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Basic_microbiome_functions
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Basic_microbiome_functions
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# Microbiome scripts R
# By: Ranko Gacesa, UMCG
# ==============================================
library(coin)
library(vegan)
doMicrobiomeAdonis <- function(inDF,
idCol = "ID",
permNR = 10000,
adonisVarsTouse,
adonisVarsToSkip = c("ID"),
targetData = "Taxa",
pwyType = "MetaCyc",
verbose = T,
nrThreads = 4,
measureTime = F) {
if (!(targetData %in% c("Taxa","PWYs"))) {
exit("ERROR: targetData must be 'Taxa' or 'PWYs' ")
}
adonisResults <- NULL
adonisVarsTouse <- adonisVarsTouse[!(adonisVarsTouse %in% adonisVarsToSkip)]
inPhenos <- inDF
if (verbose) {
if (targetData=="Taxa") {
spDF <- subsetMicrobiomeDF(inDF = inDF,verbose = verbose,getPWYs = F,getVFs = F,getTaxa = T,getCARDs = F,getPhenos = F,
getDivs = F,pwyType = pwyType,getID = T,idName = idCol,idToRowName = F)
} else if (targetData=="PWYs") {
spDF <- subsetMicrobiomeDF(inDF = inDF,verbose = verbose,getPWYs = T,getVFs = F,getTaxa = F,getCARDs = F,getPhenos = F,
getDivs = F,pwyType = pwyType,getID = T,idName = idCol,idToRowName = F)
}
}
for (i in adonisVarsTouse) {
if (verbose) {
print (paste(' >>> ANALYSING VARIABLE <',i,'> <<<'))
print (' >> collecting complete cases')
}
if (measureTime) {t1 <- Sys.time()}
inPhenosOneVarID <- inPhenos[,colnames(inPhenos) %in% c(i,idCol)]
allDF <- merge(x=inPhenosOneVarID,by.x=idCol,y=spDF,by.y=idCol)
rownames(allDF) <- allDF$DAG3_sampleID
allDF[[idCol]] <- NULL
allDF <- allDF[complete.cases(allDF),]
av <- allDF[[i]]
allDF[[i]] <- NULL
if (verbose) {print (' >> calculating B/C distance')}
inBC <- vegdist(allDF,method = "bray")
if (verbose) {print (' >> doing adonis')}
nrRows <- length(av)
if (length(av) < 3 | length(unique(av)) < 2) {
if (verbose) {print(paste0(' >> WARNING: ',i,' has no useful data!!'))}
} else {
#print(paste0(' NR NAs: ',sum(is.na(av))))
ad <- adonis(inBC ~ av,permutations=permNR,parallel = nrThreads)
aov_table <- ad$aov.tab
# accumulate results
oneRow <- data.frame(Var=i,
NR_nonNA=nrRows,
DF=aov_table[1,1],
SumsOfSqs=aov_table[1,2],
MeanSqs=aov_table[1,3],
FModel=aov_table[1,4],
R2=aov_table[1,5],
pval=aov_table[1,6],
FDR.BH=NA,
Significant=NA)
print(oneRow)
adonisResults <- rbind.data.frame(adonisResults,oneRow)
if (verbose) {print (paste0('--- ',i,' DONE! ---'))}
if (measureTime) {t2 <- Sys.time()}
if (verbose & measureTime) {print(t2-t1)}
}
}
rownames(adonisResults) = adonisResults$Var
adonisResults$FDR.BH=p.adjust(adonisResults$pval, method = "BH")
adonisResults$Significant="No"
adonisResults$Significant[adonisResults$FDR.BH<0.05]="Yes"
adonisResults <- adonisResults[order(adonisResults$pval),]
adonisResults #write.table(adonisResults,'phenotypes_adonis_univar.csv',sep=',',row.names = F)
# g <- ggplot(adonisResults, aes(reorder(row.names(adonisResults), R2), R2, fill=Significant)) +
# geom_bar(stat = "identity") + coord_flip() + theme_bw() +
# ylab ("Explained variance (R^2) ") + xlab ("Factor") + theme(text = element_text(size=11))
# ggsave(plot = g,filename = 'phenotypes_adonis_univar.png')
}
# PREP DATA (CEDNN)
# ===============================================================
# > load CeDNN microbiome (metaphlan "raw" output / taxa)
# ===============================================================
prepCleanMetaphlan <- function(inPath,
unclassifiedPercDrop=95,
presenceFilter = -1,
minRelativeAbundance = -1,
rescaleTaxa = T,
keepDomains = c("Archaea","Bacteria"),
keepLevels=c("P","C","O","F","G","S"),
novogeneIdsClean = T
) {
inTaxa <- read.table(inPath,sep='\t',header=T)
# reshuffle id column, transpose, turn to data trame
rownames(inTaxa) <- inTaxa$ID
inTaxa$ID <- NULL
inTaxa <- as.data.frame(t.data.frame(inTaxa))
inTaxa$ID <- rownames(inTaxa)
print(paste('Loaded Metaphlan (',nrow(inTaxa),'samples)'))
# transform sample ID names to real-world ones
# (Novogene adds lots of stuff to names, we need to get rid of it)
if (novogeneIdsClean) {
print ('NOTE: doing cleaning of Novogene IDs (keeping format <AB>_<CD>_<EFG...>)')
for (i in c(1:nrow(inTaxa))) {
ss <- strsplit(inTaxa$ID[i],'_')[[1]]
sss <- paste0(ss[1],'_',ss[2],'_',ss[3])
inTaxa$ID[i] <- sss
}
}
# get rid of duplicates (Novogene randomly sequenced some samples mores then once)
if (sum(duplicated(inTaxa$ID) > 0)) {
print(paste('WARNING: found ',sum(duplicated(inTaxa$ID) > 0),'duplicates, dropping them!'))
}
inTaxa <- inTaxa[!duplicated(inTaxa$ID),]
rownames(inTaxa) <- NULL
# drop failed samples (ideally these would not exist)
if (sum(inTaxa$unclassified > unclassifiedPercDrop)) {
print(paste('WARNING: found ',sum(inTaxa$unclassified > unclassifiedPercDrop),'bad samples (with unclassified reads >',unclassifiedPercDrop,'%), dropping them!'))
