Title |
CellMapper: rapid and accurate inference of gene expression in difficult-to-isolate cell types
|
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Published in |
Genome Biology, September 2016
|
DOI | 10.1186/s13059-016-1062-5 |
Pubmed ID | |
Authors |
Bradlee D. Nelms, Levi Waldron, Luis A. Barrera, Andrew W. Weflen, Jeremy A. Goettel, Guoji Guo, Robert K. Montgomery, Marian R. Neutra, David T. Breault, Scott B. Snapper, Stuart H. Orkin, Martha L. Bulyk, Curtis Huttenhower, Wayne I. Lencer |
Abstract |
We present a sensitive approach to predict genes expressed selectively in specific cell types, by searching publicly available expression data for genes with a similar expression profile to known cell-specific markers. Our method, CellMapper, strongly outperforms previous computational algorithms to predict cell type-specific expression, especially for rare and difficult-to-isolate cell types. Furthermore, CellMapper makes accurate predictions for human brain cell types that have never been isolated, and can be rapidly applied to diverse cell types from many tissues. We demonstrate a clinically relevant application to prioritize candidate genes in disease susceptibility loci identified by GWAS. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 40% |
United Kingdom | 1 | 20% |
India | 1 | 20% |
Unknown | 1 | 20% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 60% |
Science communicators (journalists, bloggers, editors) | 1 | 20% |
Scientists | 1 | 20% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Israel | 1 | 1% |
Canada | 1 | 1% |
Unknown | 85 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 26 | 30% |
Researcher | 21 | 24% |
Student > Master | 6 | 7% |
Student > Bachelor | 5 | 6% |
Other | 5 | 6% |
Other | 12 | 14% |
Unknown | 12 | 14% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 29 | 33% |
Biochemistry, Genetics and Molecular Biology | 18 | 21% |
Computer Science | 8 | 9% |
Medicine and Dentistry | 7 | 8% |
Neuroscience | 5 | 6% |
Other | 8 | 9% |
Unknown | 12 | 14% |