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

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.Comment: 9 pages, 5 figure

Vertical intra-industry trade and the EU accession: The case of Hungarian agri-food sector

The aim of this paper is to examine the relationship between the factor endowment and the pattern of intra-industry trade. Our empirical analysis relates to Hungary’s intra-industry trade in agri-food products with 26 member states of the EU over the period 1999-2010. Estimations reject the comparative advantage explanation of vertical intra-industry trade and provide partial support the prediction of Flam and Helpman model. Findings highlight that nature of factor endowments play also important role in explanation of vertical intra-industry trade. Other variables like market size and distance confirm the theoretical expectations. In addition, trade with new member states positively, whilst the EU accession ambigouosly influence the share of vertical IIT