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mgc_plotting_functions.R
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mgc_plotting_functions.R
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################################
# Name: MacIntosh Cornwell
# Email: mcornwell1957@gmail.com
################################
## Plotting Functions
## Load in Libraries
packagelist = c("ggplot2", "reshape2", "DESeq2", "grid", "gridExtra", "scales", "ggrepel", "circlize", "viridis", "RColorBrewer", "tools", "ggpubr", "ggsignif", "ComplexHeatmap", "stringr", "tidyverse", "ggsci", "Rtsne", "GGally", "corrplot", "waffle", "ggthemes")
junk <- lapply(packagelist, function(xxx) suppressMessages(require(xxx, character.only = TRUE,quietly=TRUE,warn.conflicts = FALSE)))
ht_opt$message = FALSE
# https://nanx.me/ggsci/reference/pal_npg.html
## npg_color_list: c("#E64B35FF", "#4DBBD5FF", "#00A087FF", "#3C5488FF", "#F39B7FFF", "#8491B4FF", "#91D1C2FF", "#DC0000FF", "#7E6148FF", "#B09C85FF")
# --------------------------------- barchart ---------------------------------
# infile <- "/Users/tosh/Desktop/Ruggles_Lab/projects/newman-pace/output_newcohorts_age_covar/gsea/comp_cohort__pace_v_healthy/comp_cohort__pace_v_healthy_gsea_HALL.csv"
# intable <- read.table(infile, sep = ",", header = TRUE)
# bartab <- intable[intable[,"p.adjust"] < 0.1,c(1,6,8)]
# labsparam <- list(title = "top pathways", x = "pathway", y = "NES", fill = "Adjpvalue")
# pout <- plot_barchart(bartab, colorcol = 3, labsparam = labsparam)
# errorbarparam - what to do error bars off of? {ci, sd (standard dev)} ## ADD MORE OPTIONS AS NEEDED
plot_barchart <- function(bartab, colorvar = NULL, errorbarvar = NULL, labsparam, strwrap_characternum = 20){
plotdata <- bartab
## First - replace the label strings with spaces so we can wrap nicely
plotdata[,1] <- gsub("_", " ", plotdata[,1])
## Stop reordering
plotdata[,1] <- factor(plotdata[,1], levels = plotdata[,1])
## Add colors if input
if (!is.null(colorvar)) {plotdata$colorvar = colorvar[,1]}
pout <- ggplot(plotdata, mapping = aes(x = str_wrap(plotdata[,1], strwrap_characternum), y = plotdata[,2]))
# if (!is.null(colorcol)) {pout <- ggplot(plotdata, mapping = aes(x = str_wrap(plotdata[,1], strwrap_characternum), y = plotdata[,2], fill = plotdata[,colorcol]))}
if (!is.null(colorvar)) {
if (is.numeric(plotdata[,3])){
pout <- pout + aes(fill=plotdata$colorvar)
colormin <- min(plotdata$colorvar, 0)
colormax <- max(plotdata$colorvar, 0)
pout <- pout + scale_fill_viridis(limits = c(colormin, colormax), direction = -1, option = "viridis")
} else {
pout <- pout + aes(fill=plotdata$colorvar)
## If literally a color - then use the color - 2022-11-03
if (sum(areColors(plotdata$colorvar)) == length(plotdata$colorvar) & !is.numeric(plotdata$colorvar)) { ## If your colorvar are literally colors and NOT number - then use them
pout <- pout + scale_color_identity()
pout <- pout + scale_fill_identity()
}
## If literally a color - then use the color - 2022-11-03
}
}
pout <- pout + geom_bar(stat = "identity")
# pout <- pout + scale_x_discrete(limits=rev(str_wrap(plotdata[,1], strwrap_characternum)))
pout <- pout + scale_x_discrete(limits=str_wrap(plotdata[,1], strwrap_characternum))
if (!is.null(errorbarvar)) {
pout <- pout + geom_errorbar(aes(ymax = plotdata[,2] + errorbarvar[,1], ymin = plotdata[,2] - errorbarvar[,1]),
width = 0.5, position = position_dodge(width = 0.9))}
pout <- pout + labs(x = labsparam$x, y = labsparam$y, fill = labsparam$fill, title = labsparam$title)
pout <- pout + theme_pubr(x.text.angle = 90, legend = "right")
pout
return(pout)
}
# Make the table for a boxplot
# indata - standard data table, samples on y, features on x, with row and col names
# inmeta - standard meta data, samples on x, categories on y, col names, FIRST COLUMN IS SAMPLES
# FOI - features of interest, with values to be plotted in the boxplot
# COI - category of interest - the primary category to split data on
# COIaddon - categorical information that will be added onto features if you want subsections of features
# ex) C1F1, C1F2, C2F1, C2F2 for 4 boxplots
make_boxplot_table <- function(indata, inmeta, FOI, COI, COIaddon=NULL) {
## Combine the data and metadata into one table
temp = rbind(t(inmeta[match(inmeta[,1], colnames(indata)),c(2:ncol(inmeta)), drop=FALSE]), as.matrix(indata))
colnames(temp) = colnames(indata)
# tempmelt = as.matrix(melt(temp, varnames = c("vars","sample")))
tempmelt = melt(temp, varnames = c("vars","sample"))
## Select the primary category of interest and replace samples with it
tempmelt[,2] = inmeta[,COI][match(tempmelt[,2], inmeta[,1])]
## Add on any data from COIaddon to features
if (!is.null(COIaddon)) {tempmelt[,1] =
paste(as.character(tempmelt[tempmelt[,1]==COIaddon,3]), as.character.factor(tempmelt[,1]), sep = "_")}
## Select the FOI
featureindex = c()
for (FOIcount in seq_len(length(FOI))){
# featureindex = c(featureindex, grep(FOI[FOIcount], tempmelt[,1], fixed = TRUE))
## Return the index that has the GOI from the melted table
# featureindex = c(featureindex, rownames(tempmelt[tempmelt[,1] == FOI,])) # edit 20200410
featureindex = c(featureindex, rownames(tempmelt[tempmelt[,1] == FOI[FOIcount],]))
}
tempmelt = tempmelt[featureindex,]
tempmelt[,1] = as.character(tempmelt[,1])
tempmelt[,2] = as.character(tempmelt[,2])
tempmelt[,3] = as.numeric(as.character(tempmelt[,3]))
return(data.frame(tempmelt))
}
# --------------------------------- monster function for boxplots ---------------------------------
## GGplot the boxplots for SOI for each gene in the item - MAJOR REWRITE - but necessary.
# Input the alread melted boxplot (function above)
# if want to label with second axis, then NAME the second axis, and the second axis will be scaled to the first
# ex) group1 and group2, if you NAME group2, then group1 will get the MAJOR markings, and group2 gets the SECOND markings
# xsplit - is the param by which you split your x-axis on <"category" | "feature" > Do you want a boxplot(s) for each category or for each feature? feature is the feature in your count table, category is the grouping in your metadata
# colorparam - named vector with the names being the features, and the named item being the color you want it
# plotstats - < "inter", "intra" > to say if you want stats for INTERgroup comparisons or INTRAgroup comparisons - intra will automatically facet the plot, inter will leave as one plot
# comparisonparam - if inputted, should be a list of features/categories to compare
###### TOSH - IT GOES FEATURE, CATEGORY, VALUE. this may not be how you want it, but it is what it is.
# labsparam - title, x, y, fill, catorder, featorder
# boxplot_plotter <- function(boxplottable,
# xsplit = "category",
# labsparam, colorparam = NULL, secondaxis=NULL,
# plotstats = FALSE, comparisonparam = NULL, testtypeparam = "wilcox.test", pointcoloring = FALSE,
# plotpoints = TRUE ## Added 2022-08-01
# ) {
#
# ## INPUTTING THE PREMADE MELTED TABLE
# # ## Turn counttab and and metatable into melted plottab
# # ## Melt the table down
# # meltedtab = data.frame(melt(as.matrix(intable), varnames = c("features", "samples")), stringsAsFactors = FALSE)
# # ## Reassign the samples as the class
# # meltedtab[,2] = metatable[,2][match(meltedtab[,2], metatable[,1])]
# # meltedtab[,1] = as.character(meltedtab[,1])
# # plottab = meltedtab
# plottab = boxplottable
#
# ## Create failsafe if you omit the catorder and featorder ##28-07-2021
# if (is.null(labsparam$featorder)) {labsparam$featorder <- unique(plottab[,1])}
# if (is.null(labsparam$catorder)) {labsparam$catorder <- unique(plottab[,2])}
#
# ## To get numbers in the same world for plotting purpose - we are going to round them to the nearest base 10
# ## Really only usable when you have two categories - PROBABLY BROKEN UNTIL FURTHER TESTED
# ## use the NAMED secondaxis variable, and scale that variable up to the first one, while labeling the second axis
# if (!is.null(secondaxis)) {
# roundUp <- function(x) 10^ceiling(log10(x))
# for (i in seq_len(length(secondaxis))) {
# temptab1 = plottab[plottab[,1] == secondaxis[i],]
# temptab2 = plottab[plottab[,1] != secondaxis[i],]
# tempval = mean(na.omit(temptab2[,3])) / mean(na.omit(temptab1[,3]))
# scalefactor = roundUp(tempval)
# plottab[plottab[,1] == secondaxis[i],3] = plottab[plottab[,1] == secondaxis[i],3] * scalefactor
# }
# }
#
# ## Get maxval on plot - to be used for plotting purposes later
# maxval = round(max(na.omit(plottab[,3])),2)
# minval = round(min(na.omit(plottab[,3])),2)
# valrange <- abs(maxval) + abs(minval)
#
# if (xsplit == "feature") {
# xcol = 1
# zcol = 2
# ## 3/17/12
# # bporder = labsparam$featorder
# # bporderintra = labsparam$catorder
# bporder = factor(labsparam$featorder, levels = labsparam$featorder)
# bporderintra = factor(labsparam$catorder, levels = labsparam$catorder)
# plottab[,xcol] <- factor(plottab[,xcol], levels = labsparam$featorder)
# plottab[,zcol] <- factor(plottab[,zcol], levels = labsparam$catorder)
# ## 3/17/12
# if(is.null(bporder)) {bporder <- unique(plottab[,zcol])}
# if(is.null(bporderintra)) {bporderintra <- unique(plottab[,xcol])}
# } else { # xsplit == "category"
# xcol = 2
# zcol = 1
# ## 3/17/12
# # bporder = labsparam$catorder
# # bporderintra = labsparam$featorder
# bporder = factor(labsparam$catorder, levels = labsparam$catorder)
# bporderintra = factor(labsparam$featorder, levels = labsparam$featorder)
# plottab[,xcol] <- factor(plottab[,xcol], levels = labsparam$catorder)
# plottab[,zcol] <- factor(plottab[,zcol], levels = labsparam$featorder)
# ## 3/17/12
# if(is.null(bporder)) {bporder <- unique(plottab[,xcol])}
# if(is.null(bporderintra)) {bporderintra <- unique(plottab[,zcol])}
# }
#
# ## Add in point coloring if desired # added 9/21/2021
# if (pointcoloring == TRUE) {
# # pointcolorcol <- 4
# pointcolorparam <- plottab[,4]
# } else {
# # pointcolorcol <- zcol
# pointcolorparam <- "black"
# }
# ## STANDARD BOXPLOT WITH NO STATS
# pout <- ggplot(plottab, aes(x=plottab[,xcol], y=plottab[,3], z=plottab[,zcol], fill = plottab[,zcol]))
# pout <- pout + geom_boxplot(na.rm=TRUE, outlier.shape = NA, position = position_dodge(width=0.9))
# # pout <- pout + geom_point(data = plottab, aes(x=plottab[,xcol], y=plottab[,3], fill = plottab[,pointcolorcol]),
# # position=position_jitterdodge(dodge.width=0.9, jitter.width = 0.12), size = 0.8, shape = 16)
# if (plotpoints) {
# pout <- pout + geom_point(data = plottab, aes(x=plottab[,xcol], y=plottab[,3], color = pointcolorparam),
# position=position_jitterdodge(dodge.width=0.9, jitter.width = 0.12), size = 0.8, shape = 16)
# }
#
#
# pout <- pout + scale_x_discrete(limits = bporder)
# # pout <- pout + scale_fill_discrete(limits = bporderintra)
#
# pout <- pout + labs(title = labsparam$title, x = labsparam$x, y = labsparam$y, fill = labsparam$fill)
# if(!is.null(secondaxis)) {pout <- pout +
# scale_y_continuous(sec.axis = sec_axis(~./scalefactor, name = secondaxis[1]))}
# if(!is.null(colorparam)) {pout <- pout + scale_fill_manual(values = unname(colorparam), limits = names(colorparam))}
# pout <- pout + theme_pubr(x.text.angle = 45, base_size = 12)
#
# pout <- pout + scale_y_continuous(limits = c(round(minval-(abs(0.2*minval)), digits = 2), round(maxval*1.2, digits = 2)),
# # breaks = seq(round(minval-(abs(0.2*minval)), digits = 2), round(maxval*1.2, digits = 2),length.out = 5))
# breaks = round(seq(round(minval-(abs(0.2*minval)), digits = 2), round(maxval*1.2, digits = 2),length.out = 5),2))
#
#
# ## CREATE COMBO TABLE FOR STATS IF APPLICABLE
# if (plotstats != FALSE) {
# if (is.null(comparisonparam)) {
# combcol = ifelse(plotstats == "inter", xcol, zcol)
# ## Put a failsafe in here to only test against those with at least 2 points (cant have a t.test against 1 pt)
# # comparisontemp <- na.omit(names(table(plottab[!is.na(plottab[,combcol]),combcol])[table(plottab[!is.na(plottab[,combcol]),combcol])> 1]))
# # comparisontemp2 <- comparisontemp[order(comparisontemp)]
#
# # if (length(comparisontemp2) > 1) {
# # combtab = combn(comparisontemp2, 2, simplify=F)
# # } else {
# # combtab <- NULL
# # }
#
# combtab = combn(as.character(unique(plottab[!is.na(plottab[,combcol]),combcol])), 2, simplify=F)
#
# } else {
# if (is.character(comparisonparam) | is.factor(comparisonparam)) {
# comparisonparam <- as.character(comparisonparam)
# combtab = combn(comparisonparam, 2, simplify=F)
# }
# if (is.list(comparisonparam)) {
# combtab = comparisonparam
# }
# }
# ## Failsafe here to exclude statistical testing if not enough values in group
# excludegroups <- names(table(plottab[,2])[table(plottab[,2]) < 2])
# combtab <- combtab[!unlist(lapply(combtab, function(x) sum(x %in% excludegroups) > 0))]
# }
#
# ### THIS DOES SIGNIFICANCE BETWEEN THE X AXIS GROUPS
# if (plotstats == "inter"){
# # pout <- pout + geom_signif(comparisons = combtab, step_increase = 0.1,
# # y_position = seq(maxval*1.2, (maxval*(1.1+(0.1*length(combtab)))), 0.1*maxval),
# # map_signif_level=FALSE)}
# pout <- pout + geom_signif(comparisons = combtab, step_increase = 0.15, test = testtypeparam,
# # y_position = seq(maxval*1.1, (maxval*(1.0+(0.1*length(combtab)))), 0.1*maxval), ## 27-07-2021 - change because the step.increase does this better than the manual setting
# map_signif_level=FALSE)
# pout <- suppressMessages(pout + scale_y_continuous(limits = c(round(minval-(abs(0.2*minval)), digits = 2), round(maxval*(1.1+(0.15*length(combtab))), digits = 2)),
# # breaks = seq(round(minval-(abs(0.2*minval)), digits = 2), round(maxval*(1.1+(0.1*length(combtab))), digits = 2),length.out = 5)))
# breaks = round(seq(round(minval-(abs(0.2*minval)), digits = 2), round(maxval*(1.1+(0.1*length(combtab))), digits = 2),length.out = 5),2)))
# }
#
#
# ### THIS DOES SIGNIFICANCE BETWEEN SUBGROUPS
# if (plotstats == "intra") {
# pout <- ggplot(plottab, aes(x=plottab[,zcol], y=plottab[,3], fill = plottab[,zcol]))
# pout <- pout + facet_wrap(~factor(plottab[,xcol], levels = bporder), scales = "free")
# pout <- pout + geom_boxplot(na.rm=TRUE, outlier.shape = NA, position = position_dodge(width=0.9))
# # pout <- pout + geom_point(data = plottab, aes(x=plottab[,zcol], y=plottab[,3], fill = plottab[,pointcolorcol]),
# # position=position_jitterdodge(dodge.width=0.9, jitter.width = 0.12), size = 0.5, shape = 16)
# if (plotpoints) {
# pout <- pout + geom_point(data = plottab, aes(x=plottab[,zcol], y=plottab[,3], color = pointcolorparam),
# position=position_jitterdodge(dodge.width=0.9, jitter.width = 0.12), size = 0.5, shape = 16)
# }
#
#
#
# pout <- pout + scale_x_discrete(limits = bporderintra)
#
# pout <- pout + labs(title = labsparam$title, x = labsparam$x, y = labsparam$y, fill = labsparam$fill)
# if(!is.null(secondaxis)) {pout <- pout +
# scale_y_continuous(sec.axis = sec_axis(~./scalefactor, name = secondaxis[1]))}
# if(!is.null(colorparam)) {pout <- pout + scale_fill_manual(values = unname(colorparam), limits = names(colorparam))}
# pout <- pout + theme_pubr(x.text.angle = 60, base_size = 12, border = TRUE)
# # pout <- pout + stat_compare_means(comparisons = combtab, method.args = list(exact = FALSE), label = "p.format", size = 3,
# # label.y = seq(maxval*1.1, (maxval*(1.0+(0.1*length(combtab)))), 0.1*maxval))
# if (length(combtab) > 0) { # failsafe for if there arent actually nay viable comps
# ## Failsafe for single comp and fix rounding error:
# failsafesignparam <- sign(maxval+(0.1*length(combtab)*valrange) - maxval*1.1)
# pout <- pout + stat_compare_means(comparisons = combtab, method = testtypeparam,
# # method.args = list(exact = TRUE), ### FOR SOME REASON - THIS IS KILLING MY RUN TIME, so omitting for now
# label = "p.format", size = 3,
# # label.y = seq(maxval*1.1, (maxval*(1.0+(0.1*length(combtab)))), 0.1*maxval))
# # label.y = seq(maxval*1.1, maxval*(0.1*length(combtab)*valrange), 0.1*valrange))
# label.y = seq(maxval*1.1, maxval+(0.1*length(combtab)*valrange), failsafesignparam*0.1*valrange))
# ## bug here where the rounding screws it up... really only when you have one comp though
# # label.y = seq(ceiling(maxval*1.1, 2), ceiling(maxval+(0.1*length(combtab)*valrange),2), round(0.1*valrange,2)))
# }
#
# # pout <- pout + scale_y_continuous(limits = c(round(minval-(abs(0.2*minval)), digits = 2), round(maxval*(1.1+(0.1*length(combtab))), digits = 2)),
# # breaks = seq(round(minval-(abs(0.2*minval)), digits = 2), round(maxval*(1.1+(0.1*length(combtab))), digits = 2),length.out = 5))
# # pout <- pout + scale_y_continuous(limits = c(round(minval-(abs(0.2*minval)), digits = 2), round(0.1*length(combtab)*valrange, digits = 2)),
# # breaks = seq(round(minval-(abs(0.2*minval)), digits = 2), round(maxval*(1.1+(0.1*length(combtab))), digits = 2),length.out = 5))
# pout <- pout + scale_y_continuous(limits = c(round(minval-(abs(0.2*minval)), digits = 2), round(maxval+(0.1*length(combtab)*valrange+0.1*valrange), digits = 2)),
# # breaks = seq(round(minval-(abs(0.2*minval)), digits = 2), round(maxval*(1.1+(0.1*length(combtab))), digits = 2),length.out = 5))
# breaks = round(seq(round(minval-(abs(0.2*minval)), digits = 2), round(maxval*(1.1+(0.1*length(combtab))), digits = 2),length.out = 5),2))
#
# # expand_scale(mult = c(1.2, round(maxval*(1.1+(0.1*length(combtab))), digits = 2)))
#
# }
#
# ## Add in point coloring if desired # added 9/21/2021
# if (pointcoloring != TRUE) {
# pout <- pout + scale_color_manual(values = "black", breaks = "black")
# }
#
# pout
#
# #return(pout)
#
# }
boxplot_plotter <- function(boxplottable,
