readPattern <- function(bcl2fastqparams){
c(readlengths(bcl2fastqparams)[1],indexlengths(bcl2fastqparams)[1],
indexlengths(bcl2fastqparams)[2],readlengths(bcl2fastqparams)[2])
}
readBCLStatFile <- function(read.filename){
read.filename <- file(read.filename, "rb")
clusterNumber <- readBin(read.filename,"int",size=4,n = 1)
ACI <- readBin(read.filename,"double",size=8,n = 1)
AIAOverAIAA <- readBin(read.filename,"double",size=8,n = 1)
AICOverAICA <- readBin(read.filename,"double",size=8,n = 1)
AIGOverAIGA <- readBin(read.filename,"double",size=8,n = 1)
AITOverAITA <- readBin(read.filename,"double",size=8,n = 1)
AIAOverA <- readBin(read.filename,"double",size=8,n = 1)
AICOverC <- readBin(read.filename,"double",size=8,n = 1)
AIGOverG <- readBin(read.filename,"double",size=8,n = 1)
AITOverT <- readBin(read.filename,"double",size=8,n = 1)
NCA <- readBin(read.filename,"int",size=4,n = 1)
NCC <- readBin(read.filename,"int",size=4,n = 1)
NCG <- readBin(read.filename,"int",size=4,n = 1)
NCT <- readBin(read.filename,"int",size=4,n = 1)
NCX <- readBin(read.filename,"int",size=4,n = 1)
NCIA <- readBin(read.filename,"int",size=4,n = 1)
NCIC <- readBin(read.filename,"int",size=4,n = 1)
NCIG <- readBin(read.filename,"int",size=4,n = 1)
NCIT <- readBin(read.filename,"int",size=4,n = 1)
bclClusterTileStats <- c(clusterNumber,ACI,AIAOverAIAA,AICOverAICA,AIGOverAIGA,
AITOverAITA,AIAOverA,AICOverC,AIGOverG,
AITOverT,NCA,NCC,NCG,NCT,NCX,NCIA,NCIC,NCIG,NCIT)
close(read.filename)
return(bclClusterTileStats)
}
readBCLStatFiles <- function(read.filenames){
bclStatList <- lapply(read.filenames,readBCLStatFile)
bclStatMat <- do.call(rbind,bclStatList)
}
readExtractionMetrics <- function(extractionMetricsBin){
bytesOfFile <- file.info(extractionMetricsBin)$size
read.filename <- file(extractionMetricsBin, "rb")
bytesRead <- 0
k <- 1
versionNumber <- readBin(read.filename,"int",size=1,n = 1)
recordSize <- readBin(read.filename,"int",size=1,n = 1,signed = FALSE)
bytesRead <- bytesRead+1+1
extractionMetricsOut <- list()
while(bytesRead < bytesOfFile){
laneNumber <- readBin(read.filename,"int",size=2,n = 1)
tileNumber <- readBin(read.filename,"int",size=2,n = 1)
cycleNumber <- readBin(read.filename,"int",size=2,n = 1)
focusForChannelA <- readBin(read.filename,"double",size=4,n = 1)
focusForChannelC <- readBin(read.filename,"double",size=4,n = 1)
focusForChannelG <- readBin(read.filename,"double",size=4,n = 1)
focusForChannelT <- readBin(read.filename,"double",size=4,n = 1)
maxIntensityForChannelA <- readBin(read.filename,"int",size=2,n = 1,signed = FALSE)
maxIntensityForChannelC <- readBin(read.filename,"int",size=2,n = 1,signed = FALSE)
maxIntensityForChannelG <- readBin(read.filename,"int",size=2,n = 1,signed = FALSE)
maxIntensityForChannelT <- readBin(read.filename,"int",size=2,n = 1,signed = FALSE)
timeStamp <- readBin(read.filename,"int",size=8,n = 1)
bytesRead <- bytesRead+(2*3)+(4*4)+(2*4)+8
extractionMetricsOut[[k]] <- c(
laneNumber,tileNumber,cycleNumber,
focusForChannelA,focusForChannelC,
focusForChannelG,focusForChannelT,
maxIntensityForChannelA,maxIntensityForChannelC,
maxIntensityForChannelG,maxIntensityForChannelT,
