ISLET: individual-specific reference panel recovery improves cell-type-specific inference

Hao Feng, Guanqun Meng, Tong Lin, Hemang Parikh, Yue Pan, Ziyi Li, Jeffrey Krischer, Qian Li

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We propose a statistical framework ISLET to infer individual-specific and cell-type-specific transcriptome reference panels. ISLET models the repeatedly measured bulk gene expression data, to optimize the usage of shared information within each subject. ISLET is the first available method to achieve individual-specific reference estimation in repeated samples. Using simulation studies, we show outstanding performance of ISLET in the reference estimation and downstream cell-type-specific differentially expressed genes testing. We apply ISLET to longitudinal transcriptomes profiled from blood samples in a large observational study of young children and confirm the cell-type-specific gene signatures for pancreatic islet autoantibody. ISLET is available at https://bioconductor.org/packages/ISLET .

Original languageEnglish (US)
Article number174
JournalGenome biology
Volume24
Issue number1
DOIs
StatePublished - Dec 2023

Keywords

  • Cell-type-specific differential expression
  • Deconvolution
  • Individual-specific reference panel
  • Temporal measures

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

MD Anderson CCSG core facilities

  • Biostatistics Resource Group

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