nikolovski2012 | R Documentation |
This is the data used in Nikolovksi et al. (2012). See below for details and references.
data(nikolovski2012)
data(nikolovski2012imp)
The data is an instance of class MSnSet
from package MSnbase
.
These data are a concatenation of 4 LOPIT experiments. Experiments 1
and 2 are from Dunkley et al. 2006 (see also dunkley2006
).
Exepriments 3 and 4 are new.
In the LOPIT experiments by Dunkley et al. (2006), peripheral membrane proteins were removed by carbonate washing of the isolated membranes, while for experiments 3 and 4, no carbonate wash was performed and are, as such, enriched in peripheral and luminal proteins. See figure 1 in Nikolovski 2012 for a description of the design.
In nikolovksi2012imp
missing values have been imputed using
partial least-squares regression.
The training set used for the Naive Bayesian classifier is available
as the markers
feature meta-data. Note that Nikolovksi included
a group of markers labelled 'others', which has been retained in these
data sets. The results produced in this work are available in the
preds
feature variable (note that some organelles are
marked with a '*', which is undefined here).
Supporting Information on
http://www.plantphysiol.org/content/160/2/1037.long,
also available in the package's extdata
directory.
Nikolovski N, Rubtsov D, Segura MP, Miles GP, Stevens TJ, Dunkley TP, Munro S, Lilley KS, Dupree P. Putative glycosyltransferases and other plant Golgi apparatus proteins are revealed by LOPIT proteomics. Plant Physiol. 2012 Oct;160(2):1037-51. doi: 10.1104/pp.112.204263. Epub 2012 Aug 24. PMID: 22923678; PMCID: PMC3461528.
Dunkley TP, Hester S, Shadforth IP, Runions J, Weimar T, Hanton SL, Griffin JL, Bessant C, Brandizzi F, Hawes C, Watson RB, Dupree P, Lilley KS. Mapping the Arabidopsis organelle proteome. Proc Natl Acad Sci U S A. 2006 Apr 25;103(17):6518-23. Epub 2006 Apr 17. PubMed PMID: 16618929; PubMed Central PMCID: PMC1458916.
data(nikolovski2012)
data(nikolovski2012imp)
table(is.na(nikolovski2012))
table(is.na(nikolovski2012imp))
phenoData(nikolovski2012)
table(fData(nikolovski2012)$markers)
all.equal(sort(featureNames(nikolovski2012)),
sort(featureNames(nikolovski2012imp)))
library("pRoloc")
plot2D(nikolovski2012imp)
addLegend(nikolovski2012imp, where = "topright", bty = "n", cex =.7)
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