timeStamp
)
k <- k+1
}
extractionMetricsOutMat <- do.call(rbind,extractionMetricsOut)
colnames(extractionMetricsOutMat) <- c(
"laneNumber","tileNumber","cycleNumber",
"focusForChannelA","focusForChannelC",
"focusForChannelG","focusForChannelT",
"maxIntensityForChannelA","maxIntensityForChannelC",
"maxIntensityForChannelG","maxIntensityForChannelT",
"timeStamp"
)
close(read.filename)
extractionMetricsOutMat <- tbl_df(extractionMetricsOutMat)
}
readIndexMetrics <- function(indexMetricsBin){
bytesOfFile <- file.info(indexMetricsBin)$size
indexMetricsBin <- file(indexMetricsBin, "rb")
bytesRead <- 0
versionNumber <- readBin(indexMetricsBin,"int",size=1,n = 1)
bytesRead <- bytesRead+1
k <- 1
IndexMetricsOut <- list()
while(bytesRead < bytesOfFile){
laneNumber <- readBin(indexMetricsBin,"int",size=2,n = 1)
tileNumber <- readBin(indexMetricsBin,"int",size=2,n = 1)
readNumber <- readBin(indexMetricsBin,"int",size=2,n = 1)
indexNameLength <- readBin(indexMetricsBin,"int",size=2,n = 1)
indexName <- rawToChar(readBin(indexMetricsBin,"raw",n=indexNameLength))
identifiedAsIndex <- readBin(indexMetricsBin,"int",size=4,n = 1)
sampleNameLength <- readBin(indexMetricsBin,"int",size=2,n = 1)
sampleName <- rawToChar(readBin(indexMetricsBin,"raw",n=sampleNameLength))
projectNameLength <- readBin(indexMetricsBin,"int",size=2,n = 1)
projectName <- rawToChar(readBin(indexMetricsBin,"raw",n=projectNameLength))
bytesRead <- bytesRead + 2+2+2+2+indexNameLength+4+2+sampleNameLength+2+projectNameLength
IndexMetricsOut[[k]] <- c(
laneNumber,tileNumber,readNumber,indexName,identifiedAsIndex,sampleName, projectName
)
k <- k+1
}
close(indexMetricsBin)
IndexMetricsFrame <- do.call(rbind,IndexMetricsOut)
colnames(IndexMetricsFrame) <- c(
"laneNumber","tileNumber","readNumber","indexName","identifiedAsIndex","sampleName","projectName"
)
tbl_df(IndexMetricsFrame)
}
readQMetrics <- function(qMetricsBin){
bytesOfFile <- file.info(qMetricsBin)$size
read.filename <- file(qMetricsBin, "rb")
bytesRead <- 0
versionNumber <- readBin(read.filename,"int",size=1,n = 1)
recordSize <- readBin(read.filename,"int",size=1,n = 1,signed = FALSE)
hasBins <- readBin(read.filename,"logical",size=1,n = 1)
numberOfBins <- readBin(read.filename,"int",size=1,n = 1,signed = FALSE)
lowEndsOfBins <- readBin(read.filename,"int",size=1,n = numberOfBins,signed = FALSE)
highEndsOfBins <- readBin(read.filename,"int",size=1,n = numberOfBins,signed = FALSE)
valueOfBins <- readBin(read.filename,"int",size=1,n = numberOfBins,signed = FALSE)
bytesRead <- bytesRead+1+1+1+1+numberOfBins*3
k <- 1
qMetricsOut <- list()
while(bytesRead < bytesOfFile){
Lane <- readBin(read.filename,"int",size=2,n = 1,signed = FALSE)
Tile <- readBin(read.filename,"int",size=2,n = 1,signed = FALSE)
Cycle <- readBin(read.filename,"int",size=2,n = 1,signed = FALSE)
qhist <- readBin(read.filename,"integer",size=4,n = 50)
qMetricsOut[[k]] <-
c(Lane,Tile,Cycle,qhist)
bytesRead <- bytesRead+2+2+2+4*50
k <- k+1
}
close(read.filename)
qMetricsFrame <- do.call(rbind,qMetricsOut)
colnames(qMetricsFrame) <- c("Lane","Tile","Cycle",paste0("Q",1:50))
tbl_df(qMetricsFrame)
}
