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THetA: inferring intra-tumor heterogeneity from high-throughput DNA sequencing data

Overview of attention for article published in Genome Biology (Online Edition), January 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)

Mentioned by

twitter
9 tweeters
wikipedia
2 Wikipedia pages

Citations

dimensions_citation
196 Dimensions

Readers on

mendeley
248 Mendeley
citeulike
5 CiteULike
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Title
THetA: inferring intra-tumor heterogeneity from high-throughput DNA sequencing data
Published in
Genome Biology (Online Edition), January 2013
DOI 10.1186/gb-2013-14-7-r80
Pubmed ID
Authors

Layla Oesper, Ahmad Mahmoody, Benjamin J Raphael

Abstract

Tumor samples are typically heterogeneous, containing admixture by normal, non-cancerous cells and one or more subpopulations of cancerous cells. Whole-genome sequencing of a tumor sample yields reads from this mixture, but does not directly reveal the cell of origin for each read. We introduce THetA (Tumor Heterogeneity Analysis), an algorithm that infers the most likely collection of genomes and their proportions in a sample, for the case where copy number aberrations distinguish subpopulations. THetA successfully estimates normal admixture and recovers clonal and subclonal copy number aberrations in real and simulated sequencing data. THetA is available at http://compbio.cs.brown.edu/software/.

Twitter Demographics

The data shown below were collected from the profiles of 9 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 248 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 13 5%
Germany 3 1%
Belgium 2 <1%
Switzerland 1 <1%
Sweden 1 <1%
Italy 1 <1%
Australia 1 <1%
United Kingdom 1 <1%
Unknown 225 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 69 28%
Researcher 68 27%
Student > Master 21 8%
Student > Bachelor 21 8%
Professor 10 4%
Other 30 12%
Unknown 29 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 96 39%
Biochemistry, Genetics and Molecular Biology 50 20%
Computer Science 34 14%
Medicine and Dentistry 12 5%
Mathematics 4 2%
Other 17 7%
Unknown 35 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 January 2021.
All research outputs
#3,834,866
of 20,114,180 outputs
Outputs from Genome Biology (Online Edition)
#2,420
of 3,911 outputs
Outputs of similar age
#33,643
of 173,670 outputs
Outputs of similar age from Genome Biology (Online Edition)
#5
of 5 outputs
Altmetric has tracked 20,114,180 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,911 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.9. This one is in the 38th percentile – i.e., 38% 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 173,670 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one.