Dataset Information

Improving Gene-Set Enrichment Analysis of RNA-Seq Data with Small Replicates.

ABSTRACT: Deregulated pathways identified from transcriptome data of two sample groups have played a key role in many genomic studies. Gene-set enrichment analysis (GSEA) has been commonly used for pathway or functional analysis of microarray data, and it is also being applied to RNA-seq data. However, most RNA-seq data so far have only small replicates. This enforces to apply the gene-permuting GSEA method (or preranked GSEA) which results in a great number of false positives due to the inter-gene correlation in each gene-set. We demonstrate that incorporating the absolute gene statistic in one-tailed GSEA considerably improves the false-positive control and the overall discriminatory ability of the gene-permuting GSEA methods for RNA-seq data. To test the performance, a simulation method to generate correlated read counts within a gene-set was newly developed, and a dozen of currently available RNA-seq enrichment analysis methods were compared, where the proposed methods outperformed others that do not account for the inter-gene correlation. Analysis of real RNA-seq data also supported the proposed methods in terms of false positive control, ranks of true positives and biological relevance. An efficient R package (AbsFilterGSEA) coded with C++ (Rcpp) is available from CRAN.

SUBMITTER: Yoon S

PROVIDER: S-EPMC5102490 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Improving Gene-Set Enrichment Analysis of RNA-Seq Data with Small Replicates.

Yoon Sora S Kim Seon-Young SY Nam Dougu D

PloS one 20161109 11

Deregulated pathways identified from transcriptome data of two sample groups have played a key role in many genomic studies. Gene-set enrichment analysis (GSEA) has been commonly used for pathway or functional analysis of microarray data, and it is also being applied to RNA-seq data. However, most RNA-seq data so far have only small replicates. This enforces to apply the gene-permuting GSEA method (or preranked GSEA) which results in a great number of false positives due to the inter-gene correl ...[more]

PMID: 27829002

Similar Datasets

Project description:To infer the biological meaning from transcriptome data, it is useful to focus on genes that are regulated by the same regulator, i.e., regulons. Unfortunately, current gene set enrichment analysis (GSEA) tools do not consider whether a gene is activated or repressed by a regulator. This distinction is crucial when analyzing regulons since a regulator can work as an activator of certain genes and as a repressor of other genes, yet both sets of genes belong to the same regulon. Therefore, simply averaging expression differences of the genes of such a regulon will not properly reflect the activity of the regulator. What makes it more complicated is the fact that many genes are regulated by different transcription factors, and current transcriptome analysis tools are unable to indicate which regulator is most likely responsible for the observed expression difference of a gene. To address these challenges, we developed the gene set enrichment analysis program GINtool. Additional features of GINtool are novel graphical representations to facilitate the visualization of gene set analyses of transcriptome data, the possibility to include functional categories as gene sets for analysis, and the option to analyze expression differences within operons, which is useful when analyzing prokaryotic transcriptome and also proteome data.IMPORTANCEMeasuring the activity of all genes in cells is a common way to elucidate the function and regulation of genes. These transcriptome analyses produce large amounts of data since genomes contain thousands of genes. The analysis of these large data sets is challenging. Therefore, we developed a new software tool called GINtool that can facilitate the analysis of transcriptome data by using prior knowledge of gene sets controlled by the same regulator, the so-called regulons. An important novelty of GINtool is that it can take into account the directionality of gene regulation in these analyses, i.e., whether a gene is activated or repressed, which is crucial to assess whether a regulon or functional category is affected. GINtool also includes new graphical methods to facilitate the visual inspection of regulation events in transcriptome data sets. These and additional analysis methods included in GINtool make it a powerful software tool to analyze transcriptome data.

Dataset Information

Improving Gene-Set Enrichment Analysis of RNA-Seq Data with Small Replicates.

Publications

Improving Gene-Set Enrichment Analysis of RNA-Seq Data with Small Replicates.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets