chasemc

#Read in data
party_ready <- read_csv("Data/party_ready.csv")

#Copy data
party_time <- party_ready

#Get columns with percents
columns_with_pct <- grep("pct",colnames(party_time))

#Convert percents as strings to percents as double

chasemc

# cols is a vector of named colors

# First we convert the named colors to rgb, and store the values in a list
rgbVals<-lapply(cols,col2rgb)
# Then convert the list to a data frame and flip it
rgbVals<-t(as.data.frame(rgbVals,col.names = seq(1,length(rgbVals),by=1)))
# Then we convert the rgb values to HEX
hexVal<-sapply(1:length(rgbVal[,1]),function(x){ rgb(rgbVal[x,"red"],rgbVal[x,"green"],rgbVal[x,"blue"],maxColorValue = 255) })

chasemc

#define IncludeRtools true


#--------------------------------------

 #if IncludeRtools
          Source: "Rtools34.exe"; DestDir: "{tmp}"; Check: RtoolsNeeded
          #endif

chasemc

  #
  # output$pcaplot3d <- renderRglwidget({
  #
  #   e<-pcaCalculation()
  #
  #   if(!any(names(e) == 'd')){
  #     e<-cbind(e,d=rep("black",length(e[,1])))
  #   }
  #

chasemc

#    e<-pcaCalculation()
#
#     if(any(names(e) == 'd')){
#
#       p<-ggplot(e,aes(Dim.1,Dim.2,label=nam,col=factor(e$d)))+
#         geom_text()+
#         xlab("Dimension 1")+
#         ylab("Dimension 2")+
#         ggtitle("Zoomable PCA of Protein MALDI Spectra")+

chasemc

if(!length(grep("mzR",row.names(installed.packages())))){
  if(!length(grep("BiocInstaller",row.names(installed.packages())))){
    source("https://bioconductor.org/biocLite.R")
    biocLite("BiocInstaller",suppressUpdates = T)
    }

  source("https://bioconductor.org/biocLite.R")
  biocLite("mzR",ask=F)
}

chasemc

# capture the current working directory
# set the package search path to the app specific library
appwd <- getwd()
applibpath <- file.path(appwd, "library")

# Load functions to ensure software dependencies and check the internet
source("utils/ensure.R")

# Create app/library if it doesn't exist (e.g. first run)
# Initialize RInno

chasemc

4
MENVMSWFNIDFEIKSDNNIDKTLL
3017.418
3.918049
5
MKFYTIKLPKFLGGIVRAMLGSFRKD
3017.730
11.254379

chasemc

Parse mzXML

Chase Clark June 11, 2018

Examples of both MALDI MS1 and LC-MS/MS files

Example of IDBac mzXML conversion output... Bruker autoFlex MALDI MS1

First few lines of mzXML file:

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spongeSmallIn <- readRDS("data/spongeSmall.rds")
spongeSmall <- MALDIquant::intensityMatrix(spongeSmallIn)
spongeSmall[is.na(spongeSmall)] <- 0.000000001
spongeSmall <- prcomp(log10(spongeSmall))
spongeSmall <- spongeSmall$x[,1:50]
z <- sapply(spongeSmallIn, function(x) x@metaData$Strain )
q <- as.data.frame(spongeSmall)
rownames(q) <- z