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00-Functions.R
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00-Functions.R
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#-----------------------------------------------------------------------------
# 00-Functions.R
# Copyright (c) 2021--, Greg Poore
# Purpose: List main functions for R scripts
#-----------------------------------------------------------------------------
pcaPlotting <- function(pcaObject,pcChoices, dataLabels, factorString, titleString){
require(ggbiplot)
theme_update(plot.title = element_text(hjust = 0.5))
g <- ggbiplot(pcaObject,pcChoices, obs.scale = 1, var.scale = 1,
groups = dataLabels, ellipse = TRUE,
alpha = 0.05,
circle = TRUE,var.axes=FALSE) +
# scale_color_nejm(name = factorString) +
theme_bw() +
#theme(legend.direction = "horizontal", legend.position = "top") +
ggtitle(titleString) + theme(plot.title = element_text(hjust = 0.5))
print(g)
}
avgRAbarplot <- function(psObj, dataType, filename, yAxisLab="Mean Relative Abundance",
title="TCGA bacteria vs. fungi average relative abundances"){
figFilepath <- "Figures/Supplementary_Figures/"
dataFilepath <- "Figures_data/Supplementary_Figures/"
ps <- tax_glom(psObj, "Domain")
ps0 <- transform_sample_counts(ps, function(x) x / sum(x))
ps1 <- merge_samples(ps0, "investigation")
ps2 <- transform_sample_counts(ps1, function(x) x / sum(x))
ps2_MeltFilt <- psmelt(ps2) %>%
select(OTU, Sample, Abundance, Domain) %>%
mutate(label = ifelse(Domain == "k__Eukaryota",yes = round(Abundance, 3),no = NA)) %>%
droplevels() %>% data.frame()
plot_bar(ps2, fill="Domain") + scale_fill_manual(values = c("#0072B5FF","#BC3C29FF")) +
ylab(yAxisLab) + xlab("TCGA Cancer Type") +
scale_y_continuous(breaks = seq(0, 1, by = 0.1)) +
ggtitle(paste0(title," (Primary Tumor | ",dataType,")")) +
geom_text(data = ps2_MeltFilt, aes(x=Sample, label=label),
color = "white", position=position_dodge(width=0.9), hjust=-0.25,vjust=-0.01,angle = 90) +
theme(plot.title = element_text(hjust = 0.5))
ggsave(filename = paste0(figFilepath,filename,".pdf"),
dpi = "retina", width = 12, height = 4, units = "in")
ps2_otu <- data.frame(otu_table(ps2))
ps2_otu %>% write.csv(file = paste0(dataFilepath,filename,".csv"))
ps2Df <- data.frame(otu_table(ps2))
print("Normalized relative abundance means (%):")
print(round(100*colMeans(ps2Df),2))
return(ps2)
}
vsnmFunctionTCGA <- function(qcData, qcMetadata=metaQiitaCombined_Nonzero_DecontamV2, cancerTypeFlag=FALSE, filename){
require(limma)
require(edgeR)
require(dplyr)
require(snm)
# Set up design matrix
if(cancerTypeFlag){
covDesignNorm <- model.matrix(~0 + disease_type + sample_type +
data_submitting_center_label +
experimental_strategy,
data = qcMetadata)
} else{
covDesignNorm <- model.matrix(~0 + sample_type +
data_submitting_center_label +
experimental_strategy,
data = qcMetadata)
}
# Check row dimensions
print(dim(covDesignNorm))
print(dim(qcData))
print(dim(covDesignNorm)[1] == dim(qcData)[1])
colnames(covDesignNorm) <- gsub('([[:punct:]])|\\s+','',colnames(covDesignNorm))
# Set up counts matrix
counts <- t(qcData) # DGEList object from a table of counts (rows=features, columns=samples)
# Normalize using edgeR and then plug into voom
dge <- DGEList(counts = counts)
vdge_data <- voom(dge, design = covDesignNorm, plot = TRUE, save.plot = TRUE,
normalize.method="quantile")
vdge_dataE <- t(vdge_data$E)
# Apply
if(cancerTypeFlag){
bio.var <- model.matrix(~disease_type + sample_type,
data=qcMetadata)
} else{
bio.var <- model.matrix(~sample_type,
data=qcMetadata)
}
adj.var <- model.matrix(~data_submitting_center_label +
experimental_strategy,
data=qcMetadata)
colnames(bio.var) <- gsub('([[:punct:]])|\\s+','',colnames(bio.var))
colnames(adj.var) <- gsub('([[:punct:]])|\\s+','',colnames(adj.var))
snmDataObjOnly <- snm(raw.dat = vdge_data$E,
bio.var = bio.var,
adj.var = adj.var,
rm.adj=TRUE,
verbose = TRUE,
diagnose = TRUE)
snmData <- t(snmDataObjOnly$norm.dat)
res <- list(qcData=qcData,
vdge_dataE=vdge_dataE,
snmData=snmData)
return(res)
}
findSharedTaxa <- function(psTCGA, psWeizmann, taxLevel){
if(taxLevel == "species"){
tcgaFeat <- colnames(psFungiHiSeqFungi_Paired2Wz_species) # rownames(otu_table(psTCGA))
wzFeat <- data.frame(tax_table(psWeizmann))[,taxLevel]
} else{
tcgaFeat <- data.frame(tax_table(psTCGA))[,taxLevel] # rownames(otu_table(psTCGA))
wzFeat <- data.frame(tax_table(psWeizmann))[,taxLevel]
}
tcgaFeatFilt <- tcgaFeat[!grepl("other",tcgaFeat)]
wzFeatFilt <- wzFeat[!grepl("other",wzFeat)]
sharedFeatures <- intersect(tcgaFeatFilt, wzFeatFilt)
print(sharedFeatures)
return(sharedFeatures)
}
findSharedTaxaWISRep200 <- function(rep200TaxTableX, psWeizmann, taxLevel){
wzFeat <- data.frame(tax_table(psWeizmann))[,taxLevel]
wzFeatFilt <- wzFeat[!grepl("other",wzFeat)]
rep200TaxTableXFilt <- rep200TaxTableX[!grepl("other",rep200TaxTableX)]
sharedFeatures <- intersect(rep200TaxTableXFilt, wzFeatFilt)
print(sharedFeatures)
return(sharedFeatures)
}
findSharedTaxaBacteria <- function(psTCGA, psWeizmann, taxLevel){
