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+β₁ 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+β₁ l_k(Low/High)+β₂ 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.

DEswan example

See the vignette for examples