Srgnet Lab Analysis

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2.5. Implementation
SRGnet (acronym of the Synergistic Response to Gene mutations network) is an R package and available through the Bioconductor repository: https://www.bioconductor.org/packages/SRGnet. 2.6. Data
Polysomal RNA expression was previously measured in young adult mouse colonic epithelium (YAMC) in four conditions: YAMC control, mutant p53-expressing (mp53), activated Ras-expressing, and both mutant genes (mp53/Ras) cells using Affymetrix GeneChip® Mouse Genome 430 2.0 Arrays (McMurray, et al., 2008). The raw data are available via the Gene Expression Omnibus with accession number GSE9199. We processed the transcriptomics data using Robust Multi-array Average (RMA) with custom chip description file containing layout information
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Validation of interactions
We evaluated the robustness of gene-gene interactions in SRG network based on 10-fold cross validation and frequency of their presence in the inferred networks. We hypothesized that if an interaction between pairs of genes is strongly supported by the data, its inference should not be strongly effected by a slightly different training data set. As results, most of interactions in the network of SRGs ranked as highly stable (Figure 5) (Supplementary file 2, Table 3).
In next step, we compared 10 gene regulatory network inference tools based on the same methodology, and SRGnet, Clr, Genie3, mmet, mmetb, pcit, and zscore showed better performance than other tools (Figure 5) (Supplementary Table
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To this end, we first quantified the significance of interactions between genes, which were measured by correlation coefficients computed from the transcriptomic profiles. Next, we fitted the beta-mixture models to compute posterior probabilities for signal to noise. The beta-mixture model showed significant skew toward the positively associated pairs of genes in results of SRGnet in comparison to the model computed from differentially expressed genes (Figure 6). These findings provide evidence of joint regulation of SRGs, and highlights the importance of integrating synergistic expression patterns with coexpression clustering and reduction the noise within the

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