Principle Angle Enrichment Analysis (PAEA): Dimensionally reduced multivariate gene set enrichment analysis tool

Abstract

Gene set analysis of differential expression, which identifies collectively differentially expressed gene sets, has become an important tool for biology. The power of this approach lies in its reduction of the dimensionality of the statistical problem and its incorporation of biological interpretation by construction. Many approaches to gene set analysis have been proposed, but benchmarking their performance in the setting of real biological data is difficult due to the lack of a gold standard. In a previously published work we proposed a geometrical approach to differential expression which performed highly in benchmarking tests and compared well to the most popular methods of differential gene expression. As reported, this approach has a natural extension to gene set analysis which we call Principal Angle Enrichment Analysis (PAEA). PAEA employs dimensionality reduction and a multivariate approach for gene set enrichment analysis. However, the performance of this method has not been assessed nor its implementation as a web-based tool. Here we describe new benchmarking protocols for gene set analysis methods and find that PAEA performs highly. The PAEA method is implemented as a user-friendly web-based tool, which contains 70 gene set libraries and is freely available to the community.

Drug-induced adverse events prediction with the LINCS L1000 data

“…A signature for a compound is defined as a vector of continuous values, each representing the direction and magnitude of differential expression between control samples and compound-treated samples. The Characteristic Direction (CD) method (Clark et al, 2014) was used to compute GE signatures for drug perturbations using only the 978 directly measured landmark genes, as well as all genes both measured and imputed. The CD signatures in the space of 978 landmark genes are used as GE signatures for drugs to predict ADRs.…”

“…The CD signatures in the space of 978 landmark genes are used as GE signatures for drugs to predict ADRs. A geometric extension of CD called Principal Angle Enrichment Analysis (PAEA) (Clark et al, 2015) was used to compute enrichment p-values for each CD signature in the space of all genes against gene sets created from the Gene Ontology (GO) including Biological Processes, Cellular Components and Molecular Functions and other gene set libraries available from the Enrichr tool (Chen et al, 2013). The cell morphological (MC) profiles were downloaded from the MLPCN project website (Wawer et al, 2014).…”

MEACA: efficient gene-set interpretation of expression data using mixed models

“…Finally, we note that by letting ∆ i = β 0 + β 1 G i + ψ i , equation (8) is equivalent to model (1) whose mean and variance are given by equations (9) and (10). The random effects ψ i 's capture the heterogeneity of the DE effects that are conditional on whether gene i belongs to the test set (G i = 1) or not (G i = 0).…”

“…We first consider the less interesting case with uncorrelated genes, in which C equals I, an m-dimensional identity matrix. Under the quasi-likelihood model for U given in equations (9) and (10), the quasi-score statistic for β 1 has the form S ∝ G T (U −β 0 1 m ), whereβ 0 = U is an estimate for β 0 and 1 m is a m-dimensional vector of 1's. To perform a quasi-score test, one would divide S 2 by its estimated variance under H 0 and the assumption that C = I.…”

“…Depending on the null hypothesis that is tested, there are two types of gene-set tests [3]: self-contained tests and competitive tests (also called enrichment tests in some literature). A self-contained test examines the DE signals of genes in the test set without reference to other genes in the genome, with the null being that no genes in the test set are differentially expressed [4,5,6,7,8,9]. A competitive test compares DE signals of genes in the test set to those of the genes not in the test set, trying to detect whether the former are more abundant and/or profound than the latter [10,11,12].…”