dropEst: pipeline for accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Viktor Petukhov, Jimin Guo, Ninib Baryawno, Nicolas Severe, David T. Scadden, Maria G. Samsonova, Peter V. Kharchenko

ABSTRACT

Recent single-cell RNA-seq protocols based on droplet microfluidics use massively multiplexed barcoding to enable simultaneous measurements of transcriptomes for thousands of individual cells. The increasing complexity of such data creates challenges for subsequent computational processing and troubleshooting of these experiments, with few software options currently available. Here, we describe a flexible pipeline for processing droplet-based transcriptome data that implements barcode corrections, classification of cell quality, and diagnostic information about the droplet libraries. We introduce advanced methods for correcting composition bias and sequencing errors affecting cellular and molecular barcodes to provide more accurate estimates of molecular counts in individual cells. More... »

PAGES

78

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s13059-018-1449-6

DOI

http://dx.doi.org/10.1186/s13059-018-1449-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1104989805

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29921301


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