}
inTaxa <- inTaxa[inTaxa$unclassified < unclassifiedPercDrop,]
# get rid of "nothing" taxon
inTaxa$unclassified <- NULL
# more fixing of ID column
rownames(inTaxa) <- inTaxa$ID
inTaxa$ID <- NULL
# filter metaphlan: keep Bacteria and Archaea, drop strains, rescale relative abundances to make sure each level sums to 1
inTaxaF <- filterMetaGenomeDF(inTaxa,presPerc = presenceFilter,minMRelAb=minRelativeAbundance,minMedRelAb = -1,rescaleTaxa = rescaleTaxa,verbose = T,keepDomains = keepDomains,
keepLevels = keepLevels)
print(paste('Done, returning ',nrow(inTaxaF),'samples'))
inTaxaF$ID <- rownames(inTaxaF)
inTaxaF
}
# > load CeDNN microbiome (humann pathways, MetaCyc)
#TODO: this should also be done for other humann data layers (GO, InfoGO, KEGG, Pfam, ...)
# ===============================================================
prepCleanHumann <- function(inPath,
dropUnintegrated = T,
dropUnmapped = T,
dropTaxonSpecific = T,
presenceFilter = -1,
minRelativeAbundance = -1,
rescaleTaxa = T,
novogeneIdsClean = T
) {
inDF <- read.table(inPath,sep='\t',header=T,quote ='',comment.char = '')
#fix pathway ID (these tend to be weird coming out of humann)
rownames(inDF) <- inDF$X..Pathway
inDF$X..Pathway <- NULL
#drop "junk" from humann (unintegrated/unmapped data & taxon-specific pathways)
if (dropTaxonSpecific) {
inDF <- inDF[grep('\\|',rownames(inDF),invert = T),]
}
if (dropUnintegrated) {
inDF <- inDF[grep('UNINTEGRATED',rownames(inDF),invert = T),]
}
if (dropUnmapped) {
inDF <- inDF[grep('UNMAPPED',rownames(inDF),invert = T),]
}
rownames(inDF)[grep('PWY',rownames(inDF),invert = T)] <- paste0('PWY_',rownames(inDF)[grep('PWY',rownames(inDF),invert = T)])
inDF <- as.data.frame(t.data.frame(inDF))
# fix sample IDs, remove duplicates
inDF$ID <- rownames(inDF)
if (novogeneIdsClean) {
print ('NOTE: doing cleaning of Novogene IDs (keeping format <AB>_<CD>_<EFG...>)')
for (i in c(1:nrow(inDF))) {
ss <- strsplit(inDF$ID[i],'_')[[1]]
sss <- paste0(ss[1],'_',ss[2],'_',ss[3])
inDF$ID[i] <- sss
}
}
if (sum(duplicated(inDF$ID) > 0)) {
print(paste('WARNING: found ',sum(duplicated(inDF$ID) > 0),'duplicates, dropping them!'))
}
inDF <- inDF[!duplicated(inDF$ID),]
rownames(inDF) <- inDF$ID
inDF$ID <- NULL
# make sure columns are actually numbers (otherwise filter dies; NOTE: this should not be necessary, but... )
for (c in colnames(inDF)) {inDF[[c]] <- as.numeric(inDF[[c]])}
# clean, rescale and save
inDFt2 <- filterHumannDF(inDF,presPerc = presenceFilter,minMRelAb = minRelativeAbundance,minMedRelAb = -1,rescale = T,minSum = 1,verbose = T)
inDFt2 <- inDFt2[,colSums(inDFt2)!=0]
inDFt2$ID <- row.names(inDFt2)
print(paste('Done, returning ',nrow(inDFt2),'samples'))
inDFt2
}
purgeMGNames <- function(dF) {
cn <- colnames(dF)
cnt = 1
for (i in cn) {
if (grepl('k__',i)) {
cn[cnt] <- strsplit(x = i,split = '\\.')[[1]][length(strsplit(x = i,split = '\\.')[[1]])]
} else {
cn[cnt] <- i
}
cnt = cnt + 1
}
colnames(dF) <- cn
dF
}
purgeMGNameOne <- function(oneName) {
if (grepl('\\.',oneName)) {
cn <- strsplit(x = oneName,split = '\\.')[[1]][length(strsplit(x = oneName,split = '\\.')[[1]])]
} else if (grepl('\\|',oneName)) {
cn <- strsplit(x = oneName,split = '\\|')[[1]][length(strsplit(x = oneName,split = '\\|')[[1]])]
} else {cn <- oneName}
cn
}
purgeMGNamesRaw <- function(dF) {
cn <- colnames(dF)
cnt = 1
for (i in cn) {
if (grepl('k__',i)) {
cn[cnt] <- strsplit(x = i,split = '\\|')[[1]][length(strsplit(x = i,split = '\\|')[[1]])]
} else {
cn[cnt] <- i
}
cnt = cnt + 1
}
colnames(dF) <- cn
dF
}
# =========== parser for cor function, returns dataframe =============
# ====================================================================
findCorrelationsR <- function(corMat,cutoff=0.5) {
corsDF <- data.frame()
for (i in 1:nrow(corMat)){
cors <- which(corMat[i,] > cutoff & corMat[i,] != 1.0)
if (length(cors) > 0) {
for (j in cors) {
corsDF <- rbind.data.frame(corsDF,data.frame(A=i,B=j,AName=colnames(corMat)[i],
BName=rownames(corMat)[j],cor=corMat[i,j]))
}
}
}
corsDF <- corsDF[order(corsDF$cor,decreasing = T),]
corsDF
}
