inst/scripts/E14TG2a.R
In lgatto/pRolocdata: Data accompanying the pRoloc package
library("MSnbase")
library("pRoloc")
makeAndy <- function(filename, date, stringency) {
csv <- read.csv(filename, row.names=1, header = TRUE, stringsAsFactors=FALSE)
Uniprot.ID <- substr(rownames(csv), start=4, stop=9)
Accession.No <- substr(rownames(csv), start=11, stop=nchar(rownames(csv)))
rownames(csv) <- Accession.No
ind <- grep("n1", colnames(csv))
.exprs <- csv[,ind]
.exprs <- as.matrix(.exprs)
ind <- c(grep("GO", colnames(csv)), grep("Train", colnames(csv)))
.fData <- cbind(Uniprot.ID, csv[,c(1:3,ind)])
null <- which(.fData$GOannotation=="null")
.fData$GOannotation[null] <- rep("unknown", length(null))
na <- which(.fData$Training=="")
.fData$Training[na] <- rep("unknown", length(na))
ind <- which(colnames(.fData) == "Training")
colnames(.fData)[ind] <- "markers"
.fData <- new("AnnotatedDataFrame", .fData)
.fData@varMetadata[,1] <- c("UniProtKB accession number",
"Full protein name",
"Peptides",
"Peptide spectrum match",
"Localisation inferred from GO: Andy' output from his quickGO program",
"Andy's own curated training set")
.pData <- new("AnnotatedDataFrame",
data.frame(Fraction.information=c(rep("High Density", 7),
"Soluble/cytosolic"),
row.names=colnames(.exprs)))
.experiment <- new("MIAPE",
lab = "Cambridge Centre for Proteomics (CCP)",
name = "Andy Christoforou",
contact = "Kathryn S. Lilley",
email = "k.s.lilley@bioc.cam.ac.uk",
samples = list(
species = "Mouse",
tissue = "Cell",
cellLine = "Murine pluripotent embryonic stem cells (E14Tg2a)",
operator = "Andy Christoforou"
),
title="",
abstract = "",
pubMedIds = "",
url = "",
instrumentModel = "LTQ Orbitrap Velos",
instrumentManufacturer = "ThermoScientific",
ionSource = "New Objective PicoView nano-electrospray",
analyser = "Orbitrap",
detectorType = "Orbitrap",
softwareName = "Mascot Search Engine",
isolationWidth = 0.5,
collisionEnergy = "45% CE HCD (two-stepped, 10% width)",
other = list(
experimentType = "Experiment Type: Standard LOPIT experimental design on E14TG2a embryonic stem cells. Sample: E14TG2a mouse pluripotent embryonic stem cells cultured under conditions favouring self-renewal (serum+LIF). Label: iTRAQ 8-plex. Instrument: LTQ Orbitrap Velos. Scan Mode for Identification: MS2-HCD. Scan Mode for Quantitation: MS2-HCD. Scan Setup: Nth order double play, Top 10 HCD. MS1 Scan: FTMS, resolution = 30000, scan range m/z 380 - 1600. MS2 Scan: FTMS, resolution = 7500, scan range m/z 100 - 2000. Precursor Ion Selection Window: 0.5 Da on 'stringent' and 1.2 Da on 'relaxed' setting (see stringencySetting slot). Collision Energy: 45% CE HCD (two-stepped, 10% width)",
searchParameters = "Search Engine: Mascot. Search Database: UniProt Mouse. Fixed Modifications: iTRAQ 8-plex (N-term), iTRAQ-8plex (K), Methylthio (C). Variable Modifications: iTRAQ 8-plex (Y), Oxidation (M). Enzyme: Trypsin. Max. Missed Cleavages: 2. Decoy Type: None (see Percolator parameters below). Peptide Charge: 5+. Peptide Tolerance: +/- 10 ppm. MS/MS Tolerance: +/- 0.2 Da. Instrument: ESI-ORBITRAP-HCD",
postProcessing = "Unquantifiable spectra (E-value for PSM > 0.05, non-unique sequences, very low ion counts, >2 zero value reporter ions ) removed
Spectra filtered based on several criteria (precursor relative signal, position of switch relative to peak apex, reporter ion intensity) to pick a single 'peptidotypic' spectrum per peptide
Peptides were merged into proteins by intensity weighted mean",
percolatorParameters = "Percolator Parameters: Percolator Version Used: None. Percolator was misbehaving when this data was processed so Mascot E-values were used to benchmark ID significance rather than PEPs. Any E-values less than 0.05 were accepted for quantitation. The E-value is less stringent than PEP but my general impression is this has made little difference to the overall quality of data. Although no decoy searches were run for Percolating they were performed to check that the FDR was tolerable at the default Mascot p-value of 0.05.
Additional filtering was applied to the data after percolating to refine protein inference within iSPY. Swissprot accessions were given precedence over trEMBL accessions, and isoforms from the same UniProt accession were collapsed together. These assumptions substantially reduce the redundancy of the database, allowing more 'unique' peptides to be taken forward for quantitation.",
stringencySetting = stringency,
MS = "iTRAQ8",
spatexp = "LOPIT",
markers.fcol = "markers",
prediction.fcol = NA
),
dateStamp = date
)
.process <- new("MSnProcess",
processing=c(
paste("Loaded on ",date(),".",sep=""),
paste("Normalised to sum of intensities.")),
normalised=TRUE,
files=filename)
obj <- new("MSnSet",
exprs = .exprs,
phenoData = .pData,
experimentData = .experiment,
featureData = .fData)
obj@processingData <- .process
if (validObject(obj))
return (obj)
}
f1s <- "../extdata/E14TG2a_2011/E14TG2a_2011_12_stringentsettings/E14TG2a_2011_12_proteins.csv"
f2s <- "../extdata/E14TG2a_2011/E14TG2a_2012_01_stringentsettings/E14TG2a_2012_01_proteins.csv"
f1r <- "../extdata/E14TG2a_2011/E14TG2a_2011_12_relaxedsettings/E14TG2a_2011_12_1p2_proteins.csv"
date1 <- "December 2011"
date2 <- "January 2012"
H <- "High (stringent)"
L <- "Low (relaxed)"
E14TG2aS1 <- makeAndy(f1s, date1, H)
E14TG2aS2 <- makeAndy(f2s, date2, H)
E14TG2aR <- makeAndy(f1r, date1, L)
## Make uniprot accession number featureNames
foo <- function(data) {
fData(data)$UniprotName <- featureNames(data)
featureNames(data) <- fData(data)$Uniprot.ID
fData(data) <- fData(data)[, c(1, 7, 2:6)]
fData(data)$markers.orig <- fData(data)$markers
fData(data)$markers <- NULL
return(data)
}
E14TG2aS1 <- foo(E14TG2aS1)
E14TG2aS2 <- foo(E14TG2aS2)
E14TG2aR <- foo(E14TG2aR)
## Add updated marker list
load("../extdata/markersE14.rda")
## --E14TG2aS1
E14TG2aS1 <- addMarkers(E14TG2aS1, markers = mrk, verbose = FALSE)
E14TG2aS1 <- minMarkers(E14TG2aS1, 6)
fData(E14TG2aS1)$markers <- fData(E14TG2aS1)$markers6
fData(E14TG2aS1)$markers6 <- NULL
## Remove annotation mismatches
#torm <- c("O55143", "Q9D1B9", "Q9CPX7", "P52503", "Q8C2E4", "Q8R0G7", "Q9CXW2",
# "Q99N87", "P19096", "Q8VDF2", "Q62315", "Q9JKR6", "Q6P5E4", "Q9CY27")
torm <- c(103, 946, 366, 545, 788, 821, 939, 172, 212, 346, 689, 97, 306, 369)
fData(E14TG2aS1)$markers[torm] <- rep("unknown", length(torm))
## --E14TG2aS2
E14TG2aS2 <- addMarkers(E14TG2aS2, mrk, verbose = FALSE)
E14TG2aS2 <- minMarkers(E14TG2aS2, 6)
fData(E14TG2aS2)$markers <- fData(E14TG2aS2)$markers6
fData(E14TG2aS2)$markers6 <- NULL
# c("Q9CXW2", "Q99N87", "P62858", "P52503", "Q8R0G7")
ind <- c(724, 763, 873, 958, 1042)
fData(E14TG2aS2)$markers[ind] <- rep("unknown", length(ind))
## --E14TG2aR
E14TG2aR <- addMarkers(E14TG2aR, mrk, verbose = FALSE)
E14TG2aR <- minMarkers(E14TG2aR, 6)
fData(E14TG2aR)$markers <- fData(E14TG2aR)$markers6
fData(E14TG2aR)$markers6 <- NULL
# c("P06745", "P52503", "Q99N87", "Q3UMR5", "Q8R0G7", "Q9CXW2", "Q8VDF2", "Q62315")
ind<- c(203, 810, 1029, 1402, 1643, 1799, 248, 1448)
