SSBER: removing batch effect for single-cell RNA sequencing data.
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.
['Zhang|Yin|Y|', 'Wang|Fei|F|http://orcid.org/0000-0001-5890-0448']
D000465:Algorithms D012313:RNA D017423:Sequence Analysis, RNA D059010:Single-Cell Analysis D059467:Transcriptome D000073359:Whole Exome Sequencing
Batch effect Data integration Supervised cell type assignment The shared cell type