Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics.
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 us to pinpoint the sub-cellular distribution of thousands of proteins in a specific sys...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2016-05-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004920&type=printable |
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| author | Lisa M Breckels Sean B Holden David Wojnar Claire M Mulvey Andy Christoforou Arnoud Groen Matthew W B Trotter Oliver Kohlbacher Kathryn S Lilley Laurent Gatto |
| author_facet | Lisa M Breckels Sean B Holden David Wojnar Claire M Mulvey Andy Christoforou Arnoud Groen Matthew W B Trotter Oliver Kohlbacher Kathryn S Lilley Laurent Gatto |
| author_sort | Lisa M Breckels |
| collection | DOAJ |
| description | 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 us 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 four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an 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. |
| format | Article |
| id | doaj-art-c25995a02a4b4247953bc222acc05e9e |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2016-05-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-c25995a02a4b4247953bc222acc05e9e2025-08-20T03:24:36ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582016-05-01125e100492010.1371/journal.pcbi.1004920Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics.Lisa M BreckelsSean B HoldenDavid WojnarClaire M MulveyAndy ChristoforouArnoud GroenMatthew W B TrotterOliver KohlbacherKathryn S LilleyLaurent GattoSub-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 us 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 four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an 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.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004920&type=printable |
| spellingShingle | Lisa M Breckels Sean B Holden David Wojnar Claire M Mulvey Andy Christoforou Arnoud Groen Matthew W B Trotter Oliver Kohlbacher Kathryn S Lilley Laurent Gatto Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics. PLoS Computational Biology |
| title | Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics. |
| title_full | Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics. |
| title_fullStr | Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics. |
| title_full_unstemmed | Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics. |
| title_short | Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics. |
| title_sort | learning from heterogeneous data sources an application in spatial proteomics |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004920&type=printable |
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