hyperLOPITU2OS2017 | R Documentation |
This data contains 4 different datasets generated from U2OS cells.
The lopitdcU2OS2018
was generated using the LOPIT-DC method
and all other datasets have been generated using the hyperLOPIT
protocol (see Christoforou et al. 2016 and Mulvey et al. 2017). The
lopitdcU2OS2018
dataset contains 3 replicates, 10 fractions
per replicate. The hyperLOPITU2OS2017
dataset contains 2
replicates, in which the quantitation was obtained using two sets of
TMT 10-plex per replicate, producing a total of 40 quanitation
channels, while in hyperLOPITU2OS2017b
, 3 fractions with low
protein yields have been remove (see example below). The
hyperLOPITU2OS2018
dataset contains a third replicate, thus
giving 57 quantitation channels in total.
data("hyperLOPITU2OS2017")
An object of class MSnSet
, defined in the MSnbase
package.
The data (expression and feature variable) contain:
UniProt Accession for Protein Group (no isoform information): Unique UniProt accession for quantified protein group reported by Proteome Discoverer (1% FDR) - isoform information not retained.
Normalized TMT 10-plex Reporter Ion Distribution: ReplicateX TMT SetX-126 Normalized TMT 10-plex reporter ion values, representing the distribution of each protein across the fractionation scheme for each experiment. Protein-level reporter ion values were calculated by taking the median of all quantifiable PSMs for the protein group, then normalized so that the sum of all 10 channels was equal to 1. The numeric value in the tag name corresponds to the nominal mass of each TMT reporter ion. The N and C suffixes differentiates between the 15N or 13C isotopologue variants of TMT 10-plex reporter ions of the same nominal mass.
UniProt Accession for Protein Group (with isoform information): Unique UniProt accession for quantified protein group reported by Proteome Discoverer (1% FDR) - isoform information retained.
UniProt Protein Description: UniProt description for protein accession.
Coverage: Percentage of protein sequence covered by identified peptides.
Quantified Proteins: Number of quantified protein groups.
Quantified Unique Peptides: Number of unique quantified peptides. Only these peptides were used for quantification.
Quantified Peptides: Number of quantified peptides. Only peptides that were unique to a single protein group were used for quantification.
Quantified PSMs: Number of quantified peptide-spectrum matches.
Score - ReplicateX TMT SetX: Total score of identified protein group for each experiment. This score is equal to the sum of the individual peptide scores.
Coverage - ReplicateX TMT SetX: Percentage of protein sequence covered by identified peptides for each experiment.
Quantified Peptides - ReplicateX TMT SetX: Number of quantified peptides for each experiment. Only peptides that were unique to a single protein group were used for quantification.
Quantified PSMs - ReplicateX TMT SetX: Number of quantified peptide-spectrum matches for each experiment.
SVM Marker Set: Final marker set used for SVM classification of protein subcellular localization to 14 subcellular compartments.
SVM Classification: Subcellular class to which the protein group was assigned by SVM classification. All proteins are assigned to a single class by SVM.
SVM Score: Confidence score for localization assignment, ranging from 0 to 1. A score close to 0 represents a very low confidence assignment, whereas a score of 1 indicates a very high confidence assignment.
Final SVM Classification (5% FDR) (assignment): Predicted localization, with SVM score thresholds determined empirically by comparison to GO annotation and protein database annotation. The SVM score thresholds were set individually for each class so that the false discovery rate of the SVM classification was equal or lower than (5%).
Thul PJ et al. A subcellular map of the human proteome. Science. 2017 May 26;356(6340). pii: eaal3321. doi: 10.1126/science.aal3321. Epub 2017 May 11. PubMed PMID: 28495876.
data(hyperLOPITU2OS2017)
hyperLOPITU2OS2017
library("pRoloc")
plot2D(hyperLOPITU2OS2017, addLegend = "bottomleft")
## removing low intensity fractions
sort(colSums(exprs(hyperLOPITU2OS2017)))
i <- order(colSums(exprs(hyperLOPITU2OS2017)))[1:3]
x <- hyperLOPITU2OS2017[, -i]
plot2D(x, mirrorY = TRUE)
data(hyperLOPITU2OS2017b)
## only difference if subsetting date
all.equal(hyperLOPITU2OS2017b, x)
processingData(hyperLOPITU2OS2017b)
processingData(x)
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