As gene expression measurement technology is shifting from microarrays to sequencing, the statistical tools available for their analysis must be adapted since RNA-seq data are measured as counts. It has been proposed to model RNA-seq counts as continuous variables using ...
Read More »SeqGSA – gene set analysis with length bias adjustment for RNA-seq data
In gene set analysis, the researchers are interested in determining the gene sets that are significantly correlated with an outcome, e.g. disease status or treatment. With the rapid development of high throughput sequencing technologies, Ribonucleic acid sequencing (RNA-seq) has become ...
Read More »gsa4mirna – Integrated gene set analysis for microRNA studies
Functional interpretation of miRNA expression data is currently done in a three step procedure: select differentially expressed miRNAs, find their target genes, and carry out gene set overrepresentation analysis Nevertheless, major limitations of this approach have already been described at ...
Read More »GeneAnalytics – An Integrative Gene Set Analysis Tool for Next Generation Sequencing, RNAseq and Microarray Data
Postgenomics data are produced in large volumes by life sciences and clinical applications of novel omics diagnostics and therapeutics for precision medicine. To move from “data-to-knowledge-to-innovation,” a crucial missing step in the current era is, however, our limited understanding of ...
Read More »PAGODA – Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis
The transcriptional state of a cell reflects a variety of biological factors, from cell-type-specific features to transient processes such as the cell cycle, all of which may be of interest. However, identifying such aspects from noisy single-cell RNA-seq data remains ...
Read More »Gene set analysis approaches for RNA-seq data – performance evaluation and application guideline
Transcriptome sequencing (RNA-seq) is gradually replacing microarrays for high-throughput studies of gene expression. The main challenge of analyzing microarray data is not in finding differentially expressed genes, but in gaining insights into the biological processes underlying phenotypic differences. To interpret ...
Read More »Comparative evaluation of gene set analysis approaches for RNA-Seq data
Over the last few years transcriptome sequencing (RNA-Seq) has almost completely taken over microarrays for high-throughput studies of gene expression. Currently, the most popular use of RNA-Seq is to identify genes which are differentially expressed between two or more conditions. ...
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