  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 downstream analysis and may also cause results to be inconclusive. Thus,
it is essential to correct for these effects. This can be solved using a novel
Generative Adversarial Networks (GANs) based framework that is proposed here,
advantage of using this framework over other prior approaches is that here it
is not required to choose a reproducing kernel and define its parameters.
Results of the framework on a mass cytometry dataset are reported.
