PRANA

PRANA performs differential co-expression network analysis on RNA-sequencing (RNA-seq) data as an R package, incorporating pseudo-value regression and covariate adjustment to detect changes in gene connectivity across conditions.

Key Features:

• Differential Network Analysis: Performs differential co-expression network analysis to identify changes in gene connectivity between biological conditions without requiring pre-established pathway knowledge.
• Covariate Adjustment: Adjusts for multiple covariates to mitigate confounding effects in RNA-seq differential connectivity analyses.
• Pseudo-Value Regression Approach: Employs a pseudo-value regression technique as the core statistical method for estimating differential connectivity in RNA-seq data.
• Complementary Functions: Provides functions to extract adjusted p-values and coefficient estimates for all or selected variables for each gene.
• Gene Identification: Identifies genes that exhibit differential connections across subjects in different biological states.

Scientific Applications:

• Complex disease studies (e.g., COPD): Enables analysis of gene interaction changes in complex diseases such as chronic obstructive pulmonary disease (COPD) by combining differential co-expression analysis with covariate adjustment.

Methodology:

Analysis is based on a pseudo-value regression approach applied to RNA-sequencing (RNA-seq) data with explicit adjustment for multiple covariates to estimate differential co-expression networks.