xsplit = "category",
labsparam, colorparam = NULL, secondaxis=NULL,
plotstats = FALSE, comparisonparam = NULL, testtypeparam = "wilcox.test", pointcoloring = FALSE,
plotpoints = TRUE, ## Added 2022-08-01
violin = FALSE ## Added 2022-10-17
) {
## INPUTTING THE PREMADE MELTED TABLE
# ## Turn counttab and and metatable into melted plottab
# ## Melt the table down
# meltedtab = data.frame(melt(as.matrix(intable), varnames = c("features", "samples")), stringsAsFactors = FALSE)
# ## Reassign the samples as the class
# meltedtab[,2] = metatable[,2][match(meltedtab[,2], metatable[,1])]
# meltedtab[,1] = as.character(meltedtab[,1])
# plottab = meltedtab
plottab = boxplottable
## Create failsafe if you omit the catorder and featorder ##28-07-2021
if (is.null(labsparam$featorder)) {labsparam$featorder <- unique(plottab[,1])}
if (is.null(labsparam$catorder)) {labsparam$catorder <- unique(plottab[,2])}
## To get numbers in the same world for plotting purpose - we are going to round them to the nearest base 10
## Really only usable when you have two categories - PROBABLY BROKEN UNTIL FURTHER TESTED
## use the NAMED secondaxis variable, and scale that variable up to the first one, while labeling the second axis
if (!is.null(secondaxis)) {
roundUp <- function(x) 10^ceiling(log10(x))
for (i in seq_len(length(secondaxis))) {
temptab1 = plottab[plottab[,1] == secondaxis[i],]
temptab2 = plottab[plottab[,1] != secondaxis[i],]
tempval = mean(na.omit(temptab2[,3])) / mean(na.omit(temptab1[,3]))
scalefactor = roundUp(tempval)
plottab[plottab[,1] == secondaxis[i],3] = plottab[plottab[,1] == secondaxis[i],3] * scalefactor
}
}
## Get maxval on plot - to be used for plotting purposes later
maxval = round(max(na.omit(plottab[,3])),2)
minval = round(min(na.omit(plottab[,3])),2)
valrange <- abs(maxval) + abs(minval)
if (xsplit == "feature") {
xcol = 1
zcol = 2
## 3/17/12
# bporder = labsparam$featorder
# bporderintra = labsparam$catorder
bporder = factor(labsparam$featorder, levels = labsparam$featorder)
bporderintra = factor(labsparam$catorder, levels = labsparam$catorder)
plottab[,xcol] <- factor(plottab[,xcol], levels = labsparam$featorder)
plottab[,zcol] <- factor(plottab[,zcol], levels = labsparam$catorder)
## 3/17/12
if(is.null(bporder)) {bporder <- unique(plottab[,zcol])}
if(is.null(bporderintra)) {bporderintra <- unique(plottab[,xcol])}
} else { # xsplit == "category"
xcol = 2
zcol = 1
## 3/17/12
# bporder = labsparam$catorder
# bporderintra = labsparam$featorder
bporder = factor(labsparam$catorder, levels = labsparam$catorder)
bporderintra = factor(labsparam$featorder, levels = labsparam$featorder)
plottab[,xcol] <- factor(plottab[,xcol], levels = labsparam$catorder)
plottab[,zcol] <- factor(plottab[,zcol], levels = labsparam$featorder)
## 3/17/12
if(is.null(bporder)) {bporder <- unique(plottab[,xcol])}
if(is.null(bporderintra)) {bporderintra <- unique(plottab[,zcol])}
}
## Add in point coloring if desired # added 9/21/2021
if (pointcoloring == TRUE) {
# pointcolorcol <- 4
pointcolorparam <- plottab[,4]
} else {
# pointcolorcol <- zcol
pointcolorparam <- "black"
}
## STANDARD BOXPLOT WITH NO STATS
pout <- ggplot(plottab, aes(x=plottab[,xcol], y=plottab[,3], z=plottab[,zcol], fill = plottab[,zcol]))