readCorrectedIntMetrics <- function(correctedIntMetricsBin){
bytesOfFile <- file.info(correctedIntMetricsBin)$size
read.filename <- file(correctedIntMetricsBin, "rb")
bytesRead <- 0
k <- 1
versionNumber <- readBin(read.filename,"int",size=1,n = 1)
recordSize <- readBin(read.filename,"int",size=1,n = 1,signed = FALSE)
bytesRead <- bytesRead+1+1
correctedIntMetricsOut <- list()
while(bytesRead < bytesOfFile){
laneNumber <- readBin(read.filename,"int",size=2,n = 1)
tileNumber <- readBin(read.filename,"int",size=2,n = 1)
cycleNumber <- readBin(read.filename,"int",size=2,n = 1)
averageCycleIntensity <- readBin(read.filename,"int",size=2,n = 1)
averageCorrectedIntensityForChannelA <- readBin(read.filename,"int",size=2,n = 1)
averageCorrectedIntensityForChannelC <- readBin(read.filename,"int",size=2,n = 1)
averageCorrectedIntensityForChannelG <- readBin(read.filename,"int",size=2,n = 1)
averageCorrectedIntensityForChannelT <- readBin(read.filename,"int",size=2,n = 1)
averageCorrectedIntForCalledClustersForBaseA <- readBin(read.filename,"int",size=2,n = 1)
averageCorrectedIntForCalledClustersForBaseC <- readBin(read.filename,"int",size=2,n = 1)
averageCorrectedIntForCalledClustersForBaseG <- readBin(read.filename,"int",size=2,n = 1)
averageCorrectedIntForCalledClustersForBaseT <- readBin(read.filename,"int",size=2,n = 1)
numberOfBaseCallsForNoCall <- readBin(read.filename,"int",size=4,n = 1)
numberOfBaseCallsForBaseA <- readBin(read.filename,"int",size=4,n = 1)
numberOfBaseCallsForBaseC <- readBin(read.filename,"int",size=4,n = 1)
numberOfBaseCallsForBaseG <- readBin(read.filename,"int",size=4,n = 1)
numberOfBaseCallsForBaseT <- readBin(read.filename,"int",size=4,n = 1)
signalToNoiseRatio <- readBin(read.filename,"double",size=4,n = 1)
bytesRead <- bytesRead+(2*3)+(2*9)+(4*5)+4
correctedIntMetricsOut[[k]] <- c(laneNumber,tileNumber,cycleNumber,averageCycleIntensity,
averageCorrectedIntensityForChannelA,averageCorrectedIntensityForChannelC,
averageCorrectedIntensityForChannelG,averageCorrectedIntensityForChannelT,
averageCorrectedIntForCalledClustersForBaseA,averageCorrectedIntForCalledClustersForBaseC,
averageCorrectedIntForCalledClustersForBaseG,averageCorrectedIntForCalledClustersForBaseT,
numberOfBaseCallsForNoCall,
numberOfBaseCallsForBaseA,numberOfBaseCallsForBaseC,
numberOfBaseCallsForBaseG,numberOfBaseCallsForBaseT)
k <- k+1
}
close(read.filename)
correctedIntMetricsMat <- do.call(rbind,correctedIntMetricsOut)
}
readImageMetrics <- function(imageMetricsBin){
bytesOfFile <- file.info(imageMetricsBin)$size
read.filename <- file(imageMetricsBin, "rb")
bytesRead <- 0
k <- 1
versionNumber <- readBin(read.filename,"int",size=1,n = 1)
recordSize <- readBin(read.filename,"int",size=1,n = 1,signed = FALSE)
bytesRead <- bytesRead+1+1
imageMetricsOut <- list()
while(bytesRead < bytesOfFile){
laneNumber <- readBin(read.filename,"int",size=2,n = 1)
tileNumber <- readBin(read.filename,"int",size=2,n = 1)
cycleNumber <- readBin(read.filename,"int",size=2,n = 1)
channelNumber <- readBin(read.filename,"int",size=2,n = 1,signed = FALSE)
minimumContrast <- readBin(read.filename,"int",size=2,n = 1,signed = FALSE)
