Work

Cell-type-aware analysis of RNA-seq data

Year: 2021

Type: article

Source: Nature Computational Science

DOI: 10.1038/s43588-021-00055-6

Authors: Chong Jin, Mengjie Chen, Dan-Yu Lin, Wei Sun

Institutions: University of North Carolina at Chapel Hill, University of Chicago, University of Washington, Fred Hutch Cancer Center

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Science Foundation, U.S. Department of Health and Human Services, Alfred P. Sloan Foundation, Directorate for Mathematical and Physical Sciences, National Heart, Lung, and Blood Institute, National Human Genome Research Institute, National Cancer Institute, National Institute of General Medical Sciences

Awards: G2095348026, G249436669, G2507033445, G2775946608, G2969161798, Semiparametric Analysis of Big Censored Data, G345745147, Estimation and association analysis of biomarkers for tumor immune microenvironment, Statistical Methods for Cancer Clinical Trials, Unlocking complex co-expression network using graphical models, Statistical Methods in Trans-Omics Chronic Disease Research, Statistical Methods for Intra-Tumor Heterogeneity Studies Using Sequencing Data, G8435788039, Statistical Methods for RNA-seq Data Analysis, New directions in single cell genomics method development

OpenAlex ID: W3156482864

FWCI: 1.804

Cites: 43

Cited by: 27

Related to: 10