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Deconvolution of gene expression from cell populations across the C. eleganslineage

Overview of attention for article published in BMC Bioinformatics, June 2013
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Title
Deconvolution of gene expression from cell populations across the C. eleganslineage
Published in
BMC Bioinformatics, June 2013
DOI 10.1186/1471-2105-14-204
Pubmed ID
Authors

Joshua T Burdick, John Isaac Murray

Abstract

Knowledge of when and in which cells each gene is expressed across multicellular organisms is critical in understanding both gene function and regulation of cell type diversity. However, methods for measuring expression typically involve a trade-off between imaging-based methods, which give the precise location of a limited number of genes, and higher throughput methods such as RNA-seq, which include all genes, but are more limited in their resolution to apply to many tissues. We propose an intermediate method, which estimates expression in individual cells, based on high-throughput measurements of expression from multiple overlapping groups of cells. This approach has particular benefits in organisms such as C. elegans where invariant developmental patterns make it possible to define these overlapping populations of cells at single-cell resolution.

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X Demographics

The data shown below were collected from the profiles of 2 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 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Netherlands 1 2%
United States 1 2%
Unknown 39 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 33%
Student > Ph. D. Student 10 24%
Professor > Associate Professor 4 10%
Student > Bachelor 3 7%
Other 3 7%
Other 7 17%
Unknown 1 2%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 48%
Biochemistry, Genetics and Molecular Biology 7 17%
Engineering 3 7%
Neuroscience 3 7%
Computer Science 2 5%
Other 4 10%
Unknown 3 7%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 22 June 2013.
All research outputs
#17,690,153
of 22,712,476 outputs
Outputs from BMC Bioinformatics
#5,919
of 7,259 outputs
Outputs of similar age
#141,326
of 196,753 outputs
Outputs of similar age from BMC Bioinformatics
#74
of 86 outputs
Altmetric has tracked 22,712,476 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,259 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 13th percentile – i.e., 13% 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 196,753 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 86 others from the same source and published within six weeks on either side of this one. This one is in the 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.