maximumContrast <- readBin(read.filename,"int",size=2,n = 1,signed = FALSE)
bytesRead <- bytesRead+(2*3)+(2*3)
imageMetricsOut[[k]] <- c(laneNumber,tileNumber,cycleNumber,
channelNumber,minimumContrast,maximumContrast)
k <- k+1
}
close(read.filename)
imageMetricsMat <- do.call(rbind,imageMetricsOut)
}
readTileMetrics <- function(tileMetricsBin){
bytesOfFile <- file.info(tileMetricsBin)$size
read.filename <- file(tileMetricsBin, "rb")
bytesRead <- 0
k <- 1
versionNumber <- readBin(read.filename,"int",size=1,n = 1)
recordSize <- readBin(read.filename,"int",size=1,n = 1,signed = FALSE)
bytesRead <- bytesRead+1+1
tileMetricsOut <- list()
while(bytesRead < bytesOfFile){
laneNumber <- readBin(read.filename,"int",size=2,n = 1)
tileNumber <- readBin(read.filename,"int",size=2,n = 1)
codeTile <- readBin(read.filename,"int",size=2,n = 1,signed = FALSE)
valueTile <- readBin(read.filename,"double",size=4,n = 1)
bytesRead <- bytesRead+(2*2)+2+4
tileMetricsOut[[k]] <- c(laneNumber,tileNumber,
codeTile,valueTile)
k <- k+1
}
close(read.filename)
tileMetricsMat <- do.call(rbind,tileMetricsOut)
colnames(tileMetricsMat) <- c(
"laneNumber","tileNumber",
"code","value"
)
tbl_df(tileMetricsMat) %>%
mutate(code = str_c("c",code)) %>%
group_by(laneNumber,tileNumber,code) %>%
summarise_all(max) %>%
ungroup() %>%
mutate(Lane=laneNumber,Tile=tileNumber) %>%
mutate(code=stringr::str_replace_all(code,"c100","ClusterDensity")) %>%
mutate(code=stringr::str_replace_all(code,"c101","ClusterDensityPF")) %>%
mutate(code=stringr::str_replace_all(code,"c102","NumberOfClusters")) %>%
mutate(code=stringr::str_replace_all(code,"c103","NumberOfClustersPF")) %>%
mutate(code=stringr::str_replace_all(code,"c200",
paste0("PhasingFor",
names(readPattern(bcl2fastqparams))[readPattern(bcl2fastqparams) != 0][1]))) %>%
mutate(code=stringr::str_replace_all(code,"c201",
paste0("PrePhasingFor",
names(readPattern(bcl2fastqparams))[readPattern(bcl2fastqparams) != 0][1]))) %>%
mutate(code=stringr::str_replace_all(code,"c202",
paste0("PhasingFor",
names(readPattern(bcl2fastqparams))[readPattern(bcl2fastqparams) != 0][2]))) %>%
mutate(code=stringr::str_replace_all(code,"c203",
paste0("PrePhasingFor",
names(readPattern(bcl2fastqparams))[readPattern(bcl2fastqparams) != 0][2]))) %>%
mutate(code=stringr::str_replace_all(code,"c204",
paste0("PhasingFor",
names(readPattern(bcl2fastqparams))[readPattern(bcl2fastqparams) != 0][3]))) %>%
mutate(code=stringr::str_replace_all(code,"c205",
paste0("PrePhasingFor",
names(readPattern(bcl2fastqparams))[readPattern(bcl2fastqparams) != 0][3]))) %>%
mutate(code=stringr::str_replace_all(code,"c206",
paste0("PhasingFor",
names(readPattern(bcl2fastqparams))[readPattern(bcl2fastqparams) != 0][4]))) %>%
mutate(code=stringr::str_replace_all(code,"c207",
paste0("PrePhasingFor",
names(readPattern(bcl2fastqparams))[readPattern(bcl2fastqparams) != 0][4]))) %>%
filter(code!="PrePhasingForNA" & code!="PhasingForNA" ) %>%
spread(code,value) %>%
dplyr::select(-laneNumber,-tileNumber,
-starts_with("c",ignore.case = FALSE))
}
readPhasingMetrics <- function(phasingMetricsBin){
bytesOfFile <- file.info(phasingMetricsBin)$size
