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Robust prediction of clinical outcomes using cytometry data

Overview of attention for article published in Bioinformatics, August 2018
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53 Mendeley
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Title
Robust prediction of clinical outcomes using cytometry data
Published in
Bioinformatics, August 2018
DOI 10.1093/bioinformatics/bty768
Pubmed ID
Authors

Zicheng Hu, Benjamin S Glicksberg, Atul J Butte

Abstract

Flow cytometry and mass cytometry are widely used to diagnose diseases and to predict clinical outcomes. When associating clinical features with cytometry data, traditional analysis methods require cell gating as an intermediate step, leading to information loss and susceptibility to batch effects. Here, we wish to explore an alternative approach that predicts clinical features from cytometry data without the cell-gating step. We also wish to test if such a gating-free approach increases the accuracy and robustness of the prediction. We propose a novel strategy (CytoDx) to predict clinical outcomes using cytometry data without cell gating. Applying CytoDx on real-world datasets allow us to predict multiple types of clinical features. In particular, CytoDx is able to predict the response to influenza vaccine using highly heterogeneous datasets, demonstrating that it is not only accurate but also robust to batch effects and cytometry platforms. CytoDx is available as an R package on Bioconductor (bioconductor.org/packages/CytoDx). Data and scripts for reproducing the results are available on bitbucket.org/zichenghu_ucsf/cytodx_study_code/downloads. Supplementary data are available at Bioinformatics online.

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The data shown below were collected from the profiles of 3 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 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 53 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 17%
Researcher 9 17%
Student > Master 5 9%
Student > Bachelor 3 6%
Student > Doctoral Student 2 4%
Other 7 13%
Unknown 18 34%
Readers by discipline Count As %
Immunology and Microbiology 8 15%
Agricultural and Biological Sciences 7 13%
Biochemistry, Genetics and Molecular Biology 6 11%
Computer Science 5 9%
Mathematics 2 4%
Other 6 11%
Unknown 19 36%
Attention Score in Context

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 10 September 2018.
All research outputs
#13,824,594
of 23,577,761 outputs
Outputs from Bioinformatics
#6,596
of 9,035 outputs
Outputs of similar age
#171,361
of 336,434 outputs
Outputs of similar age from Bioinformatics
#134
of 200 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,035 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. 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 336,434 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 200 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.