Computational protocol: Global Transcriptome Analysis in Influenza-Infected Mouse Lungs Reveals the Kinetics of Innate and Adaptive Host Immune Responses

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Protocol publication

[…] Data were analyzed using the R software, several packages from BioConductor including the packages Agi4×44PreProcess, affycoretools, annotate, RankProd, GOstats, SPIA, KEGGSOAP, Cairo, psych, gplots, RColorBrewer, mgug4122a.db, and gtools. Preprocessing steps included background correction (“normexp”), quantile normalization, probe summarization, and log2 transformation. For background correction, fitted intensities were calculated by the convolution of normal and exponential distributions to the observed foreground and background intensities . For a robust analysis, median values were calculated for each gene of 3–9 replicates. We then identified all genes that were differentially expressed (DE) genes. DE genes were defined as genes that exhibited at least a two-fold change in expression levels compared to the controls and the fold-changes had to be significant with an FDR corrected p-value of 0.1 using the rank product method , . Cluster analysis (density clustering, which eliminates non-associated genes; data normalized to the mean and standard deviation, Euclidean distance derived by transformation of the Pearson correlation coefficient , ) was used to find genes with similar expression profiles. In order to further functionally characterize DE genes, GOstats was applied to determine the over-representation of gene ontology terms and SPIA, the signaling pathway impact analysis, to examine KEGG pathways . NK cell signature genes were derived from the transcriptomes of sorted NK cells compared to T cells and whole lungs (using a threshold of differential NK cell expression of 20.5 fold, data not published). […]

Pipeline specifications

Software tools affycoretools, RankProd, GOstats, SPIA, gplots
Application Gene expression microarray analysis
Organisms Mus musculus
Diseases Ataxia Telangiectasia, Infection, HIV Infections