title: Validation of noise models for single-cell transcriptomics.
abstract: 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.
authors.affiliation: 1] Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences), Utrecht, The Netherlands. [2] University Medical Center Utrecht, Cancer Genomics Netherlands, Utrecht, The Netherlands. [3].
authors.forename: Dominic
authors.initials: D
authors.lastname: Grün
authors.affiliation: 1] Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences), Utrecht, The Netherlands. [2] University Medical Center Utrecht, Cancer Genomics Netherlands, Utrecht, The Netherlands. [3].
authors.forename: Lennart
authors.initials: L
authors.lastname: Kester
authors.affiliation: 1] Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences), Utrecht, The Netherlands. [2] University Medical Center Utrecht, Cancer Genomics Netherlands, Utrecht, The Netherlands.
authors.forename: Alexander
authors.initials: A
authors.lastname: van Oudenaarden
mesh_terms: D000818:Animals; D053595:Embryonic Stem Cells / Q000378:metabolism; D020869:Gene Expression Profiling / Q000379:methods* / Q000592:standards*; D005786:Gene Expression Regulation; D051379:Mice; D008954:Models, Biological; D015588:Observer Variation; D015983:Selection Bias; D017423:Sequence Analysis, RNA; D059629:Signal-To-Noise Ratio; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D014157:Transcription Factors
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