tcgaFeat <- data.frame(tax_table(psTCGA))
wzFeat <- data.frame(tax_table(psWeizmann))
tcgaFeat$formatted <- gsub('([[:punct:]])|\\s+','',tcgaFeat[,taxLevel])
wzFeat$formatted <- gsub('([[:punct:]])|\\s+','',wzFeat[,taxLevel])
tcgaFeatFilt1 <- tcgaFeat[!tcgaFeat[,taxLevel] == "other",]
wzFeatFilt1 <- wzFeat[!wzFeat[,taxLevel] == "other",]
sharedFeatures <- intersect(tcgaFeatFilt1$formatted, wzFeatFilt1$formatted)
tcgaFeatFilt2 <- tcgaFeatFilt1[tcgaFeatFilt1$formatted %in% sharedFeatures,]
wzFeatFilt2 <- wzFeatFilt1[wzFeatFilt1$formatted %in% sharedFeatures,]
print(unique(tcgaFeatFilt2[,taxLevel]))
print(sprintf("Number of uniquely shared features: %d",length(unique(tcgaFeatFilt2[,taxLevel]))))
res <- list(intersectedOGUs = rownames(tcgaFeatFilt2),
intersectedTaxa = tcgaFeatFilt2[,taxLevel])
return(res)
}
findPrevGenus <- function(metaData=metaQiitaCombined_Nonzero_AllSeqPlatforms_8cancer_GenusShared,
countData=rep200FungiAllSeqPlatformsGenusShared_8cancer_Nonzero,
sampleType=c("Primary Tumor","Solid Tissue Normal"),
cancerType=c("Breast Cancer"), seqCenter=c("Harvard Medical School"),
taxaFlag=FALSE, weizmannIntersectFlag=FALSE, weizmannTissue=c("breast"),
weizmannTissueType=c("tumor","nat"),
weizmannMeta=weizmannMetaGenusShared_Nonzero, weizmannData=weizmannGenusShared_Nonzero){
metaData_seqCenter_Dz <- metaData %>% filter(data_submitting_center_label %in% seqCenter) %>%
filter(sample_type %in% sampleType) %>% filter(disease_type %in% cancerType) %>% droplevels()
countData_subset <- countData[rownames(metaData_seqCenter_Dz),]
taxaBoolean <- (colSums(countData_subset) != 0)
taxaWithCounts <- names(colSums(countData_subset))[taxaBoolean]
taxaWithCountsNamed <- taxaWithCounts # rep200FungiTaxIntersect[taxaWithCounts, "Genus"]
print(sprintf("TCGA taxa with counts: %d", sum(taxaBoolean)))
# print(taxaWithCountsNamed)
taxaWithCountsNamedFormatted <- taxaWithCountsNamed # gsub('^g__|([[:punct:]])','',taxaWithCountsNamed)
if(taxaFlag){print(sort(taxaWithCountsNamedFormatted))}
if(weizmannIntersectFlag){
weizmannMeta_Dz <- weizmannMeta %>% filter(tissue %in% weizmannTissue) %>% filter(type.detail %in% weizmannTissueType) %>% droplevels()
weizmannData_Dz <- weizmannData[rownames(weizmannMeta_Dz),]
weizmannTaxaWithCounts <- names(colSums(weizmannData_Dz))[colSums(weizmannData_Dz) != 0]
# print(weizmannTaxaWithCounts)
weizmannTaxaWithCountsFormatted <- weizmannTaxaWithCounts # gsub('([[:punct:]])', ' ', gsub('^g__','',weizmannTaxaWithCounts))
# weizmannTaxaWithCountsFormatted <- gsub('^s__', '', gsub('([[:punct:]])',' ',weizmannTaxaWithCounts))
# print(weizmannTaxaWithCountsFormatted)
taxaIntersection <- intersect(taxaWithCountsNamedFormatted, weizmannTaxaWithCountsFormatted)
cat(sprintf("\nNumber of intersected taxa: %d/%d (Weizmann) | %d/%d (TCGA)\n",
length(taxaIntersection), length(weizmannTaxaWithCountsFormatted),
length(taxaIntersection), length(taxaWithCountsNamedFormatted)))
print(taxaIntersection)
}
}
findPrevSpecies <- function(metaData=metaQiitaCombined_Nonzero_AllSeqPlatforms_8cancer_SpeciesShared,
countData=rep200FungiAllSeqPlatformsSpeciesShared_8cancer_Nonzero,
sampleType=c("Primary Tumor","Solid Tissue Normal"),
cancerType=c("Breast Cancer"), seqCenter=c("Harvard Medical School"),
taxaFlag=FALSE, weizmannIntersectFlag=FALSE, weizmannTissue=c("breast"),
weizmannTissueType=c("tumor","nat"),
weizmannMeta=weizmannMetaSpeciesShared_Nonzero, weizmannData=weizmannSpeciesShared_Nonzero){
metaData_seqCenter_Dz <- metaData %>% filter(data_submitting_center_label %in% seqCenter) %>%
filter(sample_type %in% sampleType) %>% filter(disease_type %in% cancerType) %>% droplevels()
countData_subset <- countData[rownames(metaData_seqCenter_Dz),]
taxaBoolean <- (colSums(countData_subset) != 0)
taxaWithCounts <- names(colSums(countData_subset))[taxaBoolean]
taxaWithCountsNamed <- taxaWithCounts # rep200FungiTaxIntersect[taxaWithCounts, "Species"]
print(sprintf("TCGA taxa with counts: %d", sum(taxaBoolean)))
taxaWithCountsNamedFormatted <- taxaWithCountsNamed # gsub('^s__|([[:punct:]])','',taxaWithCountsNamed)
if(taxaFlag){print(sort(taxaWithCountsNamedFormatted))}
if(weizmannIntersectFlag){
weizmannMeta_Dz <- weizmannMeta %>% filter(tissue %in% weizmannTissue) %>% filter(type.detail %in% weizmannTissueType) %>% droplevels()
weizmannData_Dz <- weizmannData[rownames(weizmannMeta_Dz),]
weizmannTaxaWithCounts <- names(colSums(weizmannData_Dz))[colSums(weizmannData_Dz) != 0]
weizmannTaxaWithCountsFormatted <- weizmannTaxaWithCounts # gsub('([[:punct:]])', ' ', gsub('^s__','',weizmannTaxaWithCounts))
# weizmannTaxaWithCountsFormatted <- gsub('^s__', '', gsub('([[:punct:]])',' ',weizmannTaxaWithCounts))
# print(weizmannTaxaWithCountsFormatted)
taxaIntersection <- intersect(taxaWithCountsNamedFormatted, weizmannTaxaWithCountsFormatted)
cat(sprintf("\nNumber of intersected taxa: %d/%d (Weizmann) | %d/%d (TCGA)\n",
length(taxaIntersection), length(weizmannTaxaWithCountsFormatted),
length(taxaIntersection), length(taxaWithCountsNamedFormatted)))
print(taxaIntersection)
}
}
compareTvsNATbrayCurtis <- function(featureData, metaData = metaQiitaCombined_Nonzero_8cancer,
# taxTable = psRep200FungiTax,
scalar=10^6, snmFlag = TRUE, axes=c(1,2),
grepFlag = TRUE, greplString = "Lung|Melanoma|Breast|Colorectal",
plotlyAspectmode='cube', apiUploadFlag = FALSE, brayHullFlag = TRUE){
require(tibble)
require(stringr)
require(plotly)
# metaData processing
metaDataFilt <- droplevels(metaData[metaData$sample_type %in% c("Primary Tumor","Solid Tissue Normal"),])
metaDataFilt$sample_type <- ifelse(metaDataFilt$sample_type == "Primary Tumor", yes = "PT", no = "NAT")
# metaDataFilt$sample_type[metaDataFilt$sample_type == "Primary Tumor"] <- "PT"
# metaDataFilt$sample_type[metaDataFilt$sample_type == "Solid Tissue Normal"] <- "NAT"
metaDataFilt$disease_and_sample_type <- paste(metaDataFilt$disease_type,metaDataFilt$sample_type)
print(table(metaDataFilt$disease_and_sample_type))
# Rescaling VSNM data
if(snmFlag){
countData <- floor(((2^featureData)/rowSums(2^featureData))*scalar)
} else{
countData <- featureData
}
# Calculating relative abundances on rescaled data
countDataRA <- countData/rowSums(countData)
countDataRAfilt <- countDataRA[rownames(metaDataFilt),]
# Calculate mean across groups
groupedCountDataRA <- aggregate(countDataRAfilt,
by = list(metaDataFilt$disease_and_sample_type),
FUN = mean)
# rownames(groupedCountDataRA) <- levels(metaDataFilt$disease_and_sample_type)
groupedCountDataRAformatted <- groupedCountDataRA %>% column_to_rownames("Group.1")
ptNatVec <- ifelse(grepl("NAT",rownames(groupedCountDataRAformatted)), yes="NAT",no="PT")
dzSplitVec <- sapply(strsplit(rownames(groupedCountDataRAformatted)," NAT| PT"), `[`, 1)
groupedMetaDataFilt <- data.frame(disease_type = dzSplitVec,
sample_type = ptNatVec,
disease_and_sample_type = levels(factor(metaDataFilt$disease_and_sample_type)))
rownames(groupedMetaDataFilt) <- levels(factor(metaDataFilt$disease_and_sample_type))
# print(groupedMetaDataFilt)
if(grepFlag){
groupedMetaDataFiltGrep <- droplevels(groupedMetaDataFilt[grepl(greplString,groupedMetaDataFilt$disease_type),])
groupedCountDataRAformattedGrep <- groupedCountDataRAformatted[rownames(groupedMetaDataFiltGrep),]
} else{
groupedMetaDataFiltGrep <- groupedMetaDataFilt
groupedCountDataRAformattedGrep <- groupedCountDataRAformatted
}
combinedPSobj <- phyloseq(otu_table(groupedCountDataRAformattedGrep, taxa_are_rows = FALSE),
sample_data(groupedMetaDataFiltGrep))
bray_dist = phyloseq::distance(combinedPSobj, method="bray") # need to rarefy
bray_ordination = ordinate(combinedPSobj, method="PCoA", distance=bray_dist)
brayPlot2D <- plot_ordination(combinedPSobj, bray_ordination, color="disease_type", shape = "sample_type", axes = axes) +
theme(aspect.ratio=1) + theme_pubr() + geom_point(size = 3) + scale_color_nejm() + coord_fixed() + theme(legend.position = "right") +
labs(shape="Sample type", color="Disease type")
if(brayHullFlag){
brayPlot2D <- brayPlot2D + geom_mark_hull(aes(group = disease_type, color = disease_type), expand = 0)
}
brayData <- data.frame(bray_ordination$vectors, groupedMetaDataFiltGrep)
brayPlot3D <- plot_ly(data = brayData, x= ~Axis.1, y= ~Axis.2, z= ~Axis.3) %>%
add_markers(color= ~disease_type, symbol = ~sample_type, symbols = c(200, 18)) %>%
layout(scene = list(xaxis = list(title = 'PCoA 1'),
yaxis = list(title = 'PCoA 2'),
zaxis = list(title = 'PCoA 3'),
aspectmode=plotlyAspectmode))
if(apiUploadFlag){
api_create(brayPlot3D)
}
print(brayPlot3D)
res <- list(brayPlot2D=brayPlot2D,
brayPlot3D=brayPlot3D,
bray_dist=bray_dist,
bray_ordination=bray_ordination,
brayData=brayData,
countDataRAfilt=countDataRAfilt,
metaDataFilt=metaDataFilt,
groupedCountDataRAformattedGrep=groupedCountDataRAformattedGrep,
groupedMetaDataFiltGrep=groupedMetaDataFiltGrep,
combinedPSobj=combinedPSobj)
return(res)
}
calcCorrAlpha <- function(psAll, seqCenter="Harvard Medical School", sampleType="Primary Tumor",
fungi_gOTUs=fungiOGUs, bacteria_gOTUs=bacteriaOGUs,
dataType="WGS", rarefyNumber=NA, statCorMethod = "Pearson", libraryCorrFlag = FALSE,
filePath = "Figures/Supplementary_Figures/"){
if(statCorMethod == "Pearson"){
corCoefName <- "R"
} else if(statCorMethod == "Spearman"){
corCoefName <- "rho"
}
# print(seqCenter)
if(seqCenter=="All Seq Centers"){
filtBoolean <- ((sample_data(psAll)[["sample_type"]] == sampleType) &
(sample_data(psAll)[["experimental_strategy"]] == dataType))
figWidthPerCT <- 18
figRowsPerCT <- 4
} else{
filtBoolean <- ((sample_data(psAll)[["data_submitting_center_label"]] == seqCenter) &
(sample_data(psAll)[["sample_type"]] == sampleType) &
(sample_data(psAll)[["experimental_strategy"]] == dataType))
figWidthPerCT <- 12
figRowsPerCT <- 3
}
psAll_Filt <- prune_samples(filtBoolean, psAll)
if(!is.na(rarefyNumber)){
rarefyLevelInt <- rarefyNumber
} else{
print("Sample read distribution:")
print(summary(sample_sums(psAll_Filt)))
rarefyLevel <- readline(prompt="Enter rarefaction amount for sample data: ")
rarefyLevelInt <- as.integer(rarefyLevel)
}
print("Ok. Rarefying sample data...")