# ================================================================================
# function for filtering pathway results (HUMANN)
# - takes dataframe (any)
# - converts NAs to 0
# - filters for relative abundances (median & mean)
#
#
# ================================================================================
filterHumannDF <- function(inDF,presPerc = 0.05,minMRelAb = 0.001,minMedRelAb=0.0,minSum=90.0, rescale=T,verbose=T,type='MetaCyc') {
nonPWYpwys <- c("ARG+POLYAMINE-SYN: superpathway of arginine and polyamine biosynthesis",
"CHLOROPHYLL-SYN: chlorophyllide a biosynthesis I (aerobic, light-dependent)",
"GLYCOLYSIS-E-D: superpathway of glycolysis and Entner-Doudoroff",
"GLYCOLYSIS-TCA-GLYOX-BYPASS: superpathway of glycolysis, pyruvate dehydrogenase, TCA, and glyoxylate bypass",
"GLYCOLYSIS: glycolysis I (from glucose 6-phosphate)",
"GLYOXYLATE-BYPASS: glyoxylate cycle",
"HEME-BIOSYNTHESIS-II: heme biosynthesis I (aerobic)",
"MANNOSYL-CHITO-DOLICHOL-BIOSYNTHESIS: protein N-glycosylation (eukaryotic, high mannose)",
"NAD-BIOSYNTHESIS-II: NAD salvage pathway II",
"REDCITCYC: TCA cycle VIII (helicobacter)",
"TCA-GLYOX-BYPASS: superpathway of glyoxylate bypass and TCA",
"TCA: TCA cycle I (prokaryotic)")
colnames(inDF)[colnames(inDF) %in% nonPWYpwys] <- paste0('PWY_',colnames(inDF)[colnames(inDF) %in% nonPWYpwys])
if (type=='MetaCyc') {
nonPWYdf <- as.data.frame(inDF[,-grep('PWY',colnames(inDF))])
cnsNonPWYdf <- colnames(inDF[colnames(inDF)[-grep('PWY',colnames(inDF))] ])
} else if (type=='EC') {
nonPWYdf <- as.data.frame(inDF[,-grep('^EC_',colnames(inDF))])
cnsNonPWYdf <- colnames(inDF[colnames(inDF)[-grep('^EC_',colnames(inDF))] ])
} else if (type=='RXN') {
nonPWYdf <- as.data.frame(inDF[,-grep('RXN',colnames(inDF))])
cnsNonPWYdf <- colnames(inDF[colnames(inDF)[-grep('RXN',colnames(inDF))] ])
} else if (type=='PFAM') {
nonPWYdf <- as.data.frame(inDF[,-grep('^PF[01]',colnames(inDF))])
cnsNonPWYdf <- colnames(inDF[colnames(inDF)[-grep('^PF[01]',colnames(inDF))] ])
} else if (type=='GO') {
nonPWYdf <- as.data.frame(inDF[,-grep('^GO',colnames(inDF))])
cnsNonPWYdf <- colnames(inDF[colnames(inDF)[-grep('^GO',colnames(inDF))] ])
} else if (type=='KEGG') {
nonPWYdf <- as.data.frame(inDF[,-grep('^K[012]',colnames(inDF))])
cnsNonPWYdf <- colnames(inDF[colnames(inDF)[-grep('^K[012]',colnames(inDF))] ])
}
colnames(nonPWYdf) <- cnsNonPWYdf
if (type=='MetaCyc') {
yesPWYdf <- as.data.frame(inDF[,grep('PWY',colnames(inDF))])
cnsYesPWYdf <- colnames(inDF[colnames(inDF)[grep('PWY',colnames(inDF))] ])
} else if (type=='EC') {
yesPWYdf <- as.data.frame(inDF[,grep('^EC_',colnames(inDF))])
cnsYesPWYdf <- colnames(inDF[colnames(inDF)[grep('^EC_',colnames(inDF))] ])
} else if (type=='RXN') {
yesPWYdf <- as.data.frame(inDF[,grep('RXN',colnames(inDF))])
cnsYesPWYdf <- colnames(inDF[colnames(inDF)[grep('RXN',colnames(inDF))] ])
} else if (type=='PFAM') {
yesPWYdf <- as.data.frame(inDF[,grep('^PF[01]',colnames(inDF))])
cnsYesPWYdf <- colnames(inDF[colnames(inDF)[grep('^PF[01]',colnames(inDF))] ])
} else if (type=='GO') {
yesPWYdf <- as.data.frame(inDF[,grep('^GO',colnames(inDF))])
cnsYesPWYdf <- colnames(inDF[colnames(inDF)[grep('^GO',colnames(inDF))] ])
} else if (type=='KEGG') {
yesPWYdf <- as.data.frame(inDF[,grep('^K[012]',colnames(inDF))])
cnsYesPWYdf <- colnames(inDF[colnames(inDF)[grep('^K[012]',colnames(inDF))] ])
}
# replaces NAs with 0s
for (c in colnames(yesPWYdf)) {
yesPWYdf[,c][is.na(yesPWYdf[,c])] <- 0.0
}
# rescale to rel ab (if rescale = T)
if (rescale==T) {
if (verbose) {print (' >> rescaling')}
rsums <- rowSums(yesPWYdf)
rsums[rsums==0] <- 1.0
yesPWYdf <- yesPWYdf/rsums
}
# filter for presence
# -----------------
nrRemoved = 0
toRemove = c()
for (c in colnames(yesPWYdf)) {
nrnZ = as.numeric(sum(yesPWYdf[,c]!=0.0))
if (nrnZ/as.numeric(nrow(yesPWYdf)) < presPerc) {
nrRemoved = nrRemoved + 1
toRemove <- c(toRemove,c)
}
}
if (length(toRemove) > 0) {
yesPWYdf <- yesPWYdf[,!(colnames(yesPWYdf) %in% toRemove)]
}
if (verbose) {print (paste(' > presence filter: Removed',nrRemoved,'pathways!, ',length(colnames(yesPWYdf)),'pathways left!')); }
