ArticleBioinformatics (Oxford, England)2019
Robust prediction of clinical outcomes using cytometry data.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 44 citations in OpenAlex.
- cytoGPNet: Enhancing clinical outcome prediction accuracy using longitudinal cytometry data in small cohort studies.Patterns (New York, N.Y.) · 2025Article
- Microglia Single-Cell RNA-Seq Enables Robust and Applicable Markers of Biological Aging.Aging cell · 2025Article
- Conditional similarity triplets enable covariate-informed representations of single-cell data.BMC bioinformatics · 2025Article
- Unveiling the power of high-dimensional cytometry data with cyCONDOR.Nature communications · 2024Article
- Leveraging Single-Cell RNA-Seq to Generate Robust Microglia Aging Clocks.bioRxiv : the preprint server for biology · 2024Article
- Conditional Similarity Triplets Enable Covariate-Informed Representations of Single-Cell Data.Research square · 2024Article
- Dynamic immune signatures of patients with advanced non-small-cell lung cancer for infection prediction after immunotherapy.Frontiers in immunology · 2024Article
- A cell-level discriminative neural network model for diagnosis of blood cancers.Bioinformatics (Oxford, England) · 2023Article
- In-silico generation of high-dimensional immune response data in patients using a deep neural network.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2023Article
- A cytotoxic-skewed immune set point predicts low neutralizing antibody levels after Zika virus infection.Cell reports · 2022Article
- Automated identification of maximal differential cell populations in flow cytometry data.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2022Article
- Systematic identification of ACE2 expression modulators reveals cardiomyopathy as a risk factor for mortality in COVID-19 patients.Genome biology · 2022Article
- treekoR: identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data.Genome biology · 2021Article
- CytoPy: An autonomous cytometry analysis framework.PLoS computational biology · 2021Article
- A robust and interpretable end-to-end deep learning model for cytometry data.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- A Cancer Biologist's Primer on Machine Learning Applications in High-Dimensional Cytometry.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2020Review
- VoPo leverages cellular heterogeneity for predictive modeling of single-cell data.Nature communications · 2020Article
- Machine Learning of Discriminative Gate Locations for Clinical Diagnosis.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2020Article
- Metronomic capecitabine as an immune modulator in glioblastoma patients reduces myeloid-derived suppressor cells.JCI insight · 2019Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
motivationFlow 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.
resultsWe 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.