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Dada_PlotFunctions.R
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Dada_PlotFunctions.R
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tol14rainbow=c("#882E72", "#B178A6", "#D6C1DE", "#1965B0", "#5289C7", "#7BAFDE", "#4EB265", "#90C987", "#CAE0AB", "#F7EE55", "#F6C141", "#F1932D", "#E8601C", "#DC050C")
tol15rainbow=c("#114477", "#4477AA", "#77AADD", "#117755", "#44AA88", "#99CCBB", "#777711", "#AAAA44", "#DDDD77", "#771111", "#AA4444", "#DD7777", "#771144", "#AA4477", "#DD77AA")
tol18rainbow=c("#771155", "#AA4488", "#CC99BB", "#114477", "#4477AA", "#77AADD", "#117777", "#44AAAA", "#77CCCC", "#777711", "#AAAA44", "#DDDD77", "#774411", "#AA7744", "#DDAA77", "#771122", "#AA4455", "#DD7788")
# ...and finally, the Paul Tol 21-color salute
tol21rainbow= c("#771155", "#AA4488", "#CC99BB", "#114477", "#4477AA", "#77AADD", "#117777", "#44AAAA", "#77CCCC", "#117744", "#44AA77", "#88CCAA", "#777711", "#AAAA44", "#DDDD77", "#774411", "#AA7744", "#DDAA77", "#771122", "#AA4455", "#DD7788")
#######################################
#### IndivQualPlot
#######################################
IndivQualPlot <- function(QStatsList, SampleName, xlim_low = -5, xlim_high = 305) {
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
df <- QStatsList[[SampleName]]
ggplot(data = df, aes(x = Cycle, y = Mean_QS)) +
geom_hline(yintercept = 25, color = 'darkred', linetype = "dashed", size = .35) +
geom_line(color = "#009E73") +
geom_line(aes(y = Median_QS), color = "#E69F00") +
#geom_line(aes(y = q25_QS), color = "#FC8D62", linetype = "dashed") +
#geom_line(aes(y = q75_QS), color = "#FC8D62", linetype = "dashed") +
ylab("Quality Score") +
xlab("Cycle") +
coord_cartesian(xlim=c(xlim_low, xlim_high),
ylim = c(8,42), expand = FALSE) +
## I added this
ggtitle(paste("Sample:", SampleName, " No Reads: ", df$NoReads[1])) +
theme_bw() +
theme(panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_line(color = "#999999", size = .15))
}
#######################################
#### QS_Median_OverviewPlot
#######################################
QS_Median_OverviewPlot <- function(QStatsList, SampleNames, Prefix = "FW", xlim_low = -5, xlim_high = 305, ylim_low=8, ylim_high=42) {
# Input:
# QStatsList: list of QStats data frames, such as FW_QualityStats
# SampleNames: List of Sample names that must be the names of the QStatsList
if (!all(SampleNames %in% names(QStatsList))) {
stop("not all SampleNames were found in the QStatsList")
}
ReadLengths <- sapply(1:length(SampleNames), function(x) dim(QStatsList[[SampleNames[x]]])[1])
x <- range(ReadLengths)/mean(ReadLengths)
# if (!isTRUE(all.equal(x[1], x[2], tolerance = .Machine$double.eps ^ 0.5))) {
#
# stop("Samples are of different read lengths")
# }
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
QList <- QStatsList[SampleNames]
# add sample names to the data frames
for (i in seq_along(QList)) {
QList[[i]]$Sample <- names(QList[i])
}
# combine all df in the list to one big df
df <- do.call("rbind", QList)
ggplot(data = df, aes(x = Cycle, y = Median_QS, colour = NoReads, group = Sample)) +
geom_hline(yintercept = 25, color = 'darkred', linetype = "dashed", size = .35) +
geom_line() +
#geom_point() +
ylab("Quality Score") +
xlab("Cycle") +
coord_cartesian(xlim=c(xlim_low, xlim_high),
ylim = c(ylim_low,ylim_high), expand = FALSE) +
scale_color_gradient2(name = "No reads", limits = c(10000, 60000), midpoint = 35000, low = "#009E73", high = "#E69F00", mid = "#999999") +
#scale_color_gradient2(name = "No reads", limits = c(min(df$NoReads), max(df$NoReads)), midpoint = min(df$NoReads) + (range(df$NoReads)[2]- range(df$NoReads)[1])/2, low = "#009E73", high = "#E69F00", mid = "#999999")
ggtitle(paste(Prefix, "reads, Median_QS, NoSamples:", length(SampleNames))) +
theme_bw() +
theme(panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_line(color = "#999999", size = .15))
}
#####################################
QS_Median_OverviewPlot_ITS2 <- function(QStatsList, SampleNames, Prefix = "FW", xlim_low = -5, xlim_high = 305) {
# Input:
# QStatsList: list of QStats data frames, such as FW_QualityStats
# SampleNames: List of Sample names that must be the names of the QStatsList
if (!all(SampleNames %in% names(QStatsList))) {
stop("not all SampleNames were found in the QStatsList")
}
ReadLengths <- sapply(1:length(SampleNames), function(x) dim(QStatsList[[SampleNames[x]]])[1])
x <- range(ReadLengths)/mean(ReadLengths)
# if (!isTRUE(all.equal(x[1], x[2], tolerance = .Machine$double.eps ^ 0.5))) {
#
# stop("Samples are of different read lengths")
# }
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
QList <- QStatsList[SampleNames]
# add sample names to the data frames
for (i in seq_along(QList)) {
QList[[i]]$Sample <- names(QList[i])
}
# combine all df in the list to one big df
df <- do.call("rbind", QList)
ggplot(data = df, aes(x = Cycle, y = Median_QS, colour = NoReads, group = Sample)) +
geom_hline(yintercept = 25, color = 'darkred', linetype = "dashed", size = .35) +
geom_line() +
#geom_point() +
ylab("Quality Score") +
xlab("Cycle") +
coord_cartesian(xlim=c(xlim_low, xlim_high),
ylim = c(8,42), expand = FALSE) +
scale_color_gradient2(name = "No reads", limits = c(10000, 60000), midpoint = 35000, low = "#009E73", high = "#E69F00", mid = "#999999") +
#scale_color_gradient2(name = "No reads", limits = c(min(df$NoReads), max(df$NoReads)), midpoint = min(df$NoReads) + (range(df$NoReads)[2]- range(df$NoReads)[1])/2, low = "#009E73", high = "#E69F00", mid = "#999999")
ggtitle(paste(Prefix, "reads, Median_QS, NoSamples:", length(SampleNames))) +
theme_bw() +
theme(panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_line(color = "#999999", size = .15))
}
###
#######################################
#### QS_Mean_OverviewPlot
#######################################
QS_Mean_OverviewPlot <- function(QStatsList, SampleNames, Prefix = "FW", xlim_low = -5, xlim_high = 255) {
# Input:
# QStatsList: list of QStats data frames, such as FW_QualityStats
# SampleNames: List of Sample names that must be the names of the QStatsList
if (!all(SampleNames %in% names(QStatsList))) {
stop("not all SampleNames were found in the QStatsList")
}
ReadLengths <- sapply(1:length(SampleNames), function(x) dim(QStatsList[[SampleNames[x]]])[1])
x <- range(ReadLengths)/mean(ReadLengths)
if (!isTRUE(all.equal(x[1], x[2], tolerance = .Machine$double.eps ^ 0.5))) {
stop("Samples are of different read lengths")
}
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
QList <- QStatsList[SampleNames]
# add sample names to the data frames
for (i in seq_along(QList)) {
QList[[i]]$Sample <- names(QList[i])
}
df <- do.call("rbind", QList)
ggplot(data = df, aes(x = Cycle, y = Mean_QS, colour = NoReads, group = Sample)) +
geom_hline(yintercept = 25, color = 'darkred', linetype = "dashed", size = .35) +
geom_line() +
#geom_point() +
ylab("Quality Score") +
xlab("Cycle") +
coord_cartesian(xlim=c(xlim_low, xlim_high),
ylim = c(8,42), expand = FALSE) +
scale_color_gradient2(name = "No reads", limits = c(10000, 60000), midpoint = 35000, low = "#009E73", high = "#E69F00", mid = "#999999") +
#scale_color_gradient2(name = "No reads", limits = c(min(df$NoReads), max(df$NoReads)), midpoint = min(df$NoReads) + (range(df$NoReads)[2]- range(df$NoReads)[1])/2, low = "#009E73", high = "#E69F00", mid = "#999999")
ggtitle(paste(Prefix, " reads, Mean_QS, NoSamples", length(SampleNames))) +
theme_bw() +
theme(panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_line(color = "#999999", size = .15))
}
#######################################
#### NoReads_DotPlot
#######################################
NoReads_DotPlot <- function(QStatsList, SampleNames) {
# Input:
# QStatsList: list of QStats data frames, such as FW_QualityStats
# SampleNames: List of Sample names that must be the names of the QStatsList
if (!all(SampleNames %in% names(QStatsList))) {
stop("not all SampleNames were found in the QStatsList")
}
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
QList <- QStatsList[SampleNames]
# add sample names to the data frames
for (i in seq_along(QList)) {
QList[[i]]$Sample <- names(QList[i])
}
# combine all df in the list to one big df
df <- do.call("rbind", QList)
df <- df[!duplicated(df$Sample), c("Sample", "NoReads")]
df <- dplyr::arrange(df, desc(NoReads))
df$Sample <- factor(df$Sample)
# relevel so samples are shown from min NoReads to max NoReads
LevelsWant <- as.character(df$Sample)
for (i in 1:length(LevelsWant)) {
df$Sample <- relevel(df$Sample, ref = LevelsWant[i])
}
ggplot(data = df, aes(x = Sample, y = NoReads)) +
geom_hline(yintercept = 10000, color = 'darkred', linetype = "dashed", size = .35) +
geom_point(color = "#E69F00", size = 1) +
ylab("No Reads") +
xlab("") +
theme_bw() +
theme(panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_line(color = "#999999", size = .15),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 6))
}
#######################################
#### NoReads_Histogram
#######################################
NoReads_Histogram <- function(QStatsList, SampleNames) {
# Input:
# QStatsList: list of QStats data frames, such as FW_QualityStats
# SampleNames: List of Sample names that must be the names of the QStatsList
if (!all(SampleNames %in% names(QStatsList))) {
stop("not all SampleNames were found in the QStatsList")
}
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
QList <- QStatsList[SampleNames]
# add sample names to the data frames
for (i in seq_along(QList)) {
QList[[i]]$Sample <- names(QList[i])
}
# combine all df in the list to one big df
df <- do.call("rbind", QList)
df <- df[!duplicated(df$Sample), c("Sample", "NoReads")]
ggplot(data = df, aes(x = NoReads)) +
geom_histogram(binwidth = 1000, col = "black", fill = "#E69F00")+
geom_vline(xintercept = 10000, color = 'darkred', linetype = "dashed", size = .35) +
geom_rug() +
ylab("No Samples") +
xlab("No Reads") +
theme_bw() +
theme(panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_line(color = "#999999", size = .15))
}
#######################################
#### NoReads_DotPlot_Type
#######################################
NoReads_DotPlot_Type <- function(QStatsList = NULL, derepFs = NULL, mergers = NULL, mergers.nochim = NULL, SampleNames, sort = TRUE) {
# Input:
# QStatsList: list of QStats data frames, such as FW_QualityStats
# SampleNames: List of Sample names that must be names of the QStatsList, derepFs, mergers, and mergers.nochim
# sort: if the samples should be sorted in the plot based on NoReads
if (all(c(is.null(QStatsList), is.null(derepFs), is.null(mergers), is.null(mergers.nochim)))) {
stop("QStatsList, derepFs, mergers, mergers.nochim can not all be NULL")
}
if (!all(SampleNames %in% c(names(QStatsList), names(derepFs), names(mergers), names(mergers.nochim)))) {
stop("not all SampleNames were found in the given data")
}
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
## Construct the plotting data frame
df.all <- NULL
if (!is.null(QStatsList) & any(SampleNames %in% names(QStatsList))) {
QStatsList <- QStatsList[SampleNames[which(SampleNames %in% names(QStatsList))]]
for (i in seq_along(QStatsList)) {
QStatsList[[i]]$Sample <- names(QStatsList[i])
}
df.all <- do.call("rbind",QStatsList)
df.all <- df.all[!duplicated(df.all$Sample), c("Sample", "NoReads")]
df.all$Type <- "all"
}
df.filtered <- NULL
if (!is.null(derepFs) & any(SampleNames %in% names(derepFs))) {
derepFs <- derepFs[SampleNames[which(SampleNames %in% names(derepFs))]]
df.filtered <- data.frame(Sample = names(derepFs), NoReads = 0, Type = "filtered")
for (i in seq_along(derepFs)) {
df.filtered$NoReads[i] <- sum(derepFs[[i]]$uniques)
}
}
df.merged <- NULL
if (!is.null(mergers) & any(SampleNames %in% names(mergers))) {
mergers <- mergers[SampleNames[which(SampleNames %in% names(mergers))]]
df.merged <- data.frame(Sample = names(mergers), NoReads = 0, Type = "merged")
for(i in seq_along(mergers)) {
df.merged$NoReads[i] <- sum(mergers[[i]]$abundance)
}
}
df.nochim <- NULL
if (!is.null(mergers.nochim) & any(SampleNames %in% names(mergers.nochim))) {
mergers.nochim <- mergers.nochim[SampleNames[which(SampleNames %in% names(mergers.nochim))]]
df.nochim <- data.frame(Sample = names(mergers.nochim), NoReads = 0, Type = "nochim")
for(i in seq_along(mergers.nochim)) {
df.nochim$NoReads[i] <- sum(mergers.nochim[[i]]$abundance)
}
}
df.plot <- rbind(df.all, df.filtered, df.merged, df.nochim)
if (sort) {
# sort always by highest level: all, filtered, merged, nochim
if (!is.null(df.all)) {
df.all <- dplyr::arrange(df.all, desc(NoReads))
LevelsWant <- as.character(df.all$Sample)
} else if (!is.null(df.filtered)) {