# filter for abundance (mean)
# ---------------------------
nrRemoved = 0
toRemove = c()
for (c in colnames(yesPWYdf)) {
mn = mean(yesPWYdf[,c])
if ( mn < minMRelAb) {
nrRemoved = nrRemoved + 1
toRemove <- c(toRemove,c)
}
}
if (length(toRemove) > 0) {
yesPWYdf <- yesPWYdf[,!(colnames(yesPWYdf) %in% toRemove)]
}
if (verbose) {print (paste(' > mean abundance filter: Removed',nrRemoved,'pathways!, ',length(colnames(yesPWYdf)),'pathways left!')); }
# filter for abundance (median)
# -----------------------------
nrRemoved = 0
toRemove = c()
for (c in colnames(yesPWYdf)) {
mn = median(yesPWYdf[,c])
if ( mn < minMedRelAb) {
nrRemoved = nrRemoved + 1
toRemove <- c(toRemove,c)
}
}
if (length(toRemove) > 0) {
yesPWYdf <- yesPWYdf[,!(colnames(yesPWYdf) %in% toRemove)]
}
if (verbose) {print (paste(' > median abundance filter: Removed',nrRemoved,'pathways!, ',length(colnames(yesPWYdf)),'pathways left!')); }
# do final rescale
if (rescale==T) {
if (verbose) {print (' >> rescaling')}
rsums <- rowSums(yesPWYdf)
rsums[rsums==0] <- 1.0
yesPWYdf <- yesPWYdf/rsums
}
inDF <- cbind.data.frame(nonPWYdf,yesPWYdf)
if (verbose) {print ('> DONE')}
inDF
}
# ================================================================================
# function for filtering CARD results
# - takes dataframe (any)
# - converts NAs to 0
# - filters for relative abundances (median & mean) and prevalence (0 vs non-0)
#================================================================================
filterCardDF <- function(inDF,presPerc = 0.05,minMRelAb = 0.001,grepPat="'gb\\|'",minMedRelAb=0.0,rescale=T,verbose=T) {
nonPWYdf <- as.data.frame(inDF[,-grep(grepPat,colnames(inDF))])
cnsNonPWYdf <- colnames(inDF[colnames(inDF)[-grep(grepPat,colnames(inDF))] ])
colnames(nonPWYdf) <- cnsNonPWYdf
yesPWYdf <- as.data.frame(inDF[,grep(grepPat,colnames(inDF))])
cnsYesPWYdf <- colnames(inDF[colnames(inDF)[grep(grepPat,colnames(inDF))] ])
# replaces NAs with 0s
for (c in colnames(yesPWYdf)) {
yesPWYdf[,c][is.na(yesPWYdf[,c])] <- 0.0
}
# rescale to rel ab (if rescale = T)
if (rescale==T) {
if (verbose) {print (' >> rescaling')}
rsums <- rowSums(yesPWYdf)
rsums[rsums==0] <- 1.0
yesPWYdf <- yesPWYdf/rsums
}
# filter for presence
# -----------------
nrRemoved = 0
toRemove = c()
for (c in colnames(yesPWYdf)) {
nrnZ = as.numeric(sum(yesPWYdf[,c]!=0.0))
if (nrnZ/as.numeric(nrow(yesPWYdf)) < presPerc) {
nrRemoved = nrRemoved + 1
toRemove <- c(toRemove,c)
}
}
if (length(toRemove) > 0) {
yesPWYdf <- yesPWYdf[,!(colnames(yesPWYdf) %in% toRemove)]
}
if (verbose) {print (paste(' > presence filter: Removed',nrRemoved,'CARDs!, ',length(colnames(yesPWYdf)),'CARDs left!')); }
# filter for abundance (mean)
# ---------------------------
nrRemoved = 0
toRemove = c()
for (c in colnames(yesPWYdf)) {
mn = mean(yesPWYdf[,c])
if ( mn < minMRelAb) {
nrRemoved = nrRemoved + 1
toRemove <- c(toRemove,c)
}
}
if (length(toRemove) > 0) {
yesPWYdf <- yesPWYdf[,!(colnames(yesPWYdf) %in% toRemove)]
}
if (verbose) {print (paste(' > mean abundance filter: Removed',nrRemoved,'CARDs!, ',length(colnames(yesPWYdf)),'CARDs left!')); }
# filter for abundance (median)
# -----------------------------
nrRemoved = 0
toRemove = c()
for (c in colnames(yesPWYdf)) {
mn = median(yesPWYdf[,c])
if ( mn < minMedRelAb) {
nrRemoved = nrRemoved + 1
toRemove <- c(toRemove,c)
}
}
if (length(toRemove) > 0) {
yesPWYdf <- yesPWYdf[,!(colnames(yesPWYdf) %in% toRemove)]
}
if (verbose) {print (paste(' > median abundance filter: Removed',nrRemoved,'CARDs!, ',length(colnames(yesPWYdf)),'CARDs left!')); }
# do final rescale
if (rescale==T) {
if (verbose) {print (' >> rescaling')}
rsums <- rowSums(yesPWYdf)
rsums[rsums==0] <- 1.0
yesPWYdf <- yesPWYdf/rsums
}
inDF <- cbind.data.frame(nonPWYdf,yesPWYdf)
if (verbose) {print ('> DONE')}
inDF
}
# ================================================================================
# function for filtering VFDB results
# - takes dataframe (any)
# - converts NAs to 0
# - filters for relative abundances (median & mean) and prevalence (0 vs non-0)
#================================================================================