psAll_Filt_rarefied <- rarefy_even_depth(psAll_Filt, sample.size = rarefyLevelInt,
rngseed = 42, replace = FALSE, trimOTUs = TRUE, verbose = FALSE)
rarefactionTitle <- sprintf("Rarefied %d reads",rarefyLevelInt)
plotTitlePerCT <- paste(rarefactionTitle, seqCenter, sampleType, dataType, statCorMethod, sep = " | ")
plotTitleAggregated <- paste("Aggregated data",rarefactionTitle, seqCenter, sampleType, dataType, statCorMethod, sep = " | ")
taxFungiBoolean <- (rownames(tax_table(psAll_Filt_rarefied)) %in% fungi_gOTUs)
taxBacteriaBoolean <- (rownames(tax_table(psAll_Filt_rarefied)) %in% bacteria_gOTUs)
psFungi_Filt_rarefied <- prune_taxa(taxFungiBoolean, psAll_Filt_rarefied)
psBacteria_Filt_rarefied <- prune_taxa(taxBacteriaBoolean, psAll_Filt_rarefied)
meta2Keep <- sample_data(psFungi_Filt_rarefied)[,c("experimental_strategy","disease_type","sample_type",
"cgc_platform","investigation","data_submitting_center_label",
"pathologic_stage_label","library_size","library_size_log10")]
print("Now calculating richness...")
fungi_rarefied_alpha <- data.frame(estimate_richness(psFungi_Filt_rarefied, measures = c("Observed","Chao1", "ACE", "Shannon", "Simpson", "InvSimpson")))
colnames(fungi_rarefied_alpha) <- paste0("fungi_",colnames(fungi_rarefied_alpha))
fungi_rarefied_alpha_formatted <- fungi_rarefied_alpha %>% rownames_to_column("sampleid")
bacteria_rarefied_alpha <- data.frame(estimate_richness(psBacteria_Filt_rarefied, measures = c("Observed","Chao1", "ACE", "Shannon", "Simpson", "InvSimpson")))
colnames(bacteria_rarefied_alpha) <- paste0("bacteria_",colnames(bacteria_rarefied_alpha))
bacteria_rarefied_alpha_formatted <- bacteria_rarefied_alpha %>% rownames_to_column("sampleid")
fungi_rarefied_alpha_formatted %>%
left_join(bacteria_rarefied_alpha_formatted, by = "sampleid") %>%
column_to_rownames("sampleid") -> fungi_and_bacteria_alpha
rownames(meta2Keep) <- paste0("X",rownames(meta2Keep))
fungi_and_bacteria_alpha_with_meta <- cbind(fungi_and_bacteria_alpha, meta2Keep[rownames(fungi_and_bacteria_alpha),])
maxFungiObserved <- max(fungi_and_bacteria_alpha_with_meta$fungi_Observed)
fungi_and_bacteria_alpha_with_meta %>%
ggplot(aes(bacteria_Observed,fungi_Observed)) +
geom_point() +
xlab("Bacterial richness") + ylab("Fungal richness") + theme_pubr() +
facet_wrap(facets = vars(investigation), nrow = figRowsPerCT) +
scale_y_continuous(limits = c(0, maxFungiObserved+10)) +
geom_smooth(method='lm') +
ggtitle(plotTitlePerCT) +
theme(plot.title = element_text(hjust = 0.5)) +
# scale_x_log10() +
# rotate_x_text(30) +
scale_fill_igv() +
stat_cor(method = tolower(statCorMethod), cor.coef.name = corCoefName) -> richnessCorrPlotPerCT
print(richnessCorrPlotPerCT)
baseName <- paste(paste0("rarefied",rarefyLevelInt),
gsub('([[:punct:]])|\\s+','',seqCenter),
gsub('([[:punct:]])|\\s+','',sampleType),
gsub('([[:punct:]])|\\s+','',dataType),
statCorMethod, sep = "_")
ggsave(plot=richnessCorrPlotPerCT, filename = paste0("alpha_div_per_CT_",baseName,".png"), path = filePath,
dpi = "retina", units = "in", width = figWidthPerCT)
fungi_and_bacteria_alpha_with_meta %>%
ggplot(aes(bacteria_Observed,fungi_Observed)) +
geom_point() +
xlab("Bacterial richness") + ylab("Fungal richness") + theme_pubr() +
scale_y_continuous(limits = c(0, NA)) +
geom_smooth(method='lm') +
ggtitle(plotTitleAggregated) +
theme(plot.title = element_text(hjust = 0.5)) +
# scale_x_log10() +
# rotate_x_text(30) +
scale_fill_igv() +
stat_cor(method = tolower(statCorMethod), cor.coef.name = corCoefName) -> richnessCorrPlotAggregated
print(richnessCorrPlotAggregated)
ggsave(plot=richnessCorrPlotAggregated, filename = paste0("alpha_div_All_CT_",baseName,".png"), path = filePath,
dpi = "retina", units = "in", width = 12)
res <- list(fungi_and_bacteria_alpha_with_meta=fungi_and_bacteria_alpha_with_meta,
richnessCorrPlotPerCT=richnessCorrPlotPerCT,
richnessCorrPlotAggregated=richnessCorrPlotAggregated,
meta2Keep=meta2Keep)
if(libraryCorrFlag){
plotTitlePerCT_Library_Fungi <- paste("Fungi vs. Library Size", rarefactionTitle, seqCenter, sampleType, dataType, statCorMethod, sep = " | ")
plotTitleAggregated_Library_Fungi <- paste("Fungi vs. Library Size", "Aggregated data",rarefactionTitle, seqCenter, sampleType, dataType, statCorMethod, sep = " | ")
plotTitlePerCT_Library_Bacteria <- paste("Bacteria vs. Library Size", rarefactionTitle, seqCenter, sampleType, dataType, statCorMethod, sep = " | ")
plotTitleAggregated_Library_Bacteria <- paste("Bacteria vs. Library Size", "Aggregated data",rarefactionTitle, seqCenter, sampleType, dataType, statCorMethod, sep = " | ")
## Fungi vs. library size
fungi_and_bacteria_alpha_with_meta %>%
ggplot(aes(library_size_log10,fungi_Observed)) +
geom_point(aes(colour = library_size_log10)) +
scale_colour_gradient(low = "yellow", high = "red", na.value = NA) +
xlab("Original microbial library Size (log10)") + ylab("Fungal richness") + theme_pubr() +
# facet_wrap(facets = vars(investigation), scales = "free", nrow = 3) +
facet_wrap(facets = vars(investigation), nrow = 3) +
