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Hunting complex differential gene interaction patterns across molecular contexts.

ABSTRACT: Heterogeneity in genetic networks across different signaling molecular contexts can suggest molecular regulatory mechanisms. Here we describe a comparative chi-square analysis (CP?(2)) method, considerably more flexible and effective than other alternatives, to screen large gene expression data sets for conserved and differential interactions. CP?(2) decomposes interactions across conditions to assess homogeneity and heterogeneity. Theoretically, we prove an asymptotic chi-square null distribution for the interaction heterogeneity statistic. Empirically, on synthetic yeast cell cycle data, CP?(2) achieved much higher statistical power in detecting differential networks than alternative approaches. We applied CP?(2) to Drosophila melanogaster wing gene expression arrays collected under normal conditions, and conditions with overexpressed E2F and Cabut, two transcription factor complexes that promote ectopic cell cycling. The resulting differential networks suggest a mechanism by which E2F and Cabut regulate distinct gene interactions, while still sharing a small core network. Thus, CP?(2) is sensitive in detecting network rewiring, useful in comparing related biological systems.

SUBMITTER: Song M

PROVIDER: S-EPMC3985659 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Hunting complex differential gene interaction patterns across molecular contexts.

Song Mingzhou M Zhang Yang Y Katzaroff Alexia J AJ Edgar Bruce A BA Buttitta Laura L

Nucleic acids research 20140129 7

Heterogeneity in genetic networks across different signaling molecular contexts can suggest molecular regulatory mechanisms. Here we describe a comparative chi-square analysis (CPχ(2)) method, considerably more flexible and effective than other alternatives, to screen large gene expression data sets for conserved and differential interactions. CPχ(2) decomposes interactions across conditions to assess homogeneity and heterogeneity. Theoretically, we prove an asymptotic chi-square null distributi ...[more]

PMID: 24482443

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Project description:Cytosine methylation is widespread in most eukaryotic genomes and is known to play a substantial role in various regulatory pathways. Unmethylated cytosines may be converted to uracil through the addition of sodium bisulphite, allowing genome-wide quantification of cytosine methylation via high-throughput sequencing. The data thus acquired allows the discovery of methylation 'loci'; contiguous regions of methylation consistently methylated across biological replicates. The mapping of these loci allows for associations with other genomic factors to be identified, and for analyses of differential methylation to take place.The segmentSeq R package is extended to identify methylation loci from high-throughput sequencing data from multiple experimental conditions. A statistical model is then developed that accounts for biological replication and variable rates of non-conversion of cytosines in each sample to compute posterior likelihoods of methylation at each locus within an empirical Bayesian framework. The same model is used as a basis for analysis of differential methylation between multiple experimental conditions with the baySeq R package. We demonstrate the capability of this method to analyse complex data sets in an analysis of data derived from multiple Dicer-like mutants in Arabidopsis. This reveals several novel behaviours at distinct sets of loci in response to loss of one or more of the Dicer-like proteins that indicate an antagonistic relationship between the Dicer-like proteins at at least some methylation loci. Finally, we show in simulation studies that this approach can be significantly more powerful in the detection of differential methylation than many existing methods in data derived from both mammalian and plant systems.The methods developed here make it possible to analyse high-throughput sequencing of the methylome of any given organism under a diverse set of experimental conditions. The methods are able to identify methylation loci and evaluate the likelihood that a region is truly methylated under any given experimental condition, allowing for downstream analyses that characterise differences between methylated and non-methylated regions of the genome. Futhermore, diverse patterns of differential methylation may also be characterised from these data.

Dataset Information

Hunting complex differential gene interaction patterns across molecular contexts.

Publications

Hunting complex differential gene interaction patterns across molecular contexts.

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