VIRTUAL - High dimensional analysis -Thinking beyond tradition

Dimensional reduction methods are instrumental in single cell analysis and in data visualization. However, dimensional reduction does not stop at data "shrinkage" nor simply serve as a pre-processing step prior to application of graph-based methods or to facilitate visualization. How often do we question the our choice of standard methods applied in dimensional reduction?  Are there alternative methods that we can apply? Similarly integration tools have become frequently used in data assessment and processing. And yet, standard integration methods faces challenges when faced with complex datasets, such as cancer datasets. Perhaps it is time to explore newer methods developed from AI. Here in our next lecture series under our "challenge-me" series, we discuss non-traditional methods of dimensional reduction in data analysis. In addition, with the rise of AI in method developments, we introduce here a topic on deep-learning application in data integration.

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