df.filtered <- dplyr::arrange(df.filtered, desc(NoReads))
LevelsWant <- as.character(df.filtered$Sample)
} else if (!is.null(df.merged)) {
df.merged <- dplyr::arrange(df.merged, desc(NoReads))
LevelsWant <- as.character(df.merged$Sample)
} else if (!is.null(df.nochim)) {
df.nochim <- dplyr::arrange(df.nochim, desc(NoReads))
LevelsWant <- as.character(df.nochim$Sample)
}
df.plot$Sample <- factor(df.plot$Sample)
for (i in seq_along(LevelsWant)) {
df.plot$Sample <- relevel(df.plot$Sample, ref = LevelsWant[i])
}
}
ggplot(data = df.plot, aes(x = Sample, y = NoReads, color = Type)) +
geom_hline(yintercept = 10000, color = 'darkred', linetype = "dashed", size = .35) +
geom_point(size = 2) +
scale_color_manual(values = c(cbPalette[2:4],cbPalette[7])) +
ylab("No Reads") +
xlab("") +
theme_bw() +
theme(panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_line(color = "#999999", size = .15),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 6),
legend.title = element_blank())
}
#######################################
#### NoReads_StepsSimple
#######################################
NoReads_StepsSimple <- function(ReadSummary, SampleNames, sort = TRUE) {
if (!all(SampleNames %in% ReadSummary$Sample)) {
stop("not all SampleNames were found in the given ReadSummary")
}
ReadSummary <- ReadSummary[ReadSummary$Sample %in% SampleNames,]
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#C110CE","#83BA03", "#02BAB3" )
## Construct the plotting data frame
ReadSummary <- subset(ReadSummary, select = c("Sample", "NoReads", "FilteredReads", "MergedReads", "MergedReadsWOBimera"))
if (sort) {
ReadSummary <- dplyr::arrange(ReadSummary, desc(NoReads))
LevelsWant <- as.character(ReadSummary$Sample)
for (i in seq_along(LevelsWant)) {
ReadSummary$Sample <- relevel(ReadSummary$Sample, ref = LevelsWant[i])
}
}
colnames(ReadSummary)[2:5] <- c("all", "filtered", "merged", "nochim")
ReadSummary <- tidyr::gather(ReadSummary, key = Type, value = NoReads, -Sample)
Tr <- ggplot(data = ReadSummary, aes(x = Sample, y = NoReads, color = Type))
Tr <- Tr +
geom_hline(yintercept = 10000, color = 'darkred', linetype = "dashed", size = .35) +
geom_point(size = 2) +
scale_color_manual(values = c(cbPalette[2:4],cbPalette[7])) +
ylab("No Reads") +
xlab("") +
theme_bw() +
theme(panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_line(color = "#999999", size = .15),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 6),
legend.title = element_blank())
Tr
}
#######################################
#### TotalandUniqueAmplicons
#######################################
# Input:
# FinalNumers: data frame with Sample, UniqueAmplicons and NoAmplicons columns
# sort: default TRUE: sort samples after number total amplicons
# Output:
# the Tr structure
# NB: requires tidyr
TotalandUniqueAmplicons <- function(FinalNumbers, seqtab, sort =TRUE) {
## show the final amplicon numbers in a plot
FinalNumbers$TotalAmplicons <- FinalNumbers$NoAmplicons/100
FinalNumbersL <- tidyr::gather(FinalNumbers, key = Type, value = Number, -Sample, -NoAmplicons)
if(sort) {
FinalNumbersL <- dplyr::arrange(FinalNumbersL, desc(NoAmplicons))
LevelsWant <- as.character(FinalNumbersL$Sample)
for (i in seq_along(LevelsWant)) {
FinalNumbersL$Sample <- relevel(FinalNumbersL$Sample, ref = LevelsWant[i])
}
}
Tr <- ggplot(FinalNumbersL, aes(x = Sample, y = Number, col = Type)) +
geom_point() +
scale_color_manual(values = c("#E69F00", "#009E73"), labels = c("TotalAmplicons/100", "UniqueAmplicons"))+
ylab("No Amplicons") +
xlab("") +
#scale_y_continuous(breaks = c(100, 200, 300, 400), labels = c("100\n(10000)", "200\n(20000)", "300\n(30000)", "400\n(40000)")) +
ggtitle(paste("No unique amplicons in all samples",dim(seqtab)[2]))+
theme_bw() +
theme(panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_line(color = "#999999", size = .15),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 6),
legend.title = element_blank())
return(Tr)
}
#######################################
#### plotSVdistributions
#######################################
