hyperLOPIT2015: Protein and PMS-level hyperLOPIT datasets on Mouse E14TG2a...

In lgatto/pRolocdata: Data accompanying the pRoloc package

Protein and PMS-level hyperLOPIT datasets on Mouse E14TG2a embryonic stem cells from Christoforou et al. (2016).

Description

This is a spatial proteomics dataset from a hyperLOPIT experimental design on Mouse E14TG2a embryonic stem cells.

Usage

data(hyperLOPIT2015)
data(hyperLOPIT2015_se)
data(hyperLOPIT2015ms3r1)
data(hyperLOPIT2015ms3r2)
data(hyperLOPIT2015ms3r3)
data(hyperLOPIT2015ms2)
data(hyperLOPIT2015ms3r1psm)
data(hyperLOPIT2015ms3r2psm)
data(hyperLOPIT2015ms2psm)

Format

The data are an instance of class MSnSet from package MSnbase. Those ending in _se are of class SummarizedExperiment.

Details

This is a hyperLOPIT experiment. Normalised intensities for proteins for TMT 10-plex labelled fractions are available for 3 replicates acquired in MS3 mode (hyperLOPIT2015ms3r1, hyperLOPIT2015ms3r2 and hyperLOPIT2015ms3r3) using an Orbitrap Fusion mass-spectrometer. The first two replicates have also been combined as described in Trotter et al (2010) to generate dataset hyperLOPIT2015 to increase organellar resolution. A dataset acquired in MS2 mode has also been acquired (hyperLOPIT2015ms2) which was also generated using the Orbitrap Fusion and using a TMT 10-plex experimental design.

The PSM-level cvs file are available in the extdata directory and have been processed as follows: imported MSnSet instances using readMSnSet2, PSMs with missing values were filtered out with filterNA, only PSMs with feature variable Quan.Usage "Used" and a TMT6plex modification were retained and the phenoData was matched and assigned from the respective protein-level data. Finally, marker proteins are annotated based on the combined protein-level data hyperLOPIT2015 and reporter tags are normalised using the "sum" method. The processing script is scripts/hyperlopit2015psm.R.

The TAGM feature data contains the allocation results from the Baysian T-augmented Gaussian Mixture modelling approach as described in Crook et al. (2018).

Source

The data was generated by A. Christoforou and C. Mulvey in the Cambridge Centre for Proteomics. http://www.bio.cam.ac.uk/proteomics/.

References

A draft map of the mouse pluripotent stem cell spatial proteome. Christoforou A, Mulvey CM, Breckels LM, Geladaki A, Hurrell T, Hayward PC, Naake T, Gatto L, Viner R, Martinez Arias A, Lilley KS. Nat Commun. 2016 Jan 12;7:8992. doi: 10.1038/ncomms9992. PubMed PMID: 26754106; PubMed Central PMCID: PMC4729960.

A Bayesian Mixture Modelling Approach For Spatial Proteomics Oliver M Crook, Claire M Mulvey, Paul D. W. Kirk, Kathryn S Lilley, Laurent Gatto bioRxiv 282269; doi: https://doi.org/10.1101/282269

Examples

data(hyperLOPIT2015)
hyperLOPIT2015
pData(hyperLOPIT2015)
head(exprs(hyperLOPIT2015))

data(hyperLOPIT2015ms3r1psm)
x <- combineFeatures(hyperLOPIT2015ms3r1psm,
                     groupBy = fData(hyperLOPIT2015ms3r1psm)$Protein.Group.Accession,
                     fun = median)
library("pRoloc")
par(mfrow = c(1, 2))
plot2D(hyperLOPIT2015ms3r1psm, main = "PSM-level")
plot2D(x, main = "Protein-level (using mean)")

## SummarizedExperiment data
library(SummarizedExperiment)
data(hyperLOPIT2015_se)

lgatto/pRolocdata documentation built on Nov. 27, 2024, 12:12 a.m.