filterVfdbDF <- function(inDF,presPerc = 0.05,minMRelAb = 0.001,minMedRelAb=0.0,rescale=T,verbose=T) {
nonPWYdf <- as.data.frame(inDF[,-grep('^VF',colnames(inDF))])
cnsNonPWYdf <- colnames(inDF[colnames(inDF)[-grep('^VF',colnames(inDF))] ])
colnames(nonPWYdf) <- cnsNonPWYdf
yesPWYdf <- as.data.frame(inDF[,grep('^VF',colnames(inDF))])
cnsYesPWYdf <- colnames(inDF[colnames(inDF)[grep('^VF',colnames(inDF))] ])
# replaces NAs with 0s
for (c in colnames(yesPWYdf)) {
yesPWYdf[,c][is.na(yesPWYdf[,c])] <- 0.0
}
# rescale to rel ab (if rescale = T)
if (rescale==T) {
if (verbose) {print (' >> rescaling')}
rsums <- rowSums(yesPWYdf)
rsums[rsums==0] <- 1.0
yesPWYdf <- yesPWYdf/rsums
}
# filter for presence
# -----------------
nrRemoved = 0
toRemove = c()
for (c in colnames(yesPWYdf)) {
nrnZ = as.numeric(sum(yesPWYdf[,c]!=0.0))
if (nrnZ/as.numeric(nrow(yesPWYdf)) < presPerc) {
nrRemoved = nrRemoved + 1
toRemove <- c(toRemove,c)
}
}
if (length(toRemove) > 0) {
yesPWYdf <- yesPWYdf[,!(colnames(yesPWYdf) %in% toRemove)]
}
if (verbose) {print (paste(' > presence filter: Removed',nrRemoved,'VFs!, ',length(colnames(yesPWYdf)),'VFs left!')); }
# filter for abundance (mean)
# ---------------------------
nrRemoved = 0
toRemove = c()
for (c in colnames(yesPWYdf)) {
mn = mean(yesPWYdf[,c])
if ( mn < minMRelAb) {
nrRemoved = nrRemoved + 1
toRemove <- c(toRemove,c)
}
}
if (length(toRemove) > 0) {
yesPWYdf <- yesPWYdf[,!(colnames(yesPWYdf) %in% toRemove)]
}
if (verbose) {print (paste(' > mean abundance filter: Removed',nrRemoved,'VFs!, ',length(colnames(yesPWYdf)),'VFs left!')); }
# filter for abundance (median)
# -----------------------------
nrRemoved = 0
toRemove = c()
for (c in colnames(yesPWYdf)) {
mn = median(yesPWYdf[,c])
if ( mn < minMedRelAb) {
nrRemoved = nrRemoved + 1
toRemove <- c(toRemove,c)
}
}
if (length(toRemove) > 0) {
yesPWYdf <- yesPWYdf[,!(colnames(yesPWYdf) %in% toRemove)]
}
if (verbose) {print (paste(' > median abundance filter: Removed',nrRemoved,'CARDs!, ',length(colnames(yesPWYdf)),'VFs left!')); }
# do final rescale
if (rescale==T) {
if (verbose) {print (' >> rescaling')}
rsums <- rowSums(yesPWYdf)
rsums[rsums==0] <- 1.0
yesPWYdf <- yesPWYdf/rsums
}
inDF <- cbind.data.frame(nonPWYdf,yesPWYdf)
if (verbose) {print ('> DONE')}
inDF
}
# ====================================================
# > reorders phenotypes VS data (includes: taxa, pwys, CARD, VFs)
# ====================================================
reorderMicrobiomeDF <- function(inDF,verbose=T) {
if (verbose) {print ("RE-ORDERING PHENOTYPES VS DATA ...")}
nonPhenoCols <- c()
nonPhenoColsTaxa <- c(grep("^[dgtspcfko]__",colnames(inDF)))
if (verbose) {print(paste0(' > FOUND ',length(nonPhenoColsTaxa),' TAXA'))}
nonPhenoColsPWYs <- c(grep("PWY",colnames(inDF)))
if (verbose) {print(paste0(' > FOUND ',length(nonPhenoColsPWYs),' PWYs'))}
nonPhenoColsVFs <- c(grep("^VF",colnames(inDF)))
if (verbose) {print(paste0(' > FOUND ',length(nonPhenoColsVFs),' VFs'))}
nonPhenoColsCARDs <- c(grep("CARD",colnames(inDF)))
if (verbose) {print(paste0(' > FOUND ',length(nonPhenoColsCARDs),' CARDs'))}
nonPhenoCols <- c(nonPhenoCols,nonPhenoColsTaxa,nonPhenoColsPWYs,nonPhenoColsVFs,nonPhenoColsCARDs)
phenoCols <- c(1:ncol(inDF)); phenoCols <- phenoCols[!(phenoCols %in% nonPhenoCols)]
if (verbose) {print(paste0(' > FOUND ',length(phenoCols),' *Phenotypes*'))}
inDF <- inDF[, c(phenoCols,nonPhenoCols)]
}
# ====================================================
# > Subsets stuff from merged microbiome dataframe
# ====================================================
subsetMicrobiomeDF <- function(inDF,verbose=T,getPWYs=F,getVFs=F,getTaxa=T,getCARDs=F,getPhenos=T,getDivs=F,pwyType="MetaCyc",getID=T,idName="ID",idToRowName=F) {
if (!(pwyType %in% c('MetaCyc','EC','RXN','PFAM','GO','KEGG'))) {
exit("ERROR: pwyType must be one of 'MetaCyc','EC','RXN','PFAM','GO','KEGG' ")
}
if (verbose) {print ("SUBSETTING Microbiome Dataframe ...")
