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cell transcriptomic data  (Broad Clinical Labs)


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    Broad Clinical Labs cell transcriptomic data
    Identification of core genes associated with macrophage immune training and heart failure. (A) Schematic overview of human-derived macrophage trained immunity model and <t>transcriptomic</t> 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.
    Cell Transcriptomic Data, supplied by Broad Clinical Labs, used in various techniques. Bioz Stars score: 96/100, based on 896 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/transcriptome+sequencing+data/Single+Cell+Sequencing/pmc12957145-38-1-11
    Average 96 stars, based on 896 article reviews
    cell transcriptomic data - by Bioz Stars, 2026-10
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    1) Product Images from "Identification of MTURN as a trained immunity-related biomarker for heart failure via integrative transcriptomic machine learning analysis and experimental validation"

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

    Journal: Frontiers in Immunology

    doi: 10.3389/fimmu.2026.1739660

    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.
    Figure Legend 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.

    Techniques Used: 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.
    Figure Legend 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.

    Techniques Used: Expressing, In Vitro

    Related Articles

    RNA Sequencing:

    Article Title: Integrative computational analysis of public fecal lipidomics and transcriptomics datasets suggests a candidate association between the COX-2 pathway and CE(20:4) in colorectal adenoma-carcinoma progression
    Article Snippet: data for the TCGA-COAD project can be found at the Genomic Data Commons (GDC) Data Portal. The high-resolution single-cell RNA sequencing dataset (Human Colon Cancer Atlas, c295) is accessible at the Broad Institute Single Cell Portal. All analytical R scripts are openly available on GitHub at: https://github.com/bingmoon/FL-MRS_Trajectory , and an archived version is deposited on Zenodo.

    Sequencing:

    Article Title: Integrative computational analysis of public fecal lipidomics and transcriptomics datasets suggests a candidate association between the COX-2 pathway and CE(20:4) in colorectal adenoma-carcinoma progression
    Article Snippet: data for the TCGA-COAD project can be found at the Genomic Data Commons (GDC) Data Portal. The high-resolution single-cell RNA sequencing dataset (Human Colon Cancer Atlas, c295) is accessible at the Broad Institute Single Cell Portal. All analytical R scripts are openly available on GitHub at: https://github.com/bingmoon/FL-MRS_Trajectory , and an archived version is deposited on Zenodo.

    Single Cell:

    Article Title: Integrative computational analysis of public fecal lipidomics and transcriptomics datasets suggests a candidate association between the COX-2 pathway and CE(20:4) in colorectal adenoma-carcinoma progression
    Article Snippet: data for the TCGA-COAD project can be found at the Genomic Data Commons (GDC) Data Portal. The high-resolution single-cell RNA sequencing dataset (Human Colon Cancer Atlas, c295) is accessible at the Broad Institute Single Cell Portal. All analytical R scripts are openly available on GitHub at: https://github.com/bingmoon/FL-MRS_Trajectory , and an archived version is deposited on Zenodo.

    Gene Expression:

    Article Title: Integrative computational analysis of public fecal lipidomics and transcriptomics datasets suggests a candidate association between the COX-2 pathway and CE(20:4) in colorectal adenoma-carcinoma progression
    Article Snippet: data for the TCGA-COAD project can be found at the Genomic Data Commons (GDC) Data Portal. The high-resolution single-cell RNA sequencing dataset (Human Colon Cancer Atlas, c295) is accessible at the Broad Institute Single Cell Portal. All analytical R scripts are openly available on GitHub at: https://github.com/bingmoon/FL-MRS_Trajectory , and an archived version is deposited on Zenodo.

    Expressing:

    Article Title: Integrative computational analysis of public fecal lipidomics and transcriptomics datasets suggests a candidate association between the COX-2 pathway and CE(20:4) in colorectal adenoma-carcinoma progression
    Article Snippet: data for the TCGA-COAD project can be found at the Genomic Data Commons (GDC) Data Portal. The high-resolution single-cell RNA sequencing dataset (Human Colon Cancer Atlas, c295) is accessible at the Broad Institute Single Cell Portal. All analytical R scripts are openly available on GitHub at: https://github.com/bingmoon/FL-MRS_Trajectory , and an archived version is deposited on Zenodo.

    Infection:

    Article Title: Integrative computational analysis of public fecal lipidomics and transcriptomics datasets suggests a candidate association between the COX-2 pathway and CE(20:4) in colorectal adenoma-carcinoma progression
    Article Snippet: data for the TCGA-COAD project can be found at the Genomic Data Commons (GDC) Data Portal. The high-resolution single-cell RNA sequencing dataset (Human Colon Cancer Atlas, c295) is accessible at the Broad Institute Single Cell Portal. All analytical R scripts are openly available on GitHub at: https://github.com/bingmoon/FL-MRS_Trajectory , and an archived version is deposited on Zenodo.

