Revealing the functions of clonal driver gene mutations in patients based on evolutionary dependencies.

The clonal mutations in driver genes enable cells to gradually acquire growth advantage in tumor development. Therefore, revealing the functions of clonal driver gene mutations is important. Here, we proposed the method FCMP that considered evolutionary dependencies to analyze the functions of clonal driver gene mutations in a single patient. Applying our method to five cancer types from The Cancer Genome Atlas, we identified specific functions and common functions of clonal driver gene mutations. We found that the clonal driver gene mutations in the same patient played multiple functions. We also found that clonal mutations in the same driver gene performed different functions in different patients. These findings suggested that the clonal driver gene mutations showed strong tumor heterogeneity. In the pan-cancer analysis, the immune-related functions for clonal driver gene mutations were shared by multiple cancer types. In addition, clonal mutations in some driver genes predicted the survival of patients in cancers.

Annotation of cell types (ACT): a convenient web server for cell type annotation.

BACKGROUND: The advancement of single-cell sequencing has progressed our ability to solve biological questions. Cell type annotation is of vital importance to this process, allowing for the analysis and interpretation of enormous single-cell datasets. At present, however, manual cell annotation which is the predominant approach remains limited by both speed and the requirement of expert knowledge. METHODS: To address these challenges, we constructed a hierarchically organized marker map through manually curating over 26,000 cell marker entries from about 7000 publications. We then developed WISE, a weighted and integrated gene set enrichment method, to integrate the prevalence of canonical markers and ordered differentially expressed genes of specific cell types in the marker map. Benchmarking analysis suggested that our method outperformed state-of-the-art methods. RESULTS: By integrating the marker map and WISE, we developed a user-friendly and convenient web server, ACT ( http://xteam.xbio.top/ACT/ or http://biocc.hrbmu.edu.cn/ACT/ ), which only takes a simple list of upregulated genes as input and provides interactive hierarchy maps, together with well-designed charts and statistical information, to accelerate the assignment of cell identities and made the results comparable to expert manual annotation. Besides, a pan-tissue marker map was constructed to assist in cell assignments in less-studied tissues. Applying ACT to three case studies showed that all cell clusters were quickly and accurately annotated, and multi-level and more refined cell types were identified. CONCLUSIONS: We developed a knowledge-based resource and a corresponding method, together with an intuitive graphical web interface, for cell type annotation. We believe that ACT, emerging as a powerful tool for cell type annotation, would be widely used in single-cell research and considerably accelerate the process of cell type identification.