inst/doc/JProteomeRes_Supplemental.R
In HegemanLab/ProteinTurnover: Protein Turnover Experiment Analysis
## ---- setup, include=FALSE-----------------------------------------------
knitr::opts_chunk$set(eval=FALSE)
## ------------------------------------------------------------------------
# system.file(package="ProteinTurnover", "extscripts", "JProteomeRes_Supplemental.R")
## ------------------------------------------------------------------------
# f <- system.file(package="ProteinTurnover", "extscripts", "JProteomeRes_Supplemental.R")
# file.copy(f, ".")
## ------------------------------------------------------------------------
# source("http://bioconductor.org/biocLite.R")
# biocLite("xcms")
## ------------------------------------------------------------------------
# devtools::install_github("HegemanLab/ProteinTurnover")
## ------------------------------------------------------------------------
# R.version.string
# # [1] "R version 3.3.2 (2016-10-31)"
# packageVersion("ProteinTurnover")
# # [1] '1.1.1'
## ---- eval=TRUE----------------------------------------------------------
library(ProteinTurnover)
## ------------------------------------------------------------------------
# p <- list()
## ------------------------------------------------------------------------
# p$num.cores <- 1
## ------------------------------------------------------------------------
# p$dir.data <- "mzs"
## ------------------------------------------------------------------------
# p$dir.results <- "Turnover"
## ------------------------------------------------------------------------
# p$scaffoldfile <- "soluble.csv"
## ------------------------------------------------------------------------
# p$times <- c(0,4,8,16,24,32,40,48)
# p$rawfiles <- paste0("hr_", p$times, ".mzXML")
# p$rdsfiles <- paste0("hr_", p$times, ".rds")
## ------------------------------------------------------------------------
# p$isotope <- "N"
# p$confidence.level <- 80
# p$time.zero <- 0
# p$time.unit <- "Hour"
# p$mz.tol <- 0.005
# p$rt.tol <- 30
# p$extra.channels <- 5
## ------------------------------------------------------------------------
# p$regression.model <- "lm" # "lm" as linear regression
## ------------------------------------------------------------------------
# p$isotope.method <- "incorporation"
## ------------------------------------------------------------------------
# p$M.model <- "one"
## ------------------------------------------------------------------------
# p$alpha.model <- "log2k" #make log2 value of turnover rate k
## ------------------------------------------------------------------------
# p$plim <- NA
## ------------------------------------------------------------------------
# dir.create(p$dir.results)
## ------------------------------------------------------------------------
# rawToRDS(p$dir.data, p$rawfiles, p$rdsfiles)
# # reading hr_0.mzXML (time: 1m 38.3s)
# # writing hr_0.rds (time: 53.2s)
# # reading hr_4.mzXML (time: 1m 39.7s)
# # writing hr_4.rds (time: 54.2s)
# # reading hr_8.mzXML (time: 1m 49.0s)
# # writing hr_8.rds (time: 56.9s)
# # reading hr_16.mzXML (time: 1m 19.1s)
# # writing hr_16.rds (time: 41.9s)
# # reading hr_24.mzXML (time: 1m 42.0s)
# # writing hr_24.rds (time: 56.3s)
# # reading hr_32.mzXML (time: 1m 41.6s)
# # writing hr_32.rds (time: 56.4s)
# # reading hr_40.mzXML (time: 1m 42.0s)
# # writing hr_40.rds (time: 56.6s)
# # reading hr_48.mzXML (time: 1m 46.8s)
# # writing hr_48.rds (time: 53.6s)
# # Warning message:
# # 'loadRcppModules' is deprecated.
# # Use 'loadModule' instead.
# # See help("Deprecated")
## ------------------------------------------------------------------------
# dat <- readScaffoldFile(p$scaffoldfile, times=p$times, dir=p$dir.data)
# # reading Scaffold file ...
# # read soluble.csv
## ------------------------------------------------------------------------
# seqs <- makeSequences(dat,
# isotope=p$isotope,
# confidence=p$confidence.level,
# time.zero=p$time.zero,
# time.unit=p$time.unit,
# mz.tol=p$mz.tol,
# rt.tol=p$rt.tol,
# extra.channels=p$extra.channels)
# # finding Sequences ...
