New Results
A robust statistical framework to detect multiple sources of hidden variation in single-cell transcriptomes
Donghyung Lee, Anthony Cheng, Duygu Ucar
doi: https://doi.org/10.1101/151217
Donghyung Lee
1 The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, Unites States of America,
Anthony Cheng
1 The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, Unites States of America,
2 University of Connecticut Health Center, Farmington, Connecticut, Unites States of America
Duygu Ucar
1 The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, Unites States of America,
Article usage
Posted June 18, 2017.
A robust statistical framework to detect multiple sources of hidden variation in single-cell transcriptomes
Donghyung Lee, Anthony Cheng, Duygu Ucar
bioRxiv 151217; doi: https://doi.org/10.1101/151217
Subject Area
Subject Areas
- Biochemistry (11740)
- Bioengineering (8750)
- Bioinformatics (29189)
- Biophysics (14967)
- Cancer Biology (12093)
- Cell Biology (17410)
- Clinical Trials (138)
- Developmental Biology (9420)
- Ecology (14178)
- Epidemiology (2067)
- Evolutionary Biology (18301)
- Genetics (12239)
- Genomics (16797)
- Immunology (11865)
- Microbiology (28070)
- Molecular Biology (11583)
- Neuroscience (60953)
- Paleontology (451)
- Pathology (1870)
- Pharmacology and Toxicology (3238)
- Physiology (4957)
- Plant Biology (10425)
- Synthetic Biology (2884)
- Systems Biology (7338)
- Zoology (1651)