【佳學(xué)基因檢測(cè)】RNA測(cè)序結(jié)果的標(biāo)準(zhǔn)化展示——MA作圖

An MA-plot (Dudoit et al. 2002) provides a useful overview for the distribution of the estimated coefficients in the model, e.g. the comparisons of interest, across all genes. On the y-axis, the “M” stands for “minus” – subtraction of log values is equivalent to the log of the ratio – and on the x-axis, the “A” stands for “average”. You may hear this plot also referred to as a mean-difference plot, or a Bland-Altman plot.

Before making the MA-plot, we use the lfcShrink function to shrink the log2 fold changes for the comparison of dex treated vs untreated samples. There are three types of shrinkage estimators in DESeq2, which are covered in the DESeq2 vignette. Here we specify the apeglm method for shrinking coefficients, which is good for shrinking the noisy LFC estimates while giving low bias LFC estimates for true large differences (Zhu, Ibrahim, and Love 2018). To use apeglm we specify a coefficient from the model to shrink, either by name or number as the coefficient appears in resultsNames(dds).