RT Journal Article SR Electronic T1 Learning from heterogeneous data sources: an application in spatial proteomics JF bioRxiv FD Cold Spring Harbor Laboratory SP 022152 DO 10.1101/022152 A1 Lisa M. Breckels A1 Sean Holden A1 David Wonjar A1 Claire M. Mulvey A1 Andy Christoforou A1 Arnoud Groen A1 Oliver Kohlbacher A1 Kathryn S. Lilley A1 Laurent Gatto YR 2015 UL http://biorxiv.org/content/early/2015/07/16/022152.abstract AB Sub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with three different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to a experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis.