Validation of noise models for single-cell transcriptomics

TY - JOUR

T1 - Validation of noise models for single-cell transcriptomics

AU - Grün, Dominic

AU - Kester, Lennart

AU - van Oudenaarden, Alexander

PY - 2014/6

Y1 - 2014/6

N2 - Single-cell transcriptomics has recently emerged as a powerful technology to explore gene expression heterogeneity among single cells. Here we identify two major sources of technical variability: sampling noise and global cell-to-cell variation in sequencing efficiency. We propose noise models to correct for this, which we validate using single-molecule FISH. We demonstrate that gene expression variability in mouse embryonic stem cells depends on the culture condition.

AB - Single-cell transcriptomics has recently emerged as a powerful technology to explore gene expression heterogeneity among single cells. Here we identify two major sources of technical variability: sampling noise and global cell-to-cell variation in sequencing efficiency. We propose noise models to correct for this, which we validate using single-molecule FISH. We demonstrate that gene expression variability in mouse embryonic stem cells depends on the culture condition.

KW - Animals

KW - Embryonic Stem Cells

KW - Gene Expression Profiling

KW - Gene Expression Regulation

KW - Mice

KW - Models, Biological

KW - Observer Variation

KW - Selection Bias

KW - Sequence Analysis, RNA

KW - Signal-To-Noise Ratio

KW - Transcription Factors

U2 - 10.1038/nmeth.2930

DO - 10.1038/nmeth.2930

M3 - Article

C2 - 24747814

SN - 1548-7091

VL - 11

SP - 637

EP - 640

JO - Nature Methods

JF - Nature Methods

IS - 6

ER -