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SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models

Overview of attention for article published in Genome Biology (Online Edition), September 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)

Mentioned by

blogs
1 blog
twitter
37 tweeters
facebook
1 Facebook page
wikipedia
2 Wikipedia pages
googleplus
1 Google+ user

Citations

dimensions_citation
136 Dimensions

Readers on

mendeley
144 Mendeley
citeulike
3 CiteULike
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Title
SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models
Published in
Genome Biology (Online Edition), September 2017
DOI 10.1186/s13059-017-1311-2
Pubmed ID
Authors

Hamim Zafar, Anthony Tzen, Nicholas Navin, Ken Chen, Luay Nakhleh

Abstract

Single-cell sequencing enables the inference of tumor phylogenies that provide insights on intra-tumor heterogeneity and evolutionary trajectories. Recently introduced methods perform this task under the infinite-sites assumption, violations of which, due to chromosomal deletions and loss of heterozygosity, necessitate the development of inference methods that utilize finite-sites models. We propose a statistical inference method for tumor phylogenies from noisy single-cell sequencing data under a finite-sites model. The performance of our method on synthetic and experimental data sets from two colorectal cancer patients to trace evolutionary lineages in primary and metastatic tumors suggests that employing a finite-sites model leads to improved inference of tumor phylogenies.

Twitter Demographics

The data shown below were collected from the profiles of 37 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 144 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
Unknown 143 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 41 28%
Researcher 29 20%
Student > Bachelor 13 9%
Student > Master 9 6%
Student > Doctoral Student 7 5%
Other 15 10%
Unknown 30 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 38 26%
Biochemistry, Genetics and Molecular Biology 33 23%
Computer Science 27 19%
Mathematics 4 3%
Engineering 2 1%
Other 7 5%
Unknown 33 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 33. 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
#1,010,240
of 22,479,668 outputs
Outputs from Genome Biology (Online Edition)
#831
of 4,087 outputs
Outputs of similar age
#21,593
of 297,343 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 22,479,668 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,087 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has done well, scoring higher than 79% 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 297,343 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them