Dataset Information

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

ABSTRACT:

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).

SUBMITTER: McDavid A

PROVIDER: S-EPMC3570210 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature

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Publications

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

McDavid Andrew A Finak Greg G Chattopadyay Pratip K PK Dominguez Maria M Lamoreaux Laurie L Ma Steven S SS Roederer Mario M Gottardo Raphael R

Bioinformatics (Oxford, England) 20121224 4

<h4>Motivation</h4>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 singl ...[more]

PMID: 23267174

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Dataset Information

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

Motivation

Results

Availability

Publications

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

Similar Datasets

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