The goal of DeConveil is the extension of Differential Gene Expression testing by accounting for genome aneuploidy. This computational framework extends traditional DGE analysis by integrating DNA Copy Number Variation (CNV) data. This approach adjusts for dosage effects and categorizes genes as dosage-sensitive (DSG), dosage-insensitive (DIG), and dosage-compensated (DCG), separating the expression changes caused by CNVs from other alterations in transcriptional regulation. To perform this gene separation we need to perform DGE testing using both PyDESeq2 (CN-naive) and DeConveil (CN-aware) methods.
Pre-required installations before running DeConveil
Python libraries are required to be installed: pydeseq2
pip install pydeseq2
How to install DeConveil
pip install DeConveil
or git clone https://github.com/caravagnalab/DeConveil.git
Input data
DeConveil requires the following input matrices:
- matched mRNA read counts (normal and tumor samples) and absolute CN values (for normal diploid samples we assign CN=2), structured as NxG matrix, where N represents the number of samples and G represents the number of genes;
- a design matrix structured as an N × F matrix, where N is the number of samples and F is the number of features or covariates.
Example of CN data for a given gene g: if CN = [1, 2, 3, 4, 5, 6], each CN value should be normalized by dividing by 2 (i.e., CN/2).
An example of the input data can be found in the test_deconveil Jupyter Notebook.
Output data
res_CNnaive.csv (for PyDESeq2 method) and res_CNaware.csv (for DeConveil) data frames reporting log2FC and p.adjust values for both methods.
These data frames are further processed to separate gene groups using define_gene_groups() function included in DeConveil framework.
A tutorial of the analysis workflow is available in test_deconveil.ipynb