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

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

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Assessment of batch-correction methods for scRNA-seq data with a new test metric

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

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

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

A test metric for assessing single-cell RNA-seq batch correction

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

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

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