Correcting the estimated level of differential expression for gene selection bias: application to a microarray study

The level of differential gene expression may be defined as a fold change, a frequency of upregulation, or some other measure of the degree or extent of a difference in expression across groups of interest. On the basis of expression data for hundreds or thousands of genes, inferring which genes are differentially expressed or ranking genes in order of priority introduces a bias in estimates of their differential expression levels. A previous correction of this feature selection bias suffers from a lack of generality in the method of ranking genes, from requiring many biological replicates, and from unnecessarily overcompensating for the bias. For any method of ranking genes on the basis of gene expression measured for as few as three biological replicates, a simple leave-one-out algorithm corrects, with less overcompensation, the bias in estimates of the level of differential gene expression. In a microarray data set, the bias correction reduces estimates of the probability of upregulation or downregulation from 100% to as low as 60%, even for genes with estimated local false discovery rates close to 0. A simulation study quantifies both the advantage of smoothing estimates of bias before correction and the degree of overcompensation.     

Allelic imbalance metre (Allim), a new tool for measuring allele‐specific gene expression with RNA‐seq data

Estimating differences in gene expression among alleles is of high interest for many areas in biology and medicine. Here, we present a user‐friendly software tool, Allim, to estimate allele‐specific gene expression. Because mapping bias is a major problem for reliable estimates of allele‐specific gene expression using RNA‐seq, Allim combines two different strategies to account for the mapping biases. In order to reduce the mapping bias, Allim first generates a polymorphism‐aware reference genome that accounts for the sequence variation between the alleles. Then, a sequence‐specific simulation tool estimates the residual mapping bias. Statistical tests for allelic imbalance are provided that can be used with the bias corrected RNA‐seq data.