{"title":"第14章。R代表蛋白质组学","authors":"L. Breckels, Sebastian Gibb, V. Petyuk, L. Gatto","doi":"10.1039/9781782626732-00321","DOIUrl":null,"url":null,"abstract":"In this chapter, we introduce some R and Bioconductor software to process, analyse and interpret mass spectrometry and proteomics data. We describe how to programmatically access data, how to read various data formats into R, we review the existing infrastructure to reliably identify peptide-spectrum matches, describe how to analyse and process quantitative data, review MALDI and imaging mass spectrometry using Bioconductor packages and conclude with an overview of statistical and machine learning software applicable to proteomics data. All the use cases are accompanied by executable example code and further reproducible examples are provided in the companion RforProteomics package.","PeriodicalId":192946,"journal":{"name":"Proteome Informatics","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Chapter 14. R for Proteomics\",\"authors\":\"L. Breckels, Sebastian Gibb, V. Petyuk, L. Gatto\",\"doi\":\"10.1039/9781782626732-00321\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this chapter, we introduce some R and Bioconductor software to process, analyse and interpret mass spectrometry and proteomics data. We describe how to programmatically access data, how to read various data formats into R, we review the existing infrastructure to reliably identify peptide-spectrum matches, describe how to analyse and process quantitative data, review MALDI and imaging mass spectrometry using Bioconductor packages and conclude with an overview of statistical and machine learning software applicable to proteomics data. All the use cases are accompanied by executable example code and further reproducible examples are provided in the companion RforProteomics package.\",\"PeriodicalId\":192946,\"journal\":{\"name\":\"Proteome Informatics\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-11-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proteome Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1039/9781782626732-00321\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proteome Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1039/9781782626732-00321","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this chapter, we introduce some R and Bioconductor software to process, analyse and interpret mass spectrometry and proteomics data. We describe how to programmatically access data, how to read various data formats into R, we review the existing infrastructure to reliably identify peptide-spectrum matches, describe how to analyse and process quantitative data, review MALDI and imaging mass spectrometry using Bioconductor packages and conclude with an overview of statistical and machine learning software applicable to proteomics data. All the use cases are accompanied by executable example code and further reproducible examples are provided in the companion RforProteomics package.
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