    Isolation:

    Article Title: Integrative computational analysis of public fecal lipidomics and transcriptomics datasets suggests a candidate association between the COX-2 pathway and CE(20:4) in colorectal adenoma-carcinoma progression
    Article Snippet: data for the TCGA-COAD project can be found at the Genomic Data Commons (GDC) Data Portal. The high-resolution single-cell RNA sequencing dataset (Human Colon Cancer Atlas, c295) is accessible at the Broad Institute Single Cell Portal. All analytical R scripts are openly available on GitHub at: https://github.com/bingmoon/FL-MRS_Trajectory , and an archived version is deposited on Zenodo.

    Software:

    Article Title: Integrative computational analysis of public fecal lipidomics and transcriptomics datasets suggests a candidate association between the COX-2 pathway and CE(20:4) in colorectal adenoma-carcinoma progression
    Article Snippet: data for the TCGA-COAD project can be found at the Genomic Data Commons (GDC) Data Portal. The high-resolution single-cell RNA sequencing dataset (Human Colon Cancer Atlas, c295) is accessible at the Broad Institute Single Cell Portal. All analytical R scripts are openly available on GitHub at: https://github.com/bingmoon/FL-MRS_Trajectory , and an archived version is deposited on Zenodo.

    Generated:

    Article Title: Integrative computational analysis of public fecal lipidomics and transcriptomics datasets suggests a candidate association between the COX-2 pathway and CE(20:4) in colorectal adenoma-carcinoma progression
    Article Snippet: data for the TCGA-COAD project can be found at the Genomic Data Commons (GDC) Data Portal. The high-resolution single-cell RNA sequencing dataset (Human Colon Cancer Atlas, c295) is accessible at the Broad Institute Single Cell Portal. All analytical R scripts are openly available on GitHub at: https://github.com/bingmoon/FL-MRS_Trajectory , and an archived version is deposited on Zenodo.

    Transcriptomics:

    Article Title: Integrative computational analysis of public fecal lipidomics and transcriptomics datasets suggests a candidate association between the COX-2 pathway and CE(20:4) in colorectal adenoma-carcinoma progression
    Article Snippet: data for the TCGA-COAD project can be found at the Genomic Data Commons (GDC) Data Portal. The high-resolution single-cell RNA sequencing dataset (Human Colon Cancer Atlas, c295) is accessible at the Broad Institute Single Cell Portal. All analytical R scripts are openly available on GitHub at: https://github.com/bingmoon/FL-MRS_Trajectory , and an archived version is deposited on Zenodo.

    Spatial Transcriptomics:

    Article Title: Integrative computational analysis of public fecal lipidomics and transcriptomics datasets suggests a candidate association between the COX-2 pathway and CE(20:4) in colorectal adenoma-carcinoma progression
    Article Snippet: data for the TCGA-COAD project can be found at the Genomic Data Commons (GDC) Data Portal. The high-resolution single-cell RNA sequencing dataset (Human Colon Cancer Atlas, c295) is accessible at the Broad Institute Single Cell Portal. All analytical R scripts are openly available on GitHub at: https://github.com/bingmoon/FL-MRS_Trajectory , and an archived version is deposited on Zenodo.

    Labeling:

    Article Title: Integrative computational analysis of public fecal lipidomics and transcriptomics datasets suggests a candidate association between the COX-2 pathway and CE(20:4) in colorectal adenoma-carcinoma progression
    Article Snippet: data for the TCGA-COAD project can be found at the Genomic Data Commons (GDC) Data Portal. The high-resolution single-cell RNA sequencing dataset (Human Colon Cancer Atlas, c295) is accessible at the Broad Institute Single Cell Portal. All analytical R scripts are openly available on GitHub at: https://github.com/bingmoon/FL-MRS_Trajectory , and an archived version is deposited on Zenodo.

    Comparison:

    Article Title: Integrative computational analysis of public fecal lipidomics and transcriptomics datasets suggests a candidate association between the COX-2 pathway and CE(20:4) in colorectal adenoma-carcinoma progression
    Article Snippet: data for the TCGA-COAD project can be found at the Genomic Data Commons (GDC) Data Portal. The high-resolution single-cell RNA sequencing dataset (Human Colon Cancer Atlas, c295) is accessible at the Broad Institute Single Cell Portal. All analytical R scripts are openly available on GitHub at: https://github.com/bingmoon/FL-MRS_Trajectory , and an archived version is deposited on Zenodo.



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    Broad Clinical Labs cell transcriptomic data
    Identification of core genes associated with macrophage immune training and heart failure. (A) Schematic overview of human-derived macrophage trained immunity model and <t>transcriptomic</t> 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.
    Cell Transcriptomic Data, supplied by Broad Clinical Labs, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/transcriptome+sequencing+data/Single+Cell+Sequencing/pmc12957145-38-1-11
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    cell transcriptomic data - by Bioz Stars, 2026-10
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    Image Search Results


    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