Abstract
BackgroundDiseases like cancer will lead to changes in gene expression, and it is relevant to identify key regulatory genes that can be linked directly to these changes. This can be done by computing a Regulatory Impact Factor (RIF) score for relevant regulators. However, this computation is based on estimating correlated patterns of gene expression, often Pearson correlation, and an assumption about a set of specific regulators, normally transcription factors. This study explores alternative measures of correlation, using the Fisher and Sobolev metrics, and an extended set of regulators, including epigenetic regulators and long non-coding RNAs (lncRNAs). Data on prostate cancer have been used to explore the effect of these modifications.ResultsA tool for computation of RIF scores with alternative correlation measures and extended sets of regulators was developed and tested on gene expression data for prostate cancer. The study showed that the Fisher and Sobolev metrics lead to improved identification of well-documented regulators of gene expression in prostate cancer, and the sets of identified key regulators showed improved overlap with previously defined gene sets of relevance to cancer. The extended set of regulators lead to identification of several interesting candidates for further studies, including lncRNAs. Several key processes were identified as important, including spindle assembly and the epithelial-mesenchymal transition (EMT).ConclusionsThe study has shown that using alternative metrics of correlation can improve the performance of tools based on correlation of gene expression in genomic data. The Fisher and Sobolev metrics should be considered also in other correlation-based applications.
Highlights
Diseases like cancer will lead to changes in gene expression, and it is relevant to identify key regulatory genes that can be linked directly to these changes
RIF1 identifies factors that are consistently co-expressed with highly abundant differential expression (DE) genes, whereas RIF2 identifies Transcription factor (TF) with the ability to act as predictors of the abundance of DE genes
We have recently shown that correlations in gene expression can be identified more robustly [25] by using alternative correlation metrics like Fisher [26] or Sobolev [27], rather than the standard Pearson or Spearman correlation used in most studies, and we wanted to test if this could be applied to regulatory impact factor (RIF) scores
Summary
Diseases like cancer will lead to changes in gene expression, and it is relevant to identify key regulatory genes that can be linked directly to these changes. Increased expression of a gene producing a positive regulator can initiate higher expression values of the downstream genes that are controlled by the regulator, and this leads to correlation in expression values for Ehsani and Drabløs BMC Bioinformatics (2020) 21:134 these genes across a relevant biological process, like a pathway of cellular differentiation This correlation does not by itself show causality. The simplest approach for doing a more focused analysis is to specify a potential causality by postulating a specific regulator for a process and hypothesizing that correlated expression levels between the regulator and other genes indicates that this regulator has a significant regulatory impact on downstream targets in the process This approach has in particular been implemented as regulatory impact factor (RIF) scores [4, 5]. RIF1 identifies factors that are consistently co-expressed with highly abundant DE genes, whereas RIF2 identifies TFs with the ability to act as predictors of the abundance of DE genes
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