Data exploration, quality control and testing in single-cell qPCR-based gene expression experimentsMOTIVATION: 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).
Data Exploration, Quality Control and Testing in Single-Cell qPCR-Based Gene Expression ExperimentsCell 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.
Data Exploration, Quality Control and Testing in Single-Cell qPCR-Based\n Gene Expression ExperimentsCell populations are never truly homogeneous; individual cells exist in\nbiochemical states that define functional differences between them. New\ntechnology based on microfluidic arrays combined with multiplexed quantitative\npolymerase chain reactions (qPCR) now enables high-throughput single-cell gene\nexpression measurement, allowing assessment of cellular heterogeneity. However\nvery little analytic tools have been developed specifically for the statistical\nand analytical challenges of single-cell qPCR data. We present a statistical\nframework for the exploration, quality control, and analysis of single-cell\ngene expression data from microfluidic arrays. We assess accuracy and\nwithin-sample heterogeneity of single-cell expression and develop quality\ncontrol criteria to filter unreliable cell measurements. We propose a\nstatistical model accounting for the fact that genes at the single-cell level\ncan be on (and for which a continuous expression measure is recorded) or\ndichotomously off (and the recorded expression is zero). Based on this model,\nwe derive a combined likelihood-ratio test for differential expression that\nincorporates both the discrete and continuous components. Using an experiment\nthat examines treatment-specific changes in expression, we show that this\ncombined test is more powerful than either the continuous or dichotomous\ncomponent in isolation, or a t-test on the zero-inflated data. While developed\nfor measurements from a specific platform (Fluidigm), these tools are\ngeneralizable to other multi-parametric measures over large numbers of events.\n