Decloud: an unsupervised deconvolution tool for building gene expression profiles
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Deconvolution is the process of decomposing a mixed signal into its originating elements. For my thesis I created a clustering application, named DeCloud, with the intent to replace the unsupervised clustering step in the deconvolution tool, Deblender. Utilizing clustering packages in R such as optCluster, the application was built to allow for a range of new clustering algorithms. In this thesis the scope has been set to test Hierarchical clustering and two variations of PAM. A novel filtering function was created, providing a different approach to handling clusters. The novel approach has been implemented for use with the PAM clustering method, but could be applied to other algorithms as well. We have tested the resulting pipeline on the data sets used to benchmark Deblender and other tools. Comparing the results obtained by Deblender and by DeCloud, shows that DeCloud obtains marked better results on two of the three datasets used for testing. The last dataset is a complicated case, none of the applications are able to effectively cluster and deconvolve. The novel filter function applied to the PAM algorithm has been shown to be the best performer in each of the two successful deconvolution datasets.
PublisherThe University of Bergen
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