51. Simulation-based benchmarking of isoform quantification in single-cell RNA-seq
- Author
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Martin Hemberg, Anne C. Ferguson-Smith, Marcela Sjöberg Herrera, Jennifer Westoby, Hemberg, Martin [0000-0001-8895-5239], and Apollo - University of Cambridge Repository
- Subjects
0301 basic medicine ,Gene isoform ,lcsh:QH426-470 ,Population ,Cell ,Method ,RNA-Seq ,Computational biology ,Biology ,Benchmark ,03 medical and health sciences ,Bulk RNA-seq ,Mice ,0302 clinical medicine ,scRNA-seq ,medicine ,Animals ,Protein Isoforms ,Single cell ,Computer Simulation ,education ,lcsh:QH301-705.5 ,Gene ,Simulation based ,Cells, Cultured ,Isoform quantification ,education.field_of_study ,B-Lymphocytes ,Sequence Analysis, RNA ,Gene Expression Profiling ,High-Throughput Nucleotide Sequencing ,Benchmarking ,lcsh:Genetics ,030104 developmental biology ,medicine.anatomical_structure ,lcsh:Biology (General) ,Benchmark (computing) ,RNA ,Single-Cell Analysis ,030217 neurology & neurosurgery ,Software - Abstract
Single-cell RNA-seq has the potential to facilitate isoform quantification as the confounding factor of a mixed population of cells is eliminated. However, best practice for using existing quantification methods has not been established. We carry out a benchmark for five popular isoform quantification tools. Performance is generally good for simulated data based on SMARTer and SMART-seq2 data. The reduction in performance compared with bulk RNA-seq is small. An important biological insight comes from our analysis of real data which shows that genes that express two isoforms in bulk RNA-seq predominantly express one or neither isoform in individual cells. Electronic supplementary material The online version of this article (10.1186/s13059-018-1571-5) contains supplementary material, which is available to authorized users.
- Published
- 2018