| 描述信息 |
Cellular heterogeneity within the cancer tissues determines the cancer progression and treatment response. Single-cell RNA sequencing (scRNA-seq) has provided a powerful approach for investigating cellular heterogeneity of both cancer cells and stroma cells in the microenvironment. However, the common practice to characterize cell identity based on the similarity of their gene expression profiles may not really indicate functionally distinct cell populations. Generally, the cell identity and function are orchestrated by the expression of given specific genes tightly regulated by transcription factors (TFs). Therefore, deciphering TF activity is essential for gaining a better understanding of the uniqueness and functionality of each cell type. Herein, we introduce metaTF (https://github.com/wanglabsmu/metaTF), a computational machine learning framework designed to infer TF activity in scRNA-seq data, and outperforms existing methods in estimating TF activity. It presents the improved effectiveness in characterizing cell identity during mouse hematopoietic stem cell development. Furthermore, metaTF provides a superior characterization of the functional identity of breast cancer epithelial cells, and newly identifies a subset of neural-regulated T cells within the tumor immune microenvironment, which potentially activates BCL6 in response to neural-related signals. Overall, metaTF enables robust TF activity analysis from scRNA-seq data, significantly enhancing characterization of cell identity and function. |
