data(H3K27Ac, package = "MAnorm2") attr(H3K27Ac, "metaInfo") ## Make a comparison between GM12891 and GM12892 cell lines and create an MA ## plot on the comparison results. # Perform MA normalization and construct bioConds to represent the two cell # lines. norm <- normalize(H3K27Ac, 5:6, 10:11) norm <- normalize(norm, 7:8, 12:13) conds <- list(GM12891 = bioCond(norm[5:6], norm[10:11], name = "GM12891"), GM12892 = bioCond(norm[7:8], norm[12:13], name = "GM12892")) autosome <- !(H3K27Ac$chrom %in% c("chrX", "chrY")) conds <- normBioCond(conds, common.peak.regions = autosome) # Variations in ChIP-seq signals across biological replicates of a cell line # are generally of a low level, and their relationship with the mean signal # intensities is expected to be well modeled by the presumed parametric # form. conds <- fitMeanVarCurve(conds, method = "parametric", occupy.only = TRUE) summary(conds[[1]]) plotMeanVarCurve(conds, subset = "occupied") # Perform differential tests between the two cell lines. res <- diffTest(conds[[1]], conds[[2]]) head(res) # Visualize the overall test results. MAplot(res, padj = 0.001) abline(h = 0, lwd = 2, lty = 5, col = "green3")
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