## ------------------------------------------------------------------------
# seqs <- addEICs(seqs, files=p$rdsfiles, type="rds", times=p$times, dir=p$dir.data)
# # reading hr_0.rds (time: 27.0s)
# # adding EICs for Time 0 (time: 4.5s)
# # reading hr_4.rds (time: 27.4s)
# # adding EICs for Time 4 (time: 3.7s)
# # reading hr_8.rds (time: 28.9s)
# # adding EICs for Time 8 (time: 2.9s)
# # reading hr_16.rds (time: 21.0s)
# # adding EICs for Time 16 (time: 2.2s)
# # reading hr_24.rds (time: 28.3s)
# # adding EICs for Time 24 (time: 2.5s)
# # reading hr_32.rds (time: 3.2s)
# # adding EICs for Time 32 (time: 2.0s)
# # reading hr_40.rds (time: 3.2s)
# # adding EICs for Time 40 (time: 1.7s)
# # reading hr_48.rds (time: 28.6s)
# # adding EICs for Time 48 (time: 1.8s)
## ------------------------------------------------------------------------
# save(dat, seqs, p, file=file.path(p$dir.results, "eicdata.Rdata"))
## ------------------------------------------------------------------------
# dir.results <- "Turnover"
# load(file.path(dir.results, "eicdata.Rdata"))
## ------------------------------------------------------------------------
# if(!is.null(p$num.cores)) { options(mc.cores=p$num.cores) }
## ------------------------------------------------------------------------
# seqs <- mclapply.progress(seqs, function(seq) {
# relAb <- makeRelAb(seq, regression.model=p$regression.model)
# fit <- makeFit(seq=seq, relAb=relAb, M=p$M.model, alpha=p$alpha.model, se=TRUE, isotope.method=p$isotope.method)
# list(seq=seq, relAb=relAb, fit=fit)
# })
# # Processing 1854 items, in parallel on 8 cores
# # Started Sat Jul 15 16:03:51 2017
# # ================================================================================
# # Ended Sat Jul 15 16:06:08 2017; elapsed time: 2m 17.0s
## ------------------------------------------------------------------------
# save(dat, seqs, p, file=file.path(p$dir.results, "fit-log2k.Rdata"))
## ------------------------------------------------------------------------
# dir.results <- "Turnover"
# load(file.path(dir.results, "fit-log2k.Rdata"))
## ------------------------------------------------------------------------
# fits <- getPars(seqs)
# # There were 50 or more warnings (use warnings() to see the first 50)
# write.csv(fits, file=file.path(p$dir.results, "fits-log2k.csv"), na="")
## ------------------------------------------------------------------------
# dir.results <- "Turnover"
# load(file.path(dir.results, "fit-log2k.Rdata"))
## ------------------------------------------------------------------------
# if(!is.null(p$num.cores)) { options(mc.cores=p$num.cores) }
## ------------------------------------------------------------------------
# out <- makeSequenceHTML(seqs, dir=p$dir.results, file="results-log2k")
# # Creating output for sequences with errors (these are faster).
# # Processing 866 items, in parallel on 8 cores
# # Started Sat Jul 15 16:06:20 2017
# # ================================================================================
# # Ended Sat Jul 15 16:06:55 2017; elapsed time: 35.0s
# # Creating output for sequences without errors.
# # Processing 988 items, in parallel on 8 cores
# # Started Sat Jul 15 16:06:55 2017
# # ================================================================================
# # Ended Sat Jul 15 16:19:49 2017; elapsed time: 12m 54.0s
# # There were 50 or more warnings (use warnings() to see the first 50)
## ------------------------------------------------------------------------
# dir.results <- "Turnover"
# load(file.path(dir.results, "fit-log2k.Rdata"))
## ---- eval=TRUE, include=FALSE-------------------------------------------
s <- ProteinTurnover:::example2
## ------------------------------------------------------------------------
# s <- seqs[[2]]
## ---- eval=TRUE----------------------------------------------------------
s$fit
