Statistical analysis of multi-dimensional, temporal gene expression of stem cells to elucidate colony size-dependent neural differentiation

摘要

High throughput gene expression analysis using qPCR is commonly used to identify molecular markersof complex cellular processes. However, statistical analysis of multi-dimensional, temporal geneexpression data is complicated by limited biological replicates and large number of measurements.Moreover, many available statistical tools for analysis of time series data assume that the data sequenceis static and does not evolve over time. With this assumption, the parameters used to model the timeseries are fixed and thus, can be estimated by pooling data together. However, in many cases, dynamicprocesses of biological systems involve abrupt changes at unknown time points, making the assumptionof stationary time series break down. We addressed this problem using a combination of statisticalmethods including hierarchical clustering, change point detection, and multiple testing. We applied thismulti-step method to multi-dimensional, temporal gene expression data that resulted from our study ofcolony size-dependent neural cell differentiation of stem cells. The gene expression data were timeseries as the observations were recorded sequentially over time. Hierarchical clustering segregated thegenes into three distinct clusters based on their temporal expression profiles; change point detectionidentified specific time points at which the entire dataset was divided into several homogenous subsetsto allow a separate analysis of each subset; and multiple testing procedure identified the differentiallyexpressed genes in each cluster within each subset of data. We established that our multi-step approachpinpoints specific sets of genes that underlie colony size-mediated neural differentiation of stem cellsand demonstrated its advantages over conventional parametric and non-parametric tests that do nottake into account temporal dynamics of the data. Importantly, our proposed approach is broadlyapplicable to any multivariate data sets of limited sample size from high throughput and high contentscreening such as in drug and biomarker discovery studies.