if (getPhenos) {print(" > getting phenotypes")}
if (getTaxa) {print(" > getting taxa")}
if (getPWYs) {print(paste0(" > getting pathways [",pwyType,"]"))}
if (getCARDs) {print(" > getting CARDs")}
if (getVFs) {print(" > getting VFs")}
if (getDivs) {print(" > getting Diversities")}
if (getID) {print(paste0(" > getting ID column = ",idName))}
}
nonPhenoCols <- c()
nonPhenoColsTaxa <- c(grep("^[dgtspcfko]__",colnames(inDF)))
toGet <- c()
if (getTaxa) {
toGet <- nonPhenoColsTaxa
if (verbose) {print(paste0(' > FOUND ',length(nonPhenoColsTaxa),' TAXA'))}
}
nonPhenoColsPWYs <- c()
if (pwyType=='MetaCyc' | pwyType=='All' | pwyType=='Metacyc') {
nonPhenoColsPWYs <- c(nonPhenoColsPWYs,grep("PWY",colnames(inDF)))
nonPhenoColsPWYs <- nonPhenoColsPWYs[!(nonPhenoColsPWYs %in% grep('^DIV\\.',colnames(inDF)))]
}
if (pwyType=='EC' | pwyType=='All') {
nonPhenoColsPWYs <- c(nonPhenoColsPWYs,grep("^EC_",colnames(inDF)))
}
if (pwyType=='RXN' | pwyType=='All') {
nonPhenoColsPWYs <- c(nonPhenoColsPWYs,grep("RXN",colnames(inDF)))
}
if (pwyType=='PFAM' | pwyType=='All' | pwyType=='Pfam') {
nonPhenoColsPWYs <- c(nonPhenoColsPWYs,grep("^PF[01]",colnames(inDF)))
}
if (pwyType=='GO' | pwyType=='All') {
nonPhenoColsPWYs <- c(nonPhenoColsPWYs,grep("^GO",colnames(inDF)))
}
if (pwyType=='KEGG' | pwyType=='All' | pwyType=='Kegg') {
nonPhenoColsPWYs <- c(nonPhenoColsPWYs,grep("^K[012]",colnames(inDF)))
}
nonPhenoColsPWYs <- unique(nonPhenoColsPWYs)
if (getPWYs) {
toGet <- c(toGet,nonPhenoColsPWYs)
if (verbose) {print(paste0(' > FOUND ',length(nonPhenoColsPWYs),' PWYs'))}
}
nonPhenoColsVFs <- c(grep("^VF",colnames(inDF)))
if (getVFs) {
toGet <- c(toGet,nonPhenoColsVFs)
if (verbose) {print(paste0(' > FOUND ',length(nonPhenoColsVFs),' VFs'))}
}
nonPhenoColsCARDs <- c(grep("^CARD",colnames(inDF)))
if (getCARDs) {
toGet <- c(toGet,nonPhenoColsCARDs)
if (verbose) {print(paste0(' > FOUND ',length(nonPhenoColsCARDs),' CARDs'))}
}
divCols <- grep("^DIV\\.",colnames(inDF))
if (getDivs) {
if (verbose) {print(paste0(' > FOUND ',length(divCols),' *Diversity metrics*'))}
toGet <- c(toGet,divCols)
}
idCols <- grep(paste0("^",idName,"$"),colnames(inDF))
if (getID) {
toGet <- unique(c(toGet,idCols))
if (verbose) {print(paste0(' > FOUND ',length(idCols),' *ID COLUMNS*'))}
}
nonPhenoCols <- c(nonPhenoCols,nonPhenoColsTaxa,nonPhenoColsPWYs,nonPhenoColsVFs,nonPhenoColsCARDs,divCols,idCols)
phenoCols <- c(1:ncol(inDF)); phenoCols <- phenoCols[!(phenoCols %in% nonPhenoCols)]
if (getPhenos) {
toGet <- c(phenoCols,toGet)
if (verbose) {print(paste0(' > FOUND ',length(phenoCols),' *Phenotypes*'))}
}
inDF <- inDF[, unique(toGet)]
if (idToRowName) {
rownames(inDF) <- inDF[[idName]]
inDF[[idName]] <- NULL
}
inDF
}
# ================================================================================
# calculator for diversity metrics
# ================================================================================
calcDIVMetrics <- function(inDF,IDcol="RowNames",pwyType="MetaCyc",
metrics=c("shannon","simpson","invsimpson","richness"),
DIVlvls=c("taxS","taxG","taxF","taxO","taxC","taxP","PWY","CARD","VF") ) {
# select IDs column
if (IDcol == "RowNames") {
DIVMatrix <- data.frame(RN=rownames(inDF))
} else {
DIVMatrix <- data.frame(IDcol=inDF[[IDcol]])
colnames(DIVMatrix)[1] <- IDcol
}
# iterate over stuff to calculate
# > diversity levels (taxa per taxon lvl, pathways, VFs, CARDs)
for (l in DIVlvls) {
# TAXA
# ================================
if (grepl('^tax.$',l)) {
toUse <- gsub('^tax','',l)
inDFf <- subsetMicrobiomeDF(inDF,getTaxa = T,getPhenos = F,getVFs = F,getPWYs = F,getCARDs = F,pwyType = pwyType,getID = T)
if (IDcol=="RowNames") {
} else {
rownames(inDFf) <- inDFf[[IDcol]]
inDFf[[IDcol]] <- NULL
}
inDFf <- filterMetaGenomeDF(inDFf,presPerc = -1,minMRelAb = -1,minMedRelAb = -1,rescaleTaxa = T,verbose = T,
keepDomains = "All",keepLevels = c(toUse))
# iterate over metrics, calculate each
# NOTE: richness is not implemented in vegan, requires special treatment
for (m in metrics) {
print(paste0(' > calculating ',m,'[',l,']'))
if (m=="richness") {
inDFpa <- inDFf
inDFpa[inDFpa > 0] <- 1
dv <- rowSums(inDFpa)
} else {
dv <- diversity(inDFf,index = m)
}
DIVMatrix <- cbind.data.frame(DIVMatrix,dv)
colnames(DIVMatrix)[ncol(DIVMatrix)] <- paste0('DIV.',toUse,'.',m)
}
# PWYs
# =======================
} else if (l=="PWY") {
inDFf <- subsetMicrobiomeDF(inDF,getTaxa = F,getPhenos = F,getVFs = F,getPWYs = T,getCARDs = F,getID = T,pwyType = pwyType)
if (IDcol=="RowNames") {
} else {
rownames(inDFf) <- inDFf[[IDcol]]
inDFf[[IDcol]] <- NULL
}
for (m in metrics) {
print(paste0(' > calculating ',m, '[PWYs,',pwyType,']'))
if (m=="richness") {
inDFpa <- inDFf
inDFpa[inDFpa > 0] <- 1
dv <- rowSums(inDFpa)
} else {
dv <- diversity(inDFf,index = m)
}
DIVMatrix <- cbind.data.frame(DIVMatrix,dv)
colnames(DIVMatrix)[ncol(DIVMatrix)] <- paste0('DIV.PWY.',m)
}
# CARD
# ==========================
} else if (l=="CARD") {
inDFf <- subsetMicrobiomeDF(inDF,getTaxa = F,getPhenos = F,getVFs = F,getPWYs = F,getCARDs = T,getID = T)
