New Results
Airpart: Interpretable statistical models for analyzing allelic imbalance in single-cell datasets
View ORCID ProfileWancen Mu, View ORCID ProfileHirak Sarkar, View ORCID ProfileAvi Srivastava, View ORCID ProfileKwangbom Choi, View ORCID ProfileRob Patro, View ORCID ProfileMichael I. Love
doi: https://doi.org/10.1101/2021.10.15.464546
Wancen Mu
1Department of Biostatistics, University of North Carolina-Chapel Hill
Hirak Sarkar
2Department of Computer Science, University of Maryland, College Park, MD
Avi Srivastava
3New York Genome Center, New York, NY 10013, USA
Kwangbom Choi
4The Jackson Laboratory, Bar Harbor, ME 04609, USA
Rob Patro
2Department of Computer Science, University of Maryland, College Park, MD
Michael I. Love
1Department of Biostatistics, University of North Carolina-Chapel Hill
5Department of Genetics, University of North Carolina-Chapel Hill
Posted October 16, 2021.
Airpart: Interpretable statistical models for analyzing allelic imbalance in single-cell datasets
Wancen Mu, Hirak Sarkar, Avi Srivastava, Kwangbom Choi, Rob Patro, Michael I. Love
bioRxiv 2021.10.15.464546; doi: https://doi.org/10.1101/2021.10.15.464546
Subject Area
Subject Areas
- Biochemistry (11730)
- Bioengineering (8743)
- Bioinformatics (29179)
- Biophysics (14964)
- Cancer Biology (12080)
- Cell Biology (17399)
- Clinical Trials (138)
- Developmental Biology (9417)
- Ecology (14174)
- Epidemiology (2067)
- Evolutionary Biology (18294)
- Genetics (12233)
- Genomics (16791)
- Immunology (11858)
- Microbiology (28051)
- Molecular Biology (11575)
- Neuroscience (60919)
- Paleontology (451)
- Pathology (1870)
- Pharmacology and Toxicology (3238)
- Physiology (4955)
- Plant Biology (10422)
- Synthetic Biology (2881)
- Systems Biology (7338)
- Zoology (1650)