## SORRY FOR THE TERMINOLOGY CONFUSION< from now on counts is used instead of amplicons in the plots
## REQUIRES dplyr
## Input:
# Seqtab: seqtab from dada2 wrapper
# prevalence: a percentage of samples (bw 0 and 100)
## Output:
# TrList: List of four Trelis objects, plus distribution data.frame
plotSVdistributions <- function(seqtab, prevalence = 10) {
FinalNumbersSeq <- data.frame(Sequence = colnames(seqtab), InNumberSamples = colSums(seqtab != 0), TotalCounts = colSums(seqtab))
# a look at it shows you that seqtab is ordered after total counts
FinalNumbersSeq <- group_by(FinalNumbersSeq, InNumberSamples)
CountDistribution <- dplyr::summarise(FinalNumbersSeq, No_ASVs = n(), TotalCounts = sum(TotalCounts))
CountDistribution$CumSumUnique <- rev(cumsum(rev(CountDistribution$No_ASVs)))
CountDistribution$CumPerCUnique <- rev(cumsum(rev(CountDistribution$No_ASVs/ncol(seqtab))))
CountDistribution$CumSumTotal <- rev(cumsum(rev(CountDistribution$TotalCounts)))
CountDistribution$CumPerCTotal <- rev(cumsum(rev(CountDistribution$TotalCounts/sum(colSums(seqtab)))))
PCValue <- ceiling((prevalence/100)*dim(seqtab)[1]) # tells you in how many samples a SV must be present to meet the prevalence
# Diff <- CountDistribution$InNumberSamples - PCValue
# index <- which.max(Diff[Diff<0]) + which.min(Diff[Diff>=0])
index <- which(CountDistribution$InNumberSamples >= PCValue)[1]
PCKeptAtPCValue <- CountDistribution$CumPerCTotal[index]
SVskeptAtPCValue <- CountDistribution$CumPerCUnique[index]
# The number of samples the SVs are present in
Tr <- ggplot(CountDistribution, aes(x = InNumberSamples, y = No_ASVs))
Tr <- Tr + geom_point(col = "#E69F00", size = 3) +
xlab("prevalence") +
ylab("number of ASVs") +
theme_bw() +
theme(panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_line(color = "#999999", size = .15))
In1Index <- which(CountDistribution$InNumberSamples == 1)
if (length(In1Index) != 0) {
Tr <- Tr + ggtitle(paste(CountDistribution$No_ASVs[In1Index], " of ", CountDistribution$CumSumUnique[1], " ASVs (", round(100*CountDistribution$No_ASVs[In1Index]/CountDistribution$CumSumUnique[1], 1), " %)", " were only found in 1 sample", sep = ""))
}
# Cumulative Percentage of SVs
Tr1 <- ggplot(CountDistribution, aes(x = InNumberSamples, y = CumPerCUnique))
Tr1 <- Tr1 + geom_point(col = "#E69F00", size = 3) +
xlab("prevalence") +
ylab("cumulative percentage of ASVs") +
theme_bw() +
theme(panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_line(color = "#999999", size = .15))
Tr1 <- Tr1 +
geom_hline(yintercept = SVskeptAtPCValue, lty = "dashed") +
geom_vline(xintercept = (prevalence/100)*dim(seqtab)[1], lty = 'dashed') +
ggtitle(paste("prevalence ", prevalence, " % = ", round((prevalence/100)*dim(seqtab)[1],1), "; ", CountDistribution$CumSumUnique[index], " of ", CountDistribution$CumSumUnique[1],
" ASVs (", round(100*CountDistribution$CumSumUnique[index]/CountDistribution$CumSumUnique[1], 1), " %) have higher prevalence", sep = ""))
Tr2 <- ggplot(CountDistribution, aes(x = InNumberSamples, y = TotalCounts))
Tr2 <- Tr2 + geom_point(col = "#E69F00", size = 3) +
xlab("prevalence") +
ylab("total counts of ASVs with given prevalence") +
theme_bw() +
theme(panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_line(color = "#999999", size = .15))
Tr2 <- Tr2 + ggtitle(paste(CountDistribution$CumSumTotal[1] - CountDistribution$CumSumTotal[index], " of ",
CountDistribution$CumSumTotal[1], " (", round(100*(CountDistribution$CumSumTotal[1] - CountDistribution$CumSumTotal[index])/CountDistribution$CumSumTotal[1], 2),
" %) counts are from ASVs present in less than ", round((prevalence/100)*dim(seqtab)[1],1), " samples.", sep = ""))
Tr3 <- ggplot(CountDistribution, aes(x = InNumberSamples, y = CumPerCTotal))
Tr3 <- Tr3 + geom_point(col = "#E69F00", size = 3) +
xlab("prevalence") +
ylab("cumulative percentage of counts") +
theme_bw() +
theme(panel.grid.minor = element_blank(),
panel.grid.major.y = element_line(color = "#999999", size = .15),
panel.grid.major.x = element_line(color = "#999999", size = .15))
Tr3 <- Tr3 +
geom_hline(yintercept = PCKeptAtPCValue, lty = "dashed") +
geom_vline(xintercept = (prevalence/100)*dim(seqtab)[1], lty = 'dashed') +
ggtitle(paste("prevalence ", prevalence, " % = ", round((prevalence/100)*dim(seqtab)[1],1), "; ", CountDistribution$CumSumTotal[index], " of ", CountDistribution$CumSumTotal[1],
