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However, existing methods remain limited in their ability to characterize heterogeneity at the individual level. To address this gap, we present scHILL, a framework that integrates a masked autoencoder (MAE) with a multilayer perceptron (MLP) to decipher phenotypic heterogeneity arises from immune cell heterogeneity among individuals under specific disease conditions. The MAE, pretrained with data augmentation, enables self-supervised feature learning without labels and effectively mitigates the challenge of limited sample size. The MLP further generates a score for each individual to quantify the functional significance of cells and genes. Across multiple datasets, scHILL outperforms existing methods in phenotype prediction and reveals individual-level immune cell heterogeneity in infectious disease, autoimmune disease, and cancer. scHILL provides a generalizable framework for interpreting individual-level scRNA-seq data, thereby facilitating the future realization of personalized medicine.<\/jats:p>","DOI":"10.1093\/bib\/bbag286","type":"journal-article","created":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T11:32:13Z","timestamp":1779017533000},"source":"Crossref","is-referenced-by-count":0,"title":["scHILL: deciphering individual-level immune cell heterogeneity with single-cell RNA sequencing data"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9397-0673","authenticated-orcid":false,"given":"Yi","family":"Wang","sequence":"first","affiliation":[{"name":"National Genomics Data Center, China National Center for Bioinformation , No. 1 Beichen West Road, Chaoyang District, 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