Detecting cognizable trends of gene expression in a time series RNA-sequencing experiment: a bootstrap approach
Study of temporal trajectory of gene expression is important. RNA sequencing is popular in genome-scale studies of transcription. Because of high expenses involved, many time-course RNA sequencing studies are challenged by inadequacy of sample sizes. This poses difficulties in conducting formal statistical tests of significance of null hypotheses. We propose a bootstrap algorithm to identify ‘cognizable’ ‘time-trends’ of gene expression. Properties of the proposed algorithm are derived using a simulation study. The proposed algorithm captured known ‘time-trends’ in the simulated data with a high probability of success, even when sample sizes were small (n<10). The proposed statistical method is efficient and robust to capture ‘cognizable’ ‘time-trends’ in RNA sequencing data.
In this paper, we give simple mathematical results that allow us to get all cut-off points that maximize the overall proportion of correct classifications in any binary classification method (and, in particular, in binary logistic regression). In addition, we give results that allow us to get all cut-off points that maximize a weighted combination of specificity and sensitivity. In addition, we discuss measures of association between predicted probabilities and observed responses, and, in particular, we discuss the calculation of the overall percentages of concordant, discordant, and tied pairs of input observations with different responses. We mention that the calculation of these quantities by SAS and Minitab is sometimes incorrect. The concepts and methods of the paper are illustrated by a hypothetical example of school retention data.