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BayesFlow: latent modeling of flow cytometry cell populations

Overview of attention for article published in BMC Bioinformatics, January 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

Mentioned by

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10 tweeters

Citations

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14 Dimensions

Readers on

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62 Mendeley
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Title
BayesFlow: latent modeling of flow cytometry cell populations
Published in
BMC Bioinformatics, January 2016
DOI 10.1186/s12859-015-0862-z
Pubmed ID
Authors

Kerstin Johnsson, Jonas Wallin, Magnus Fontes

Abstract

Flow cytometry is a widespread single-cell measurement technology with a multitude of clinical and research applications. Interpretation of flow cytometry data is hard; the instrumentation is delicate and can not render absolute measurements, hence samples can only be interpreted in relation to each other while at the same time comparisons are confounded by inter-sample variation. Despite this, most automated flow cytometry data analysis methods either treat samples individually or ignore the variation by for example pooling the data. A key requirement for models that include multiple samples is the ability to visualize and assess inferred variation, since what could be technical variation in one setting would be different phenotypes in another. We introduce BayesFlow, a pipeline for latent modeling of flow cytometry cell populations built upon a Bayesian hierarchical model. The model systematizes variation in location as well as shape. Expert knowledge can be incorporated through informative priors and the results can be supervised through compact and comprehensive visualizations. BayesFlow is applied to two synthetic and two real flow cytometry data sets. For the first real data set, taken from the FlowCAP I challenge, BayesFlow does not only give a gating which would place it among the top performers in FlowCAP I for this dataset, it also gives a more consistent treatment of different samples than either manual gating or other automated gating methods. The second real data set contains replicated flow cytometry measurements of samples from healthy individuals. BayesFlow gives here cell populations with clear expression patterns and small technical intra-donor variation as compared to biological inter-donor variation. Modeling latent relations between samples through BayesFlow enables a systematic analysis of inter-sample variation. As opposed to other joint gating methods, effort is put at ensuring that the obtained partition of the data corresponds to actual cell populations, and the result is therefore directly biologically interpretable. BayesFlow is freely available at GitHub.

Twitter Demographics

The data shown below were collected from the profiles of 10 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 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
France 1 2%
Australia 1 2%
Unknown 59 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 29%
Researcher 18 29%
Student > Bachelor 7 11%
Student > Master 5 8%
Student > Doctoral Student 3 5%
Other 6 10%
Unknown 5 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 31%
Computer Science 9 15%
Mathematics 7 11%
Biochemistry, Genetics and Molecular Biology 6 10%
Immunology and Microbiology 6 10%
Other 9 15%
Unknown 6 10%

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 15 February 2016.
All research outputs
#2,588,186
of 10,444,782 outputs
Outputs from BMC Bioinformatics
#1,408
of 4,169 outputs
Outputs of similar age
#90,436
of 330,976 outputs
Outputs of similar age from BMC Bioinformatics
#56
of 134 outputs
Altmetric has tracked 10,444,782 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,169 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 65% 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 330,976 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 72% of its contemporaries.
We're also able to compare this research output to 134 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 58% of its contemporaries.