  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.
