In lehallib/DEswan: Differential Expression Sliding Window ANalaysis

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DEswan package

Run a Differential Expression - Sliding Window ANalysis (DEswan) as described by Lehallier et al. (https://www.biorxiv.org/content/10.1101/751115v1)

Installation

You can install DEswan from github with:

# install.packages("devtools")
devtools::install_github("lehallib/DEswan",build_vignettes = T)

DEswan approach

DEswan explore linear and non linear relationships between a quantitative trait (l) and one or more features.

Considering a vector l of k unique values, we iteratively use l~k~ as the center of a window of size x and group samples in parcels below and above l~k~

i.e. [l~k~-x/2 ; l~k~[ and ]l~k~ ; l~k~+x/2]

To test for differential expression, we use the following linear model:

Feature~i~ ~ α+β~1~ l~k~(below/above)+ε

l~k~(below-above) being binarized according to the parcels around l~k~. Covariates can be included in the modeling as follows:

Feature~i~ ~ α+β~1~ l~k~(Low/High)+β~2~ Covariate~a~+...+β~x~ Covariate~x~+ε

Type II sum of squares are calculated using the Anova function of the R car package.

When analyzing the links between l and more than one feature, we recommend to estimate q-values for each l~k~ using Benjamini–Hochberg correction. To assess the robustness and relevance of DE-SWAN results, we recommend to test multiple parcel widths and different p/q-values thresholds.