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.
Volume 102, 2023
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