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single-cell transcriptomic data  (Omics Data Automation)

 
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    Omics Data Automation single-cell transcriptomic data
    Overview of the GCN-SC framework on single-cell <t>multi-omics</t> <t>data</t> integration. GCN-SC can take single-cell multi-omics data as input, including data of gene expression (scRNA-seq), chromatin accessibility (scATAC-seq) and protein expression (CITE-seq). Each omics data was pre-processed to a uniform count matrix, in which each row represents one feature and each column represents one cell. As gene expression has much higher frequency of dropout compared to other omics data, scImpute was conducted on the matrix to reduce the sparsity of the matrix. Next, one scRNA-seq data were used as the reference, while the rest were considered as the query. By using MNN algorithm, cell-pairs with the closest Euclidean distance were identified, and then a mixed graph was constructed based on cell–cell relationships, which includes both inter-dataset edges and intra-dataset edges. A GCN that includes four layers further took this mixed graph to adjust matrices from the query dataset. Finally, NMF was implemented for dimension reduction on the adjusted matrices from the query and the matrix from the reference.
    Single Cell Transcriptomic Data, supplied by Omics Data Automation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    1) Product Images from "A universal framework for single-cell multi-omics data integration with graph convolutional networks"

    Article Title: A universal framework for single-cell multi-omics data integration with graph convolutional networks

    Journal: Briefings in Bioinformatics

    doi: 10.1093/bib/bbad081

    Overview of the GCN-SC framework on single-cell multi-omics data integration. GCN-SC can take single-cell multi-omics data as input, including data of gene expression (scRNA-seq), chromatin accessibility (scATAC-seq) and protein expression (CITE-seq). Each omics data was pre-processed to a uniform count matrix, in which each row represents one feature and each column represents one cell. As gene expression has much higher frequency of dropout compared to other omics data, scImpute was conducted on the matrix to reduce the sparsity of the matrix. Next, one scRNA-seq data were used as the reference, while the rest were considered as the query. By using MNN algorithm, cell-pairs with the closest Euclidean distance were identified, and then a mixed graph was constructed based on cell–cell relationships, which includes both inter-dataset edges and intra-dataset edges. A GCN that includes four layers further took this mixed graph to adjust matrices from the query dataset. Finally, NMF was implemented for dimension reduction on the adjusted matrices from the query and the matrix from the reference.
    Figure Legend Snippet: Overview of the GCN-SC framework on single-cell multi-omics data integration. GCN-SC can take single-cell multi-omics data as input, including data of gene expression (scRNA-seq), chromatin accessibility (scATAC-seq) and protein expression (CITE-seq). Each omics data was pre-processed to a uniform count matrix, in which each row represents one feature and each column represents one cell. As gene expression has much higher frequency of dropout compared to other omics data, scImpute was conducted on the matrix to reduce the sparsity of the matrix. Next, one scRNA-seq data were used as the reference, while the rest were considered as the query. By using MNN algorithm, cell-pairs with the closest Euclidean distance were identified, and then a mixed graph was constructed based on cell–cell relationships, which includes both inter-dataset edges and intra-dataset edges. A GCN that includes four layers further took this mixed graph to adjust matrices from the query dataset. Finally, NMF was implemented for dimension reduction on the adjusted matrices from the query and the matrix from the reference.

    Techniques Used: Biomarker Discovery, Gene Expression, Expressing, Construct

    Integration of single-cell multi-omics data. ( A ) The UMAP visualization of the scRNA-seq and scATAC-seq data from the same group of cells (dataset 5). ( B ) The UMAP visualization of the integrated data processed by GCN-SC. The UMAP visualization of the integration data by Seurat ( C ), GLUER ( D ) and GCNSC ( E ). ( F ) The alignment score map of the three integration algorithms Seurat, GLUER and GCN-SC on dataset 5.
    Figure Legend Snippet: Integration of single-cell multi-omics data. ( A ) The UMAP visualization of the scRNA-seq and scATAC-seq data from the same group of cells (dataset 5). ( B ) The UMAP visualization of the integrated data processed by GCN-SC. The UMAP visualization of the integration data by Seurat ( C ), GLUER ( D ) and GCNSC ( E ). ( F ) The alignment score map of the three integration algorithms Seurat, GLUER and GCN-SC on dataset 5.

    Techniques Used: Biomarker Discovery

    Related Articles

    Biomarker Discovery:

    Article Title: A universal framework for single-cell multi-omics data integration with graph convolutional networks
    Article Snippet: Integrating these two is not only able to reveal gene regulatory relationships associated with cell heterogeneity but can also increase the power for classification of subcellular population comparing to using only single omics data [ , ].