read.filename <- file(phasingMetricsBin, "rb")
bytesRead <- 0
k <- 1
versionNumber <- readBin(read.filename,"int",size=1,n = 1)
recordSize <- readBin(read.filename,"int",size=1,n = 1,signed = FALSE)
bytesRead <- bytesRead+1+1
phasingMetricsOut <- list()
while(bytesRead < bytesOfFile){
laneNumber <- readBin(read.filename,"int",size=2,n = 1)
tileNumber <- readBin(read.filename,"int",size=2,n = 1)
cycleNumber <- readBin(read.filename,"int",size=2,n = 1,signed = FALSE)
phasingWeight <- readBin(read.filename,"double",size=4,n = 1)
prephasingWeight <- readBin(read.filename,"double",size=4,n = 1)
bytesRead <- bytesRead+(2*3)+(4*2)
phasingMetricsOut[[k]] <- c(laneNumber,tileNumber,cycleNumber,
phasingWeight,prephasingWeight)
k <- k+1
}
close(read.filename)
phasingMetricsMat <- do.call(rbind,phasingMetricsOut)
}
readErrorMetrics <- function(errorMetricsBin){
bytesOfFile <- file.info(errorMetricsBin)$size
read.filename <- file(errorMetricsBin, "rb")
bytesRead <- 0
k <- 1
versionNumber <- readBin(read.filename,"int",size=1,n = 1)
recordSize <- readBin(read.filename,"int",size=1,n = 1,signed = FALSE)
bytesRead <- bytesRead+1+1
tileMetricsOut <- list()
while(bytesRead < bytesOfFile){
laneNumber <- readBin(read.filename,"int",size=2,n = 1)
tileNumber <- readBin(read.filename,"int",size=2,n = 1)
cycleNumber <- readBin(read.filename,"int",size=2,n = 1,signed = FALSE)
errorRate <- readBin(read.filename,"double",size=4,n = 1)
numberOfPerfectReads <- readBin(read.filename,"int",size=4,n = 1)
numberOfReadsWithOneError <- readBin(read.filename,"int",size=4,n = 1)
numberOfReadsWithTwoError <- readBin(read.filename,"int",size=4,n = 1)
numberOfReadsWithThreeError <- readBin(read.filename,"int",size=4,n = 1)
numberOfReadsWithFourError <- readBin(read.filename,"int",size=4,n = 1)
bytesRead <- bytesRead+(2*3)+(4*6)
tileMetricsOut[[k]] <- c(laneNumber,tileNumber,cycleNumber,
errorRate,numberOfPerfectReads,
numberOfReadsWithOneError,numberOfReadsWithTwoError,
numberOfReadsWithThreeError,numberOfReadsWithFourError)
k <- k+1
}
close(read.filename)
tileMetricsMat <- do.call(rbind,tileMetricsOut)
}
readInterOpsMetrics <- function(bcl2fastqparams,verbose=TRUE,
interopsToParse=c("TileMetricsOut","QMetricsOut")){
interOpsMetrics <- list()
if(any(interopsToParse %in% "TileMetricsOut")){
if(verbose){message("Parsing InterOps Tile binary file..",appendLF = FALSE)}
tileMetricsFile <- file.path(bcl2fastqparams@RunDir,"InterOp","TileMetricsOut.bin")
if(file.exists(tileMetricsFile)){
interOpsMetrics[["tileMet"]] <- readTileMetrics(tileMetricsFile)
}
if(verbose){message("done",appendLF = TRUE)}
}
if(any(interopsToParse %in% "QMetricsOut")){
if(verbose){message("Parsing InterOps QMetrics binary file..",appendLF = FALSE)}
qMetricsFile <- file.path(bcl2fastqparams@RunDir,"InterOp","QMetricsOut.bin")
if(file.exists(qMetricsFile)){
interOpsMetrics[["qMet"]] <- readQMetrics(qMetricsFile)
}
if(verbose){message("done",appendLF = TRUE)}
}
return(interOpsMetrics)
}
#' Function to parse InterOps files and generate summary reports
#'
#' Parses the InterOps binary files produced by Illumina's Real Time Analysis sofware and
#' used by Illumina's SAV sofware.