scale_y_continuous(limits = c(0, maxFungiObserved+10)) +
geom_smooth(method='lm') +
ggtitle(plotTitlePerCT_Library_Fungi) +
theme(plot.title = element_text(hjust = 0.5)) +
# scale_x_log10() +
# rotate_x_text(30) +
scale_fill_igv() +
stat_cor(method = tolower(statCorMethod), cor.coef.name = corCoefName) -> libraryFungiCorrPlotPerCT
print(libraryFungiCorrPlotPerCT)
## Bacteria vs. library size
fungi_and_bacteria_alpha_with_meta %>%
ggplot(aes(library_size_log10,bacteria_Observed)) +
geom_point(aes(colour = library_size_log10)) +
scale_colour_gradient(low = "yellow", high = "red", na.value = NA) +
xlab("Original microbial library Size (log10)") + ylab("Bacterial richness") + theme_pubr() +
# facet_wrap(facets = vars(investigation), scales = "free", nrow = 3) +
facet_wrap(facets = vars(investigation), nrow = 3) +
scale_y_continuous(limits = c(0, maxFungiObserved+10)) +
geom_smooth(method='lm') +
ggtitle(plotTitlePerCT_Library_Bacteria) +
theme(plot.title = element_text(hjust = 0.5)) +
# scale_x_log10() +
# rotate_x_text(30) +
scale_fill_igv() +
stat_cor(method = tolower(statCorMethod), cor.coef.name = corCoefName) -> libraryBacteriaCorrPlotPerCT
print(libraryBacteriaCorrPlotPerCT)
## Aggregated fungi vs. library size
fungi_and_bacteria_alpha_with_meta %>%
ggplot(aes(library_size_log10,fungi_Observed)) +
geom_point(aes(colour = library_size_log10)) +
scale_colour_gradient(low = "yellow", high = "red", na.value = NA) +
xlab("Original microbial library Size (log10)") + ylab("Fungal richness") + theme_pubr() +
scale_y_continuous(limits = c(0, NA)) +
# facet_wrap(facets = vars(investigation), scales = "free", nrow = 3) +
# facet_wrap(facets = vars(investigation), nrow = 3) +
geom_smooth(method='lm') +
ggtitle(paste(plotTitleAggregated_Library_Fungi)) +
theme(plot.title = element_text(hjust = 0.5)) +
# scale_x_log10() +
# rotate_x_text(30) +
scale_fill_igv() +
stat_cor(method = tolower(statCorMethod), cor.coef.name = corCoefName) -> libraryFungiCorrPlotAggregated
print(libraryFungiCorrPlotAggregated)
## Aggregated bacteria vs. library size
fungi_and_bacteria_alpha_with_meta %>%
ggplot(aes(library_size_log10,bacteria_Observed)) +
geom_point(aes(colour = library_size_log10)) +
scale_colour_gradient(low = "yellow", high = "red", na.value = NA) +
xlab("Original microbial library Size (log10)") + ylab("Bacterial richness") + theme_pubr() +
scale_y_continuous(limits = c(0, NA)) +
# facet_wrap(facets = vars(investigation), scales = "free", nrow = 3) +
# facet_wrap(facets = vars(investigation), nrow = 3) +
geom_smooth(method='lm') +
ggtitle(plotTitleAggregated_Library_Bacteria) +
theme(plot.title = element_text(hjust = 0.5)) +
# scale_x_log10() +
# rotate_x_text(30) +
scale_fill_igv() +
stat_cor(method = tolower(statCorMethod), cor.coef.name = corCoefName) -> libraryBacteriaCorrPlotAggregated
print(libraryBacteriaCorrPlotAggregated)
res <- list(libraryFungiCorrPlotPerCT=libraryFungiCorrPlotPerCT,
libraryBacteriaCorrPlotPerCT=libraryBacteriaCorrPlotPerCT,
libraryFungiCorrPlotAggregated=libraryFungiCorrPlotAggregated,
libraryBacteriaCorrPlotAggregated=libraryBacteriaCorrPlotAggregated,
fungi_and_bacteria_alpha_with_meta=fungi_and_bacteria_alpha_with_meta,
richnessCorrPlotPerCT=richnessCorrPlotPerCT,
richnessCorrPlotAggregated=richnessCorrPlotAggregated,
meta2Keep=meta2Keep)
}
return(res)
}
mlCristiano <- function(metaData=metaCristianoTxNaiveFilt,
countData,
col2Predict = "cancer_status",
dzOfInterest = "Cancer vs. Healthy",
stageNum = "I",
modelType = "gbm",
dataString = "fungi_only",
# trainTestFlag = TRUE,
cutPoint = 0.5,
numKFold = 10,
numResampleIter = 1,
varImpFlag = FALSE){
require(caret) # for model building
require(gbm) # for machine learning
require(PRROC) # for precision-recall curves
require(MLmetrics) # for multiclass ML
require(e1071)
require(ggpubr)
if(col2Predict == "phenotype"){
dzOfInterestFixed <- gsub('([[:punct:]])|\\s+','',dzOfInterest)
metaDataFixed <- droplevels(metaData[metaData$cancer_status == "Cancer",])
metaDataFixed$condition <- factor(ifelse(metaDataFixed[,col2Predict] == dzOfInterest,
yes = dzOfInterestFixed, no = "Other"),
levels = c(dzOfInterestFixed,"Other"))
positiveClass <- dzOfInterestFixed
negativeClass <- "Other"
} else if(col2Predict == "cancer_status"){
metaDataFixed <- metaData
metaDataFixed$condition <- metaDataFixed$cancer_status
positiveClass <- "Cancer"
negativeClass <- "Healthy"
} else if(col2Predict == "one_cancer_vs_healthy"){
dzOfInterestFixed <- gsub('([[:punct:]])|\\s+','',dzOfInterest)
metaDataFixed <- droplevels(metaData[metaData$phenotype %in% c("Healthy",dzOfInterest),])
metaDataFixed$condition <- factor(ifelse(metaDataFixed[,"phenotype"] == dzOfInterest,
yes = dzOfInterestFixed, no = "Healthy"),
levels = c(dzOfInterestFixed,"Healthy"))
positiveClass <- dzOfInterestFixed
negativeClass <- "Healthy"
} else if(col2Predict == "stage"){
metaDataFixed <- metaData %>% filter(stage %in% c(stageNum, "Healthy")) %>% droplevels()
