2-AIN-506 a 2-AIN-252: Seminár z bioinformatiky (2) a (4)
Leto 2016

Sean Simmons, Jian Peng, Jadwiga Bienkowska, Bonnie Berger. Discovering What Dimensionality Reduction Really Tells Us About RNA-Seq Data. Journal of computational biology : a journal of computational molecular cell biology, 22(8):715-718. 2015.

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Biology is being inundated by noisy, high-dimensional data to an extent never
before experienced. Dimensionality reduction techniques such as principal
component analysis (PCA) are common approaches for dealing with this onslaught.
Though these unsupervised techniques can help uncover interesting structure in
high-dimensional data they give little insight into the biological and technical 
considerations that might explain the uncovered structure. Here we introduce a
hybrid approach--component selection using mutual information (CSUMI)--that uses 
a mutual information--based statistic to reinterpret the results of PCA in a
biologically meaningful way. We apply CSUMI to RNA-seq data from GTEx. Our hybrid
approach enables us to unveil the previously hidden relationship between
principal components (PCs) and the underlying biological and technical sources of
variation across samples. In particular, we look at how tissue type affects PCs
beyond the first two, allowing us to devise a principled way of choosing which
PCs to consider when exploring the data. We further apply our method to RNA-seq
data taken from the brain and show that some of the most biologically informative
PCs are higher-dimensional PCs; for instance, PC 5 can differentiate the basal
ganglia from other tissues. We also use CSUMI to explore how technical artifacts 
affect the global structure of the data, validating previous results and
demonstrating how our method can be viewed as a verification framework for
detecting undiscovered biases in emerging technologies. Finally we compare CSUMI 
to two correlation-based approaches, showing ours outperforms both. A python
implementation is available online on the CSUMI website.