#' InterOp binary files contain information on phasing/prephsing, yield,read numbers
#' and basecalling quality score distributions per cycle.
#' This interOpsReport functions parses and summarises the InterOps files, TileMetrics.bin and QMetrics.bin, and the
#' Stats directory XML files, ConversionStats.xml and DemultiplexingStats.xml.
#'
#'
#' @docType methods
#' @name interOpsReport
#' @rdname interOpsReport
#'
#' @author Thomas Carroll.
#' @param bcl2fastqparams A BCL2FastQparams object.
#' @param verbose TRUE or FALSE . TRUE reports progress through file parsing.
#' @return A named list of length 3 containing machine and run information,
#' basecalling quality information and demultiplexing information.
#' @details The interOpsReport function returns a list of machine and run
#' information, basecalling quality information and demultiplexing information.
#' The three named elements are descibed below.
#' \itemize{
#' \item{"machineReport"}{ A data.frame containing information machine and software parameters
#' }
#' \item{"sequencingReport"}{ A data.frame of mean cluster density, percentage clusters passing filter, phasing
#' and prephasing percentages, number of reads total/passing filter and percent of reads with mean quality score > Q30
#' grouped by lane and read
#' }
#' \item{"demuxReport"}{ A data.frame of demultiplexing results containing yield, number of reads, percentage of reads
#' with quality scores greater than >Q30 and the percent of total reads per lane.
#' Results are summarised per lane for samples, underdetermined indexes and all indexes (identifed and unidentified).
#' }
#' }
#'
#' @import stringr XML RColorBrewer methods raster BiocStyle
#' @examples
#'
#' fileLocations <- system.file("extdata",package="basecallQC")
#' runXML <- dir(fileLocations,pattern="runParameters.xml",full.names=TRUE)
#' config <- dir(fileLocations,pattern="config.ini",full.names=TRUE)
#' bcl2fastqparams <- BCL2FastQparams(runXML,config,runDir=getwd(),verbose=FALSE)
#'
#' # myRes_BCAGJ8ANXX <- interOpsReport(bcl2fastqparams,verbose=TRUE)
#' @export
interOpsReport <- function(bcl2fastqparams,verbose=TRUE){
if(verbose){message("Parsing XML files..",appendLF = FALSE)}
basecallmetrics <- baseCallMetrics(bcl2fastqparams)
demultiplexmetrics <- demultiplexMetrics(bcl2fastqparams)
dmx <- demultiplexmetrics$demuxStatsProcessed
conv <- basecallmetrics$convStatsProcessed
if(verbose){message("done",appendLF = TRUE)}
dmxSum <- dmx %>%
tbl_df %>%
dplyr::select(Sample,Barcode,Lane) %>%
distinct(Sample,Barcode,Lane) %>%
mutate(Lane=factor(as.numeric(str_replace_all(Lane,"Lane",""))))
convSum <- conv %>%
tbl_df %>%
group_by(Sample,Lane,Filter) %>%
summarise(YieldSum=sum(as.numeric(Yield)),
Yield30Sum=sum(as.numeric(Yield30)),
Reads=sum(ClusterCount)) %>%
filter(Filter=="Pf") %>%
group_by(Lane) %>%
mutate(PercentageOfGreaterQ30_Bases_PF=(Yield30Sum/YieldSum)*100,
