{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Hu Y"],"funding":["Chinese Foundation for Primary Health Care","Haiyan Foundation of Harbin Medical University Cancer Hospital","National Natural Science Foundation of China Excellent Young Fund","Guangdong Basic and Applied Basic Research Foundation","Basic and Applied Basic Research Foundation of Guangdong Province","National Natural Science Foundation of China","Joint Funds of the National Natural Science Foundation of China","National Key Research and Development Project of China"],"pagination":["e10745"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12199337"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["12(23)"],"pubmed_abstract":["Cellular heterogeneity within cancer tissues determines cancer progression and treatment response. Single-cell RNA sequencing (scRNA-seq) has provided a powerful approach for investigating the cellular heterogeneity of both cancer cells and stroma cells in the tumor microenvironment. However, the common practice to characterize cell identity based on the similarity of their gene expression profiles may not really indicate distinct cellular populations with unique roles. 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, metaTF, a computational fram"],"journal":["Advanced science (Weinheim, Baden-Wurttemberg, Germany)"],"pubmed_title":["Accurate Transcription Factor Activity Inference to Decipher Cell Identity from Single-Cell Transcriptomic Data with MetaTF."],"pmcid":["PMC12199337"],"funding_grant_id":["2022YFA0806303","cphcf-2023-018","82273364","32422026","U24A20724","82370106","JJZD2024-02","2024A1515011769"],"pubmed_authors":["Yi Y","Wu L","Hu Y","Zhu Y","Liu M","Chen J","Shan M","Wang D","Tang G","Li X","Zheng H","Li Z","Huang Y","Zhang X","Tan P"],"additional_accession":[]},"is_claimable":false,"name":"Accurate Transcription Factor Activity Inference to Decipher Cell Identity from Single-Cell Transcriptomic Data with MetaTF.","description":"Cellular heterogeneity within cancer tissues determines cancer progression and treatment response. Single-cell RNA sequencing (scRNA-seq) has provided a powerful approach for investigating the cellular heterogeneity of both cancer cells and stroma cells in the tumor microenvironment. However, the common practice to characterize cell identity based on the similarity of their gene expression profiles may not really indicate distinct cellular populations with unique roles. 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, metaTF, a computational fram","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jun","modification":"2026-06-02T08:42:24.648Z","creation":"2026-04-16T03:12:32.738Z"},"accession":"S-EPMC12199337","cross_references":{"pubmed":["40397381"],"doi":["10.1002/advs.202410745"]}}