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Article

PIPET: predicting relevant subpopulations in single-cell data using phenotypic information from bulk data.

Briefings in bioinformatics - 23 May 2024

Ruan Xinjia, Cheng Yu, Ye Yuqing, Wang Yuhang, Chen Xinyi, Yang Yuqing, Liu Tiantian, Yan Fangrong

Abstract excerpt

Single-cell RNA sequencing has revealed cellular heterogeneity in complex tissues, notably benefiting research on diseases such as cancer. However, the integration of single-cell data from small samples with extensive clinical features in bulk data remains underexplored. In this study, we introduce PIPET, an algorithmic method for predicting relevant subpopulations in single-cell data based on multivariate...

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Identifiers

PMID: 38819254
DOI: 10.1093/bib/bbae260
PMCID: PMC11141296

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TP53

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

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