if (!violin) {
pout <- pout + geom_boxplot(na.rm=TRUE, outlier.shape = NA, position = position_dodge(width=0.9))
} else {
pout <- pout + geom_violin(draw_quantiles = c(0.25, 0.5, 0.75), trim = TRUE)
}
# pout <- pout + geom_point(data = plottab, aes(x=plottab[,xcol], y=plottab[,3], fill = plottab[,pointcolorcol]),
# position=position_jitterdodge(dodge.width=0.9, jitter.width = 0.12), size = 0.8, shape = 16)
if (plotpoints) {
pout <- pout + geom_point(data = plottab, aes(x=plottab[,xcol], y=plottab[,3], color = pointcolorparam),
position=position_jitterdodge(dodge.width=0.9, jitter.width = 0.12), size = 0.8, shape = 16)
}
pout <- pout + scale_x_discrete(limits = bporder)
# pout <- pout + scale_fill_discrete(limits = bporderintra)
pout <- pout + labs(title = labsparam$title, x = labsparam$x, y = labsparam$y, fill = labsparam$fill)
if(!is.null(secondaxis)) {pout <- pout +
scale_y_continuous(sec.axis = sec_axis(~./scalefactor, name = secondaxis[1]))}
if(!is.null(colorparam)) {pout <- pout + scale_fill_manual(values = unname(colorparam), limits = names(colorparam))}
pout <- pout + theme_pubr(x.text.angle = 45, base_size = 12)
## 2022-12-13 COMBO OF THESE THINGS is fucking this up again, gah, so... maybe we dont need to scale of the geom_signif works well????
if (plotstats == FALSE) {
pout <- pout + scale_y_continuous(limits = c(round(minval-(abs(0.2*minval)), digits = 2), round(maxval*1.2, digits = 2)),
# breaks = seq(round(minval-(abs(0.2*minval)), digits = 2), round(maxval*1.2, digits = 2),length.out = 5))
breaks = round(seq(round(minval-(abs(0.2*minval)), digits = 2), round(maxval*1.2, digits = 2),length.out = 5),2))
}
## CREATE COMBO TABLE FOR STATS IF APPLICABLE
if (plotstats != FALSE) {
if (is.null(comparisonparam)) {
combcol = ifelse(plotstats == "inter", xcol, zcol)
## Put a failsafe in here to only test against those with at least 2 points (cant have a t.test against 1 pt)
# comparisontemp <- na.omit(names(table(plottab[!is.na(plottab[,combcol]),combcol])[table(plottab[!is.na(plottab[,combcol]),combcol])> 1]))
# comparisontemp2 <- comparisontemp[order(comparisontemp)]
# if (length(comparisontemp2) > 1) {
# combtab = combn(comparisontemp2, 2, simplify=F)
# } else {
# combtab <- NULL
# }
combtab = combn(as.character(unique(plottab[!is.na(plottab[,combcol]),combcol])), 2, simplify=F)
} else {
if (is.character(comparisonparam) | is.factor(comparisonparam)) {
comparisonparam <- as.character(comparisonparam)
combtab = combn(comparisonparam, 2, simplify=F)
}
if (is.list(comparisonparam)) {
combtab = comparisonparam
}
}
## Failsafe here to exclude statistical testing if not enough values in group
excludegroups <- names(table(plottab[,2])[table(plottab[,2]) < 2])
combtab <- combtab[!unlist(lapply(combtab, function(x) sum(x %in% excludegroups) > 0))]
}
### THIS DOES SIGNIFICANCE BETWEEN THE X AXIS GROUPS
if (plotstats == "inter"){
# pout <- pout + geom_signif(comparisons = combtab, step_increase = 0.1,
# y_position = seq(maxval*1.2, (maxval*(1.1+(0.1*length(combtab)))), 0.1*maxval),
# map_signif_level=FALSE)}
pout <- pout + geom_signif(comparisons = combtab, step_increase = 0.15, test = testtypeparam,
# y_position = seq(maxval*1.1, (maxval*(1.0+(0.1*length(combtab)))), 0.1*maxval), ## 27-07-2021 - change because the step.increase does this better than the manual setting
map_signif_level=FALSE)
## 2022-12-13 COMBO OF THESE THINGS is fucking this up again, gah, so... maybe we dont need to scale of the geom_signif works well????