fData(E14TG2aR)$markers[ind] <- rep("unknown", length(ind))
## Add results from Breckels et al 2015 to feature data of E14TG2aS1 dataset
load("../extdata/breckels2015.rda")
if (!all(featureNames(E14TG2aS1) == featureNames(breckels2015)))
stop("Feature names do not match between E14TG2aS1 and Breckels et al 2015 data")
fData(E14TG2aS1)$markers.tl <- fData(breckels2015)$markers.tl
fData(E14TG2aS1)$knntl.breckels2015 <- fData(breckels2015)$knntl.breckels2015
fData(E14TG2aS1)$knntl.scores.breckels2015 <- fData(breckels2015)$knntl.scores.breckels2015
fData(E14TG2aS1)$knntl.final.assignment <- fData(breckels2015)$knntl.final.assignment
fData(E14TG2aS1)$svmtl.breckels2015 <- fData(breckels2015)$svmtl.breckels2015
fData(E14TG2aS1)$svmtl.scores.breckels2015 <- fData(breckels2015)$svmtl.scores.breckels2015
fData(E14TG2aS1)$svmtl.final.assignment <- fData(breckels2015)$svmtl.final.assignment
## Update fvarMetaData slots
fvarMetadata(E14TG2aS1)["markers.orig", 1] <- "Initial markers defined by Christoforou"
fvarMetadata(E14TG2aS2)["markers.orig", 1] <- "Initial markers defined by Christoforou"
fvarMetadata(E14TG2aR)["markers.orig", 1] <- "Initial markers defined by Christoforou"
fvarMetadata(E14TG2aS1)["markers", 1] <- "Hand curated updated marker list defined by Christoforou, Mulvey and Breckels"
fvarMetadata(E14TG2aS2)["markers", 1] <- "Hand curated updated marker list defined by Christoforou, Mulvey and Breckels"
fvarMetadata(E14TG2aR)["markers", 1] <- "Hand curated updated marker list defined by Christoforou, Mulvey and Breckels"
fvarMetadata(E14TG2aS1)["markers.tl", 1] <- "Markers used for transfer learning in Breckels et al 2015, this is a subset of markers from using minMarkers function to set a minimum of 13 proteins per cluster"
fvarMetadata(E14TG2aS1)["knntl.breckels2015", 1] <- "Classification results from using the knntl algorithm as detailed in Breckels et al 2015"
fvarMetadata(E14TG2aS1)["knntl.scores.breckels2015", 1] <- "Scores output from using the knntl algorithm as detailed in Breckels et al 2015"
fvarMetadata(E14TG2aS1)["knntl.final.assignment", 1] <- "Final assignment from using the knntl algorithm as detailed in Breckels et al 2015"
fvarMetadata(E14TG2aS1)["svmtl.breckels2015", 1] <- "Classification results from using the svmtl algorithm as detailed in Breckels et al 2015"
fvarMetadata(E14TG2aS1)["svmtl.scores.breckels2015", 1] <- "Scores output from using the svmtl algorithm as detailed in Breckels et al 2015"
fvarMetadata(E14TG2aS1)["svmtl.final.assignment", 1] <- "Final assignment from using the svmtl algorithm as detailed in Breckels et al 2015"
## TL results from Breckels et al 2016
experimentData(E14TG2aS1)@other$knntl$go$markers.fcol = "markers.tl"
experimentData(E14TG2aS1)@other$knntl$go$prediction.fcol = "knntl.final.assignment"
experimentData(E14TG2aS1)@other$knntl$go$thetas = c("40S Ribosome" = 1/3,
"60S Ribosome" = 2/3,
"Cytosol" = 2/3,
"Endoplasmic reticulum" = 1,
"Lysosome" = 1/3,
"Mitochondrion" = 1,