score.visual(s$fit)
## ---- fig.width=15, fig.height=5, eval=TRUE------------------------------
regressionPlot(s$relAb)
## ---- fig.width=15, fig.height=5, eval=TRUE------------------------------
plot(s$relAb)
## ---- eval=TRUE, fig.width=7, fig.height=3-------------------------------
plot(s$fit)
## ------------------------------------------------------------------------
# dir.results <- "Turnover"
# load(file.path(dir.results, "eicdata.Rdata"))
## ------------------------------------------------------------------------
# p$alpha.model <- "many"
## ------------------------------------------------------------------------
# if(!is.null(p$num.cores)) { options(mc.cores=p$num.cores) }
## ------------------------------------------------------------------------
# seqs <- mclapply.progress(seqs, function(seq) {
# relAb <- makeRelAb(seq, regression.model=p$regression.model)
# fit <- makeFit(seq=seq, relAb=relAb, M=p$M.model, alpha=p$alpha.model, se=TRUE, isotope.method=p$isotope.method)
# list(seq=seq, relAb=relAb, fit=fit)
# curve <- makeCurve(fit, plim=p$plim, isotope.method=p$isotope.method)
# list(seq=seq, relAb=relAb, fit=fit, curve=curve)
# })
# # Processing 1854 items, in parallel on 8 cores
# # Started Sat Jul 15 16:20:01 2017
# # ================================================================================
# # Ended Sat Jul 15 16:23:44 2017; elapsed time: 3m 43.0s
## ------------------------------------------------------------------------
# save(dat, seqs, p, file=file.path(p$dir.results, "fit-many.Rdata"))
## ------------------------------------------------------------------------
# fits <- getPars(seqs)
# # There were 50 or more warnings (use warnings() to see the first 50)
# write.csv(fits, file=file.path(p$dir.results, "fits-many.csv"), na="")
## ------------------------------------------------------------------------
# out <- makeSequenceHTML(seqs, dir=p$dir.results, file="results-many")
# # Creating output for sequences with errors (these are faster).
# # Processing 853 items, in parallel on 8 cores
# # Started Sat Jul 15 16:23:55 2017
# # ================================================================================
# # Ended Sat Jul 15 16:24:13 2017; elapsed time: 18.0s
# # Creating output for sequences without errors.
# # Processing 1001 items, in parallel on 8 cores
# # Started Sat Jul 15 16:24:13 2017
# # ================================================================================
# # Ended Sat Jul 15 16:38:23 2017; elapsed time: 14m 10.0s
# # There were 50 or more warnings (use warnings() to see the first 50)
## ---- eval=TRUE, include=FALSE-------------------------------------------
s <- ProteinTurnover:::example2
## ------------------------------------------------------------------------
# s <- seqs[[2]]
## ---- eval=TRUE-----------------------------------------------------------------------------------
options(width=100)
s$fit
score.visual(s$fit)
## ---- eval=TRUE, fig.width=7, fig.height=3--------------------------------------------------------
plot(s$fit)
## ---- eval=TRUE, fig.width=6, fig.height=3--------------------------------------------------------
plot(s$curve)
## ---- include=FALSE, eval=FALSE-------------------------------------------------------------------
# download.file("http://users.stat.umn.edu/~rend0020/ProteinTurnoverScript/Turnover/fit-log2k.Rdata", "fit-log2k.Rdata")
# load("fit-log2k.Rdata")
# example1 <- seqs[[2]]
#
# download.file("http://users.stat.umn.edu/~rend0020/ProteinTurnoverScript/Turnover/fit-many.Rdata", "fit-many.Rdata")
# load("fit-many.Rdata")
# example2 <- seqs[[2]]
#
# aaTable <- ProteinTurnover:::aaTable
# LocusNumbers <- ProteinTurnover:::LocusNumbers
# devtools::use_data(aaTable, LocusNumbers, example1, example2, internal = TRUE)
#
# file.remove("fit-log2k.Rdata")
# file.remove("fit-many.Rdata")
HegemanLab/ProteinTurnover documentation built on May 6, 2019, 11:50 p.m.