    Gene Expression:

    Article Title: A universal framework for single-cell multi-omics data integration with graph convolutional networks
    Article Snippet: Integrating these two is not only able to reveal gene regulatory relationships associated with cell heterogeneity but can also increase the power for classification of subcellular population comparing to using only single omics data [ , ].

    Expressing:

    Article Title: A universal framework for single-cell multi-omics data integration with graph convolutional networks
    Article Snippet: Integrating these two is not only able to reveal gene regulatory relationships associated with cell heterogeneity but can also increase the power for classification of subcellular population comparing to using only single omics data [ , ].

    Construct:

    Article Title: A universal framework for single-cell multi-omics data integration with graph convolutional networks
    Article Snippet: Integrating these two is not only able to reveal gene regulatory relationships associated with cell heterogeneity but can also increase the power for classification of subcellular population comparing to using only single omics data [ , ].



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    Image Search Results


    Single‐cell transcriptome landscape in aging cohort. (A) UMAP visualization of cell‐type‐specific annotation among the aging cohort, showing 9 cell groups in different colors. (B) UMAP visualization of immune cell subpopulation annotation across different age groups, displaying 21 subpopulations in different colors. (C) The proportion of 21 different cell types across age groups.

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    Article Title: The Immune Cell Atlas of “Longevity Molecular Tag”: Identification of Principal Immune Cell Subsets and Their Underlying Molecular Regulatory Mechanisms

    doi: 10.1111/acel.70431

    Figure Lengend Snippet: Single‐cell transcriptome landscape in aging cohort. (A) UMAP visualization of cell‐type‐specific annotation among the aging cohort, showing 9 cell groups in different colors. (B) UMAP visualization of immune cell subpopulation annotation across different age groups, displaying 21 subpopulations in different colors. (C) The proportion of 21 different cell types across age groups.

    Article Snippet: Single‐cell transcriptomic data from 56 healthy individuals aged from birth to over 90 years were acquired from the Shanghai Pudong Cohort ( NCT05206643 ) (Synapse: syn61609846) (Wang, Li, et al. ).

    Techniques: Single Cell

    Centenarian phenotype‐associated immune cell type analysis at single‐cell resolution. (A) UMAP visualization of cell‐type‐specific annotation among immune cells, showing 9 cell groups in different colors. (B) UMAP visualization of subcellular annotation among immune cell subpopulations, showing 21 subpopulations in different colors. (C) UMAP visualization of Scissor + and Scissor − cells. (D, E) Proportional fractions of identified cell types across Scissor +/− conditions among extracted immune cells.

    Journal: Aging Cell

    Article Title: The Immune Cell Atlas of “Longevity Molecular Tag”: Identification of Principal Immune Cell Subsets and Their Underlying Molecular Regulatory Mechanisms

    doi: 10.1111/acel.70431

    Figure Lengend Snippet: Centenarian phenotype‐associated immune cell type analysis at single‐cell resolution. (A) UMAP visualization of cell‐type‐specific annotation among immune cells, showing 9 cell groups in different colors. (B) UMAP visualization of subcellular annotation among immune cell subpopulations, showing 21 subpopulations in different colors. (C) UMAP visualization of Scissor + and Scissor − cells. (D, E) Proportional fractions of identified cell types across Scissor +/− conditions among extracted immune cells.

    Article Snippet: Single‐cell transcriptomic data from 56 healthy individuals aged from birth to over 90 years were acquired from the Shanghai Pudong Cohort ( NCT05206643 ) (Synapse: syn61609846) (Wang, Li, et al. ).

    Techniques: Single Cell

    PTHrP expression in IPF and BLM-induced PF in humans. a Procedure for bioinformatics-based transcriptome analysis. b Identification of 714 commonly up- or downregulated genes in human IPF lungs using publicly available transcriptome datasets. c Top 9 activated gene sets identified by KEGG pathway analysis based on 714 common genes. d Identification of 5 genes through the intersection of genes related to soluble mediators, PTH synthesis, secretion, and action and 714 common genes. e Heatmap of PTHLH expression in normal and IPF samples. f PTHLH mRNA in normal and IPF samples. g Representative images of IF staining of PTHrP and quantification of the intensity of expression of PTHrP in human pulmonary interstitial fibrosis tissue microarrays from patients with IPF ( n = 23) and healthy donors ( n = 4). A magnified view of the region highlighted in the red box is shown. Scale bar: 50 μm and 100 μm (low magnification). a , b , d were created with BioRender.com. Data are shown as the mean ± SEM. P values were determined by two-tailed Student’s t test ( f , g ). *** P < 0.001

