## ----eval=FALSE--------------------------------------------------------------- # if (!requireNamespace("BiocManager", quietly=TRUE)) # install.packages("BiocManager") # BiocManager::install("MSstatsQC") ## ---- eval=TRUE--------------------------------------------------------------- #A typical multi peptide and multi metric system suitability dataset #This dataset was generated during CPTAC Study 9.1 at Site 54 library(MSstatsQC) data <- MSstatsQC::S9Site54 ## ---- eval=FALSE-------------------------------------------------------------- # MSnbaseToMSstatsQC(msfile) ## ---- eval=TRUE--------------------------------------------------------------- data<-DataProcess(data) ## ---- eval=TRUE, fig.width=8, fig.height=5------------------------------------ #An X chart when a guide set (1-20 runs) is used to monitor the mean of retention time XmRChart( data, peptide = "TAAYVNAIEK", L = 1, U = 20, metric = "BestRetentionTime", normalization = FALSE, ytitle = "X Chart : retention time", type = "mean", selectMean = NULL ,selectSD = NULL ) #An X chart when a guide set (1-20 runs) is used to monitor the mean of total peak area XmRChart( data, peptide = "TAAYVNAIEK", L = 1, U = 20, metric = "TotalArea", normalization = FALSE, ytitle = "X Chart : peak area", type = "mean", selectMean = NULL ,selectSD = NULL ) ## ---- eval=TRUE, fig.width=8, fig.height=5------------------------------------ #An mR chart when a guide set (1-20 runs) is used to monitor the variability of total peak area XmRChart( data, peptide = "TAAYVNAIEK", L = 1, U = 20, metric = "TotalArea", normalization = TRUE, ytitle = "mR Chart : peak area", type = "variability", selectMean = NULL, selectSD = NULL ) #An mR chart when a guide set (1-20 runs) is used to monitor the variability of retention time XmRChart( data, peptide = "TAAYVNAIEK", L = 1, U = 20, metric = "BestRetentionTime", normalization = TRUE, ytitle = "mR Chart : retention time", type = "variability", selectMean = NULL, selectSD = NULL ) #Mean and standard deviation of LVNELTEFAK is known XmRChart( data, "LVNELTEFAK", metric = "BestRetentionTime", selectMean = 28.5, selectSD = 0.5 ) ## ---- eval=TRUE, fig.width=8, fig.height=5------------------------------------ #Mean and standard deviation of LVNELTEFAK is known XmRChart( data, "LVNELTEFAK", metric = "BestRetentionTime", selectMean = 28.5, selectSD = 0.5 ) ## ---- eval=TRUE, echo =FALSE, fig.width=8, fig.height=5----------------------- #A CUSUMm chart when a guide set (1-20 runs) is used to monitor the mean of retention time CUSUMChart( data, peptide = "TAAYVNAIEK", L = 1, U = 20, metric = "BestRetentionTime", normalization = TRUE, ytitle = "CUSUMm Chart : retention time", type = "mean", referenceValue = 0.5, decisionInterval = 5, selectMean = NULL ,selectSD = NULL ) #A CUSUMm chart when a guide set (1-20 runs) is used to monitor the mean of total peak area CUSUMChart( data, peptide = "TAAYVNAIEK", L = 1, U = 20, metric = "TotalArea", normalization = TRUE, ytitle = "CUSUMm Chart : peak area", type = "mean", referenceValue = 0.5, decisionInterval = 5, selectMean = NULL ,selectSD = NULL ) ## ---- eval=TRUE, echo =FALSE, fig.width=8, fig.height=5----------------------- #A CUSUMv chart when a guide set (1-20 runs) is used to monitor the variability of retention time CUSUMChart( data, peptide = "TAAYVNAIEK", L = 1, U = 20, metric = "BestRetentionTime", normalization = TRUE, ytitle = "CUSUMv Chart : retention time", type = "variability", referenceValue = 0.5, decisionInterval = 5, selectMean = NULL ,selectSD = NULL ) #A CUSUMv chart when a guide set (1-20 runs) is used to monitor the variability of total peak area CUSUMChart( data, peptide = "TAAYVNAIEK", L = 1, U = 20, metric = "TotalArea", normalization = TRUE, ytitle = "CUSUMv Chart : peak area", type = "variability", referenceValue = 0.5, decisionInterval = 5, selectMean = NULL ,selectSD = NULL ) ## ---- eval=TRUE, fig.width=8, fig.height=5------------------------------------ # Retention time >> first 20 observations are used as a guide set XmRChart(data, "TAAYVNAIEK", metric = "BestRetentionTime", type="mean", L = 1, U = 20) ChangePointEstimator(data, "TAAYVNAIEK", metric = "BestRetentionTime", type="mean", L = 1, U = 20) ## ---- eval=TRUE, fig.width=8, fig.height=5------------------------------------ # Retention time >> first 20 observations are used as a guide set XmRChart(data, "YSTDVSVDEVK", metric = "BestRetentionTime", type="mean", L = 1, U = 20) ChangePointEstimator(data, "YSTDVSVDEVK", metric = "BestRetentionTime", type="variability", L = 1, U = 20) ## ---- eval=TRUE, fig.width=8, fig.height=5------------------------------------ # Retention time >> first 20 observations are used as a guide set RiverPlot(data = S9Site54, L = 1, U = 20, method = "XmR") RiverPlot(data = S9Site54, L = 1, U = 20, method = "CUSUM") RadarPlot(data = S9Site54, L = 1, U = 20, method = "XmR") RadarPlot(data = S9Site54, L = 1, U = 20, method = "CUSUM") ## ---- eval=TRUE,fig.width=10, fig.height=5------------------------------------ # A decision map for Site 54 can be generated using the following script # Retention time >> first 20 observations are used as a guide set DecisionMap(data,method="XmR",peptideThresholdRed = 0.25,peptideThresholdYellow = 0.10, L = 1, U = 20,type = "mean",title = "Decision map",listMean = NULL,listSD = NULL) ## ---- eval=TRUE, fig.width=8, fig.height=5------------------------------------ mydata<-DataProcess(MSstatsQC::QCloudDDA) #Creating a missing data map MissingDataMap(mydata) XmRChart(mydata, "EACFAVEGPK", metric = "missing", type="mean", L = 1, U = 15) mydata<-RemoveMissing(mydata) RiverPlot(mydata[,-9], L=1, U=15, method="XmR") RadarPlot(mydata[,-9], L=1, U=15, method="XmR") ## ---- eval=TRUE, fig.width=8, fig.height=5------------------------------------ mydata<-DataProcess(MSstatsQC::QCloudDDA) #Creating a missing data map MissingDataMap(mydata) ## ---- eval=TRUE, fig.width=8, fig.height=5------------------------------------ #Creating an X chart for missing counts XmRChart(mydata, "EACFAVEGPK", metric = "missing", type="mean", L = 1, U = 15) ## ---- eval=TRUE, fig.width=8, fig.height=5------------------------------------ #Removing missing values and analyzing the data mydata<-RemoveMissing(mydata) RiverPlot(mydata[,-9], L=1, U=15, method="XmR") RadarPlot(mydata[,-9], L=1, U=15, method="XmR") ## ---- eval=TRUE, fig.width=8, fig.height=5------------------------------------ #Checking missing values and analyzing the data MissingDataMap(MSstatsQC::QuiCDIA) RiverPlot(data = QuiCDIA, L = 1, U = 20, method = "XmR") RadarPlot(data = QuiCDIA, L = 1, U = 20, method = "XmR") ## ---- eval=TRUE, fig.width=8, fig.height=5------------------------------------ #Checking missing values and analyzing the data MissingDataMap(MSstatsQC::QCloudSRM) RiverPlot(data = QCloudSRM, L = 1, U = 20, method = "CUSUM") RadarPlot(data = QCloudSRM, L = 1, U = 20, method = "CUSUM") ## ---- eval=FALSE-------------------------------------------------------------- # #Saving plots generated by plotly # p<-XmRChart( data, peptide = "TAAYVNAIEK", L = 1, U = 20, metric = "BestRetentionTime", normalization = FALSE, # ytitle = "X Chart : retention time", type = "mean", selectMean = NULL, selectSD = NULL ) # htmlwidgets::saveWidget(p, "Aplot.html") # export(p, file = "Aplot.png") # # #Saving plots generated by ggplot2 # p<-RiverPlot(data, L=1, U=20) # ggsave(filename="Summary.pdf", plot=p) # #or # ggsave(filename="Summary.png", plot=p) ## ----si----------------------------------------------------------------------- sessionInfo()