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## ---- setup, include=FALSE-----------------------------------------------
knitr::opts_chunk$set(eval=FALSE)
## ------------------------------------------------------------------------
# system.file(package="ProteinTurnover", "extscripts", "JProteomeRes_Supplemental.R")
## ------------------------------------------------------------------------
# f <- system.file(package="ProteinTurnover", "extscripts", "JProteomeRes_Supplemental.R")
# file.copy(f, ".")
## ------------------------------------------------------------------------
# source("http://bioconductor.org/biocLite.R")
# biocLite("xcms")
## ------------------------------------------------------------------------
# devtools::install_github("HegemanLab/ProteinTurnover")
## ------------------------------------------------------------------------
# R.version.string
# # [1] "R version 3.3.2 (2016-10-31)"
# packageVersion("ProteinTurnover")
# # [1] '1.1.1'
## ---- eval=TRUE----------------------------------------------------------
library(ProteinTurnover)
## ------------------------------------------------------------------------
# p <- list()
## ------------------------------------------------------------------------
# p$num.cores <- 1
## ------------------------------------------------------------------------
# p$dir.data <- "mzs"
## ------------------------------------------------------------------------
# p$dir.results <- "Turnover"
## ------------------------------------------------------------------------
# p$scaffoldfile <- "soluble.csv"
## ------------------------------------------------------------------------
# p$times <- c(0,4,8,16,24,32,40,48)
# p$rawfiles <- paste0("hr_", p$times, ".mzXML")
# p$rdsfiles <- paste0("hr_", p$times, ".rds")
## ------------------------------------------------------------------------
# p$isotope <- "N"
# p$confidence.level <- 80
# p$time.zero <- 0
# p$time.unit <- "Hour"
# p$mz.tol <- 0.005
# p$rt.tol <- 30
# p$extra.channels <- 5
## ------------------------------------------------------------------------
# p$regression.model <- "lm" # "lm" as linear regression
## ------------------------------------------------------------------------
# p$isotope.method <- "incorporation"
## ------------------------------------------------------------------------
# p$M.model <- "one"
## ------------------------------------------------------------------------
# p$alpha.model <- "log2k" #make log2 value of turnover rate k
## ------------------------------------------------------------------------
# p$plim <- NA
## ------------------------------------------------------------------------
# dir.create(p$dir.results)
## ------------------------------------------------------------------------
# rawToRDS(p$dir.data, p$rawfiles, p$rdsfiles)
# # reading hr_0.mzXML (time: 1m 38.3s)
# # writing hr_0.rds (time: 53.2s)
# # reading hr_4.mzXML (time: 1m 39.7s)
# # writing hr_4.rds (time: 54.2s)
# # reading hr_8.mzXML (time: 1m 49.0s)
# # writing hr_8.rds (time: 56.9s)
# # reading hr_16.mzXML (time: 1m 19.1s)
# # writing hr_16.rds (time: 41.9s)
# # reading hr_24.mzXML (time: 1m 42.0s)
# # writing hr_24.rds (time: 56.3s)
# # reading hr_32.mzXML (time: 1m 41.6s)
# # writing hr_32.rds (time: 56.4s)
# # reading hr_40.mzXML (time: 1m 42.0s)
# # writing hr_40.rds (time: 56.6s)
# # reading hr_48.mzXML (time: 1m 46.8s)
# # writing hr_48.rds (time: 53.6s)
# # Warning message:
# # 'loadRcppModules' is deprecated.
# # Use 'loadModule' instead.
# # See help("Deprecated")
## ------------------------------------------------------------------------
# dat <- readScaffoldFile(p$scaffoldfile, times=p$times, dir=p$dir.data)
# # reading Scaffold file ...
# # read soluble.csv
## ------------------------------------------------------------------------
# seqs <- makeSequences(dat,
# isotope=p$isotope,
# confidence=p$confidence.level,
# time.zero=p$time.zero,
# time.unit=p$time.unit,
# mz.tol=p$mz.tol,
# rt.tol=p$rt.tol,
# extra.channels=p$extra.channels)
# # finding Sequences ...
