Islet: Individual-Specific Reference Panel Recovery Improves Cell-Type-Specific Inference

Authors

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

Publication Date

7-26-2023

Journal

Genome Biology

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.

Keywords

Child, Humans, Child, Preschool, Islets of Langerhans, Transcriptome, Computer Simulation, Autoantibodies, Gene Expression Profiling, Deconvolution, Temporal measures, Cell-type-specific differential expression, Individual-specific reference panel

DOI

10.1186/s13059-023-03014-8

PMID

37496087

PMCID

PMC10373385

PubMedCentral® Posted Date

7-26-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes