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BTR: training asynchronous Boolean models using single-cell expression data

Overview of attention for article published in BMC Bioinformatics, September 2016
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Mentioned by

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4 tweeters

Citations

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54 Dimensions

Readers on

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93 Mendeley
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1 CiteULike
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Title
BTR: training asynchronous Boolean models using single-cell expression data
Published in
BMC Bioinformatics, September 2016
DOI 10.1186/s12859-016-1235-y
Pubmed ID
Authors

Chee Yee Lim, Huange Wang, Steven Woodhouse, Nir Piterman, Lorenz Wernisch, Jasmin Fisher, Berthold Göttgens

Abstract

Rapid technological innovation for the generation of single-cell genomics data presents new challenges and opportunities for bioinformatics analysis. One such area lies in the development of new ways to train gene regulatory networks. The use of single-cell expression profiling technique allows the profiling of the expression states of hundreds of cells, but these expression states are typically noisier due to the presence of technical artefacts such as drop-outs. While many algorithms exist to infer a gene regulatory network, very few of them are able to harness the extra expression states present in single-cell expression data without getting adversely affected by the substantial technical noise present. Here we introduce BTR, an algorithm for training asynchronous Boolean models with single-cell expression data using a novel Boolean state space scoring function. BTR is capable of refining existing Boolean models and reconstructing new Boolean models by improving the match between model prediction and expression data. We demonstrate that the Boolean scoring function performed favourably against the BIC scoring function for Bayesian networks. In addition, we show that BTR outperforms many other network inference algorithms in both bulk and single-cell synthetic expression data. Lastly, we introduce two case studies, in which we use BTR to improve published Boolean models in order to generate potentially new biological insights. BTR provides a novel way to refine or reconstruct Boolean models using single-cell expression data. Boolean model is particularly useful for network reconstruction using single-cell data because it is more robust to the effect of drop-outs. In addition, BTR does not assume any relationship in the expression states among cells, it is useful for reconstructing a gene regulatory network with as few assumptions as possible. Given the simplicity of Boolean models and the rapid adoption of single-cell genomics by biologists, BTR has the potential to make an impact across many fields of biomedical research.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 93 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Unknown 92 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 33%
Researcher 13 14%
Student > Master 12 13%
Student > Bachelor 8 9%
Other 4 4%
Other 10 11%
Unknown 15 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 24 26%
Agricultural and Biological Sciences 18 19%
Computer Science 17 18%
Mathematics 5 5%
Engineering 3 3%
Other 10 11%
Unknown 16 17%

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 08 September 2016.
All research outputs
#11,149,324
of 17,800,904 outputs
Outputs from BMC Bioinformatics
#4,178
of 6,267 outputs
Outputs of similar age
#147,581
of 272,821 outputs
Outputs of similar age from BMC Bioinformatics
#9
of 28 outputs
Altmetric has tracked 17,800,904 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,267 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one is in the 28th percentile – i.e., 28% 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 272,821 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 28 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 64% of its contemporaries.