Title: Removal of Batch Effects using Generative Adversarial Networks,
Abstract: Many biological data analysis processes like Cytometry or Next Generation
Sequencing (NGS) produce massive amounts of data which needs to be processed in
batches for down-stream analysis. Such datasets are prone to technical
variations due to difference in handling the batches possibly at different
times, by different experimenters or under other different conditions. This
adds variation to the batches coming from the same source sample. These
variations are known as Batch Effects. It is possible that these variations and
natural variations due to biology confound but such situations can be avoided
by performing experiments in a carefully planned manner. Batch effects can
hamper down-stream analysis and may also cause results to be inconclusive.
Thus, it is essential to correct for these effects. Some recent methods propose
deep learning based solution to solve this problem. We demonstrate that this
can be solved using a novel Generative Adversarial Networks (GANs) based
framework. The advantage of using this framework over other prior approaches is
that here we do not require to choose a reproducing kernel and define its
parameters.We demonstrate results of our framework on a Mass Cytometry dataset.