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CloudPred: Predicting Patient Phenotypes From Single-cell RNA-seq.

Bryan He¹, Matthew Thomson, Meena Subramaniam

¹Department of Computer Science, Stanford University, United States, bryanhe@stanford.edu.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|December 10, 2021

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This study introduces CloudPred, a machine learning tool predicting disease from single-cell RNA sequencing (scRNA-seq) data. CloudPred accurately identifies disease phenotypes and relevant cell types, advancing precision medicine.

Area of Science:

Computational Biology
Genomics
Machine Learning

Background:

Single-cell RNA sequencing (scRNA-seq) offers high-resolution biological data for disease prognosis and precision medicine.
Predicting phenotypes from scRNA-seq is challenging due to variable cell counts and heterogeneity, with standard methods losing single-cell resolution.

Purpose of the Study:

To develop an interpretable machine learning algorithm, CloudPred, for predicting disease phenotypes from scRNA-seq data.
To address limitations of standard methods by preserving single-cell resolution and avoiding bias from prior annotations.

Main Methods:

Developed CloudPred, an end-to-end differentiable learning algorithm integrated with a biologically informed mixture of cell types model.
Created a systematic simulation platform for evaluating CloudPred and alternative methods.

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Validated CloudPred on a real-world scRNA-seq dataset from lupus patients.

Main Results:

CloudPred automatically infers salient cell subpopulations for phenotype prediction without prior annotations.
CloudPred outperformed alternative methods in various simulation settings.
Achieved an AUROC of 0.98 on a lupus dataset, identifying CD4 T cells as a key indicator.

Conclusions:

CloudPred is a novel framework for predicting clinical phenotypes from scRNA-seq data.
The algorithm effectively identifies disease-relevant cell subpopulations, enhancing precision medicine applications.
CloudPred represents a significant advancement in leveraging scRNA-seq for clinical insights.