Related papers:
1. Evans, C., Hardin, J., Stoebel, D. Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions. Briefings in Bioinformatics, 19(5): 776–792, 2018.
2. Wang, Z., Gerstein, M. and Snyder, M., 2009. RNA-Seq: a revolutionary tool for transcriptomics. Nature reviews genetics, 10(1), pp.57-63.
3. Bullard, J.H., Purdom, E., Hansen, K.D. and Dudoit, S., 2010. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC bioinformatics, 11(1), p.94.
4. Dillies, M.A., Rau, A., Aubert, J., Hennequet-Antier, C., Jeanmougin, M., Servant, N., Keime, C., Marot, G., Castel, D., Estelle, J. and Guernec, G., 2013. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Briefings in bioinformatics, 14(6), pp.671-683.
5. Lovén, J., Orlando, D.A., Sigova, A.A., Lin, C.Y., Rahl, P.B., Burge, C.B., Levens, D.L., Lee, T.I. and Young, R.A., 2012. Revisiting global gene expression analysis. Cell, 151(3), pp.476-482.