hyperLOPIT2015 | R Documentation |
This is a spatial proteomics dataset from a hyperLOPIT experimental design on Mouse E14TG2a embryonic stem cells.
data(hyperLOPIT2015)
data(hyperLOPIT2015_se)
data(hyperLOPIT2015ms3r1)
data(hyperLOPIT2015ms3r2)
data(hyperLOPIT2015ms3r3)
data(hyperLOPIT2015ms2)
data(hyperLOPIT2015ms3r1psm)
data(hyperLOPIT2015ms3r2psm)
data(hyperLOPIT2015ms2psm)
The data are an instance of class MSnSet
from package
MSnbase
. Those ending in _se
are of class
SummarizedExperiment
.
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).
The data was generated by A. Christoforou and C. Mulvey in the Cambridge Centre for Proteomics. http://www.bio.cam.ac.uk/proteomics/.
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
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)
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