Removal of Batch Effects from Single-cell RNA-Seq

Sources of variability in experimentally derived data include measurement error in addition to the physical phenomena of interest. This measurement error is a combination of systematic components, originating from the measuring instrument, and random measurement errors. Novel biological technologies, such as single-cell RNA-seq, are plagued with systematic errors that may severely affect statistical analysis if the data is not properly calibrated.

Yale University researchers propose a novel deep learning approach for removing systematic batch effects. This method is based on a residual neural network, trained to minimize the Maximum Mean Discrepancy (MMD) between the multivariate distributions of two replicates, measured in different batches. They apply this method to  single-cell RNA-seq datasets, and demonstrate that it effectively attenuates batch effects.

 Calibration of scRNA-seq


Top: t-SNE plots before (left) and after (right) calibration using MMD-ResNet. Bottom: Calibration of cells with high expression of Prkca. t-SNE plots before calibration (left), after calibration using Combat (middle) and MMD-ResNet (right).

Availability – the codes and data are publicly available at:

Contact –

Shaham U, Stanton KP, Zhao J, Li H, Raddassi K, Montgomery R, Kluger Y. (2017) Removal of Batch Effects using Distribution-Matching Residual Networks. Bioinformatics [Epub ahead of print]. [abstract]

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