The iGC package is used to identify CNA-driven differentially expressed genes. The iGC package provides four categories of important functions: 'create_sample_desc', 'create_gene_ex', 'create_gene_cna' and 'find_cna_drive_gene'.
The create_sample_desc function is provided for creating a sample description table containing all required inputs.
The create_gene_exp function is used to rearrange the input gene expression files into a gene expression list of entire samples.
The create_gene_cna function maps CNA data to human genes and then defines the mapped human genes as CN gain or loss based on the CN threshold, whose default values are set as 2.5 for gain and 1.5 for loss. These mapped genes will be assigned values in +1, -1 or 0, where +1 stands for CNA-gain, -1 stands for CNA-loss and 0 stands for neutral.
The find_cna_driven_gene function identifies CNA-driven differentially expressed genes. The input mapped genes remain for further analyses if its ratio of the number of CN changed samples, CNA-gain (G) or CNA-loss (L), to the number of total samples is larger than a given threshold. Here the default setting is that only genes showing CNAs in at least 20 statistical tests, T-test and Wilcoxon rank sum test, are performed in the GE level by classifying the samples as G and L plus Nertral (N) groups or L and G plus N groups, depending on the CN of the interested gene increases or decreases.
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