Differential gene expression bias due to effect of an individual sample I am analysing a human single cell RNA seq experiment, where we have 4 groups, four samples each. Data has been analysed using Seurat, with the canonical workflow. I have tried DE using various methods (Wilcox, MAST, LR), and for each of them the top genes (biggest fold change, lowest adjusted p) are genes over expressed by specific samples, e.g.:


Looking at the average expression for these genes per each subject, and plotting on a Boxplot, it does not seem to be a genuine difference (e.g. for the gene above):


Besides the subject, there is no other parameter (e.g. age, sex) that is biased across groups and that shows up as differentially represented in the PCA plot.

Is there a DE method that takes into account this behaviour and corrects for it?
How can these results be presented? 
Besides increasing number of subjects (not really feasible), is there a solution to analyse these data?

Thanks a lot!
Leave-one-out cross validation is an iterative method that leaves out one sample until each sample has been left out once. You can then identify the intersection of the differentially expressed genes from the sample space of repeated analyses to produce a set of genes that are consistently differentially expressed independent of individual sample biases.
more info here:
http://efavdb.com/leave-one-out-cross-validation/