PRANA: an R package for differential co-expression network analysis with the presence of additional covariates

• Published in: BMC genomics Vol. 24; no. 1; pp. 687 - 7
• Main Authors: Ahn, Seungjun, Datta, Somnath
• Format: Journal Article
• Language: English
• Published: London BioMed Central 16.11.2023; BioMed Central Ltd; BMC
• Subjects: Algorithms; Analysis; Animal Genetics and Genomics; Base Sequence; Biomedical and Life Sciences; Chronic obstructive pulmonary disease; Covariate adjustment; Data processing; Datasets; Differential network analysis; Estimates; Gene expression; Gene sequencing; Genes; Genomics; Genotype & phenotype; Humans; Life Sciences; Lung diseases; Microarrays; Microbial Genetics and Genomics; Monte Carlo simulation; Network analysis; Obstructive lung disease; Plant Genetics and Genomics; Proteomics; Pseudo-value regression; Ribonucleic acid; RNA; RNA sequencing; RNA-Seq data; Sequence Analysis, RNA; Software; Statistical analysis; Statistical methods; Variables; United States; Differential network analysis; Pseudo-value regression; RNA-Seq data; Covariate adjustment
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-023-09787-3

Background Advances in sequencing technology and cost reduction have enabled an emergence of various statistical methods used in RNA-sequencing data, including the differential co-expression network analysis (or differential network analysis). A key benefit of this method is that it takes into consideration the interactions between or among genes and do not require an established knowledge in biological pathways. As of now, none of existing softwares can incorporate covariates that should be adjusted if they are confounding factors while performing the differential network analysis. Results We develop an R package PRANA which a user can easily include multiple covariates. The main R function in this package leverages a novel pseudo-value regression approach for a differential network analysis in RNA-sequencing data. This software is also enclosed with complementary R functions for extracting adjusted p -values and coefficient estimates of all or specific variable for each gene, as well as for identifying the names of genes that are differentially connected (DC, hereafter) between subjects under biologically different conditions from the output. Conclusion Herewith, we demonstrate the application of this package in a real data on chronic obstructive pulmonary disease. PRANA is available through the CRAN repositories under the GPL-3 license: https://cran.r-project.org/web/packages/PRANA/index.html .