#####################################################################################
#Copyright (C) <2012>
#
#Permission is hereby granted, free of charge, to any person obtaining a copy of
#this software and associated documentation files (the "Software"), to deal in the
#Software without restriction, including without limitation the rights to use, copy,
#modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
#and to permit persons to whom the Software is furnished to do so, subject to
#the following conditions:
#
#The above copyright notice and this permission notice shall be included in all copies
#or substantial portions of the Software.
#
#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
#INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
#PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
#HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
#OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
#SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
#
# This file is a component of the MaAsLin (Multivariate Associations Using Linear Models),
# authored by the Huttenhower lab at the Harvard School of Public Health
# (contact Timothy Tickle, ttickle@hsph.harvard.edu).
#####################################################################################
inlinedocs <- function(
##author<< Curtis Huttenhower <chuttenh@hsph.harvard.edu> and Timothy Tickle <ttickle@hsph.harvard.edu>
##description<< Creates a summary of association detail files.
) { return( pArgs ) }
#Logging class
suppressMessages(library(logging, warn.conflicts=FALSE, quietly=TRUE, verbose=FALSE))
# Get logger
c_logrMaaslin <- getLogger( "maaslin" )
funcSummarizeDirectory = function(
### Summarizes the massline detail files into one file based on significance.
astrOutputDirectory,
### The output directory to find the MaAsLin results.
strBaseName,
### The prefix string used in maaslin to start the detail files.
astrSummaryFileName,
### The summary file's name, should be a path not a file name
astrKeyword,
### The column name of the data to check significance before adding a detail to the summary
afSignificanceLevel
### The value of significance the data must be at or below to be included in the summary (0.0 is most significant; like p-values)
){
#Store significant data elements
dfSignificantData = NULL
#Get detail files in output directory
astrlsDetailFiles = list.files(astrOutputDirectory, pattern=paste(strBaseName,"-","[[:print:]]*",c_sDetailFileSuffix,sep=""), full.names=TRUE)
logdebug(format(astrlsDetailFiles),c_logrMaaslin)
#For each file after the first file
for(astrFile in astrlsDetailFiles)
{
#Read in data and reduce to significance
dfDetails = read.table(astrFile, header=TRUE, sep=c_cTableDelimiter)
dfDetails = dfDetails[which(dfDetails[astrKeyword] <= afSignificanceLevel),]
#Combine with other data if it exists
if(is.null(dfSignificantData))
{
dfSignificantData = dfDetails
} else {
dfSignificantData = rbind(dfSignificantData,dfDetails)
}
}
#Write data to file
unlink(astrSummaryFileName)
if(is.null(dfSignificantData))
{
funcWrite("No significant data found.",astrSummaryFileName)
return( NULL )
} else {
#Sort by metadata and then significance
dfSignificantData = dfSignificantData[order(dfSignificantData$Value, dfSignificantData$P.value, decreasing = FALSE),]
funcWriteTable( dfSignificantData, astrSummaryFileName, fAppend = FALSE )
# Sort by q.value and return
return( dfSignificantData[ order( dfSignificantData$P.value, decreasing = FALSE ), ] )
}
}
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