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A mixture model for expression deconvolution from RNA-seq in heterogeneous tissues

Overview of attention for article published in BMC Bioinformatics, April 2013
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

Mentioned by

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6 X users

Citations

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40 Dimensions

Readers on

mendeley
141 Mendeley
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1 CiteULike
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Title
A mixture model for expression deconvolution from RNA-seq in heterogeneous tissues
Published in
BMC Bioinformatics, April 2013
DOI 10.1186/1471-2105-14-s5-s11
Pubmed ID
Authors

Yi Li, Xiaohui Xie

Abstract

RNA-seq, a next-generation sequencing based method for transcriptome analysis, is rapidly emerging as the method of choice for comprehensive transcript abundance estimation. The accuracy of RNA-seq can be highly impacted by the purity of samples. A prominent, outstanding problem in RNA-seq is how to estimate transcript abundances in heterogeneous tissues, where a sample is composed of more than one cell type and the inhomogeneity can substantially confound the transcript abundance estimation of each individual cell type. Although experimental methods have been proposed to dissect multiple distinct cell types, computationally "deconvoluting" heterogeneous tissues provides an attractive alternative, since it keeps the tissue sample as well as the subsequent molecular content yield intact.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 141 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 11 8%
United Kingdom 2 1%
Brazil 1 <1%
Israel 1 <1%
Canada 1 <1%
France 1 <1%
Argentina 1 <1%
New Zealand 1 <1%
Unknown 122 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 45 32%
Researcher 35 25%
Student > Master 15 11%
Student > Bachelor 15 11%
Professor > Associate Professor 5 4%
Other 17 12%
Unknown 9 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 68 48%
Biochemistry, Genetics and Molecular Biology 25 18%
Computer Science 12 9%
Mathematics 7 5%
Medicine and Dentistry 7 5%
Other 9 6%
Unknown 13 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 24 August 2016.
All research outputs
#7,372,420
of 22,712,476 outputs
Outputs from BMC Bioinformatics
#2,983
of 7,259 outputs
Outputs of similar age
#65,616
of 199,484 outputs
Outputs of similar age from BMC Bioinformatics
#60
of 135 outputs
Altmetric has tracked 22,712,476 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
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 has gotten more attention than average, scoring higher than 58% of its peers.
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 199,484 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 135 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.