" counts (", round(100*CountDistribution$CumSumTotal[index]/CountDistribution$CumSumTotal[1], 1), " %) would remain", sep = ""))
TrList <- list(Tr, Tr1, Tr2, Tr3, CountDistribution)
return(TrList)
}
#######################################
#### plotAlphaDiversity
#######################################
# plotAlphaDiversity is related to phyloseq::plot_richness but also offers boxplots (when group != NULL) and
# always renders each plot individually in a list instead of using facet_wraps
plotAlphaDiversity <- function(physeq, measures = NULL, x = "samples", color = NULL, shape = NULL,
group = NULL, defCol = "#E69F00"){
AlphaDiv <- suppressWarnings(estimate_richness(physeq, measures = measures))
measures = colnames(AlphaDiv)
ses = colnames(AlphaDiv)[grep("^se\\.", colnames(AlphaDiv))]
measures = measures[!measures %in% ses]
if("Observed" %in% measures){
measures[measures == "Observed"] <- "Richness"
colnames(AlphaDiv)[colnames(AlphaDiv) == "Observed"] <- "Richness"
}
if (!is.null(sample_data(physeq, errorIfNULL = FALSE))) {
DF <- data.frame(AlphaDiv, sample_data(physeq))
} else {
DF <- data.frame(AlphaDiv)
}
DF$samples <- sample_names(physeq)
if (!is.null(x)) {
if (x %in% c("sample", "samples", "sample_names", "sample.names", "Sample", "Samples")) {
x <- "samples"
}
} else {
x <- "samples"
}
if(!is.null(group)){
TrList <- list()
for (i in 1:length(measures)) {
aes_map = aes_string(x = group, y = measures[i], shape = shape, color = color, group = group)
if(is.null(color)){
Tr = ggplot(DF, aes_map) + geom_boxplot(na.rm = TRUE, col = defCol)
} else {
Tr = ggplot(DF, aes_map) + geom_boxplot(na.rm = TRUE)
}
Tr = Tr + geom_jitter(width = .2, height = 0, alpha = 0.65)
Tr <- Tr + scale_colour_manual(values = cbPalette[2:11])
Tr <- Tr + theme_bw() + xlab("")
Tr = Tr + theme(panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_line(color = "#999999", size = .15))
TrList[[i]] <- Tr
}
} else {
TrList <- list()
for (i in 1:length(measures)) {
aes_map = aes_string(x = x, y = measures[i], shape = shape, color = color)
if(is.null(color)){
Tr = ggplot(DF, aes_map) + geom_point(na.rm = TRUE, col = defCol)
} else {
Tr = ggplot(DF, aes_map) + geom_point(na.rm = TRUE)
}
if (measures[i] == "Chao1") {
Tr = Tr + geom_errorbar(aes(ymax = Chao1 + se.chao1, ymin = Chao1 -
se.chao1), width = 0.1)
}
if (measures[i] == "ACE") {
Tr = Tr + geom_errorbar(aes(ymax = ACE + se.ACE, ymin = ACE -
se.ACE), width = 0.1)
}
Tr <- Tr + scale_colour_manual(values = cbPalette[2:11])
Tr <- Tr + theme_bw()
Tr = Tr + theme(panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_line(color = "#999999", size = .15),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 6))
TrList[[i]] <- Tr
}
}
names(TrList) <- measures
return(TrList)
}
#######################################
#### boxplot_alphaDiv_fromDF
#######################################
# see plotAlphaDiversity
boxplot_alphaDiv_fromDF <- function(DF, measures, color = NULL, shape = NULL,
group = NULL, defCol = "#E69F00") {
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#C110CE","#83BA03", "#02BAB3" )
TrList <- list()
if (!is.null(DF$Total) && !is.null(DF$filtered_reads)) {
measures <- c(measures, "Total", "filtered_reads")
}
for (i in 1:length(measures)) {
aes_map = aes_string(x = group, y = measures[i], color = color)
if(is.null(color)){
Tr = ggplot(DF, aes_map) + geom_boxplot(na.rm = TRUE, col = defCol)
} else {
Tr = ggplot(DF, aes_map) + geom_boxplot(na.rm = TRUE)
}
Tr = Tr + geom_jitter(aes_string(shape = shape), width = .2, height = 0, alpha = 0.65)
Tr <- Tr + scale_colour_manual("", values = cbPalette[2:11])
Tr <- Tr + theme_bw() + xlab("") +theme(legend.position = "none")
Tr = Tr + theme(panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_line(color = "#999999", size = .15))
TrList[[i]] <- Tr
}
names(TrList) <- measures
return(TrList)
}
#######################################
#### plot_alphaDivVstotalCounts
#######################################
# Function plots the alpha diversity measures from estimate_richness against the total number of reads/amplicons per sample
# and adds a linear fit.
# if color is given the fit is individual on the different colors, but the p-value in the title is still to an overall fit!!
