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Adjustments to the reference dataset design improve cell type label transfer

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Item Type:Article
Title:Adjustments to the reference dataset design improve cell type label transfer
Creators Name:Mölbert, C. and Haghverdi, L.
Abstract:The transfer of cell type labels from pre-annotated (reference) to newly collected data is an important task in single-cell data analysis. As the number of publicly available annotated datasets which can be used as reference, as well as the number of computational methods for cell type label transfer are constantly growing, rationals to understand and decide which reference design and which method to use for a particular query dataset are needed. Using detailed data visualisations and interpretable statistical assessments, we benchmark a set of popular cell type annotation methods, test their performance on different cell types and study the effects of the design of reference data (e.g., cell sampling criteria, inclusion of multiple datasets in one reference, gene set selection) on the reliability of predictions. Our results highlight the need for further improvements in label transfer methods, as well as preparation of high-quality pre-annotated reference data of adequate sampling from all cell types of interest, for more reliable annotation of new datasets.
Keywords:Single-Cell RNA-Seq, Cell Type Annotation, Label Transfer, Reference Data, Benchmark, Interpretability
Source:Frontiers in Bioinformatics
ISSN:2673-7647
Publisher:Frontiers Media SA
Volume:3
Page Range:1150099
Date:5 April 2023
Official Publication:https://doi.org/10.3389/fbinf.2023.1150099
PubMed:View item in PubMed

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