    Journal: Signal Transduction and Targeted Therapy

    Article Title: Parathyroid hormone–related protein is a therapeutic target in idiopathic pulmonary fibrosis

    doi: 10.1038/s41392-026-02578-8

    Figure Lengend Snippet: PTHrP expression in IPF and BLM-induced PF in humans. a Procedure for bioinformatics-based transcriptome analysis. b Identification of 714 commonly up- or downregulated genes in human IPF lungs using publicly available transcriptome datasets. c Top 9 activated gene sets identified by KEGG pathway analysis based on 714 common genes. d Identification of 5 genes through the intersection of genes related to soluble mediators, PTH synthesis, secretion, and action and 714 common genes. e Heatmap of PTHLH expression in normal and IPF samples. f PTHLH mRNA in normal and IPF samples. g Representative images of IF staining of PTHrP and quantification of the intensity of expression of PTHrP in human pulmonary interstitial fibrosis tissue microarrays from patients with IPF ( n = 23) and healthy donors ( n = 4). A magnified view of the region highlighted in the red box is shown. Scale bar: 50 μm and 100 μm (low magnification). a , b , d were created with BioRender.com. Data are shown as the mean ± SEM. P values were determined by two-tailed Student’s t test ( f , g ). *** P < 0.001

    Article Snippet: To confirm the predominance of tissue-specific expression of PTHLH in human tissues, we reanalyzed publicly available single-cell transcriptome data provided by The Human Protein Atlas.

    Techniques: Expressing, Staining, Two Tailed Test

    Identification of core genes associated with macrophage immune training and heart failure. (A) Schematic overview of human-derived macrophage trained immunity model and transcriptomic profiling workflow ( GSE235897 ). (B) The volcano plot and (C) DEGs heatmap of hMDMs from trained (n=3) and untrained (n=3) samples in the macrophage-trained immunity dataset GSE235897 (|log2FC| ≥ 0.585, p < 0.05). (D) Sample clustering dendrogram of GSE135055 dataset based on gene expression profiles. (E) Scale-free topology fit index and (F) mean connectivity analysis across a range of soft-thresholding powers. (G) Cluster dendrogram of genes showing co-expression modules identified by WGCNA in database GSE135055 . (H) Module-trait heatmap values represent correlation coefficients between healthy controls and HF samples (* p < 0.05, ** p < 0.01). (I) Venn diagram showing the overlap among heart failure DEGs, trained-immunity DEGs, and WGCNA module genes.

    Journal: Frontiers in Immunology

    Article Title: Identification of MTURN as a trained immunity-related biomarker for heart failure via integrative transcriptomic machine learning analysis and experimental validation

    doi: 10.3389/fimmu.2026.1739660

    Figure Lengend Snippet: Identification of core genes associated with macrophage immune training and heart failure. (A) Schematic overview of human-derived macrophage trained immunity model and transcriptomic profiling workflow ( GSE235897 ). (B) The volcano plot and (C) DEGs heatmap of hMDMs from trained (n=3) and untrained (n=3) samples in the macrophage-trained immunity dataset GSE235897 (|log2FC| ≥ 0.585, p < 0.05). (D) Sample clustering dendrogram of GSE135055 dataset based on gene expression profiles. (E) Scale-free topology fit index and (F) mean connectivity analysis across a range of soft-thresholding powers. (G) Cluster dendrogram of genes showing co-expression modules identified by WGCNA in database GSE135055 . (H) Module-trait heatmap values represent correlation coefficients between healthy controls and HF samples (* p < 0.05, ** p < 0.01). (I) Venn diagram showing the overlap among heart failure DEGs, trained-immunity DEGs, and WGCNA module genes.

    Article Snippet: For single-cell transcriptomic data, we accessed the SCP1303 project from the Broad Institute ( https://singlecell.broadinstitute.org/single_cell ), which includes raw scRNA-seq data from failing human hearts with dilated and hypertrophic cardiomyopathy.

    Techniques: Derivative Assay, Gene Expression, Expressing

    Five heart failure transcriptomic datasets were integrated with a macrophage-trained immunity model to identify immune-related biomarkers. Through DEGs analysis, WGCNA, CIBERSORT, and six machine learning algorithms, hub genes were prioritized with MTURN emerging as the top candidate. Its potential was further validated by scRNA-seq analysis, which confirmed MTURN enrichment in cardiac macrophages. Finally, MTURN expression was validated using previously published heart failure transcriptomic data and in vitro experiments.