if (IDcol=="RowNames") {
} else {
rownames(inDFf) <- inDFf[[IDcol]]
inDFf[[IDcol]] <- NULL
}
for (m in metrics) {
print(paste0(' > calculating ',m, '[CARDs]'))
if (m=="richness") {
inDFpa <- inDFf
inDFpa[inDFpa > 0] <- 1
dv <- rowSums(inDFpa)
} else {
dv <- diversity(inDFf,index = m)
}
DIVMatrix <- cbind.data.frame(DIVMatrix,dv)
colnames(DIVMatrix)[ncol(DIVMatrix)] <- paste0('DIV.CARD.',m)
}
# VFs
# =======================
} else if (l=="VF") {
inDFf <- subsetMicrobiomeDF(inDF,getTaxa = F,getPhenos = F,getVFs = T,getPWYs = F,getCARDs = F,getID = T)
if (IDcol=="RowNames") {
} else {
rownames(inDFf) <- inDFf[[IDcol]]
inDFf[[IDcol]] <- NULL
}
for (m in metrics) {
print(paste0(' > calculating ',m, '[VFs]'))
if (m=="richness") {
inDFpa <- inDFf
inDFpa[inDFpa > 0] <- 1
dv <- rowSums(inDFpa)
} else {
dv <- diversity(inDFf,index = m)
}
DIVMatrix <- cbind.data.frame(DIVMatrix,dv)
colnames(DIVMatrix)[ncol(DIVMatrix)] <- paste0('DIV.VF.',m)
}
}
}
return(DIVMatrix)
}
# ================================================================================
# ================================================================================
# calculator for bray-curtis metrix
# ================================================================================
calcBCMatrix <- function(inDF,IDcol="RowNames",pwyType="MetaCyc",
div="taxS" ) {
# find and calculate bray-curtis
# > diversity levels (taxa per taxon lvl, pathways, VFs, CARDs)
# TAXA
# ================================
l <- div
if (grepl('^tax.$',l)) {
toUse <- gsub('^tax','',l)
inDFf <- subsetMicrobiomeDF(inDF,getTaxa = T,getPhenos = F,getVFs = F,getPWYs = F,getCARDs = F,pwyType = pwyType,getID = T)
if (IDcol=="RowNames") {
} else {
rownames(inDFf) <- inDFf[[IDcol]]
inDFf[[IDcol]] <- NULL
}
inDFf <- filterMetaGenomeDF(inDFf,presPerc = -1,minMRelAb = -1,minMedRelAb = -1,rescaleTaxa = T,verbose = T,
keepDomains = "All",keepLevels = c(toUse))
# PWYs
# =======================
} else if (l=="PWY") {
inDFf <- subsetMicrobiomeDF(inDF,getTaxa = F,getPhenos = F,getVFs = F,getPWYs = T,getCARDs = F,getID = T,pwyType = pwyType)
if (IDcol=="RowNames") {
} else {
rownames(inDFf) <- inDFf[[IDcol]]
inDFf[[IDcol]] <- NULL
}
# CARD
# ==========================
} else if (l=="CARD") {
inDFf <- subsetMicrobiomeDF(inDF,getTaxa = F,getPhenos = F,getVFs = F,getPWYs = F,getCARDs = T,getID = T)
if (IDcol=="RowNames") {
} else {
rownames(inDFf) <- inDFf[[IDcol]]
inDFf[[IDcol]] <- NULL
}
# VFs
# =======================
} else if (l=="VF") {
inDFf <- subsetMicrobiomeDF(inDF,getTaxa = F,getPhenos = F,getVFs = T,getPWYs = F,getCARDs = F,getID = T)
if (IDcol=="RowNames") {
} else {
rownames(inDFf) <- inDFf[[IDcol]]
inDFf[[IDcol]] <- NULL
}
}
# actually calc B/C
dv <- vegdist(inDFf,method = "bray")
return(dv)
}
# ================================================================================
# ================================================================================
# ================================================================================
# function for filtering microbiome (metaphlan) results
# - takes dataframe (any)
# - replaces NAs with 0.0
# - filters OUT taxa present in less then presPerc samples
# - filters OUT taxa with mean releative abundance < minMRelAb
# - filters OUT taxa with median relative abundance < minMedRelAb
# - filters OUT taxonomic levels not in keepLevels ()
# -> keepLevels should be vector of following: T = strain, S = species, G = Genera, F = families
# O = Orders, C = classes, P = phyla, K = Kingdoms
# -> example: keepLevels=c('S','G') keeps only species and genera
# - filters OUT domains not in keepDomains:
# -> input is vector of following: Eukaryota, Bacteria, Viruses, Archaea
# -> example: keepDomains=c('B') keeps only bacteria, removes viruses, archaea and eukarya
# - if rescaleTaxa = True, rescales relative abundancies after filtering
# returns modified dataframe
# NOTES:
# - assumes metaphlan encoding (k__<kingdom>.o__<order> ... ); it will mess stuff
# up if non-metagenome rows are encoded like this!
# DEFAULTS:
# - removes non-bacteria
# - keeps all except kingdoms
# ================================================================================
#TODO: implement keepDomains
filterMetaGenomeDF <- function(inDF,presPerc = 0.1,minMRelAb = 0.01,minMedRelAb=0.0, rescaleTaxa=F,verbose=T,
keepDomains=c('Bacteria','Archaea'),
keepLevels=c('T','S','G','F','O','C','P'),
keepUnknown=T) {
# -- drop unknown & rescale? --
if (!keepUnknown) {
inDF$UNKNOWN <- NULL
}
tCols = grep('k__',colnames(inDF)) # colums with microbiome
tColsNMG = grep('k__',colnames(inDF),invert = T) # colums with microbiome
# replaces NAs in microbiome with 0s
for (c in tCols) {
inDF[,c][is.na(inDF[,c])] <- 0.0
}
# filter for presence
# -----------------
nrRemoved = 0
toRemove = c()
for (c in tCols) {
nrnZ = as.numeric(sum(inDF[,c]!=0.0))
if ( (nrnZ/as.numeric(nrow(inDF))) < presPerc) {
# if (verbose) {
# print (paste('col',c,': ',colnames(inDF)[c],'; nr non Zero:',nrnZ,'=',nrnZ/as.numeric(nrow(inDF)),'>> Column removed!'))