## ------------------------------------------------------------------------
# seqs <- addEICs(seqs, files=p$rdsfiles, type="rds", times=p$times, dir=p$dir.data)
# # reading hr_0.rds (time: 27.0s)
# # adding EICs for Time 0 (time: 4.5s)
# # reading hr_4.rds (time: 27.4s)
# # adding EICs for Time 4 (time: 3.7s)
# # reading hr_8.rds (time: 28.9s)
# # adding EICs for Time 8 (time: 2.9s)
# # reading hr_16.rds (time: 21.0s)
# # adding EICs for Time 16 (time: 2.2s)
# # reading hr_24.rds (time: 28.3s)
# # adding EICs for Time 24 (time: 2.5s)
# # reading hr_32.rds (time: 3.2s)
# # adding EICs for Time 32 (time: 2.0s)
# # reading hr_40.rds (time: 3.2s)
# # adding EICs for Time 40 (time: 1.7s)
# # reading hr_48.rds (time: 28.6s)
# # adding EICs for Time 48 (time: 1.8s)
## ------------------------------------------------------------------------
# save(dat, seqs, p, file=file.path(p$dir.results, "eicdata.Rdata"))
## ------------------------------------------------------------------------
# dir.results <- "Turnover"
# load(file.path(dir.results, "eicdata.Rdata"))
## ------------------------------------------------------------------------
# if(!is.null(p$num.cores)) { options(mc.cores=p$num.cores) }
## ------------------------------------------------------------------------
# seqs <- mclapply.progress(seqs, function(seq) {
# relAb <- makeRelAb(seq, regression.model=p$regression.model)
# fit <- makeFit(seq=seq, relAb=relAb, M=p$M.model, alpha=p$alpha.model, se=TRUE, isotope.method=p$isotope.method)
# list(seq=seq, relAb=relAb, fit=fit)
# })
# # Processing 1854 items, in parallel on 8 cores
# # Started Sat Jul 15 16:03:51 2017
# # ================================================================================
# # Ended Sat Jul 15 16:06:08 2017; elapsed time: 2m 17.0s
## ------------------------------------------------------------------------
# save(dat, seqs, p, file=file.path(p$dir.results, "fit-log2k.Rdata"))
## ------------------------------------------------------------------------
# dir.results <- "Turnover"
# load(file.path(dir.results, "fit-log2k.Rdata"))
## ------------------------------------------------------------------------
# fits <- getPars(seqs)
# # There were 50 or more warnings (use warnings() to see the first 50)
# write.csv(fits, file=file.path(p$dir.results, "fits-log2k.csv"), na="")
## ------------------------------------------------------------------------
# dir.results <- "Turnover"
# load(file.path(dir.results, "fit-log2k.Rdata"))
## ------------------------------------------------------------------------
# if(!is.null(p$num.cores)) { options(mc.cores=p$num.cores) }
## ------------------------------------------------------------------------
# out <- makeSequenceHTML(seqs, dir=p$dir.results, file="results-log2k")
# # Creating output for sequences with errors (these are faster).
# # Processing 866 items, in parallel on 8 cores
# # Started Sat Jul 15 16:06:20 2017
# # ================================================================================
# # Ended Sat Jul 15 16:06:55 2017; elapsed time: 35.0s
# # Creating output for sequences without errors.
# # Processing 988 items, in parallel on 8 cores
# # Started Sat Jul 15 16:06:55 2017
# # ================================================================================
# # Ended Sat Jul 15 16:19:49 2017; elapsed time: 12m 54.0s
# # There were 50 or more warnings (use warnings() to see the first 50)
## ------------------------------------------------------------------------
# dir.results <- "Turnover"
# load(file.path(dir.results, "fit-log2k.Rdata"))
## ---- eval=TRUE, include=FALSE-------------------------------------------
s <- ProteinTurnover:::example2
## ------------------------------------------------------------------------
# s <- seqs[[2]]
## ---- eval=TRUE----------------------------------------------------------
s$fit
