T-profiler: scoring the activity of predefined groups of genes using gene expression data

Abstract

One of the key challenges in the analysis of gene expression data is how to relate the expression level of individual genes to the underlying transcriptional programs and cellular state. Here we describe T-profiler, a tool that uses the t-test to score changes in the average activity of predefined groups of genes. The gene groups are defined based on Gene Ontology categorization, ChIP-chip experiments, upstream matches to a consensus transcription factor binding motif or location on the same chromosome. If desired, an iterative procedure can be used to select a single, optimal representative from sets of overlapping gene groups. T-profiler makes it possible to interpret microarray data in a way that is both intuitive and statistically rigorous, without the need to combine experiments or choose parameters. Currently, gene expression data from Saccharomyces cerevisiae and Candida albicans are supported. Users can upload their microarray data for analysis on the web at http://www.t-profiler.org.

Figure 1

Screenshots of the various T-profiler analysis results. (A) Statistics of the uploaded gene expression dataset for cells assayed 80 min after the temperature shift from 30 to 37°C (23). The type of analysis can be selected from the panels to the right. (B) Scoring consensus motifs. Only significantly scoring motifs are shown (E-value < 0.05). By selecting the motifs in the left column, information about the genes containing this motif and their expression levels can be obtained. (C) Scoring GO categories. Only a subset of the 50 significantly changed categories is shown. (D) Scoring ChIP-chip based gene groups. (E) Graph showing the t-value for each chromosome, obtained from the gene expression profile of the mutant pfd2Δ, in which chromosome 14 is duplicated. (F) The same result as in (C), but now with redundant gene groups removed by our iterative procedure.