A Bayesian inference transcription factor activity model for the analysis of single-cell transcriptomes.

Area of Science:

• Genomics
• Computational Biology
• Molecular Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) is crucial for studying cellular heterogeneity.
• Integrating diverse biological data in scRNA-seq analysis remains a challenge for understanding cell-specific functions.
• Identifying transcription factor activities is key to elucidating gene regulatory networks.

Purpose of the Study:

• To develop a novel computational approach for inferring transcription factor activities from scRNA-seq data.
• To integrate transcription factor binding site information into scRNA-seq analysis.
• To identify key regulatory transcription factors controlling cell function and fate.

Main Methods:

• Developed the Bayesian inference transcription factor activity model (BITFAM).
• BITFAM integrates ChIP-seq transcription factor binding data with scRNA-seq data.
• Applied BITFAM to infer transcription factor activities in distinct cell populations.

Main Results:

• Inferred transcription factor activities accurately identified known regulatory transcription factors.
• Demonstrated that BITFAM reveals biologically meaningful transcription factor activities.
• Highlighted the model's ability to pinpoint regulators of cell function and fate.

Conclusions:

• BITFAM provides a robust method for inferring transcription factor activities from scRNA-seq data.
• The approach offers valuable insights into transcription factor regulatory mechanisms.
• Facilitates a deeper understanding of cell-type-specific gene regulation.