Impact of imputation on the cell type identification in scrna-seq datasets

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RSG-HIBIT22_paper_54.pdf

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2022-10

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Avşar, Gülben
Pir, Pınar

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Single-cell RNA sequencing technologies allow investigation of the cellular heterogeneity and processes in high resolution. However, the technical limitations and biological factors give rise to challenges in the analyses and interpretations of the scRNA-seq data. One of the most challenging features of the scRNA-seq datasets is the sparsity which is caused by dropouts. To eliminate the effects of the dropouts on downstream analysis, numerous methods have been generated with distinct approaches to impute the dropouts. Understanding the impact of imputation on the detection of the cell type specific marker genes is crucial for the downstream analyses which usually start with cell type identification. We evaluated the effects of three imputation methods on cell type annotation in the scRNA-seq datasets. MAGIC was found to be best performer among the three methods. However, the imputation should be done with caution for the marker genes with weak signals

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https://hibit2022.ims.metu.edu.tr/
https://hdl.handle.net/11511/101309

Conference Name

The International Symposium on Health Informatics and Bioinformatics

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Graduate School of Informatics, Conference / Seminar

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Citation Formats

G. Avşar and P. Pir, “Impact of imputation on the cell type identification in scrna-seq datasets,” Erdemli, Mersin, TÜRKİYE, 2022, p. 1054, Accessed: 00, 2023. [Online]. Available: https://hibit2022.ims.metu.edu.tr/.