metaDataFixed$condition <- factor(metaDataFixed$stage, levels = c(stageNum,"Healthy"))
# print(summary(metaDataFixed$condition))
positiveClass <- stageNum
negativeClass <- "Healthy"
}
mlDataY <- metaDataFixed
mlDataX <- countData[rownames(mlDataY),]
seedNum <- 42
set.seed(seedNum)
trainX <- mlDataX
trainY <- mlDataY[,"condition"]
refactoredTrainY <- factor(gsub('([[:punct:]])|\\s+','',trainY))
# print(refactoredTrainY)
set.seed(seedNum)
ctrl <- trainControl(method = "repeatedcv",
number = numKFold,
repeats = numResampleIter,
sampling = "up",
summaryFunction = twoClassSummary,
classProbs = TRUE,
verboseIter = TRUE,
savePredictions = TRUE,
allowParallel=TRUE)
set.seed(seedNum)
mlModel <- train(x = trainX,
y = refactoredTrainY,
method = modelType,
preProcess = c("zv"),
trControl = ctrl,
tuneGrid=data.frame(n.trees=150, interaction.depth=3,
shrinkage=0.1,
n.minobsinnode=1),
verbose = TRUE,
metric = "ROC")
# print(mlModel)
resPred <- mlModel$pred
## Calculate performance on concatenated fold predictions
predProbs <- resPred
multiClass <- resPred
# print(head(multiClass))
multiClass$pred <- relevel(multiClass$pred, positiveClass)
multiClass$obs <- relevel(multiClass$obs, positiveClass)
fg <- predProbs[resPred$obs == positiveClass,positiveClass]
bg <- predProbs[resPred$obs == negativeClass,positiveClass]
confusionMatrix <- confusionMatrix(multiClass$pred, multiClass$obs)
prroc_roc <- roc.curve(scores.class0 = fg, scores.class1 = bg, curve = T)
prroc_pr <- pr.curve(scores.class0 = fg, scores.class1 = bg, curve = T, rand.compute=T)
## Estimate AUROC CIs using cvAUC on concatenated fold predictions
require(cvAUC)
out95 <- ci.cvAUC(predictions = resPred[,positiveClass], labels = resPred$obs, label.ordering = c(negativeClass,positiveClass), folds = resPred$Resample, confidence = 0.95)
out99 <- ci.cvAUC(predictions = resPred[,positiveClass], labels = resPred$obs, label.ordering = c(negativeClass,positiveClass), folds = resPred$Resample, confidence = 0.99)
# resCvAUC <- data.frame(estimate = c(out95$cvAUC, out99$cvAUC), se = c(out95$se, out99$se), lowerCI = c(out95$ci[1], out99$ci[1]), upperCI = c(out95$ci[1], out99$ci[2]), levelCI = c(0.95,0.99))
## Split folds and calculate perf on each fold
resPredSplit <- split(resPred, resPred$Resample)
repX_perf <- list()
for(zz in seq_along(resPredSplit)){
resPredSingleRep <- resPredSplit[[zz]]
predProbsSingleRep <- resPredSingleRep
multiClassSingleRep <- resPredSingleRep
multiClassSingleRep$pred <- relevel(multiClassSingleRep$pred, positiveClass)
multiClassSingleRep$obs <- relevel(multiClassSingleRep$obs, positiveClass)
fgSingleRep <- predProbsSingleRep[resPredSingleRep$obs == positiveClass,positiveClass]
bgSingleRep <- predProbsSingleRep[resPredSingleRep$obs == negativeClass,positiveClass]
rep_roc <- roc.curve(scores.class0 = fgSingleRep, scores.class1 = bgSingleRep, curve = T)
rep_pr <- pr.curve(scores.class0 = fgSingleRep, scores.class1 = bgSingleRep, curve = T, rand.compute=T)
repX_perf[[zz]] <- data.frame(auroc=rep_roc$auc,
aupr=rep_pr$auc.integral,
rep=paste0("Fold",zz),
dataString=dataString,
minorityClassSize=sum(multiClass$obs==positiveClass),
majorityClassSize=sum(multiClass$obs==negativeClass),
diseaseType = dzOfInterest,
col2Predict = col2Predict)
}
# SUMMARIZE MODEL PERFORMANCES
rep_perf <- do.call(rbind, repX_perf)
perf <- data.frame(auroc = prroc_roc$auc,
aupr = prroc_pr$auc.integral,
aucEstimate = out95$cvAUC, # either out95 or out99 work (same result)
aucSE95 = out95$se,
lowerCI95 = out95$ci[1],
upperCI95 = out95$ci[2],
aucSE99 = out99$se,
lowerCI99 = out99$ci[1],
upperCI99 = out99$ci[2],
diseaseType = dzOfInterest,
dataString=dataString,
col2Predict = col2Predict)
# predProbs <- mlModel$pred
# multiClass <- mlModel$pred
# multiClass$pred <- relevel(multiClass$pred, positiveClass)
# multiClass$obs <- relevel(multiClass$obs, positiveClass)
# fg <- predProbs[mlModel$pred$obs == positiveClass,positiveClass]
# bg <- predProbs[mlModel$pred$obs == negativeClass,positiveClass]
#
# prroc_roc <- roc.curve(scores.class0 = fg, scores.class1 = bg, curve = T)
# prroc_pr <- pr.curve(scores.class0 = fg, scores.class1 = bg, curve = T, rand.compute=T)
#
print(confusionMatrix(multiClass$pred, multiClass$obs))
print(multiClassSummary(multiClass, lev = levels(multiClass$obs)))
varImpBestModelDF <- as.data.frame(varImp(mlModel$finalModel, scale = FALSE))
varImpBestModelDF2 <- rownames_to_column(varImpBestModelDF, "Taxa")
varImpBestModelDF2Ordered <- varImpBestModelDF2[order(-varImpBestModelDF2$Overall),]
colnames(varImpBestModelDF2Ordered)[2] <- "varImp"
varImpBestModelDF2OrderedNonzero <- varImpBestModelDF2Ordered[varImpBestModelDF2Ordered$varImp != 0,]
if(varImpFlag){
print(head(varImpBestModelDF2OrderedNonzero))
}
plot(prroc_pr)
plot(prroc_roc)
res <- list(mlModel=mlModel,
perf=perf,
rep_perf=rep_perf,
multiClass=multiClass,
varImp=varImpBestModelDF2OrderedNonzero)
return(res)
}