score.visual(s$fit)
## ---- fig.width=15, fig.height=5, eval=TRUE------------------------------
regressionPlot(s$relAb)
## ---- fig.width=15, fig.height=5, eval=TRUE------------------------------
plot(s$relAb)
## ---- eval=TRUE, fig.width=7, fig.height=3-------------------------------
plot(s$fit)
## ------------------------------------------------------------------------
# dir.results <- "Turnover"
# load(file.path(dir.results, "eicdata.Rdata"))
## ------------------------------------------------------------------------
# p$alpha.model <- "many"
## ------------------------------------------------------------------------
# if(!is.null(p$num.cores)) { options(mc.cores=p$num.cores) }
## ------------------------------------------------------------------------
# seqs <- mclapply.progress(seqs, function(seq) {
# relAb <- makeRelAb(seq, regression.model=p$regression.model)
# fit <- makeFit(seq=seq, relAb=relAb, M=p$M.model, alpha=p$alpha.model, se=TRUE, isotope.method=p$isotope.method)
# list(seq=seq, relAb=relAb, fit=fit)
# curve <- makeCurve(fit, plim=p$plim, isotope.method=p$isotope.method)
# list(seq=seq, relAb=relAb, fit=fit, curve=curve)
# })
# # Processing 1854 items, in parallel on 8 cores
# # Started Sat Jul 15 16:20:01 2017
# # ================================================================================
# # Ended Sat Jul 15 16:23:44 2017; elapsed time: 3m 43.0s
## ------------------------------------------------------------------------
# save(dat, seqs, p, file=file.path(p$dir.results, "fit-many.Rdata"))
## ------------------------------------------------------------------------
# fits <- getPars(seqs)
# # There were 50 or more warnings (use warnings() to see the first 50)
# write.csv(fits, file=file.path(p$dir.results, "fits-many.csv"), na="")
## ------------------------------------------------------------------------
# out <- makeSequenceHTML(seqs, dir=p$dir.results, file="results-many")
# # Creating output for sequences with errors (these are faster).
# # Processing 853 items, in parallel on 8 cores
# # Started Sat Jul 15 16:23:55 2017
# # ================================================================================
# # Ended Sat Jul 15 16:24:13 2017; elapsed time: 18.0s
# # Creating output for sequences without errors.
# # Processing 1001 items, in parallel on 8 cores
# # Started Sat Jul 15 16:24:13 2017
# # ================================================================================
# # Ended Sat Jul 15 16:38:23 2017; elapsed time: 14m 10.0s
# # There were 50 or more warnings (use warnings() to see the first 50)
## ---- eval=TRUE, include=FALSE-------------------------------------------
s <- ProteinTurnover:::example2
## ------------------------------------------------------------------------
# s <- seqs[[2]]
## ---- eval=TRUE-----------------------------------------------------------------------------------
options(width=100)
s$fit
score.visual(s$fit)
## ---- eval=TRUE, fig.width=7, fig.height=3--------------------------------------------------------
plot(s$fit)
## ---- eval=TRUE, fig.width=6, fig.height=3--------------------------------------------------------
plot(s$curve)
## ---- include=FALSE, eval=FALSE-------------------------------------------------------------------
# download.file("http://users.stat.umn.edu/~rend0020/ProteinTurnoverScript/Turnover/fit-log2k.Rdata", "fit-log2k.Rdata")
# load("fit-log2k.Rdata")
# example1 <- seqs[[2]]
#
# download.file("http://users.stat.umn.edu/~rend0020/ProteinTurnoverScript/Turnover/fit-many.Rdata", "fit-many.Rdata")
# load("fit-many.Rdata")
# example2 <- seqs[[2]]
#
# aaTable <- ProteinTurnover:::aaTable
# LocusNumbers <- ProteinTurnover:::LocusNumbers
# devtools::use_data(aaTable, LocusNumbers, example1, example2, internal = TRUE)
#
# file.remove("fit-log2k.Rdata")
# file.remove("fit-many.Rdata")
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