    Journal: Frontiers in Immunology

    Article Title: Identification of MTURN as a trained immunity-related biomarker for heart failure via integrative transcriptomic machine learning analysis and experimental validation

    doi: 10.3389/fimmu.2026.1739660

    Figure Lengend Snippet: Five heart failure transcriptomic datasets were integrated with a macrophage-trained immunity model to identify immune-related biomarkers. Through DEGs analysis, WGCNA, CIBERSORT, and six machine learning algorithms, hub genes were prioritized with MTURN emerging as the top candidate. Its potential was further validated by scRNA-seq analysis, which confirmed MTURN enrichment in cardiac macrophages. Finally, MTURN expression was validated using previously published heart failure transcriptomic data and in vitro experiments.

    Article Snippet: For single-cell transcriptomic data, we accessed the SCP1303 project from the Broad Institute ( https://singlecell.broadinstitute.org/single_cell ), which includes raw scRNA-seq data from failing human hearts with dilated and hypertrophic cardiomyopathy.

    Techniques: Expressing, In Vitro

    Platelet-Driven CAF Activation and ECM Barriers in the Tumor Microenvironment. A. Expression levels of TGFB1 and PDGFB were assessed using pan-tissue single-cell RNA-sequencing data from the Human Protein Atlas. Normalized counts (nCPM) were aggregated at the cell-type level. Among all surveyed human cell types, platelets showed the highest expression of TGFB1 and were among the top expressors of PDGFB, highlighting their distinct capacity as a concentrated source of these exclusion-related factors. B. Activated platelets engage CAFs via CLEC-2–podoplanin interaction and release TGF-β, PDGF, and SDF-1, inducing fibroblast, epithelial cell, and MSC differentiation into CAFs. MSCs activate platelets via PAF, forming a feedback loop. CAFs (α-SMA/FAP + ) remodel the ECM and promote desmoplasia, creating a barrier to T cell infiltration and sustaining immune suppression in the TME. Abbreviations: CAF: Cancer-Associated Fibroblast, CLEC-2: C-type Lectin-like Receptor 2, PDPN: Podoplanin, TGF-β: Transforming Growth Factor Beta, PDGF: Platelet-Derived Growth Factor, SDF-1: Stromal Cell-Derived Factor 1, MSC: Mesenchymal Stem Cell, PAF: Platelet-Activating Factor, α-SMA: Alpha-Smooth Muscle Actin, FAP: Fibroblast Activation Protein, ECM: Extracellular Matrix, TME: Tumor Microenvironment

    Journal: Cellular Oncology (Dordrecht, Netherlands)

    Article Title: Platelets in the tumor microenvironment: potential mediators of immune exclusion and resistance to immune checkpoint inhibitor therapy

    doi: 10.1007/s13402-025-01129-7

    Figure Lengend Snippet: Platelet-Driven CAF Activation and ECM Barriers in the Tumor Microenvironment. A. Expression levels of TGFB1 and PDGFB were assessed using pan-tissue single-cell RNA-sequencing data from the Human Protein Atlas. Normalized counts (nCPM) were aggregated at the cell-type level. Among all surveyed human cell types, platelets showed the highest expression of TGFB1 and were among the top expressors of PDGFB, highlighting their distinct capacity as a concentrated source of these exclusion-related factors. B. Activated platelets engage CAFs via CLEC-2–podoplanin interaction and release TGF-β, PDGF, and SDF-1, inducing fibroblast, epithelial cell, and MSC differentiation into CAFs. MSCs activate platelets via PAF, forming a feedback loop. CAFs (α-SMA/FAP + ) remodel the ECM and promote desmoplasia, creating a barrier to T cell infiltration and sustaining immune suppression in the TME. Abbreviations: CAF: Cancer-Associated Fibroblast, CLEC-2: C-type Lectin-like Receptor 2, PDPN: Podoplanin, TGF-β: Transforming Growth Factor Beta, PDGF: Platelet-Derived Growth Factor, SDF-1: Stromal Cell-Derived Factor 1, MSC: Mesenchymal Stem Cell, PAF: Platelet-Activating Factor, α-SMA: Alpha-Smooth Muscle Actin, FAP: Fibroblast Activation Protein, ECM: Extracellular Matrix, TME: Tumor Microenvironment

    Article Snippet: Our analysis of pan-tissue single-cell transcriptomic data from the Human Protein Atlas identifies platelets as a dominant cellular source of TGF-β and among the highest expressors of PDGFB across human cell types (Fig. A), providing a strong molecular basis for their impact on CAF activation and differentiation.

    Techniques: Activation Assay, Expressing, RNA Sequencing, Derivative Assay