ml1VsAllCristiano10kRep1_Iterate <- function(metaData=metaCristianoTxNaiveFilt,
listOfData = list(cristiano_rep200Data_Filt,
cristianoRep200BacteriaSpecies,
cristianoRep200FungiSpecies,
cristiano_rep200Data_Filt_Fungi_Decontam,
cristianoRep200FungiSpeciesShared,
cristianoRep200BacteriaSpeciesShared,
cbind(cristianoRep200FungiSpeciesShared,
cristianoRep200BacteriaSpeciesShared),
topXHopkinsData),
dataStringList = c("full_rep200",
"bacteria_only",
"fungi_only",
"fungi_decontam",
"fungi_intersected_with_Weizmann",
"bacteria_intersected_with_Weizmann",
"fungi_and_bacteria_intersected_with_Weizmann",
"topX_fungi"),
col2Predict = "one_cancer_vs_healthy",
dzOfInterestList = list("Bile Duct Cancer",
"Breast Cancer",
"Colorectal Cancer",
"Gastric cancer",
"Lung Cancer",
"Ovarian Cancer",
"Pancreatic Cancer"),
stageNum = "stageI",
scrambleFlag=FALSE,
shuffleFlag=FALSE,
modelType = "gbm",
cutPoint = 0.5,
numKFold = 10,
numResampleIter = 1,
varImpFlag = FALSE){
require(caret) # for model building
require(gbm) # for machine learning
require(PRROC) # for precision-recall curves
require(MLmetrics) # for multiclass ML
require(e1071)
require(ggpubr)
rep_perfTmp <- list()
perfTmp <- list()
rep_perfTmp2 <- list()
perfTmp2 <- list()
for(jj in seq_along(listOfData)){
snmData <- listOfData[[jj]]
dataString <- dataStringList[[jj]]
print(sprintf("Working on dataset: %s",dataString))
for(ii in seq_along(dzOfInterestList)){
dzOfInterest <- dzOfInterestList[[ii]]
print(sprintf("Working on cancer type: %s",dzOfInterest))
if(col2Predict == "phenotype"){
dzOfInterestFixed <- gsub('([[:punct:]])|\\s+','',dzOfInterest)
metaDataFixed <- droplevels(metaData[metaData$cancer_status == "Cancer",])
metaDataFixed$condition <- factor(ifelse(metaDataFixed[,col2Predict] == dzOfInterest,
yes = dzOfInterestFixed, no = "Other"),
levels = c(dzOfInterestFixed,"Other"))
positiveClass <- dzOfInterestFixed
negativeClass <- "Other"
} else if(col2Predict == "cancer_status"){
metaDataFixed <- metaData
metaDataFixed$condition <- metaDataFixed$cancer_status
positiveClass <- "Cancer"
negativeClass <- "Healthy"
} else if(col2Predict == "one_cancer_vs_healthy"){
dzOfInterestFixed <- gsub('([[:punct:]])|\\s+','',dzOfInterest)
metaDataFixed <- droplevels(metaData[metaData$phenotype %in% c("Healthy",dzOfInterest),])
metaDataFixed$condition <- factor(ifelse(metaDataFixed[,"phenotype"] == dzOfInterest,
yes = dzOfInterestFixed, no = "Healthy"),
levels = c(dzOfInterestFixed,"Healthy"))
positiveClass <- dzOfInterestFixed
negativeClass <- "Healthy"
} else if(col2Predict == "stage"){
metaDataFixed <- metaData %>% filter(stage %in% c(stageNum, "Healthy")) %>% droplevels()
metaDataFixed$condition <- factor(metaDataFixed$stage, levels = c(stageNum,"Healthy"))
# print(summary(metaDataFixed$condition))
positiveClass <- stageNum
negativeClass <- "Healthy"
}
mlDataY <- metaDataFixed
mlDataX <- snmData[rownames(mlDataY),]
if(scrambleFlag){
set.seed(42)
mlDataY[,"condition"] <- sample(mlDataY[,"condition"])
} else if(shuffleFlag){
set.seed(42)
mlDataX_shuffled <- mlDataX[sample(nrow(mlDataX)),]
rownames(mlDataX_shuffled) <- rownames(mlDataX)
mlDataX <- mlDataX_shuffled
}
set.seed(42)
trainX <- mlDataX
trainY <- mlDataY[,"condition"]
refactoredTrainY <- factor(gsub('([[:punct:]])|\\s+','',trainY))
set.seed(42)
ctrl <- trainControl(method = "repeatedcv",
number = numKFold,
repeats = numResampleIter,
sampling = "up",
summaryFunction = twoClassSummary,
classProbs = TRUE,
verboseIter = TRUE,
savePredictions = TRUE,
allowParallel=TRUE)
set.seed(42)
mlModel <- train(x = trainX,
y = refactoredTrainY,
method = modelType,
preProcess = c("zv"),
trControl = ctrl,
tuneGrid=data.frame(n.trees=150, interaction.depth=3,
shrinkage=0.1,
n.minobsinnode=1),
verbose = TRUE,
metric = "ROC")
# print(mlModel)
resPred <- mlModel$pred
## Calculate performance on concatenated fold predictions
predProbs <- resPred
multiClass <- resPred
# print(head(multiClass))
multiClass$pred <- relevel(multiClass$pred, positiveClass)
multiClass$obs <- relevel(multiClass$obs, positiveClass)
fg <- predProbs[resPred$obs == positiveClass,positiveClass]
bg <- predProbs[resPred$obs == negativeClass,positiveClass]
confusionMatrix <- confusionMatrix(multiClass$pred, multiClass$obs)
prroc_roc <- roc.curve(scores.class0 = fg, scores.class1 = bg, curve = T)
prroc_pr <- pr.curve(scores.class0 = fg, scores.class1 = bg, curve = T, rand.compute=T)
## Estimate AUROC CIs using cvAUC on concatenated fold predictions
require(cvAUC)
out95 <- ci.cvAUC(predictions = resPred[,positiveClass], labels = resPred$obs, label.ordering = c(negativeClass,positiveClass), folds = resPred$Resample, confidence = 0.95)
out99 <- ci.cvAUC(predictions = resPred[,positiveClass], labels = resPred$obs, label.ordering = c(negativeClass,positiveClass), folds = resPred$Resample, confidence = 0.99)
# resCvAUC <- data.frame(estimate = c(out95$cvAUC, out99$cvAUC), se = c(out95$se, out99$se), lowerCI = c(out95$ci[1], out99$ci[1]), upperCI = c(out95$ci[1], out99$ci[2]), levelCI = c(0.95,0.99))
## Split folds and calculate perf on each fold
resPredSplit <- split(resPred, resPred$Resample)
