Accurate Transcription Factor Activity Inference to Decipher Cell Identity from Single-Cell Transcriptomic Data with...

Area of Science:

• Computational Biology
• Genomics
• Cancer Research

Background:

• Cellular heterogeneity in tumors impacts cancer progression and treatment.
• Single-cell RNA sequencing (scRNA-seq) reveals cellular diversity in the tumor microenvironment.
• Current methods for cell identity characterization using gene expression similarity may not capture distinct functional roles.

Purpose of the Study:

• To introduce metaTF, a novel computational framework for inferring transcription factor (TF) activity from scRNA-seq data.
• To evaluate metaTF's performance against existing methods for TF activity estimation.
• To demonstrate metaTF's utility in characterizing cell identity and function in biological contexts.

Main Methods:

• Development of metaTF, a computational framework for TF activity inference.
• Application of metaTF to scRNA-seq datasets, including mouse hematopoietic stem cell development and breast cancer.
• Comparative analysis of metaTF against existing TF activity inference methods.

Main Results:

• metaTF outperforms existing methods in estimating TF activity.
• metaTF effectively characterizes cell identity during mouse hematopoietic stem cell development.
• metaTF provides superior functional characterization of breast cancer cells and identifies a novel subset of neural-regulated T cells.

Conclusions:

• metaTF enables robust TF activity analysis from scRNA-seq data.
• The framework significantly enhances the characterization of cell identity and function.
• metaTF aids in understanding cellular uniqueness and functionality within complex tissues.