Strategies for aggregating gene expression data: The collapseRows R function
By Jeremy A. Miller, Chaochao Cai, Peter Langfelder, Daniel H. Geschwind, Sunil M. Kurian, D. Salomon, Steve Horvath. BMC Bioinformatics, 2011
317 citations recorded by the source corpus.
BackgroundGenomic and other high dimensional analyses often require one to summarize multiple related variables by a single representative. This task is also variously referred to as collapsing, combining, reducing, or aggregating variables. Examples include summarizing several probe measurements corresponding to a single gene, representing the expression profiles of a co-expression module by a single expression profile, and aggregating cell-type marker information to de-convolute expression data. Several standard statistical summary techniques can be used, but network methods also provide useful alternative methods to find representatives. Currently few collapsing functions are developed and widely applied.ResultsWe introduce the R function collapseRows that implements several collapsing methods and evaluate its performance in three applications. First, we study a crucial step of the meta-analysis of microarray data: the merging of independent gene expression data sets, which may have been measured on different platforms. Toward this end, we collapse multiple microarray probes for a single gene and then merge the data by gene identifier. We find that choosing the probe with the highest average expression leads to best between-study consistency. Second, we study methods for summarizing the gene expression profiles of a co-expression module. Several gene co-expression network analysis applications show that the optimal collapsing strategy depends on the analysis goal. Third, we study aggregating the information of cell type marker genes when the aim is to predict the abundance of cell types in a tissue sample based on gene expression data ("expression deconvolution"). We apply different collapsing methods to predict cell type abundances in peripheral human blood and in mixtures of blood cell lines. Interestingly, the most accurate prediction method involves choosing the most highly connected "hub" marker gene. Finally, to facilitate biological interpretation of collapsed gene lists, we introduce the function userListEnrichment, which assesses the enrichment of gene lists for known brain and blood cell type markers, and for other published biological pathways.ConclusionsThe R function collapseRows implements several standard and network-based collapsing methods. In various genomic applications we provide evidence that both types of methods are robust and biologically relevant tools.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/56fe1ce14961800208a16c0f6ecd4278cf3066ae
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In Molecular Biology
- Steve Horvath
- Wolf Reik
- Peter Langfelder
- W. Dean
- Jörn Walter
- Felix Krueger
- Fátima Santos
- Simon R. Andrews
Citations in the corpus
- The Dynamics of Genome-wide DNA Methylation Reprogramming in Mouse Primordial Germ Cells (2012, result): “This is in line with previous reports using IF and locus-specific bisulfite sequencing (Seki et al., 2005; Guibert et al., 2012) but differs from the expectation that global erasure of methylation marks occurs concomitantly with imprint erasure from E11.”
- A practical guide to linking brain-wide gene expression and neuroimaging data (2018, methodology): “This analysis suggests that RNA-seq data can be used as a reference to select brain-relevant and reliably measured genes. a This measure is implemented in the R function ‘ collapseRows ’ from WGCNA package (Miller et al., 2011).”
- Aging effects on DNA methylation modules in human brain and blood tissue (2012, methodology): “To study the properties of lists of genes whose promoters contain CpG sites that are part of the aging (green) module, we also used the userListEnrichment function [25] (which is part of the WGCNA R package) since it contains lists of known marker genes for blood, brain, and stem cell types.”
- Canonical Genetic Signatures of the Adult Human Brain (2015, unknown): “Module enrichment analysis was performed using the userListEnrichment R function in the WGCNA library 47 .”