SSBER: removing batch effect for single-cell RNA sequencing data
Abstract Background With the continuous maturity of sequencing technology, different laboratories or different sequencing platforms have generated a large amount of single-cell transcriptome sequencing data for the same or different tissues. Due to batch effects and high dimensions of scRNA data, do...
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doaj-8b2835e340ce4b52b41dd4c5759738be2021-05-16T11:36:22ZengBMCBMC Bioinformatics1471-21052021-05-0122112010.1186/s12859-021-04165-wSSBER: removing batch effect for single-cell RNA sequencing dataYin Zhang0Fei Wang1Shanghai Key Lab of Intelligent Information ProcessingShanghai Key Lab of Intelligent Information ProcessingAbstract Background With the continuous maturity of sequencing technology, different laboratories or different sequencing platforms have generated a large amount of single-cell transcriptome sequencing data for the same or different tissues. Due to batch effects and high dimensions of scRNA data, downstream analysis often faces challenges. Although a number of algorithms and tools have been proposed for removing batch effects, the current mainstream algorithms have faced the problem of data overcorrection when the cell type composition varies greatly between batches. Results In this paper, we propose a novel method named SSBER by utilizing biological prior knowledge to guide the correction, aiming to solve the problem of poor batch-effect correction when the cell type composition differs greatly between batches. Conclusions SSBER effectively solves the above problems and outperforms other algorithms when the cell type structure among batches or distribution of cell population varies considerably, or some similar cell types exist across batches.https://doi.org/10.1186/s12859-021-04165-wData integrationBatch effectThe shared cell typeSupervised cell type assignment |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yin Zhang Fei Wang |
spellingShingle |
Yin Zhang Fei Wang SSBER: removing batch effect for single-cell RNA sequencing data BMC Bioinformatics Data integration Batch effect The shared cell type Supervised cell type assignment |
author_facet |
Yin Zhang Fei Wang |
author_sort |
Yin Zhang |
title |
SSBER: removing batch effect for single-cell RNA sequencing data |
title_short |
SSBER: removing batch effect for single-cell RNA sequencing data |
title_full |
SSBER: removing batch effect for single-cell RNA sequencing data |
title_fullStr |
SSBER: removing batch effect for single-cell RNA sequencing data |
title_full_unstemmed |
SSBER: removing batch effect for single-cell RNA sequencing data |
title_sort |
ssber: removing batch effect for single-cell rna sequencing data |
publisher |
BMC |
series |
BMC Bioinformatics |
issn |
1471-2105 |
publishDate |
2021-05-01 |
description |
Abstract Background With the continuous maturity of sequencing technology, different laboratories or different sequencing platforms have generated a large amount of single-cell transcriptome sequencing data for the same or different tissues. Due to batch effects and high dimensions of scRNA data, downstream analysis often faces challenges. Although a number of algorithms and tools have been proposed for removing batch effects, the current mainstream algorithms have faced the problem of data overcorrection when the cell type composition varies greatly between batches. Results In this paper, we propose a novel method named SSBER by utilizing biological prior knowledge to guide the correction, aiming to solve the problem of poor batch-effect correction when the cell type composition differs greatly between batches. Conclusions SSBER effectively solves the above problems and outperforms other algorithms when the cell type structure among batches or distribution of cell population varies considerably, or some similar cell types exist across batches. |
topic |
Data integration Batch effect The shared cell type Supervised cell type assignment |
url |
https://doi.org/10.1186/s12859-021-04165-w |
work_keys_str_mv |
AT yinzhang ssberremovingbatcheffectforsinglecellrnasequencingdata AT feiwang ssberremovingbatcheffectforsinglecellrnasequencingdata |
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1721439376314990592 |