Data Exploration, Quality Control and Testing in Single-Cell qPCR-Based Gene Expression Experiments

TL;DR

This paper introduces a statistical framework for exploring, quality controlling, and analyzing single-cell gene expression data from microfluidic qPCR experiments, addressing heterogeneity and improving differential expression detection.

Contribution

The authors develop a novel statistical model and testing approach tailored for single-cell qPCR data, enhancing accuracy and power over existing methods.

Findings

The combined likelihood-ratio test outperforms individual component tests.

Quality control criteria effectively filter unreliable cell measurements.

The framework is adaptable to other multi-parametric single-cell data platforms.

Abstract

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 (qPCR) now enables high-throughput single-cell gene expression measurement, allowing assessment of cellular heterogeneity. However very little analytic tools have been developed specifically for the statistical and analytical challenges of single-cell qPCR data. 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 for which 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. While developed for measurements from a specific platform (Fluidigm), these tools are generalizable to other multi-parametric measures over large numbers of events.