Rapaport, F. et al. Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data.
Read count normalization and differential gene expression analysis was performed using DESeq2 version 1.10.130.
The differential gene expression analysis was performed with DESeq219 starting with gene read counts and using the GC mean content of the sample libraries as covariate.
Only those reads matching the mouse genome were considered in the differential gene expression analysis carried out with Cuffdiff (Cufflinks v2.1.0 software63).
F.C. synthesized and characterized nanoparticles under the supervision of L.G. and E.T. T.K. performed the raw data analysis of the sequencing results and the differential gene expression analysis.
Differential gene expression analysis identified key biological processes associated with vegetative to reproductive tissue transition.
However, by applying differential gene expression analysis the sensitivity and predictability of the test may be increased.
Differential gene expression analysis was performed with edgeR55,56 or DESeq257 after further normalization using the RUVg approach to remove variation between RNA samples resulting from differences in library preparation.
Finally, the quantity of sequenced and mapped reads per sample in this study (Table 2; Fig. 3b) is sufficient for robust differential gene expression analysis, however, is below the conventional threshold for thorough differential isoform analysis22.
Here our differential gene expression analysis was carried out using DESeq2 (refs 9,17), however other publicly available packages such as egdeR18 or CuffDiff1 may also be used for this analysis.
The differential gene expression analysis between TNBC, HER2+ or ER/PR+ samples identified MLK4 among the top 1% of all genes with the highest probability of being differentially expressed between TNBC and other breast cancer subtypes (Supplementary Fig. 1b-d).
