We are analyzing https://link.springer.com/article/10.1186/1471-2105-14-7.

Title:
GSVA: gene set variation analysis for microarray and RNA-Seq data | BMC Bioinformatics
Description:
Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org .
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28 years and 1 months (reg. 1997-05-29).

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Keywords {🔍}

gene, data, set, genes, expression, sets, pubmed, article, google, scholar, gsva, methods, analysis, enrichment, sample, figure, samples, gse, microarray, pathway, cas, rnaseq, scores, central, method, values, survival, power, bioinformatics, model, cancer, statistic, test, distribution, activity, file, differential, simulated, size, statistical, zscore, pmid, show, expressed, pathways, groups, simulation, authors, httpwwwncbinlmnihgovpubmed, variation,

Topics {✒️}

org/packages/release/bioc/html/gsva /nature/journal/v462/n7269/abs/nature08460 /nature/journal/v449/n7164/abs/nature06258 /science/article/b6wsn-4y3tdsf pgc-1alpha-responsive genes involved cross-species gene-expression analysis open access article gov/pmc/articles/pmc1261155/] adjusted rand index gov/pmc/articles/pmc2615629/] genome-wide expression profiles gov/pmc/articles/pmc387275/] open-ended biological analysis article download pdf org/content/39/suppl_1/d945 analogous gene-specific biases pre-clinical model alignment genome-wide significance level x-linked gene expression simple gene-level model robust multi-array average rna-seq data corresponded evaluating sample-wise enrichment ranked expression-level statistic gene-specific bandwidth parameter rna-seq data consists female-specific gene set build pathway-centric models org/content/early/2012/01/24/biostatistics resulting sequence-based measurements linear additive model individual gene z-scores combined z-score approaches gene-set activation measurement male-specific gene set rna-seq data processing article hänzelmann integrated genomic analyses obtain corrected p-values sample-wise gse methods robert castelo remove low-quality samples gov/pubmed/20610611] gov/pubmed/21900207] gov/pubmed/17303618] gov/pubmed/21720365] gov/pubmed/16936753] gov/pubmed/20132535] gov/pubmed/17537913] gov/pubmed/15772666]

Schema {🗺️}

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