title: Detecting cognizable trends of gene expression in a time series RNA-sequencing experiment: a bootstrap approach.
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.
authors.affiliation: National Institute of Biomedical Genomics, Netaji Subhas Sanatorium (T. B. Hospital), P.O.: N.S.S., Kalyani 741 251, India.pp1@nibmg.ac.in.
authors.forename: Shatakshee
authors.initials: S
authors.lastname: Chatterjee
authors.forename: Partha P
authors.initials: PP
authors.lastname: Majumder
authors.forename: Priyanka
authors.initials: P
authors.lastname: Pandey
mesh_terms: D000465:Algorithms; D002478:Cells, Cultured; D003198:Computer Simulation; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D059014:High-Throughput Nucleotide Sequencing; D054884:Host-Pathogen Interactions; D006801:Humans; D008070:Lipopolysaccharides / Q000494:pharmacology; D008089:Listeria monocytogenes / Q000254:growth & development; D008957:Models, Genetic; D009000:Monocytes / Q000166:cytology / Q000187:drug effects / Q000378:metabolism* / Q000382:microbiology; D020411:Oligonucleotide Array Sequence Analysis; D018401:Sample Size; D013997:Time Factors; D014158:Transcription, Genetic
chemical_list: D008070:Lipopolysaccharides