Format

Send to

Choose Destination
Genome Biol. 2018 Jun 19;19(1):78. doi: 10.1186/s13059-018-1449-6.

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

Author information

1
Department of Applied Mathematics, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia.
2
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
3
Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, USA.
4
Harvard Stem Cell Institute, Cambridge, MA, USA.
5
Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA.
6
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. peter.kharchenko@post.harvard.edu.
7
Harvard Stem Cell Institute, Cambridge, MA, USA. peter.kharchenko@post.harvard.edu.

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.

PMID:
29921301
PMCID:
PMC6010209
DOI:
10.1186/s13059-018-1449-6
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center