Foreword

MSnbase and pRoloc are under active developed; current functionality is evolving and new features are added on a regular basis. This software is free and open-source software. If you use it, please support the project by citing it in publications:

Gatto L. and Lilley K.S. MSnbase - an R/Bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation. Bioinformatics 28, 288-289 (2011).

Gatto L, Breckels LM, Wieczorek S, Burger T, Lilley KS. Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata. Bioinformatics. 2014 May 1;30(9):1322-4..

Breckels LM, Mulvey CM, Lilley KS and Gatto L. A Bioconductor workflow for processing and analysing spatial proteomics data. F1000Research 2016, 5:2926 doi: 10.12688/f1000research.10411.1.

If you are using the phenoDisco function, please also cite

Breckels L.M., Gatto L., Christoforou A., Groen A.J., Kathryn Lilley K.S. and Trotter M.W. The effect of organelle discovery upon sub-cellular protein localisation. J Proteomics, S1874-3919(13)00094-8 (2013).

For an introduction to spatial proteomics data analysis:

Gatto L, Breckels LM, Burger T, Nightingale DJ, Groen AJ, Campbell C, Nikolovski N, Mulvey CM, Christoforou A, Ferro M, Lilley KS. A foundation for reliable spatial proteomics data analysis. Mol Cell Proteomics. 2014 Aug;13(8):1937-52. doi: 10.1074/mcp.M113.036350.

The pRoloc package contains additional vignettes and reference material:

  • pRoloc-tutorial: pRoloc tutorial.
  • pRoloc-ml: Machine learning techniques available in pRoloc.
  • pRoloc-transfer-learning: A transfer learning algorithm for spatial proteomics.
  • pRoloc-goannotations: Annotating spatial proteomics data.

Questions and bugs

You are welcome to contact me directly about pRoloc. For bugs, typos, suggestions or other questions, please file an issue in our issue tracking system (https://github.com/lgatto/pRoloc/issues) providing as much information as possible, a reproducible example and the output of sessionInfo().

If you wish to reach a broader audience for general questions about proteomics analysis using R, you may want to use the Bioconductor support site: https://support.bioconductor.org/.

1 Introduction

1.1 Spatial proteomics

Spatial (or organelle) proteomics is the study of the localisation of proteins inside cells. The sub-cellular compartment can be organelles, i.e. structures defined by lipid bi-layers,macro-molecular assemblies of proteins and nucleic acids or large protein complexes. In this document, we will focus on mass-spectrometry based approaches that assay a population of cells, as opposed as microscopy based techniques that monitor single cells, as the former is the primary concern of pRoloc, although the techniques described below and the infrastructure in place could also be applied the processed image data. The typical experimental use-case for using pRoloc is a set of fractions, originating from a total cell lysate. These fractions can originate from a continuous gradient, like in the LOPIT (Dunkley et al. 2006) or PCP (Foster et al. 2006) approaches, or can be discrete fractions. The content of the fractions is then identified and quantified (using labelled or un-labelled quantitation techniques). Using relative quantitation of known organelle residents, termed organelle markers, organelle-specific profiles along the gradient are determined and new residents are identified based on matching of these distribution profiles. See for example (L. Gatto et al. 2010) and references therein for a detailed review on organelle proteomics.

It should be noted that large protein complexes, that are not necessarily separately enclosed within their own lipid bi-layer, can be detected by such techniques, as long as a distinct profile can be defined across the fractions.

1.2 About R and pRoloc

R (R Development Core Team 2011) is a statistical programming language and interactive working environment. It can be expanded by so-called packages to confer new functionality to users. Many such packages have been developed for the analysis of high-throughput biology, notably through the Bioconductor project (Gentleman et al. 2004). Two packages are of particular interest here, namely MSnbase (L. Gatto and Lilley 2012) and pRoloc. The former provides flexible infrastructure to store and manipulate quantitative proteomics data and the associated meta-data and the latter implements specific algorithmic technologies to analyse organelle proteomics data.

Among the advantages of R are robust statistical procedures, good visualisation capabilities, excellent documentation, reproducible research1 The content of this document is compiled (the code is executed and its output, text and figures, is displayed dynamically) to generate the pdf file., power and flexibility of the R language and environment itself and a rich environment for specialised functionality in many domains of bioinformatics: tools for many omics technologies, including proteomics, bio-statistics, gene ontology and biological pathway analysis, … Although there exists some specific graphical user interfaces (GUI), interaction with R is executed through a command line interface. While this mode of interaction might look alien to new users, experience has proven that after a first steep learning curve, great results can be achieved by non-programmers. Furthermore, specific and general documentation is plenty and beginners and advanced course material are also widely available.

Once R is started, the first step to enable functionality of a specific packages is to load them using the library function, as shown in the code chunk below:

library("MSnbase")
library("pRoloc")
library("pRolocdata")

MSnbase implements the data containers that are used by pRoloc. pRolocdata is a data package that supplies several published organelle proteomics data sets.

As a final setup step, we set the default colour palette for some of our custom plotting functionality to use semi-transparent colours in the code chunk below (see ?setStockcol for details). This facilitates visualisation of overlapping points.

setStockcol(NULL) ## reset first
setStockcol(paste0(getStockcol(), 70))

2 Data structures

2.1 Example data

The data used in this tutorial has been published in (Tan et al. 2009). The LOPIT technique (Dunkley et al. 2006) is used to localise integral and associated membrane proteins in Drosophila melanogaster embryos. Briefly, embryos were collected at 0 – 16 hours, homogenised and centrifuged to collect the supernatant, removing cell debris and nuclei. Membrane fractionation was performed on a iodixanol gradient and fractions were quantified using iTRAQ isobaric tags (Ross et al. 2004) as follows: fractions 4/5, 114; fractions 12/13, 115; fraction 19, 116 and fraction 21, 117. Labelled peptides were then separated using cation exchange chromatography and analysed by LS-MS/MS on a QSTAR XL quadrupole-time-of-flight mass spectrometer (Applied Biosystems). The original localisation analysis was performed using partial least square discriminant analysis (PLS-DA). Relative quantitation data was retrieved from the supplementary file pr800866n_si_004.xls (http://pubs.acs.org/doi/suppl/10.1021/pr800866n/suppl_file/pr800866n_si_004.xls) and imported into R as described below. We will concentrate on the first replicate.

2.2 Importing and loading data

This section illustrates how to import data in comma-separated value (csv) format into an appropriate R data structure. The first section shows the original csv (comma separated values) spreadsheet, as published by the authors, and how one can read such a file into using the read.csv function. This spreadsheet file is similar to the output of many quantitation software.

In the next section, we show 2 csv files containing a subset of the columns of original pr800866n_si_004-rep1.csv file and another short file, created manually, that will be used to create the appropriate R data.

2.2.1 The original data file

## The original data for replicate 1, available
## from the pRolocdata package
f0 <- dir(system.file("extdata", package = "pRolocdata"), 
          full.names = TRUE,
          pattern = "pr800866n_si_004-rep1.csv")
csv <- read.csv(f0)

The three first lines of the original spreadsheet, containing the data for replicate one, are illustrated below (using the function head). It contains 888 rows (proteins) and 16 columns, including protein identifiers, database accession numbers, gene symbols, reporter ion quantitation values, information related to protein identification, …

head(csv, n=3)
##   Protein.ID        FBgn Flybase.Symbol No..peptide.IDs Mascot.score
## 1    CG10060 FBgn0001104    G-ialpha65A               3       179.86
## 2    CG10067 FBg