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Cell type-specific Interaction Analysis using Doublets in scRNA-seq (CIcADA).
Schiebout, Courtney; Lust, Hannah E; Huang, Yina H; Frost, H Robert.
Afiliação
  • Schiebout C; Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH USA.
  • Lust HE; MDI Biological Laboratory, Bar Harbor, ME USA.
  • Huang YH; Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Hanover, NH USA.
  • Frost HR; Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH USA.
bioRxiv ; 2023 Feb 15.
Article em En | MEDLINE | ID: mdl-36824707
ABSTRACT
Motivation Doublets are usually considered an unwanted artifact of single-cell RNA-sequencing (scRNA-seq) and are only identified in datasets for the sake of removal. However, if cells have a juxtacrine attachment to one another in situ and maintain this association through an scRNA-seq processing pipeline that only partially dissociates the tissue, these doublets can provide meaningful biological information regarding the interactions and cell processes occurring in the analyzed tissue. This is especially true for cases such as the immune compartment of the tumor microenvironment, where the frequency and type of immune cell juxtacrine interactions can be a prognostic indicator.

Results:

We developed Cell type-specific Interaction Analysis using Doublets in scRNA-seq (CIcADA) as a pipeline for identifying and analyzing biological doublets in scRNA-seq data. CIcADA identifies putative doublets using multi-label cell type scores and characterizes interaction dynamics through a comparison against synthetic doublets of the same cell type composition. In performing CIcADA on several scRNA-seq tumor datasets, we found that the identified doublets were consistently upregulating expression of immune response genes. Contact Courtney.T.Schiebout.GR@Dartmouth.edu , Hildreth.R.Frost@Dartmouth.edu.

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article