Bayesian approach to single-cell differential expression analysis

By Peter V. Kharchenko, Lev Silberstein, D. Scadden. Nature Methods, 2014

1,271 citations recorded by the source corpus.

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.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0be6fdb55b5fc365a0eb228f994d4181b94269b4

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Citations in the corpus

SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues (2020, positive): “Differential expression tests between cells from ACE2 versus ACE2 Type II Pneumocytes were conducted using the SCDE R package with default parameters (Kharchenko et al., 2014).”
MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data (2015, background): “Previously, Kharchenko et al. [6] developed a so-called three-component mixture model to test for differential gene expression while accounting for bimodal expression.”
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, methodology): “For example, SCDE formulated the RPM (readpermillion) data for a given gene across cells as a mixture of Poisson and negative binomial components; using a Bayesian approach, probabilities of observing a given fold change are converted into empirical pvalues [192] .”
Estimating Mutual Information for Discrete-Continuous Mixtures (2017, background): “However, this technology has a problem called dropout, which implies that sometimes, even when the gene is present it is not sequenced [25, 12].”
Modelling dropouts for feature selection in scRNASeq experiments (2016, methodology): “However, differential expression methods, such as SCDE [3], require the predetermined homogeneous subpopulations.”
Bayesian Inference for a Generative Model of Transcriptome Profiles from Single-cell RNA Sequencing (2018, background): “To avoid mistaking nuisance variation for relevant biological diversity, one must therefore account for measurement bias and uncertainty, especially due to the highly abundant false negatives or “dropout” events [8].”
SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis (2015, methodology): “Recently, a number of methods specifically designed for scRNA-seq analysis have been introduced including SNN-Cliq [32], scLVM [27] and BackSPIN [33] for clustering; SAMstrt and Bayesian approach for single-cell differential expression analysis [25,34,35]; Monocle [26] and SCUBA [36] for extracting ”
Spatial reconstruction of single-cell gene expression (2015, background): “Single-cell measurements for a single gene, however, are inherently noisy, with extensive variability stemming from both biological and technical sources 21,22 .”
CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data (2016, methodology): “…of scRNA-seq data analysis, including cell cycle (cyclone [3] and scLVM [4]), normalization (scran [5]), differential expression analysis (scde [2] and MAST [6]), and temporal analysis (Monocle[7]), but few perform preprocessing steps such as dimensionality reduction and clustering, which are…”