plot_alphaDivVstotalCounts <- function(physeq, measures = NULL, color = NULL, shape = NULL, defCol = "#E69F00"){
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#C110CE","#83BA03", "#02BAB3" )
# needed to get the p-value from-linear fit objects (from stackoverflow)
lmp <- function (modelobject) {
if (class(modelobject) != "lm") stop("Not an object of class 'lm' ")
f <- summary(modelobject)$fstatistic
p <- pf(f[1],f[2],f[3],lower.tail=F)
attributes(p) <- NULL
return(p)
}
AlphaDiv <- suppressWarnings(estimate_richness(physeq, measures = measures))
measures = colnames(AlphaDiv)
ses = colnames(AlphaDiv)[grep("^se\\.", colnames(AlphaDiv))]
measures = measures[!measures %in% ses]
if("Observed" %in% measures){
measures[measures == "Observed"] <- "Richness"
colnames(AlphaDiv)[colnames(AlphaDiv) == "Observed"] <- "Richness"
}
if (!is.null(sample_data(physeq, errorIfNULL = FALSE))) {
DF <- data.frame(AlphaDiv, sample_data(physeq))
} else {
DF <- data.frame(AlphaDiv)
}
DF$samples <- sample_names(physeq)
if(taxa_are_rows(physeq)){
DF$TotalReads <- colSums(otu_table(physeq))
} else {
DF$TotalReads <- rowSums(otu_table(physeq))
}
TrList <- list()
for (i in 1:length(measures)) {
aes_map = aes_string(x = "TotalReads", y = measures[i], shape = shape, color = color)
if(is.null(color)){
Tr = ggplot(DF, aes_map) + geom_point(na.rm = TRUE, col = defCol)
} else {
Tr = ggplot(DF, aes_map) + geom_point(na.rm = TRUE)
}
if (measures[i] == "Chao1") {
Tr = Tr + geom_errorbar(aes(ymax = Chao1 + se.chao1, ymin = Chao1 -
se.chao1), width = 0.1)
}
if (measures[i] == "ACE") {
Tr = Tr + geom_errorbar(aes(ymax = ACE + se.ACE, ymin = ACE -
se.ACE), width = 0.1)
}
Tr <- Tr + scale_colour_manual(values = cbPalette[2:11])
# add the regression line and the p-values
fit <- lm(DF[,measures[i]] ~ DF[,"TotalReads"])
adjR2 <- round(summary(fit)$adj.r.squared,3)
pfit <- lmp(fit)
Tr <- Tr + geom_smooth(aes_string(x = "TotalReads", y = measures[i]), method = "lm", se = TRUE, inherit.aes = F)
Tr <- Tr + ggtitle(paste("lm fit: p-value: ", format(pfit, digits = 4), ", adj.r.squared: ", adjR2, sep = ""))
Tr <- Tr + theme_bw() + xlab("total counts in sample (sample_sums())")
Tr = Tr + theme(panel.grid.minor = element_blank())
TrList[[i]] <- Tr
}
names(TrList) <- measures
return(TrList)
}
#######################################
#### plot_alphaDivVstotalCounts
#######################################
# Function plots the alpha diversity measures from estimate_richness against the total number of reads/amplicons per sample
# and adds a linear fit.
# if color is given the fit is individual on the different colors, but the p-value in the title is still to an overall fit!!
