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CellMapper: rapid and accurate inference of gene expression in difficult-to-isolate cell types

Overview of attention for article published in Genome Biology, September 2016
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Title
CellMapper: rapid and accurate inference of gene expression in difficult-to-isolate cell types
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.

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X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.

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%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 March 2018.
All research outputs
#15,168,964
of 25,373,627 outputs
Outputs from Genome Biology
#3,931
of 4,467 outputs
Outputs of similar age
#182,309
of 330,419 outputs
Outputs of similar age from Genome Biology
#47
of 54 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 11th percentile – i.e., 11% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 330,419 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 54 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.