 and associations with clinical and molecular charac- teristics, as well as survival, were assessed in three bulk validation cohorts. 7,10,17

Journal: Cell reports. Medicine

Article Title: Cell states and neighborhoods in distinct clinical stages of primary and metastatic esophageal adenocarcinoma.

doi: 10.1016/j.xcrm.2025.102188

Figure Lengend Snippet: Figure 1. EAC primary and metastatic samples show a diverse landscape of TME and malignant cells in transcriptomic and epigenetic data (A) Schematic representation of the study workflow. Biopsies from 10 patients in our discovery cohort, including normal adjacent tissue (NAT), primary tissue, and metastatic samples, were subjected to single-nuclei RNA and ATAC sequencing using 10X Chromium technology. For a subset of these patients as well as three additional patients, matched primary and metastatic samples were profiled with 10X Visium and 10X Xenium spatial transcriptomics (ST) technologies. For single- nuclei data, cells were annotated by cell type and categorized into malignant and TME components. TME subtypes were linked to metastasis, with validation against an external pan-cancer fibroblast atlas. 16 The malignant cell components underwent analysis using consensus non-negative matrix factorization (cNMF) to uncover malignant programs, which were further characterized for transcriptional and epigenetic heterogeneity at a single-cell and spatial level and candidate master transcription factors. External validation was performed in two single-cell validation cohorts, 9,10 and associations with clinical and molecular charac- teristics, as well as survival, were assessed in three bulk validation cohorts. 7,10,17

Article Snippet: *_ga_B3E4QL2TPR* MTcxMDc3MDI1My4yMS4wLjE3MTA3Nz AzNTcuMC4wLjAhttps://tcga-pancanatlas-hub.s3.us-east-1.amazonaws.com/ download/Survival_SupplementalTable_ S1_20171025_xena_sp Fibroblast atlas single-cell data, Luo et al. https://www.nature.com/ articles/s41467-022-34395-2 GEO: GSE210347 Software and algorithms 10X Cell Ranger ARC pipeline, v2.0 10X Genomics https://www.10xgenomics.com/support/ software/cell-ranger-arc/latest 10X Space Ranger pipeline, v2.1.1 10X Genomics https://www.10xgenomics.com/support/ software/space-ranger/latest CellBender, python package, v0.2.0 https://www.nature.com/articles/ s41592-023-01943-7 https://github.com/broadinstitute/ CellBender Scrublet, python package, v0.2.3 https://www.sciencedirect.com/ science/article/pii/S2405471218304745 https://github.com/AllonKleinLab/scrublet Scanpy, python package, v1.9.5 https://genomebiology.biomedcentral. com/articles/10.1186/s13059-017-1382-0 https://github.com/scverse/scanpy Infercnvpy, python package, v0.4.5 N/A https://github.com/icbi-lab/infercnvpy Harmony, python package, v0.0.9 https://www.nature.com/articles/ s41592-019-0619-0 https://github.com/slowkow/harmonypy Signac, R package, v1.14.0 https://www.nature.com/articles/ s41592-021-01282-5 https://github.com/stuart-lab/signac MACS2, python package, v2.2.9.1 https://genomebiology.biomedcentral. com/articles/10.1186/gb-2008-9-9-r137 https://pypi.org/project/MACS2/ Seurat, R package, v5.1.0 https://www.nature.com/articles/ s41587-023-01767-y https://satijalab.org/seurat/ MILOpy, python package, v0.1.1 https://www.nature.com/articles/ s41587-021-01033-z https://github.com/emdann/milopy cNMF, python package, v1.3.4 https://elifesciences.org/articles/43803 https://github.com/dylkot/cNMF GSEA, python package, v0.10.8 https://www.pnas.org/doi/10.1073/ pnas.0506580102 https://github.com/zqfang/GSEApy (Continued on next page) Cell Reports Medicine 6, 102188, June 17, 2025 e2 OPEN ACCESS

Techniques: Sequencing, Biomarker Discovery