From: Impact of adaptive filtering on power and false discovery rate in RNA-seq experiments
Simulation | Description and data sources |
---|---|
NB | The count data are assumed to follow a negative binomial distribution (NB), dispersion and mean parameters are fixed and equal for all \(H_0\) or \(H_1\), respectively. |
NB with distributed parameters | Read counts follow a NB distribution, dispersion and mean parameters vary across genes and are based on real RNA-seq data sets according to [2] (real data sets Kidney [6], Bottomly [7], and Sultan [8], see Table 3). |
SimSeq [9] | Counts based on real data read counts adjusted by a correction factor to generate differential expressions, dependence between genes is imitated from real data sets Bottomly [7], Kidney [6], and mouse [10]. |
PROPER [11] | Read counts follow a NB distribution, dispersion and mean parameters vary across genes and are based on a real RNA-seq data set (Cheung [12]). Additional noise is introduced due to zero baseline expressions in the original data leading to many genes with zero counts only. |
PROPER with fixed sequencing depth [11] | As PROPER. Here, the empirical average expressions sampled from the Cheung data are standardised to reach a fixed sequencing depth. |