"Nucleus - Chromatin" = 1,
"Nucleus - Nucleolus" = 1/3,
"Plasma membrane" = 2/3,
"Proteasome" = 0)
experimentData(E14TG2aS1)@other$knntl$go$k <- c(kp = 5, ka = 5)
experimentData(E14TG2aS1)@other$svmtl$go$prediction.fcol = "svmtl.final.assignment"
experimentData(E14TG2aS1)@other$svmtl$go$cost <- 16
experimentData(E14TG2aS1)@other$svmtl$go$sigmas <- c(s1 = 1, s2 = 0.1)
stopifnot(pRolocdata:::valid.pRolocmetadata(pRolocmetadata(E14TG2aR)))
stopifnot(pRolocdata:::valid.pRolocmetadata(pRolocmetadata(E14TG2aS1)))
stopifnot(pRolocdata:::valid.pRolocmetadata(pRolocmetadata(E14TG2aS2)))
if (validObject(E14TG2aR))
save(E14TG2aR,file="../../data/E14TG2aR.RData",
compress = "xz", compression_level = 9)
if (validObject(E14TG2aS1))
save(E14TG2aS1,file="../../data/E14TG2aS1.RData",
compress = "xz", compression_level = 9)
if (validObject(E14TG2aS2))
save(E14TG2aS2,file="../../data/E14TG2aS2.RData",
compress = "xz", compression_level = 9)
lgatto/pRolocdata documentation built on April 7, 2023, 1:56 a.m.
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library("MSnbase")
library("pRoloc")
makeAndy <- function(filename, date, stringency) {
csv <- read.csv(filename, row.names=1, header = TRUE, stringsAsFactors=FALSE)
Uniprot.ID <- substr(rownames(csv), start=4, stop=9)
Accession.No <- substr(rownames(csv), start=11, stop=nchar(rownames(csv)))
rownames(csv) <- Accession.No
ind <- grep("n1", colnames(csv))
.exprs <- csv[,ind]
.exprs <- as.matrix(.exprs)
ind <- c(grep("GO", colnames(csv)), grep("Train", colnames(csv)))
.fData <- cbind(Uniprot.ID, csv[,c(1:3,ind)])
null <- which(.fData$GOannotation=="null")
.fData$GOannotation[null] <- rep("unknown", length(null))
na <- which(.fData$Training=="")
.fData$Training[na] <- rep("unknown", length(na))
ind <- which(colnames(.fData) == "Training")
colnames(.fData)[ind] <- "markers"
.fData <- new("AnnotatedDataFrame", .fData)
.fData@varMetadata[,1] <- c("UniProtKB accession number",
"Full protein name",
"Peptides",
"Peptide spectrum match",
"Localisation inferred from GO: Andy' output from his quickGO program",
"Andy's own curated training set")
.pData <- new("AnnotatedDataFrame",
data.frame(Fraction.information=c(rep("High Density", 7),
"Soluble/cytosolic"),
row.names=colnames(.exprs)))
.experiment <- new("MIAPE",
lab = "Cambridge Centre for Proteomics (CCP)",
name = "Andy Christoforou",
contact = "Kathryn S. Lilley",
email = "k.s.lilley@bioc.cam.ac.uk",
samples = list(
species = "Mouse",
tissue = "Cell",
cellLine = "Murine pluripotent embryonic stem cells (E14Tg2a)",
operator = "Andy Christoforou"
),
title="",
abstract = "",
pubMedIds = "",
url = "",
instrumentModel = "LTQ Orbitrap Velos",
instrumentManufacturer = "ThermoScientific",
ionSource = "New Objective PicoView nano-electrospray",
analyser = "Orbitrap",
detectorType = "Orbitrap",
softwareName = "Mascot Search Engine",
isolationWidth = 0.5,
collisionEnergy = "45% CE HCD (two-stepped, 10% width)",
other = list(
