Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata

Abstract

Motivation: Experimental spatial proteomics, i.e. the high-throughput assignment of proteins to sub-cellular compartments based on quantitative proteomics data, promises to shed new light on many biological processes given adequate computational tools.Results: Here we present , a complete infrastructure to support and guide the sound analysis of quantitative mass-spectrometry-based spatial proteomics data. It provides functionality for unsupervised and supervised machine learning for data exploration and protein classification and novelty detection to identify new putative sub-cellular clusters. The software builds upon existing infrastructure for data management and data processing.Availability: is implemented in the language and available under an open-source license from the Bioconductor project (http://www.bioconductor.org/). A vignette with a complete tutorial describing data import/export and analysis is included in the package. Test data is available in the companion package .Contact: lg390@cam.ac.uk

Deciphering Thylakoid Sub-compartments using a Mass Spectrometry-based Approach

“…The MsnSet class is derived from the "eSet" class and mimics the "ExpressionSet" class used for microarray data. An "MSnSet" object contains quantified expression data for MS proteomic data as well as the experimental meta-data, and allows the analysis of quantitative MS-based data using advanced statistical tools implemented in R, and particularly that of the pRoloc R package (46) (http://www.bioconductor.org/packages/2.12/bioc/html/pRoloc.html), which takes as input objects of the MSbase classes. Raw quantitative data were subjected to some pre-processing steps (Fig.…”

Label-Free Protein Quantification for Plant Golgi Protein Localization and Abundance

“…The marker protein distribution profiles were used to classify unlabeled proteins to either Golgi or non-Golgi classes using a support vector machine (SVM) classifier as implemented in the pRoloc software (Gatto and Lilley, 2012;Gatto et al, 2014). The optimal SVM parameters (s = 0.01, cost = 0.0625), obtained as described above, yield a macro-F1 score of 0.94 for the marker protein set.…”

Multidimensional proteomics for cell biology

“…R is free to use and combines the ability to carry out database, statistical and graphing functions and can handle very large multidimensional data sets. In addition, many biology-specific packages are available for R, such as pRoloc, which is a tool for the analysis of protein localization using the protein correlation-profiling approach 111 . However, one potential disadvantage of statistical packages such as R is that they have a considerable learning curve before most cell biologists can take full advantage of the features they offer.…”