PercentOfLane=(Reads/sum(Reads[Sample != "all"]))*100) %>%
dplyr::select(-Filter,-Yield30Sum)
dmxReport <- full_join(convSum,tbl_df(dmxSum),by = c("Sample", "Lane"))
if(verbose){message("Parsing machine information..",appendLF = FALSE)}
runPReport <- bcl2fastqparams@RunParameters$runParams %>%
dplyr::select(ComputerName,RunID,ExperimentName,
Flowcell,
ChemistryVersion,RunMode,
ApplicationName,ApplicationVersion,
RTAVersion
) %>%
mutate(basecallQC_Version=as.character(packageVersion("basecallQC"))) %>%
t
if(verbose){message("done",appendLF = TRUE)}
if(verbose){message("Parsing InterOps binary files..",appendLF = FALSE)}
interOpsMetrics <- readInterOpsMetrics(bcl2fastqparams,verbose = FALSE)
tileMet <- interOpsMetrics$tileMet %>%
group_by(Lane) %>%
summarise(meanClusterDensity=mean(ClusterDensity),
sdClusterDensity=sd(ClusterDensity),
meanPhasingForRead1=mean(PhasingForRead1,na.rm=TRUE),
meanPrePhasingForRead1=mean(PrePhasingForRead1,na.rm=TRUE),
NumberOfReads=sum(NumberOfClusters),
NumberOfReadsPF=sum(NumberOfClustersPF),
percent_PF_Clusters=mean(NumberOfClustersPF/NumberOfClusters)*100,
percent_PF_ClustersSD=sd(NumberOfClustersPF/NumberOfClusters)*100,
meanPhasingForRead2 = if(exists('PhasingForRead2', where = .)) mean(PhasingForRead2,na.rm=TRUE) else 0,
meanPrePhasingForRead2 = if(exists('PrePhasingForRead2', where = .)) mean(PrePhasingForRead2,na.rm=TRUE) else 0
) %>%
mutate(meanClusterDensity=stringr::str_c(signif(meanClusterDensity/1000,4),"+/-",signif(sdClusterDensity/1000,4)),
percent_PF_Clusters=stringr::str_c(signif(percent_PF_Clusters,3),"+/-",signif(percent_PF_ClustersSD,3)),
Phasing_PrephasingRead1=stringr::str_c(signif(meanPhasingForRead1*100,3)," / ",signif(meanPrePhasingForRead1*100,3))) %>%
{if(exists('meanPhasingForRead2', where = .)) mutate(.,Phasing_PrephasingRead2=stringr::str_c(signif(meanPhasingForRead2*100,3)," / ",signif(meanPrePhasingForRead2*100,3))) else .} %>%
dplyr::select(Lane,meanClusterDensity,percent_PF_Clusters,
starts_with("Phasing_Prephasing"),
NumberOfReads,NumberOfReadsPF)
qMet <- interOpsMetrics$qMet %>%
group_by(Lane,Cycle) %>%
dplyr::select(Lane,Cycle,starts_with("Q")) %>%
summarise_all(sum) %>%
ungroup() %>%
mutate(q30=rowSums(.[32:52])/rowSums(.[3:52])) %>%
mutate(Read = cut(Cycle,
breaks=unique(cumsum(c(0,
readPattern(bcl2fastqparams)
))),
labels= names(readPattern(bcl2fastqparams))[readPattern(bcl2fastqparams) !=0]
)) %>%
group_by(Lane,Read) %>%
summarise_all(mean) %>%
dplyr::select(Lane,Read,q30) %>%
mutate(q30=signif(q30*100,3)) %>%
mutate(Read=paste0("Q30-",Read)) %>%
spread(Read,q30)
fullSummaryDual <- full_join(tileMet,qMet,by = "Lane")
if(verbose){message("done",appendLF = TRUE)}
toReport <- list(machineReport=as.data.frame(runPReport),
sequencingReport=as.data.frame(fullSummaryDual),
demuxReport=as.data.frame(dmxReport))
return(toReport)
}
