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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 aninterpretable machine learning algorithm, CloudPred, to predict individuals'disease phenotypes from their scRNA-seq data. Predicting phenotype fromscRNA-seq is challenging for standard machine learning methods -- the number ofcells measured can vary by orders of magnitude across individuals and the cellpopulations are also highly heterogeneous. Typical analysis creates pseudo-bulksamples which are biased toward prior annotations and also lose the single cellresolution. CloudPred addresses these challenges via a novel end-to-enddifferentiable learning algorithm which is coupled with a biologically informedmixture of cell types model. CloudPred automatically infers the cellsubpopulation that are salient for the phenotype without prior annotations. Wedeveloped a systematic simulation platform to evaluate the performance ofCloudPred and several alternative methods we propose, and find that CloudPredoutperforms the alternative methods across several settings. We furthervalidated CloudPred on a real scRNA-seq dataset of 142 lupus patients andcontrols. CloudPred achieves AUROC of 0.98 while identifying a specificsubpopulation of CD4 T cells whose presence is highly indicative of lupus.CloudPred is a powerful new framework to predict clinical phenotypes fromscRNA-seq data and to identify relevant cells.

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