plot_alphaDivVstotalCounts_fromList <- function(DF_List, measures = NULL, color = NULL, shape = NULL, defCol = "#E69F00"){
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#C110CE","#83BA03", "#02BAB3" )
TrList <- list()
DF <- DF_List[[1]]
fitlist <- DF_List[["fitlist"]]
for (i in 1:length(measures)) {
aes_map = aes_string(x = "Total", y = measures[i], shape = shape, color = color)
if(is.null(color)){
Tr = ggplot(DF, aes_map) + geom_point(na.rm = TRUE, col = defCol)
} else {
Tr = ggplot(DF, aes_map) + geom_point(na.rm = TRUE)
}
if (measures[i] == "Chao1") {
Tr = Tr + geom_errorbar(aes(ymax = Chao1 + se.chao1, ymin = Chao1 -
se.chao1), width = 0.1)
}
if (measures[i] == "ACE") {
Tr = Tr + geom_errorbar(aes(ymax = ACE + se.ACE, ymin = ACE -
se.ACE), width = 0.1)
}
Tr <- Tr + scale_colour_manual("", values = cbPalette[2:11])
# add the regression line and the p-values
fit <- fitlist[[measures[i]]]
adjR2 <- round(summary(fit)$adj.r.squared,3)
if (adjR2 != 0){
pfit <- lmp(fit)
} else {
pfit <- NA
}
Tr <- Tr + geom_smooth(aes_string(x = "Total", y = measures[i]), method = "lm", se = TRUE, inherit.aes = F)
Tr <- Tr + ggtitle(paste("lm fit: p-value: ", format(pfit, digits = 4), ", adj.r.squared: ", adjR2, sep = ""))
Tr <- Tr + theme_bw() + xlab("total counts in sample (sample_sums())")
Tr = Tr + theme(panel.grid.minor = element_blank())
TrList[[i]] <- Tr
}
names(TrList) <- measures
return(TrList)
}
#######################################
#### plot_alphaDivVsfilteredReads_fromList
#######################################
# Function plots the alpha diversity measures from estimate_richness against the given number of filteredReads per sample
# and adds a linear fit.
# if color is given the fit is individual on the different colors, but the p-value in the title is still to an overall fit!!
plot_alphaDivVsfilteredReads_fromList <- function(DF_List, measures = NULL, color = NULL, shape = NULL, defCol = "#E69F00"){
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#C110CE","#83BA03", "#02BAB3" )
TrList <- list()
DF <- DF_List[[1]]
fitlist <- DF_List[["fitlist_FilteredReads"]]
for (i in 1:length(measures)) {
aes_map = aes_string(x = "filtered_reads", y = measures[i], shape = shape, color = color)
if(is.null(color)){
Tr = ggplot(DF, aes_map) + geom_point(na.rm = TRUE, col = defCol)
} else {
Tr = ggplot(DF, aes_map) + geom_point(na.rm = TRUE)
}
if (measures[i] == "Chao1") {
Tr = Tr + geom_errorbar(aes(ymax = Chao1 + se.chao1, ymin = Chao1 -
se.chao1), width = 0.1)
}
if (measures[i] == "ACE") {
Tr = Tr + geom_errorbar(aes(ymax = ACE + se.ACE, ymin = ACE -
se.ACE), width = 0.1)
}
Tr <- Tr + scale_colour_manual("", values = cbPalette[2:11])
# add the regression line and the p-values
fit <- fitlist[[measures[i]]]
adjR2 <- round(summary(fit)$adj.r.squared,3)
if (adjR2 != 0){
pfit <- lmp(fit)
} else {
pfit <- NA
}
Tr <- Tr + geom_smooth(aes_string(x = "filtered_reads", y = measures[i]), method = "lm", se = TRUE, inherit.aes = F)
Tr <- Tr + ggtitle(paste("lm fit: p-value: ", format(pfit, digits = 4), ", adj.r.squared: ", adjR2, sep = ""))
Tr <- Tr + theme_bw() + xlab("number of filtered reads that entered dada command")
Tr = Tr + theme(panel.grid.minor = element_blank())
TrList[[i]] <- Tr
}
names(TrList) <- measures
return(TrList)
}
#######################################
#### plot_alphaDivVSoriginalTotalSampleCounts
#######################################
# wanted to add this plot after rarefaction
plot_alphaDivVSoriginalTotalSampleCounts <- function(DF_alpha_rare, DF_alpha_no_rare, measures = NULL, color = NULL, shape = NULL, defCol = "#E69F00"){
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#C110CE","#83BA03", "#02BAB3" )
measures2 <- measures
if ("Observed" %in% measures2) {
measures2[measures2 == "Observed"] <- "Richness"
}
DF_alpha_rare$originalTotal <- DF_alpha_no_rare$Total
DF <- DF_alpha_rare
TrList <- list()
for (i in 1:length(measures2)) {
aes_map = aes_string(x = "originalTotal", y = measures2[i], shape = shape, color = color)
if(is.null(color)){
Tr = ggplot(DF, aes_map) + geom_point(na.rm = TRUE, col = defCol)
} else {
Tr = ggplot(DF, aes_map) + geom_point(na.rm = TRUE)
}
if (measures[i] == "Chao1") {
Tr = Tr + geom_errorbar(aes(ymax = Chao1 + se.chao1, ymin = Chao1 -
se.chao1), width = 0.1)
}
if (measures[i] == "ACE") {
Tr = Tr + geom_errorbar(aes(ymax = ACE + se.ACE, ymin = ACE -
se.ACE), width = 0.1)