repX_perf <- list()
for(zz in seq_along(resPredSplit)){
resPredSingleRep <- resPredSplit[[zz]]
predProbsSingleRep <- resPredSingleRep
multiClassSingleRep <- resPredSingleRep
multiClassSingleRep$pred <- relevel(multiClassSingleRep$pred, positiveClass)
multiClassSingleRep$obs <- relevel(multiClassSingleRep$obs, positiveClass)
fgSingleRep <- predProbsSingleRep[resPredSingleRep$obs == positiveClass,positiveClass]
bgSingleRep <- predProbsSingleRep[resPredSingleRep$obs == negativeClass,positiveClass]
rep_roc <- roc.curve(scores.class0 = fgSingleRep, scores.class1 = bgSingleRep, curve = T)
rep_pr <- pr.curve(scores.class0 = fgSingleRep, scores.class1 = bgSingleRep, curve = T, rand.compute=T)
repX_perf[[zz]] <- data.frame(auroc=rep_roc$auc,
aupr=rep_pr$auc.integral,
rep=paste0("Fold",zz),
dataString=dataString,
minorityClassSize=sum(multiClass$obs==positiveClass),
majorityClassSize=sum(multiClass$obs==negativeClass),
diseaseType = dzOfInterest,
col2Predict = col2Predict)
}
# SUMMARIZE MODEL PERFORMANCES
rep_perfTmp[[ii]] <- do.call(rbind, repX_perf)
perfTmp[[ii]] <- data.frame(auroc = prroc_roc$auc,
aupr = prroc_pr$auc.integral,
aucEstimate = out95$cvAUC, # either out95 or out99 work (same result)
aucSE95 = out95$se,
lowerCI95 = out95$ci[1],
upperCI95 = out95$ci[2],
aucSE99 = out99$se,
lowerCI99 = out99$ci[1],
upperCI99 = out99$ci[2],
diseaseType = dzOfInterest,
dataString=dataString,
col2Predict = col2Predict)
print(confusionMatrix(multiClass$pred, multiClass$obs))
print(multiClassSummary(multiClass, lev = levels(multiClass$obs)))
varImpBestModelDF <- as.data.frame(varImp(mlModel$finalModel, scale = FALSE))
varImpBestModelDF2 <- rownames_to_column(varImpBestModelDF, "Taxa")
varImpBestModelDF2Ordered <- varImpBestModelDF2[order(-varImpBestModelDF2$Overall),]
colnames(varImpBestModelDF2Ordered)[2] <- "varImp"
varImpBestModelDF2OrderedNonzero <- varImpBestModelDF2Ordered[varImpBestModelDF2Ordered$varImp != 0,]
if(varImpFlag){print(head(varImpBestModelDF2OrderedNonzero))}
plot(prroc_pr)
plot(prroc_roc)
}
rep_perfTmp2[[jj]] <- do.call(rbind, rep_perfTmp)
perfTmp2[[jj]] <- do.call(rbind, perfTmp)
}
rep_perf <- do.call(rbind, rep_perfTmp2)
perf <- do.call(rbind, perfTmp2)
res <- list(mlModel=mlModel,
perf=perf,
rep_perf=rep_perf,
multiClass=multiClass,
varImp=varImpBestModelDF2OrderedNonzero)
return(res)
}
ml1VsAllCristiano10kRep1_Iterate_Stage <- function(metaData=metaCristianoTxNaiveFilt,
listOfData = list(cristiano_rep200Data_Filt,
cristianoRep200BacteriaSpecies,
cristianoRep200FungiSpecies,
cristiano_rep200Data_Filt_Fungi_Decontam,
cristianoRep200FungiSpeciesShared,
cristianoRep200BacteriaSpeciesShared,
cbind(cristianoRep200FungiSpeciesShared,
cristianoRep200BacteriaSpeciesShared),
topXHopkinsData),
dataStringList = c("full_rep200",
"bacteria_only",
"fungi_only",
"fungi_decontam",
"fungi_intersected_with_Weizmann",
"bacteria_intersected_with_Weizmann",
"fungi_and_bacteria_intersected_with_Weizmann",
"topX_fungi"),
col2Predict = "stage",
dzOfInterestList = list("Bile Duct Cancer",
"Breast Cancer",
"Colorectal Cancer",
"Gastric cancer",
"Lung Cancer",
"Ovarian Cancer",
"Pancreatic Cancer"),
stageNumList = list("I",
"II",
"III",
"IV"),
modelType = "gbm",
cutPoint = 0.5,
numKFold = 10,
numResampleIter = 1,
varImpFlag = FALSE){
require(caret) # for model building
require(gbm) # for machine learning
require(PRROC) # for precision-recall curves
require(MLmetrics) # for multiclass ML
require(e1071)
require(ggpubr)
rep_perfTmp <- list()
perfTmp <- list()
rep_perfTmp2 <- list()
perfTmp2 <- list()
for(jj in seq_along(listOfData)){
countData <- listOfData[[jj]]
dataString <- dataStringList[[jj]]
print(sprintf("Working on dataset: %s",dataString))
for(ii in seq_along(stageNumList)){
dzOfInterest <- dzOfInterestList[[ii]]
stageNum <- stageNumList[[ii]]
print(sprintf("Working on stage: %s",stageNum))
if(col2Predict == "phenotype"){
dzOfInterestFixed <- gsub('([[:punct:]])|\\s+','',dzOfInterest)
metaDataFixed <- droplevels(metaData[metaData$cancer_status == "Cancer",])
metaDataFixed$condition <- factor(ifelse(metaDataFixed[,col2Predict] == dzOfInterest,
yes = dzOfInterestFixed, no = "Other"),
levels = c(dzOfInterestFixed,"Other"))
positiveClass <- dzOfInterestFixed
negativeClass <- "Other"
} else if(col2Predict == "cancer_status"){
metaDataFixed <- metaData
metaDataFixed$condition <- metaDataFixed$cancer_status
positiveClass <- "Cancer"
negativeClass <- "Healthy"
} else if(col2Predict == "one_cancer_vs_healthy"){
dzOfInterestFixed <- gsub('([[:punct:]])|\\s+','',dzOfInterest)
metaDataFixed <- droplevels(metaData[metaData$phenotype %in% c("Healthy",dzOfInterest),])
metaDataFixed$condition <- factor(ifelse(metaDataFixed[,"phenotype"] == dzOfInterest,
yes = dzOfInterestFixed, no = "Healthy"),
levels = c(dzOfInterestFixed,"Healthy"))
positiveClass <- dzOfInterestFixed
negativeClass <- "Healthy"