Statistical analysis of multi-dimensional, temporal gene expression of stem cells to elucidate colony size-dependent neural differentiation.
Statistical analysis of multi-dimensional, temporal gene expression of stem cells to elucidate colony size-dependent neural differentiation.
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DOI：
10.1039/c8mo00011e
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发表时间：
2018-04-16
期刊：
影响因子：
2.9
通讯作者：
Tavana H
中科院分区：
文献类型：
--
作者：
Joshi R ;Fuller B ;Li J ;Tavana H
利用定量聚合酶链式反应进行高通量基因表达分析通常被用来识别复杂细胞过程的分子标记。然而，由于有限的生物重复和大量的测量，对多维、时间的基因表达数据的统计分析是复杂的。此外，许多可用于分析时间序列数据的统计工具都假定数据序列是静态的，并且不会随着时间的推移而演变。在这一假设下，用于对时间序列建模的参数是固定的，因此可以通过将数据汇集在一起来估计。然而，在许多情况下，生物系统的动态过程涉及未知时间点的突变，使得平稳时间序列的假设被打破。我们使用包括层次聚类、变化点检测和多重测试在内的统计方法的组合来解决这个问题。我们将这种多步骤方法应用于多维、时间的基因表达数据，这些数据源于我们对干细胞集落大小依赖的神经细胞分化的研究。基因表达数据是时间序列，因为观察是随着时间的推移而顺序记录的。层次聚类根据基因的时间表达谱将基因分成三个不同的簇；变化点检测识别整个数据集被分成几个同质子集的特定时间点，以允许对每个子集进行单独分析；多个测试过程识别每个数据子集内每个簇中差异表达的基因。我们确定了我们的多步骤方法精确定位了支持干细胞群体大小介导的神经分化的特定基因集，并证明了它比传统的参数和非参数检验的优势，后者没有考虑数据的时间动态。重要的是，我们提出的方法广泛适用于任何样本大小有限的多变量数据集，例如在药物和生物标记物发现研究中的高通量和高含量筛选。我们建立了一个三步统计方法来阐明调控干细胞集落大小介导的神经分化的特定基因集。
High throughput gene expression analysis using qPCR is commonly used to identify molecular markers of complex cellular processes. However, statistical analysis of multi-dimensional, temporal gene expression 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 sequence is static and does not evolve over time. With this assumption, the parameters used to model the time series are fixed and thus, can be estimated by pooling data together. However, in many cases, dynamic processes of biological systems involve abrupt changes at unknown time points, making the assumption of stationary time series break down. We addressed this problem using a combination of statistical methods including hierarchical clustering, change point detection, and multiple testing. We applied this multi-step method to multi-dimensional, temporal gene expression data that resulted from our study of colony size-dependent neural cell differentiation of stem cells. The gene expression data were time series as the observations were recorded sequentially over time. Hierarchical clustering segregated the genes into three distinct clusters based on their temporal expression profiles; change point detection identified specific time points at which the entire dataset was divided into several homogenous subsets to allow a separate analysis of each subset; and multiple testing procedure identified the differentially expressed genes in each cluster within each subset of data. We established that our multi-step approach pinpoints specific sets of genes that underlie colony size-mediated neural differentiation of stem cells and demonstrated its advantages over conventional parametric and non-parametric tests that do not take into account temporal dynamics of the data. Importantly, our proposed approach is broadly applicable to any multivariate data sets of limited sample size from high throughput and high content screening such as in drug and biomarker discovery studies. We established a three-step statistical approach to elucidate specific sets of genes that regulate colony size-mediated neural differentiation of stem cells.
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影响因子：
2.9
作者：
Kim TK
通讯作者：
Kim TK
影响因子：
4.8
作者：
Kitchen, Robert R.;Kubista, Mikael;Tichopad, Ales
通讯作者：
Tichopad, Ales
影响因子：
1.2
作者：
Alimperti, Stella;Andreadis, Stelios T.
通讯作者：
Andreadis, Stelios T.
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
DOI：
10.1080/01621459.2013.849605
发表时间：
2014-03-01
影响因子：
3.7
作者：
Matteson, David S.;James, Nicholas A.
通讯作者：
James, Nicholas A.