title: Effects of subsampling on characteristics of RNA-seq data from triple-negative breast cancer patients.
abstract: BACKGROUND
  Data from RNA-seq experiments provide a wealth of information about the transcriptome of an organism. However, the analysis of such data is very demanding. In this study, we aimed to establish robust analysis procedures that can be used in clinical practice.
  
  
  METHODS
  We studied RNA-seq data from triple-negative breast cancer patients. Specifically, we investigated the subsampling of RNA-seq data.
  
  
  RESULTS
  The main results of our investigations are as follows: (1) the subsampling of RNA-seq data gave biologically realistic simulations of sequencing experiments with smaller sequencing depth but not direct scaling of count matrices; (2) the saturation of results required an average sequencing depth larger than 32 million reads and an individual sequencing depth larger than 46 million reads; and (3) for an abrogated feature selection, higher moments of the distribution of all expressed genes had a higher sensitivity for signal detection than the corresponding mean values.
  
  
  CONCLUSIONS
  Our results reveal important characteristics of RNA-seq data that must be understood before one can apply such an approach to translational medicine.
authors.affiliation: Computational Biology and Machine Learning Laboratory, Faculty of Medicine, Health and Life Sciences, School of Medicine, Dentistry and Biomedical Sciences, Center for Cancer Research and Cell Biology, Queen's University Belfast, 97 Lisburn Road, Belfast, BT9 7JL, UK. astupnikov01@qub.ac.uk.
authors.forename: Alexey
authors.initials: A
authors.lastname: Stupnikov
authors.affiliation: Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA. gvglazko@uams.edu.
authors.forename: Galina V
authors.initials: GV
authors.lastname: Glazko
authors.affiliation: Computational Biology and Machine Learning Laboratory, Faculty of Medicine, Health and Life Sciences, School of Medicine, Dentistry and Biomedical Sciences, Center for Cancer Research and Cell Biology, Queen's University Belfast, 97 Lisburn Road, Belfast, BT9 7JL, UK. v@bio-complexity.com.|Computational Medicine and Statistical Learning Laboratory, Department of Signal Processing, Tampere University of Technology, Korkeakoulunkatu 1, Tampere, 33720, Finland. v@bio-complexity.com.
authors.forename: Frank
authors.initials: F
authors.lastname: Emmert-Streib
mesh_terms: D020869:Gene Expression Profiling; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D012313:RNA; D059467:Transcriptome; D064726:Triple Negative Breast Neoplasms
chemical_list: D012313:RNA