joda

joda computes gene deregulation scores to quantify how regulator perturbations differentially affect gene expression between two cell populations by integrating regulator knockdown experiments with signaling pathway target knowledge.

Key Features:

• Gene deregulation scoring: Computes gene deregulation scores that quantify differential effects of regulator perturbations between two cell populations.
• Integration of perturbation and pathway knowledge: Integrates regulator knockdown experiments with signaling pathway and pathway target gene knowledge.
• Matrix regulatory model: Formalizes regulatory interactions via a matrix model that captures regulatory interactions within biological networks.
• Joint population analysis: Merges regulatory signals from both cell populations into a single deregulation score for joint analysis.
• Network rewiring detection: Detects re-wiring of regulatory networks by combining cell population-specific perturbation data with pathway knowledge.
• Identification of deregulated clusters: Identifies deregulated genes organized into functional clusters, as demonstrated by the identification of 645 deregulated genes in thirteen functional clusters in a DNA damage study.
• Connectivity and hierarchy analysis: Facilitates examination of connectivity among deregulated genes, analysis of genes with extreme deregulation scores, and exploration of indirect effects such as those exerted by ATM to hypothesize hierarchies of direct regulation.
• Implementation: Implemented in the Bioconductor environment.

Scientific Applications:

• Comparative regulatory analysis: Quantifies differential regulatory effects of perturbations between two cell populations using deregulation scores.
• Regulatory network rewiring studies: Detects widespread re-wiring events within gene regulatory networks by joint analysis of perturbation data and pathway knowledge.
• DNA damage response analysis (neocarzinostatin): Applied to neocarzinostatin-induced DNA damage in human cells to identify deregulated genes and functional clusters.
• Connectivity and pathway influence (ATM): Investigates connectivity among deregulated genes and explores indirect pathway effects such as ATM to construct hypothetical regulatory hierarchies.
• Prioritization of extreme candidates: Identifies genes with extreme deregulation scores for follow-up in DNA damage response mechanism studies.

Methodology:

Integrates regulator knockdown experiments with signaling pathway target gene knowledge into a matrix model of regulatory interactions, computes gene deregulation scores by merging regulatory signals from both cell populations, and performs joint analysis to detect network rewiring, examine extreme deregulation scores, and explore indirect pathway effects such as ATM.