Title |
ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis
|
---|---|
Published in |
Genome Biology, November 2015
|
DOI | 10.1186/s13059-015-0805-z |
Pubmed ID | |
Authors |
Emma Pierson, Christopher Yau |
Abstract |
Single-cell RNA-seq data allows insight into normal cellular function and various disease states through molecular characterization of gene expression on the single cell level. Dimensionality reduction of such high-dimensional data sets is essential for visualization and analysis, but single-cell RNA-seq data are challenging for classical dimensionality-reduction methods because of the prevalence of dropout events, which lead to zero-inflated data. Here, we develop a dimensionality-reduction method, (Z)ero (I)nflated (F)actor (A)nalysis (ZIFA), which explicitly models the dropout characteristics, and show that it improves modeling accuracy on simulated and biological data sets. |
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United Kingdom | 6 | 14% |
Germany | 3 | 7% |
France | 3 | 7% |
Australia | 2 | 5% |
Canada | 2 | 5% |
Ireland | 1 | 2% |
Denmark | 1 | 2% |
Estonia | 1 | 2% |
Other | 1 | 2% |
Unknown | 15 | 36% |
Demographic breakdown
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Scientists | 24 | 57% |
Members of the public | 16 | 38% |
Science communicators (journalists, bloggers, editors) | 2 | 5% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United Kingdom | 5 | <1% |
Sweden | 2 | <1% |
Hungary | 1 | <1% |
Denmark | 1 | <1% |
Netherlands | 1 | <1% |
Unknown | 591 | 97% |
Demographic breakdown
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Researcher | 99 | 16% |
Student > Master | 56 | 9% |
Student > Bachelor | 52 | 9% |
Student > Doctoral Student | 30 | 5% |
Other | 80 | 13% |
Unknown | 124 | 20% |
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Agricultural and Biological Sciences | 128 | 21% |
Computer Science | 88 | 14% |
Mathematics | 34 | 6% |
Engineering | 22 | 4% |
Other | 68 | 11% |
Unknown | 140 | 23% |