  Single-cell RNA sequencing (scRNA-seq) has the potential to provide powerful,
high-resolution signatures to inform disease prognosis and precision medicine.
This paper takes an important first step towards this goal by developing an
interpretable machine learning algorithm, CloudPred, to predict individuals'
disease phenotypes from their scRNA-seq data. Predicting phenotype from
scRNA-seq is challenging for standard machine learning methods -- the number of
cells measured can vary by orders of magnitude across individuals and the cell
populations are also highly heterogeneous. Typical analysis creates pseudo-bulk
samples which are biased toward prior annotations and also lose the single cell
resolution. CloudPred addresses these challenges via a novel end-to-end
differentiable learning algorithm which is coupled with a biologically informed
mixture of cell types model. CloudPred automatically infers the cell
subpopulation that are salient for the phenotype without prior annotations. We
developed a systematic simulation platform to evaluate the performance of
CloudPred and several alternative methods we propose, and find that CloudPred
outperforms the alternative methods across several settings. We further
validated CloudPred on a real scRNA-seq dataset of 142 lupus patients and
controls. CloudPred achieves AUROC of 0.98 while identifying a specific
subpopulation of CD4 T cells whose presence is highly indicative of lupus.
CloudPred is a powerful new framework to predict clinical phenotypes from
scRNA-seq data and to identify relevant cells.

Quantitative Methods;Machine Learning