cKBET: assessing goodness of batch effect correction for single-cell RNA-seq

lt;p>Single-cell RNA sequencing reveals the gene structure and gene expression status of a single cell, which can reflect the heterogeneity between cells. However, batch effects caused by non-biological factors may hinder data integration and downstream analysis. Although the batch effect can be evaluated by visualizing the data, which actually is subjective and inaccurate. In this work, we propose a quantitative method cKBET, which considers the batch and cell type information simultaneously. The cKBET method accesses batch effects by comparing the global and local fraction of cells of different batches in different cell types. We verify the performance of our cKBET method on simulated and real biological data sets. The experimental results show that our cKBET method is superior to existing methods in most cases. In general, our cKBET method can detect batch effect with either balanced or unbalanced cell types, and thus evaluate batch correction methods.</p>

Batch effects and the effective design of single-cell gene expression studies

PDF] Projected t-SNE for batch correction

Batch alignment of single-cell transcriptomics data using deep metric learning

Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors

PDF) Comparison of Scanpy-based algorithms to remove the batch effect from single-cell RNA-seq data

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A benchmark of batch-effect correction methods for single-cell RNA sequencing data, Genome Biology

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PDF) A geometrical approach based on PCA to benchmark the algorithms of batch effect correction applied to the integration of RNA-Seq data

Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors