Simulation-based benchmarking of isoform quantification in single-cell RNA-seq.
Published version
Peer-reviewed
Repository URI
Repository DOI
Change log
Authors
Westoby, Jennifer
Herrera, Marcela Sjöberg
Ferguson-Smith, Anne C
Hemberg, Martin https://orcid.org/0000-0001-8895-5239
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.
Description
Keywords
Benchmark, Bulk RNA-seq, Isoform quantification, Single cell, scRNA-seq, Animals, B-Lymphocytes, Benchmarking, Cells, Cultured, Computer Simulation, Gene Expression Profiling, High-Throughput Nucleotide Sequencing, Mice, Protein Isoforms, RNA, Sequence Analysis, RNA, Single-Cell Analysis, Software
Journal Title
Genome Biol
Conference Name
Journal ISSN
1474-7596
1474-760X
1474-760X
Volume Title
19
Publisher
Springer Science and Business Media LLC
Publisher DOI
Sponsorship
Wellcome Trust (095606/Z/11/Z)
Biotechnology and Biological Sciences Research Council (1804962)
Biotechnology and Biological Sciences Research Council (BB/M011194/1)
Medical Research Council (MR/R009791/1)
European Commission (282510)
Biotechnology and Biological Sciences Research Council (1804962)
Biotechnology and Biological Sciences Research Council (BB/M011194/1)
Medical Research Council (MR/R009791/1)
European Commission (282510)