experimentType = "Experiment Type: Standard LOPIT experimental design on E14TG2a embryonic stem cells. Sample: E14TG2a mouse pluripotent embryonic stem cells cultured under conditions favouring self-renewal (serum+LIF). Label: iTRAQ 8-plex. Instrument: LTQ Orbitrap Velos. Scan Mode for Identification: MS2-HCD. Scan Mode for Quantitation: MS2-HCD. Scan Setup: Nth order double play, Top 10 HCD. MS1 Scan: FTMS, resolution = 30000, scan range m/z 380 - 1600. MS2 Scan: FTMS, resolution = 7500, scan range m/z 100 - 2000. Precursor Ion Selection Window: 0.5 Da on 'stringent' and 1.2 Da on 'relaxed' setting (see stringencySetting slot). Collision Energy: 45% CE HCD (two-stepped, 10% width)",
searchParameters = "Search Engine: Mascot. Search Database: UniProt Mouse. Fixed Modifications: iTRAQ 8-plex (N-term), iTRAQ-8plex (K), Methylthio (C). Variable Modifications: iTRAQ 8-plex (Y), Oxidation (M). Enzyme: Trypsin. Max. Missed Cleavages: 2. Decoy Type: None (see Percolator parameters below). Peptide Charge: 5+. Peptide Tolerance: +/- 10 ppm. MS/MS Tolerance: +/- 0.2 Da. Instrument: ESI-ORBITRAP-HCD",
postProcessing = "Unquantifiable spectra (E-value for PSM > 0.05, non-unique sequences, very low ion counts, >2 zero value reporter ions ) removed
Spectra filtered based on several criteria (precursor relative signal, position of switch relative to peak apex, reporter ion intensity) to pick a single 'peptidotypic' spectrum per peptide
Peptides were merged into proteins by intensity weighted mean",
percolatorParameters = "Percolator Parameters: Percolator Version Used: None. Percolator was misbehaving when this data was processed so Mascot E-values were used to benchmark ID significance rather than PEPs. Any E-values less than 0.05 were accepted for quantitation. The E-value is less stringent than PEP but my general impression is this has made little difference to the overall quality of data. Although no decoy searches were run for Percolating they were performed to check that the FDR was tolerable at the default Mascot p-value of 0.05.
Additional filtering was applied to the data after percolating to refine protein inference within iSPY. Swissprot accessions were given precedence over trEMBL accessions, and isoforms from the same UniProt accession were collapsed together. These assumptions substantially reduce the redundancy of the database, allowing more 'unique' peptides to be taken forward for quantitation.",
stringencySetting = stringency,
MS = "iTRAQ8",
spatexp = "LOPIT",
markers.fcol = "markers",
prediction.fcol = NA
),
dateStamp = date
)
.process <- new("MSnProcess",
processing=c(
paste("Loaded on ",date(),".",sep=""),
paste("Normalised to sum of intensities.")),
normalised=TRUE,
files=filename)
obj <- new("MSnSet",
exprs = .exprs,
phenoData = .pData,
experimentData = .experiment,
featureData = .fData)
obj@processingData <- .process
if (validObject(obj))
return (obj)
}
f1s <- "../extdata/E14TG2a_2011/E14TG2a_2011_12_stringentsettings/E14TG2a_2011_12_proteins.csv"
