A complete statistical model for calibration of RNA-seq counts using external spike-ins and maximum likelihood theory
Fig 6
Global normalization in the first round ignoring spike-ins.
(A) Based on data form the yeast growth rate study. Relative log expression (RLE) plots of raw counts normalized by median size factors [34]. The condition-dependent variation in the 0.5 quantile of log relative expression of S1D Fig has been largely eliminated. (B) PCA biplot corresponding to (A). (C) RLE plots of normalized counts produced by applying RUVg normalization [15], with one factor of unwanted variation, to the median-normalized counts in panels A and B. The ERCC spike-ins (same median global normalization applied to counts from cellular RNA). The RLE plots exhibit reduced variation of relative log expression within libraries compared to (A). (D) PCA biplot, corresponding to (C). The sensible clustering before RUVg normalization (B) has been disturbed. (E) RLE plots produced by applying a different RUV technique instead, RUVs [15], to the median-normalized counts in (A) and (B). Variation within libraries is somewhat reduced compared to that with median normalization alone in panel A. (F) PCA biplots corresponding to RLE plots in (E). These PCA plots are very similar to those in (A) for median normalization only. Pairwise testing for differential gene expression between growth rates of 0.30 and 0.12 h-1 gave very similar results for median normalization with and without RUVs normalization.