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Differential gene expression were assessed using linear modeling in R using the bioconductor package Limma that allows for identification of differentially expressed genes in a multifactorial experiment79.
Microarray data were normalized by the GC-RMA algorithm using the bioconductor package of R software.
The merged dataset was normalized with RMA [20] using the Bioconductor package, affy.
Selected KEGG pathways found to be significantly associated with DSS on day 7 were accessed using the bioconductor package KEGGSOAP.
Raw data (.cel files) were imported into R and data were normalized with RMA [20] using the Bioconductor package, affy.
Initial exploratory data analysis was performed with hierarchical clustering analysis (average-linkage and metric 1 - Pearson correlation coefficient distance) using the Bioconductor package, made4[21].
Enriched expression of gene ontology (GO) terms was assessed with Ingenuity Pathway Analysis Software 4.2 (Redwood City, CA) and confirmed using the Bioconductor package GOstats.
We also determined the enriched terms among our complex disease genes compared to all genes listed in Entrez database, using the Bioconductor package TopGO [21].
Calculations were performed using the BioConductor package limma [ 40].
This was performed using the Bioconductor package HTqPCR (www.bioconductor.org).
Rank products analysis was performed using the Bioconductor package RankProd.
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