SAE-Impute: imputation for single-cell data via subspace regression and auto-encoders

SAE-Impute: imputation for single-cell data via subspace regression and auto-encoders

Abstract Background Single-cell RNA sequencing (scRNA-seq) technology has emerged as a crucial tool for studying cellular heterogeneity. However, dropouts are inherent to the sequencing process, known as dropout events, posing challenges in downstream analysis and interpretation. Imputing dropout da...

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Bibliographic Details
Main Authors: Liang Bai, Boya Ji, Shulin Wang
Format: Article
Language:English
Published: BMC 2024-10-01
Series:BMC Bioinformatics
Subjects:
Single-cell RNA-seq
Dropout events
Imputation
Subspace regression
Autoencoders.
Online Access:https://doi.org/10.1186/s12859-024-05944-x

Internet

https://doi.org/10.1186/s12859-024-05944-x

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