SDAEC Method and its Application in Batch Effect Removal for Single Cell mRNA Sequence
Objective To propose a deep stacked denoising auto encoder embedded cluster(SDAEC)algorithm and apply it to single cell mRNA sequence(scRNA-seq)data to remove the batch effect,and further to evaluate the performance of its batch effect removal.Methods Based on the characteristics of high dimension,high sparsity and high non-linear error of single-cell data,the algorithm of single cell Louvain clustering was embedded into stacked denoising auto encoder(SDAE)algorithm,and formed a SDAEC algorithm,which was used to batch effect removal for scRNA-seq data.SDAEC algorithm was utilized to scRNA-seq data of actual ovarian cancer tissue for batch effect removal,t-distributed stochastic neighbor embedding(tSNE),k-nearest-neighbor batch-effect test(kBET),adjusted rand index(ARI),normalized mutual information(NMI)and average silhouette width(ASW)were used to evaluate the performance of removing batch effect.Results The performance of SDAEC was better than Combat,mutual nearest neighbors(MNN),maximum mean discrepancy distribution-matching residual networks(MMD-ResNet)and zero-inflated negative binomial-based wanted variation extraction(ZINB-WaVE)in removing batch effect of scRNA-seq.Conclusion SDAEC algorithm can remove the batch effect of scRNA-seq data and improve the validity of downstream analysis of scRNA-seq data.
Stacked denoising auto encoder embedded clusterSingle cell mRNA sequenceBatch effectsOvarian cancer
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