Abstract
BackgroundWith 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.ResultsIn 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.ConclusionsSSBER 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.
Highlights
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
Experiments on various datasets in different scenarios show that: (1) when the cell type composition differs greatly among batches, SSBER performs better than other algorithms, such as Harmony, Seurat and LIGER. (2) When similar cell types exist among batches or quantity distributions of Results To give a comprehension evaluation of SSBER, we implement some experiments on real data under three scenarios, cell-type structure across batches is not identical, similar cell types across batches exists, and quantity distribution of cells from various cell types is seriously unbalanced
We apply SSBER to time-series datasets for comparing the variation of development trajectory, in particular compared to Harmony
Summary
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. 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. In 2009, Tang et al developed the first sequencing technology for single-cell RNA sequencing (scRNA-seq). With the rapid development of biotechnology, single-cell RNA sequencing (scRNA-seq) has become one of the most prioritized sequencing research directions in recent years [2, 3]. Removing batch effects can reduce the influence of technical or artificial errors in the process of analyzing scRNA-seq data [5]
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