MBECS: Microbiome Batch Effects Correction Suite.
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ABSTRACT:
SUBMITTER: Olbrich M
PROVIDER: S-EPMC10155362 | biostudies-literature | 2023 May
REPOSITORIES: biostudies-literature
Publications
MBECS: Microbiome Batch Effects Correction Suite.
Olbrich Michael M Künstner Axel A Busch Hauke H
BMC bioinformatics 20230503 1
Despite the availability of batch effect correcting algorithms (BECA), no comprehensive tool that combines batch correction and evaluation of the results exists for microbiome datasets. This work outlines the Microbiome Batch Effects Correction Suite development that integrates several BECAs and evaluation metrics into a software package for the statistical computation framework R. ...[more]
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Project description:Despite their widespread applications, single-cell RNA-sequencing (scRNA-seq) experiments are still plagued by batch effects and dropout events. Although the completely randomized experimental design has frequently been advocated to control for batch effects, it is rarely implemented in real applications due to time and budget constraints. Here, we mathematically prove that under two more flexible and realistic experimental designs-the reference panel and the chain-type designs-true biological variability can also be separated from batch effects. We develop Batch effects correction with Unknown Subtypes for scRNA-seq data (BUSseq), which is an interpretable Bayesian hierarchical model that closely follows the data-generating mechanism of scRNA-seq experiments. BUSseq can simultaneously correct batch effects, cluster cell types, impute missing data caused by dropout events, and detect differentially expressed genes without requiring a preliminary normalization step. We demonstrate that BUSseq outperforms existing methods with simulated and real data.
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