Shape analysis of high-throughput transcriptomics experiment data.

ABSTRACT: The recent growth of high-throughput transcriptome technology has been paralleled by the development of statistical methodologies to analyze the data they produce. Some of these newly developed methods are based on the assumption that the data observed or a transformation of the data are relatively symmetric with light tails, usually summarized by assuming a Gaussian random component. It is indeed very difficult to assess this assumption for small sample sizes. In this article, we utilize L-moments statistics as the basis of exploratory data analysis, the assessment of distributional assumptions, and the hypothesis testing of high-throughput transcriptomic data. In particular, we use L-moments ratios for assessing the shape (skewness and kurtosis) of high-throughput transcriptome data. Bas

SUBMITTER: Okrah K 

PROVIDER: S-EPMC4570582 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature