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Bioinformatics. 2019 Apr 1;35(7):1197-1203. doi: 10.1093/bioinformatics/bty768.

Robust prediction of clinical outcomes using cytometry data.

Author information

1
Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA.

Abstract

MOTIVATION:

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.

RESULTS:

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.

AVAILABILITY AND IMPLEMENTATION:

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 INFORMATION:

Supplementary data are available at Bioinformatics online.

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