DeConveil

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 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

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), and design matrix. Example of CN data for a given gene g: [1,2,3,4,5,6]. Each value of CN we divide by 2: CN/2. Example of input data is shown in 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 for both methods. These data are further processed to separate gene groups using define_gene_groups() function included in DeConveil framework.

The tutorial of the analysis workflow is shown in test_deconveil.ipynb

References

1. Michael I Love, Wolfgang Huber, and Simon Anders. Moderated estimation of fold change and dispersion for rna-seq data with deseq2. Genome biology, 15(12):1–21, 2014. doi:10.1186/s13059-014-0550-8.

2. Boris Muzellec, Maria Telenczuk, Vincent Cabeli, and Mathieu Andreux. Pydeseq2: a python package for bulk rna-seq differential expression analysis. bioRxiv, pages 2022–12, 2022. doi:10.1101/2022.12.14.520412.

3. Anqi Zhu, Joseph G Ibrahim, and Michael I Love. Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics, 35(12):2084–2092, 2019. doi:10.1093/bioinformatics/bty895.