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A semi-supervised Bayesian approach for organelle discovery

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ococrook/2019-noveltyTagm

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NoveltyTagm

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Repository for a A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection.

To reproduce the manuscript from the source Latex, this can be found in the paper folder with accompanying figures.

The script that were submitted to to CSD3 Cambridge high-performance computing can be found in code -> codeforHPC. The S4 objects and methods use to run novelty tagm are also provided in this folder, these require the MSnbase and pRoloc packages. NoveltyTagm will be available in a future bioconductor release of pRoloc.

To reproduce the figures and analysis .rmd files are provided in code -> codeforPlots

The other folders contain useful tables and summaries, that were used in the data intepretation.

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A semi-supervised Bayesian approach for organelle discovery

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