scFeatures: multi-view representations of single-cell and spatial data for disease outcome prediction

Motivation: With the recent surge of large-cohort scale single cell research, it is of critical importance that analytical methods can fully utilize the comprehensive characterization of cellular systems that single cell technologies produce to provide insights into samples from individuals. Currently, there is little consensus on the best ways to compress information from the complex data structures of these technologies to summary statistics that represent each sample (e.g. individuals). Results: Here, we present scFeatures, an approach that creates interpretable cellular and molecular representations of single-cell and spatial data at the sample level. We demonstrate that summarizing a broad collection of features at the sample level is both important for understanding underlying disease mechanisms in different experimental studies and for accurately classifying disease status of individuals.

Automated summary

This study presents a method called scFeatures that creates interpretable cellular and molecular representations of single-cell and spatial data at the sample level. Summarizing a broad collection of features at the sample level is important for understanding disease mechanisms in different experimental studies and accurately classifying disease status of individuals.