Loess in gene enrichment analysis using DESeq Often MA plots (Bland-Altman style representations of gene enrichments) are corrected using the LOESS method. Will a LOESS correction significantly affect the results of gene enrichment analysis? More specifically, will different genes appear as significantly enriched (for instance as defined by the criteria p-value < 10^-3, log2 fold change > 3)? 
And is there a recommended implementation to use the LOESS correction together with the DESeq algorithm of Anders and Huber, 2010 (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3218662/)? Ideally, Matlab-based implementations are preferred.
 A: To answer your first question, LOESS correction of MA plots was designed to affect the results of gene enrichment analysis. Given the vagaries of 2-color hybridization in early microarray studies, you were likely to overestimate the number of biologically significant changes if you simply analyzed $log_2$ fluorescence intensity differences (M) without taking into account systematic changes in typical M values with overall average expression (A). LOESS corrected for those systematic overall changes along MA plots, in experiments where most genes were not differentially expressed, so that spurious results from these technical limitations were minimized.
Even if you are not using the Bioconductor DESeq package for this method, you should read the vignette to get a good handle on the assumptions and approach. They say explicitly:

The count values must be raw counts of sequencing reads. This is important for DESeq's statistical model to hold, as only the actual counts allow assessing the measurement precision correctly. Hence, please do not supply other quantities, such as (rounded) normalized counts, or counts of covered  base pairs--this will only lead to nonsensical results.

So LOESS normalization does not seem compatible with this method. The only normalization this method seems to support is the effective library size. If an equivalent functionality to the DESeq estimateSizeFactors function, which corrects for library size, is available in the MATLAB implementation, make sure you used it. Note that uncorrected different effective library sizes might explain the systematic slopes of the middle portions of your gene-expression clouds away from the x-axis of your MA plots. 
So if you want to use the DESeq approach for changes in count variance with mean expression, you won't be able to do LOESS correction of MA plots. At this point in your study, however, it looks like you need to be focusing on the relation between apparent differential expression and mean expression before you get to that next level of sophistication.
