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comparative marker selection module genepattern  (Broad Institute Inc)

 
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    Broad Institute Inc comparative marker selection module genepattern
    Comparative Marker Selection Module Genepattern, supplied by Broad Institute Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/comparative+marker+selection+module+genepattern/genepattern+comparative+marker+selection+module/pm36831500-53-11-16
    Average 90 stars, based on 1 article reviews
    comparative marker selection module genepattern - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    Quantitative Proteomics:

    Article Title: Body Composition and Metabolic Dysfunction Really Matter for the Achievement of Better Outcomes in High-Grade Serous Ovarian Cancer.
    Article Snippet: .. Differential expression analyses were carried out using raw counts and the comparative marker selection module (genepattern, Broad Institute, Cambridge, MA, USA [18]) to calculate the significant differences in gene expression between classes. .. Gene set enrichment analysis (GSEA), gene ontology (GO), hallmarks, KEGG pathways enrichment, cell states, and analysis of cellular communities in TCGA-OV patients were carried out using GSEA 4.3.2 (Broad Institute, Cambridge, MA, USA [19]), ShinyGO 0.76.2 (South Dakota State University, Brookings, SD, USA [20]), and Ecotyper (Stanford University, Stanford, CA, USA [21]).

    Marker:

    Article Title: Body Composition and Metabolic Dysfunction Really Matter for the Achievement of Better Outcomes in High-Grade Serous Ovarian Cancer.
    Article Snippet: .. Differential expression analyses were carried out using raw counts and the comparative marker selection module (genepattern, Broad Institute, Cambridge, MA, USA [18]) to calculate the significant differences in gene expression between classes. .. Gene set enrichment analysis (GSEA), gene ontology (GO), hallmarks, KEGG pathways enrichment, cell states, and analysis of cellular communities in TCGA-OV patients were carried out using GSEA 4.3.2 (Broad Institute, Cambridge, MA, USA [19]), ShinyGO 0.76.2 (South Dakota State University, Brookings, SD, USA [20]), and Ecotyper (Stanford University, Stanford, CA, USA [21]).

    Selection:

    Article Title: Body Composition and Metabolic Dysfunction Really Matter for the Achievement of Better Outcomes in High-Grade Serous Ovarian Cancer.
    Article Snippet: .. Differential expression analyses were carried out using raw counts and the comparative marker selection module (genepattern, Broad Institute, Cambridge, MA, USA [18]) to calculate the significant differences in gene expression between classes. .. Gene set enrichment analysis (GSEA), gene ontology (GO), hallmarks, KEGG pathways enrichment, cell states, and analysis of cellular communities in TCGA-OV patients were carried out using GSEA 4.3.2 (Broad Institute, Cambridge, MA, USA [19]), ShinyGO 0.76.2 (South Dakota State University, Brookings, SD, USA [20]), and Ecotyper (Stanford University, Stanford, CA, USA [21]).

    Gene Expression:

    Article Title: Body Composition and Metabolic Dysfunction Really Matter for the Achievement of Better Outcomes in High-Grade Serous Ovarian Cancer.
    Article Snippet: .. Differential expression analyses were carried out using raw counts and the comparative marker selection module (genepattern, Broad Institute, Cambridge, MA, USA [18]) to calculate the significant differences in gene expression between classes. .. Gene set enrichment analysis (GSEA), gene ontology (GO), hallmarks, KEGG pathways enrichment, cell states, and analysis of cellular communities in TCGA-OV patients were carried out using GSEA 4.3.2 (Broad Institute, Cambridge, MA, USA [19]), ShinyGO 0.76.2 (South Dakota State University, Brookings, SD, USA [20]), and Ecotyper (Stanford University, Stanford, CA, USA [21]).



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    Comparative clinical characteristics of the PUC and TCGA-OV cohorts.

    Journal: Cancers

    Article Title: Body Composition and Metabolic Dysfunction Really Matter for the Achievement of Better Outcomes in High-Grade Serous Ovarian Cancer

    doi: 10.3390/cancers15041156

    Figure Lengend Snippet: Comparative clinical characteristics of the PUC and TCGA-OV cohorts.

    Article Snippet: Differential expression analyses were carried out using raw counts and the comparative marker selection module (genepattern, Broad Institute, Cambridge, MA, USA [ ]) to calculate the significant differences in gene expression between classes.

    Techniques: Imaging

    Comparative progression-free (PFS; A ) and overall survival (OS; B ) curves, prevalence of different TCGA-OV molecular subtypes ( C ), tumor microenvironment (TME) subtypes (desert [D], fibrotic [F], immune-enriched non-fibrotic [IE], and immune-enriched fibrotic [IE/F]; ( D ), immune subtypes ( E ), tumor mutation burden level (TMB; F ), stemness subtype ( G ), ESTIMATE score ( H ), and prevalence of carcinoma ecotypes (CE, I ) and plasma cell (PC) states (S, J ) according to obesity and lipid metabolism clustering using RNA-seq data from the TCGA-OV cohort.

    Journal: Cancers

    Article Title: Body Composition and Metabolic Dysfunction Really Matter for the Achievement of Better Outcomes in High-Grade Serous Ovarian Cancer

    doi: 10.3390/cancers15041156

    Figure Lengend Snippet: Comparative progression-free (PFS; A ) and overall survival (OS; B ) curves, prevalence of different TCGA-OV molecular subtypes ( C ), tumor microenvironment (TME) subtypes (desert [D], fibrotic [F], immune-enriched non-fibrotic [IE], and immune-enriched fibrotic [IE/F]; ( D ), immune subtypes ( E ), tumor mutation burden level (TMB; F ), stemness subtype ( G ), ESTIMATE score ( H ), and prevalence of carcinoma ecotypes (CE, I ) and plasma cell (PC) states (S, J ) according to obesity and lipid metabolism clustering using RNA-seq data from the TCGA-OV cohort.

    Article Snippet: Differential expression analyses were carried out using raw counts and the comparative marker selection module (genepattern, Broad Institute, Cambridge, MA, USA [ ]) to calculate the significant differences in gene expression between classes.

    Techniques: Mutagenesis, Clinical Proteomics, RNA Sequencing