# }
nrRemoved = nrRemoved + 1
toRemove <- c(toRemove,c)
}
}
if (length(toRemove) > 0) {
inDF <- inDF[,-toRemove]
}
tCols = grep('^[dgtspcfko]__',colnames(inDF)) # colums with microbiome
if (verbose) {print (paste(' > presence filter: Removed',nrRemoved,'taxa!, ',length(tCols),'taxa left!')); }
# filter for abundance (mean)
# ---------------------------
nrRemoved = 0
toRemove = c()
for (c in tCols) {
mn = mean(inDF[,c])
if ( mn < minMRelAb) {
#if (verbose) {
# print (paste('col',c,': ',colnames(inDF)[c],'; mean rel abundance:',mn,' >> Column removed!'))
#}
nrRemoved = nrRemoved + 1
toRemove <- c(toRemove,c)
}
}
if (length(toRemove) > 0) {
inDF <- inDF[,-toRemove]
}
tCols = grep('k__',colnames(inDF)) # colums with microbiome
if (verbose) {print (paste(' > mean abundance filter: Removed',nrRemoved,'taxa!, ',length(tCols),'taxa left!')); }
# filter for abundance (median)
# -----------------------------
nrRemoved = 0
toRemove = c()
for (c in tCols) {
mn = median(inDF[,c])
if ( mn < minMedRelAb) {
#if (verbose) {
# print (paste('col',c,': ',colnames(inDF)[c],'; median rel abundance:',mn,' >> Column removed!'))
#}
nrRemoved = nrRemoved + 1
toRemove <- c(toRemove,c)
}
}
if (length(toRemove) > 0) {
inDF <- inDF[,-toRemove]
}
if (verbose) {print (paste(' > median abundance filter: Removed',nrRemoved,'taxa!, ',length(tCols),'taxa left!')); }
# keep domains
# -----------------------------
if (keepDomains!='All' & keepDomains !="") {
toKeep = grep('k__',colnames(inDF),invert = T)
for (d in keepDomains) {
#d print(d)
toKeep = c(toKeep,grep(paste('k__',d,sep=''),colnames(inDF)))
}
inDF <- inDF[,toKeep]
}
# remove taxonomic levels
# -----------------------------
inDFnonTaxa <- as.data.frame(inDF[,grep('k__',colnames(inDF),invert=T)])
colnames(inDFnonTaxa) <- colnames(inDF)[grep('k__',colnames(inDF),invert=T)]
inDF2 <- as.data.frame(inDF[,grep('k__',colnames(inDF),invert=F)])
# pick strains (T)
taxaTCols <- grep('t__',colnames(inDF2))
taxaT <- as.data.frame(inDF2[,taxaTCols])
colnames(taxaT) <- colnames(inDF2)[grep('t__',colnames(inDF2))]
if (length(taxaTCols) > 0) {inDF2 <- inDF2[,-taxaTCols]}
# pick species (S)
taxaSCols <- grep('s__',colnames(inDF2))
if (length(taxaSCols) > 0) {
taxaS <- as.data.frame(inDF2[,taxaSCols])
colnames(taxaS) <- colnames(inDF2)[grep('s__',colnames(inDF2))]
}
if (length(taxaSCols) > 0) {inDF2 <- inDF2[,-taxaSCols]}
# pick genera (G)
taxaGCols <- grep('g__',colnames(inDF2))
if (length(taxaGCols) > 0) {
taxaG <- as.data.frame(inDF2[,taxaGCols])
colnames(taxaG) <- colnames(inDF2)[grep('g__',colnames(inDF2))]
}
if (length(taxaGCols) > 0) {inDF2 <- inDF2[,-taxaGCols]}
# pick families (F)
taxaFCols <- grep('f__',colnames(inDF2))
if (length(taxaFCols) > 0) {
taxaF <- as.data.frame(inDF2[,taxaFCols])
colnames(taxaF) <- colnames(inDF2)[grep('f__',colnames(inDF2))]
}
if (length(taxaFCols) > 0) {inDF2 <- inDF2[,-taxaFCols]}
# pick orders (O)
taxaOCols <- grep('o__',colnames(inDF2))
if (length(taxaOCols) > 0) {
taxaO <- as.data.frame(inDF2[,taxaOCols])
colnames(taxaO) <- colnames(inDF2)[grep('o__',colnames(inDF2))]
}
if (length(taxaOCols) > 0) {inDF2 <- inDF2[,-taxaOCols]}
# pick classes (C)
taxaCCols <- grep('c__',colnames(inDF2))
if (length(taxaCCols) > 0) {
taxaC <- as.data.frame(inDF2[,taxaCCols])
colnames(taxaC) <- colnames(inDF2)[grep('c__',colnames(inDF2))]
}
if (length(taxaCCols) > 0) {
if( length(colnames(inDF2)) - length(taxaCCols) == 1) {
ccn <- colnames(inDF2)[grep('c__',colnames(inDF2))]
tempN <- colnames(inDF2)[!(colnames(inDF2) %in% ccn)]
inDF2 <- as.data.frame(inDF2[,-taxaCCols])
colnames(inDF2) <- tempN
} else {
inDF2 <- inDF2[,-taxaCCols]
}
}
# pick phyla (P)
taxaPColsKeepN <- NULL
taxaPCols <- grep('p__',colnames(inDF2))
if (length(taxaPCols) > 0) {
taxaPColsN <- colnames(inDF2)[grep('p__',colnames(inDF2))]
taxaPColsKeep <- grep('p__',colnames(inDF2),invert = T)
taxaPColsKeepN <- colnames(inDF2)[grep('p__',colnames(inDF2),invert = T)]
taxaP <- as.data.frame(inDF2[,taxaPCols])
colnames(taxaP) <- taxaPColsN
}
# pick kingdoms (K)
if (length(taxaPCols) > 0) {inDF2 <- as.data.frame(inDF2[,taxaPColsKeep])}
if (!is.null(taxaPColsKeepN)){
colnames(inDF2) <- taxaPColsKeepN
}