CeTF

CeTF analyzes transcription factor coexpression networks to identify regulatory transcription factors from microarray, RNA-seq, and single-cell RNA-seq expression data using the PCIT and RIF algorithms.

Key Features:

• Integration of PCIT and RIF: Implements PCIT to compute partial correlation coefficients using information theory and RIF to assess the regulatory impact of transcription factors.
• Identification of key transcription factors: Detects transcription factors with strong regulatory influence on gene expression networks, including comparisons between tumor stromal and tumor cells.
• Versatile data compatibility: Accepts gene expression data from microarray, RNA-seq, and single-cell RNA-seq platforms.
• High-throughput analysis integration: Supports processing of large-scale datasets and integration into high-throughput analysis workflows.

Scientific Applications:

• Cancer biology: Elucidates regulatory mechanisms within tumor microenvironments by identifying influential transcription factors.
• Therapeutic target identification: Aids in prioritizing candidate regulatory pathways and transcription factors as potential therapeutic targets.
• Developmental biology: Enables analysis of gene regulation dynamics across developmental processes.
• Systems biology: Facilitates network-level investigation of gene regulatory interactions and modular organization.

Methodology:

CeTF integrates PCIT and RIF, where PCIT computes partial correlations with information theory to distinguish direct from indirect gene interactions and RIF quantifies transcription factor regulatory influence using gene expression data from microarray, RNA-seq, or single-cell RNA-seq.