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https://www.ias.ac.in/article/fulltext/jgen/095/03/0587-0593

bootstrapping; RNA sequencing; time-trends; gene expression; simulation.

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.

SHATAKSHEE CHATTERJEE PARTHA P. MAJUMDER PRIYANKA PANDEY

Published

5 September 2016

Manuscript received

26 August 2015

Manuscript revised

8 November 2015

Accepted

17 December 2015

Unedited version published online

23 December 2015

Final version published online

18 August 2016

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