Bayesian approach to single-cell differential expression analysis.
Single-cell data provide a means to dissect the composition of complex tissues and specialized cellular environments. However, the analysis of such measurements is complicated by high levels of technical noise and intrinsic biological variability. We describe a probabilistic model of expression-magnitude distortions typical of single-cell RNA-sequencing measurements, which enables detection of differential expression signatures and identification of subpopulations of cells in a way that is more tolerant of noise.
1] Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA. [2] Hematology/Oncology Program, Children's Hospital, Boston, Massachusetts, USA. [3] Harvard Stem Cell Institute, Cambridge, Massachusetts, USA.
Peter V
PV
Kharchenko
1] Harvard Stem Cell Institute, Cambridge, Massachusetts, USA. [2] Center for Regenerative Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA. [3] Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA.
Lev
L
Silberstein
1] Harvard Stem Cell Institute, Cambridge, Massachusetts, USA. [2] Center for Regenerative Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA. [3] Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA.
David T
DT
Scadden
D000465:Algorithms; D001499:Bayes Theorem; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis; D017423:Sequence Analysis, RNA / Q000379:methods*; D059010:Single-Cell Analysis / Q000379:methods*