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

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...

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Detalles Bibliográficos
Autores principales: Feng, Hao, Meng, Guanqun, Lin, Tong, Parikh, Hemang, Pan, Yue, Li, Ziyi, Krischer, Jeffrey, Li, Qian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10373385/
https://www.ncbi.nlm.nih.gov/pubmed/37496087
http://dx.doi.org/10.1186/s13059-023-03014-8
Descripción
Sumario: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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03014-8.