Is My Network Module Preserved and Reproducible?

By Peter Langfelder, Rui Luo, Michael C. Oldham, Steve Horvath. PLoS Comput. Biol., 2011

956 citations recorded by the source corpus.

In many applications, one is interested in determining which of the properties of a network module change across conditions. For example, to validate the existence of a module, it is desirable to show that it is reproducible (or preserved) in an independent test network. Here we study several types of network preservation statistics that do not require a module assignment in the test network. We distinguish network preservation statistics by the type of the underlying network. Some preservation statistics are defined for a general network (defined by an adjacency matrix) while others are only defined for a correlation network (constructed on the basis of pairwise correlations between numeric variables). Our applications show that the correlation structure facilitates the definition of particularly powerful module preservation statistics. We illustrate that evaluating module preservation is in general different from evaluating cluster preservation. We find that it is advantageous to aggregate multiple preservation statistics into summary preservation statistics. We illustrate the use of these methods in six gene co-expression network applications including 1) preservation of cholesterol biosynthesis pathway in mouse tissues, 2) comparison of human and chimpanzee brain networks, 3) preservation of selected KEGG pathways between human and chimpanzee brain networks, 4) sex differences in human cortical networks, 5) sex differences in mouse liver networks. While we find no evidence for sex specific modules in human cortical networks, we find that several human cortical modules are less preserved in chimpanzees. In particular, apoptosis genes are differentially co-expressed between humans and chimpanzees. Our simulation studies and applications show that module preservation statistics are useful for studying differences between the modular structure of networks. Data, R software and accompanying tutorials can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/ModulePreservation.

Subfields: Experimental and Cognitive Psychology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/07dd2991dfe98d3aa88425d161309185358c0913

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In Experimental and Cognitive Psychology

In Molecular Biology

Citations in the corpus

Aging effects on DNA methylation modules in human brain and blood tissue (2012, methodology): “15 Validation 72 Leukocyte Healthy children 5 1-16 Illumina 450 k [24] GSE36064”
Canonical Genetic Signatures of the Adult Human Brain (2015, positive): “Using the six brains now available, we identified a consensus network 22 common to all brains (Fig.”
Gene co-expression analysis for functional classification and gene–disease predictions (2017, methodology): “Regulatory mechanisms and genes underlying preimplantation processes conserved between humans and mice were identified by using preservation detection defined by WGCNA [12], a feature that was later added to this package [68].”
hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023, background): “Modules can be projected across datasets by computing MEs in the query dataset, and preservation of the network structure can be assessed via statistical testing.(20) For example, modules can be projected between different species to link transcriptomic changes between mouse models and human disease”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “…on the basis of pair-wise correlations between numeric variables. http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/ModulePreservation Langfelder et al., 2011 NeMo Gene co-expression networks Detects frequent co-expression modules among gene co-expression networks across various…”