Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods

By Leif Väremo, Jens Nielsen, Intawat Nookaew. Nucleic Acids Research, 2013

671 citations recorded by the source corpus.

Gene set analysis (GSA) is used to elucidate genome-wide data, in particular transcriptome data. A multitude of methods have been proposed for this step of the analysis, and many of them have been compared and evaluated. Unfortunately, there is no consolidated opinion regarding what methods should be preferred, and the variety of available GSA software and implementations pose a difficulty for the end-user who wants to try out different methods. To address this, we have developed the R package Piano that collects a range of GSA methods into the same system, for the benefit of the end-user. Further on we refine the GSA workflow by using modifications of the gene-level statistics. This enables us to divide the resulting gene set P-values into three classes, describing different aspects of gene expression directionality at gene set level. We use our fully implemented workflow to investigate the impact of the individual components of GSA by using microarray and RNA-seq data. The results show that the evaluated methods are globally similar and the major separation correlates well with our defined directionality classes. As a consequence of this, we suggest to use a consensus scoring approach, based on multiple GSA runs. In combination with the directionality classes, this constitutes a more thorough basis for an enriched biological interpretation.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1c8ee48daf08249bcf978788ff04a11e07f55639

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Citations in the corpus

Expression Atlas update—an integrated database of gene and protein expression in humans, animals and plants (2015, methodology): “For example, the overlap between the set of differentially expressed genes in each Atlas comparison, and Reactome and Plant Reactome pathways, GO terms and InterPro domains is now assessed using Fisher’s test with multiple testing correction (16).”
A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility. (2018, methodology): “We used Piano package (Väremo et al., 2013) for performing gene annotation enrichment analyses on following ontologies down- loaded from http://download.baderlab.org/EM_Genesets/June_20_2014/Mouse/symbol/: all pathways: Mouse_AllPathways_June_20_2014_symbol.gmt GO biological process:…”
Interplay between the human gut microbiome and host metabolism (2019, methodology): “Enrichment P values were evaluated using the parametric analysis of gene set enrichment (PAGE) algorithm [39] using 10,000 random permutations as implemented in the piano R package [40] (v 1.”
Reversed graph embedding resolves complex single-cell developmental trajectories (2017, methodology): “To obtain the enriched GO BP terms for the Figure 1D, we perform a hypergeometric test on the corresponding Gene Matrix Transposed file format (GMT) file for each cluster of genes based on the piano package [22].”
Classification of low quality cells from single-cell RNA-seq data (2016, methodology): “In addition, we determined differentially expressed genes using both the DESeq [32] and Piano [51] package available on Bioconductor.”