f2s <- "../extdata/E14TG2a_2011/E14TG2a_2012_01_stringentsettings/E14TG2a_2012_01_proteins.csv"
f1r <- "../extdata/E14TG2a_2011/E14TG2a_2011_12_relaxedsettings/E14TG2a_2011_12_1p2_proteins.csv"
date1 <- "December 2011"
date2 <- "January 2012"
H <- "High (stringent)"
L <- "Low (relaxed)"
E14TG2aS1 <- makeAndy(f1s, date1, H)
E14TG2aS2 <- makeAndy(f2s, date2, H)
E14TG2aR <- makeAndy(f1r, date1, L)
## Make uniprot accession number featureNames
foo <- function(data) {
fData(data)$UniprotName <- featureNames(data)
featureNames(data) <- fData(data)$Uniprot.ID
fData(data) <- fData(data)[, c(1, 7, 2:6)]
fData(data)$markers.orig <- fData(data)$markers
fData(data)$markers <- NULL
return(data)
}
E14TG2aS1 <- foo(E14TG2aS1)
E14TG2aS2 <- foo(E14TG2aS2)
E14TG2aR <- foo(E14TG2aR)
## Add updated marker list
load("../extdata/markersE14.rda")
## --E14TG2aS1
E14TG2aS1 <- addMarkers(E14TG2aS1, markers = mrk, verbose = FALSE)
E14TG2aS1 <- minMarkers(E14TG2aS1, 6)
fData(E14TG2aS1)$markers <- fData(E14TG2aS1)$markers6
fData(E14TG2aS1)$markers6 <- NULL
## Remove annotation mismatches
#torm <- c("O55143", "Q9D1B9", "Q9CPX7", "P52503", "Q8C2E4", "Q8R0G7", "Q9CXW2",
# "Q99N87", "P19096", "Q8VDF2", "Q62315", "Q9JKR6", "Q6P5E4", "Q9CY27")
torm <- c(103, 946, 366, 545, 788, 821, 939, 172, 212, 346, 689, 97, 306, 369)
fData(E14TG2aS1)$markers[torm] <- rep("unknown", length(torm))
## --E14TG2aS2
E14TG2aS2 <- addMarkers(E14TG2aS2, mrk, verbose = FALSE)
E14TG2aS2 <- minMarkers(E14TG2aS2, 6)
fData(E14TG2aS2)$markers <- fData(E14TG2aS2)$markers6
fData(E14TG2aS2)$markers6 <- NULL
# c("Q9CXW2", "Q99N87", "P62858", "P52503", "Q8R0G7")
ind <- c(724, 763, 873, 958, 1042)
fData(E14TG2aS2)$markers[ind] <- rep("unknown", length(ind))
## --E14TG2aR
E14TG2aR <- addMarkers(E14TG2aR, mrk, verbose = FALSE)
E14TG2aR <- minMarkers(E14TG2aR, 6)
fData(E14TG2aR)$markers <- fData(E14TG2aR)$markers6
fData(E14TG2aR)$markers6 <- NULL
# c("P06745", "P52503", "Q99N87", "Q3UMR5", "Q8R0G7", "Q9CXW2", "Q8VDF2", "Q62315")
ind<- c(203, 810, 1029, 1402, 1643, 1799, 248, 1448)
fData(E14TG2aR)$markers[ind] <- rep("unknown", length(ind))
## Add results from Breckels et al 2015 to feature data of E14TG2aS1 dataset
load("../extdata/breckels2015.rda")
if (!all(featureNames(E14TG2aS1) == featureNames(breckels2015)))
stop("Feature names do not match between E14TG2aS1 and Breckels et al 2015 data")
fData(E14TG2aS1)$markers.tl <- fData(breckels2015)$markers.tl
fData(E14TG2aS1)$knntl.breckels2015 <- fData(breckels2015)$knntl.breckels2015
fData(E14TG2aS1)$knntl.scores.breckels2015 <- fData(breckels2015)$knntl.scores.breckels2015
fData(E14TG2aS1)$knntl.final.assignment <- fData(breckels2015)$knntl.final.assignment
fData(E14TG2aS1)$svmtl.breckels2015 <- fData(breckels2015)$svmtl.breckels2015
fData(E14TG2aS1)$svmtl.scores.breckels2015 <- fData(breckels2015)$svmtl.scores.breckels2015
fData(E14TG2aS1)$svmtl.final.assignment <- fData(breckels2015)$svmtl.final.assignment
## Update fvarMetaData slots
fvarMetadata(E14TG2aS1)["markers.orig", 1] <- "Initial markers defined by Christoforou"
