Title |
dropEst: pipeline for accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments
|
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Published in |
Genome Biology, June 2018
|
DOI | 10.1186/s13059-018-1449-6 |
Pubmed ID | |
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. |
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United States | 5 | 18% |
Spain | 3 | 11% |
Australia | 3 | 11% |
United Kingdom | 2 | 7% |
Switzerland | 2 | 7% |
Singapore | 1 | 4% |
Germany | 1 | 4% |
Austria | 1 | 4% |
Unknown | 10 | 36% |
Demographic breakdown
Type | Count | As % |
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Scientists | 20 | 71% |
Members of the public | 7 | 25% |
Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 283 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 65 | 23% |
Student > Ph. D. Student | 63 | 22% |
Student > Master | 33 | 12% |
Student > Bachelor | 13 | 5% |
Professor | 12 | 4% |
Other | 32 | 11% |
Unknown | 65 | 23% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 95 | 34% |
Agricultural and Biological Sciences | 51 | 18% |
Medicine and Dentistry | 13 | 5% |
Computer Science | 11 | 4% |
Immunology and Microbiology | 9 | 3% |
Other | 32 | 11% |
Unknown | 72 | 25% |