Abstract
Transcriptomic studies routinely measure expression levels across numerous conditions. These datasets allow identification of genes that are specifically expressed in a small number of conditions. However, there are currently no statistically robust methods for identifying such genes. Here we present SpeCond, a method to detect condition-specific genes that outperforms alternative approaches. We apply the method to a dataset of 32 human tissues to determine 2,673 specifically expressed genes. An implementation of SpeCond is freely available as a Bioconductor package at http://www.bioconductor.org/packages/release/bioc/html/SpeCond.html.
Highlights
Cells sharing the same genomic information are able to express it in different ways to achieve cell-specific functions or respond to different environmental changes
After repeating the procedure for every gene in the dataset, SpeCond corrects all P-values for multiple testing
Detecting tissue specificity across the human genome To demonstrate the use of our method, we examined the tissue-specific gene set returned by SpeCond when applied to the Genomics Institute of the Novartis Research Foundation (GNF) dataset
Summary
Cells sharing the same genomic information are able to express it in different ways to achieve cell-specific functions or respond to different environmental changes. The first category contains genes that are expressed in most tissues at similar levels and they are thought to provide core cellular functionality [3,4]. The second category comprises genes with distinct expression in a few tissues or conditions, which are likely to be important for defining cell-specific functions. In datasets with only a few conditions, it is possible to compare pairs of conditions using standard or moderated t-tests [5,6,7]. An alternative method is the nonstandard ANOVA, which tests all possible groups of samples against each other. This involves computationally intensive dynamic programming and cannot detect specificity in individual conditions.
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