Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments

By Andrew McDavid, Greg Finak, Pratip K. Chattopadhyay, Maria Dominguez, Laurie Lamoreaux, Steven S. Ma, Mario Roederer, Raphael Gottardo. Bioinform., 2012

376 citations recorded by the source corpus.

Motivation: Cell populations are never truly homogeneous; individual cells exist in biochemical states that define functional differences between them. New technology based on microfluidic arrays combined with multiplexed quantitative polymerase chain reactions now enables high-throughput single-cell gene expression measurement, allowing assessment of cellular heterogeneity. However, few analytic tools have been developed specifically for the statistical and analytical challenges of single-cell quantitative polymerase chain reactions data. Results: We present a statistical framework for the exploration, quality control and analysis of single-cell gene expression data from microfluidic arrays. We assess accuracy and within-sample heterogeneity of single-cell expression and develop quality control criteria to filter unreliable cell measurements. We propose a statistical model accounting for the fact that genes at the single-cell level can be on (and a continuous expression measure is recorded) or dichotomously off (and the recorded expression is zero). Based on this model, we derive a combined likelihood ratio test for differential expression that incorporates both the discrete and continuous components. Using an experiment that examines treatment-specific changes in expression, we show that this combined test is more powerful than either the continuous or dichotomous component in isolation, or a t-test on the zero-inflated data. Although developed for measurements from a specific platform (Fluidigm), these tools are generalizable to other multi-parametric measures over large numbers of events. Availability: All results presented here were obtained using the SingleCellAssay R package available on GitHub (http://github.com/RGLab/SingleCellAssay). Contact: rgottard@fhcrc.org Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9c2fc4e9eb3ed915dddb04e7d1dbdfca11b030e7

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Citations in the corpus

DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors (2018, methodology): “Statistical significance was tested using the likelihood-ratio test for single-cell gene expression (McDavid et al., 2013), and marker genes were defined as statistically-significant genes with 3-fold expression enrichment.”
Orchestrating single-cell analysis with Bioconductor (2019, background): “[24] Andrew McDavid, Greg Finak, Pratip K Chattopadyay, Maria Dominguez, Laurie Lamoreaux, Steven S Ma, 534 Mario Roederer, and Raphael Gottardo.”
COMPREHENSIVE CLASSIFICATION OF RETINAL BIPOLAR NEURONS BY SINGLE-CELL TRANSCRIPTOMICS (2016, methodology): “…genes that exhibit a binary (ON/OFF) pattern of expression (see Appendix 2, and footnote 3), we used an alternative differential expression test (McDavid et al., 2013) to find ubiquitously detected genes that nonetheless exhibited a quantitative expression difference between clusters (results…”
Droplet scRNA-seq is not zero-inflated (2019, positive): “This was related to studies in single cell reverse-transcriptase qPCR (sc-qPCR) where data was considered was zero-inflated but otherwise log-normal (McDavid et al., 2013).”
Diffusion maps for high-dimensional single-cell analysis of differentiation data (2015, methodology): “It is important to properly treat such uncertainties of data (McDavid et al., 2013; Buettner et al., 2014).”
An accurate and robust imputation method scImpute for single-cell RNA-seq data (2018, methodology): “12 and Supplementary Tables 4 and 5).”
Revealing the vectors of cellular identity with single-cell genomics (2016, methodology): “These were first introduced 88 for modeling single-cell quantitative PCR (qPCR) as a mixture of log-normal and a point mass at zero with a mixing proportion that is the frequency of expression of the gene across all cells.”
MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data (2015, positive): “We and other groups [4 – 7] have shown that the proportion of cells with detectable expression reflects both technical factors and biological differences between samples.”
Single-Cell Transcriptomics Reveals a Population of Dormant Neural Stem Cells that Become Activated upon Brain Injury. (2015, positive): “We next set out to identify genes that would distinguish qNSC1 from parenchymal astrocytes by using a likelihood-ratio test (McDavid et al., 2013), which has been recently applied to single-cell transcriptomes of closely related cell populations (Macosko et al.”