fvarMetadata(E14TG2aS2)["markers.orig", 1] <- "Initial markers defined by Christoforou"
fvarMetadata(E14TG2aR)["markers.orig", 1] <- "Initial markers defined by Christoforou"
fvarMetadata(E14TG2aS1)["markers", 1] <- "Hand curated updated marker list defined by Christoforou, Mulvey and Breckels"
fvarMetadata(E14TG2aS2)["markers", 1] <- "Hand curated updated marker list defined by Christoforou, Mulvey and Breckels"
fvarMetadata(E14TG2aR)["markers", 1] <- "Hand curated updated marker list defined by Christoforou, Mulvey and Breckels"
fvarMetadata(E14TG2aS1)["markers.tl", 1] <- "Markers used for transfer learning in Breckels et al 2015, this is a subset of markers from using minMarkers function to set a minimum of 13 proteins per cluster"
fvarMetadata(E14TG2aS1)["knntl.breckels2015", 1] <- "Classification results from using the knntl algorithm as detailed in Breckels et al 2015"
fvarMetadata(E14TG2aS1)["knntl.scores.breckels2015", 1] <- "Scores output from using the knntl algorithm as detailed in Breckels et al 2015"
fvarMetadata(E14TG2aS1)["knntl.final.assignment", 1] <- "Final assignment from using the knntl algorithm as detailed in Breckels et al 2015"
fvarMetadata(E14TG2aS1)["svmtl.breckels2015", 1] <- "Classification results from using the svmtl algorithm as detailed in Breckels et al 2015"
fvarMetadata(E14TG2aS1)["svmtl.scores.breckels2015", 1] <- "Scores output from using the svmtl algorithm as detailed in Breckels et al 2015"
fvarMetadata(E14TG2aS1)["svmtl.final.assignment", 1] <- "Final assignment from using the svmtl algorithm as detailed in Breckels et al 2015"
## TL results from Breckels et al 2016
experimentData(E14TG2aS1)@other$knntl$go$markers.fcol = "markers.tl"
experimentData(E14TG2aS1)@other$knntl$go$prediction.fcol = "knntl.final.assignment"
experimentData(E14TG2aS1)@other$knntl$go$thetas = c("40S Ribosome" = 1/3,
"60S Ribosome" = 2/3,
"Cytosol" = 2/3,
"Endoplasmic reticulum" = 1,
"Lysosome" = 1/3,
"Mitochondrion" = 1,
"Nucleus - Chromatin" = 1,
"Nucleus - Nucleolus" = 1/3,
"Plasma membrane" = 2/3,
"Proteasome" = 0)
experimentData(E14TG2aS1)@other$knntl$go$k <- c(kp = 5, ka = 5)
experimentData(E14TG2aS1)@other$svmtl$go$prediction.fcol = "svmtl.final.assignment"
experimentData(E14TG2aS1)@other$svmtl$go$cost <- 16
experimentData(E14TG2aS1)@other$svmtl$go$sigmas <- c(s1 = 1, s2 = 0.1)
stopifnot(pRolocdata:::valid.pRolocmetadata(pRolocmetadata(E14TG2aR)))
stopifnot(pRolocdata:::valid.pRolocmetadata(pRolocmetadata(E14TG2aS1)))
stopifnot(pRolocdata:::valid.pRolocmetadata(pRolocmetadata(E14TG2aS2)))
if (validObject(E14TG2aR))
save(E14TG2aR,file="../../data/E14TG2aR.RData",
compress = "xz", compression_level = 9)
if (validObject(E14TG2aS1))
save(E14TG2aS1,file="../../data/E14TG2aS1.RData",
compress = "xz", compression_level = 9)
if (validObject(E14TG2aS2))
save(E14TG2aS2,file="../../data/E14TG2aS2.RData",
compress = "xz", compression_level = 9)
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