Abstract
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.
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Acknowledgements
We would like to acknowledge Rishav Ray, National Institute of Biomedical Genomics, Kalyani, India, for his contribution in generating simulated dataset and generating bootstrap results for five time-point dataset. Shatakshee Chatterjee is supported by Indian Council of Medical Research fellowship.
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[Chatterjee S., Majumder P. P. and Pandey P. 2016 Detecting cognizable trends of gene expression in a time series RNA-sequencing experiment: a bootstrap approach. J. Genet. 95, xx–xx]
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CHATTERJEE, S., MAJUMDER, P.P. & PANDEY, P. Detecting cognizable trends of gene expression in a time series RNA-sequencing experiment: a bootstrap approach. J Genet 95, 587–593 (2016). https://doi.org/10.1007/s12041-016-0681-7
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DOI: https://doi.org/10.1007/s12041-016-0681-7