# pout <- suppressMessages(pout + scale_y_continuous(limits = c(round(minval-(abs(0.2*minval)), digits = 2), round(maxval*(1.1+(0.15*length(combtab))), digits = 2)),
# # breaks = seq(round(minval-(abs(0.2*minval)), digits = 2), round(maxval*(1.1+(0.1*length(combtab))), digits = 2),length.out = 5)))
# breaks = round(seq(round(minval-(abs(0.2*minval)), digits = 2), round(maxval*(1.1+(0.1*length(combtab))), digits = 2),length.out = 5),2)))
}
### THIS DOES SIGNIFICANCE BETWEEN SUBGROUPS
if (plotstats == "intra") {
pout <- ggplot(plottab, aes(x=plottab[,zcol], y=plottab[,3], fill = plottab[,zcol]))
pout <- pout + facet_wrap(~factor(plottab[,xcol], levels = bporder), scales = "free")
if (!violin) {
pout <- pout + geom_boxplot(na.rm=TRUE, outlier.shape = NA, position = position_dodge(width=0.9))
} else {
pout <- pout + geom_violin(draw_quantiles = c(0.25, 0.5, 0.75), trim = TRUE)
}
# pout <- pout + geom_point(data = plottab, aes(x=plottab[,zcol], y=plottab[,3], fill = plottab[,pointcolorcol]),
# position=position_jitterdodge(dodge.width=0.9, jitter.width = 0.12), size = 0.5, shape = 16)
if (plotpoints) {
pout <- pout + geom_point(data = plottab, aes(x=plottab[,zcol], y=plottab[,3], color = pointcolorparam),
position=position_jitterdodge(dodge.width=0.9, jitter.width = 0.12), size = 0.5, shape = 16)
}
pout <- pout + scale_x_discrete(limits = bporderintra)
pout <- pout + labs(title = labsparam$title, x = labsparam$x, y = labsparam$y, fill = labsparam$fill)
if(!is.null(secondaxis)) {pout <- pout +
scale_y_continuous(sec.axis = sec_axis(~./scalefactor, name = secondaxis[1]))}
if(!is.null(colorparam)) {pout <- pout + scale_fill_manual(values = unname(colorparam), limits = names(colorparam))}
pout <- pout + theme_pubr(x.text.angle = 60, base_size = 12, border = TRUE)
# pout <- pout + stat_compare_means(comparisons = combtab, method.args = list(exact = FALSE), label = "p.format", size = 3,
# label.y = seq(maxval*1.1, (maxval*(1.0+(0.1*length(combtab)))), 0.1*maxval))
if (length(combtab) > 0) { # failsafe for if there arent actually nay viable comps
## Failsafe for single comp and fix rounding error:
failsafesignparam <- sign(maxval+(0.1*length(combtab)*valrange) - maxval*1.1)
pout <- pout + stat_compare_means(comparisons = combtab, method = testtypeparam,
# method.args = list(exact = TRUE), ### FOR SOME REASON - THIS IS KILLING MY RUN TIME, so omitting for now
label = "p.format", size = 3,
# label.y = seq(maxval*1.1, (maxval*(1.0+(0.1*length(combtab)))), 0.1*maxval))
# label.y = seq(maxval*1.1, maxval*(0.1*length(combtab)*valrange), 0.1*valrange))
label.y = seq(maxval*1.1, maxval+(0.1*length(combtab)*valrange), failsafesignparam*0.1*valrange))
## bug here where the rounding screws it up... really only when you have one comp though
# label.y = seq(ceiling(maxval*1.1, 2), ceiling(maxval+(0.1*length(combtab)*valrange),2), round(0.1*valrange,2)))
}
# pout <- pout + scale_y_continuous(limits = c(round(minval-(abs(0.2*minval)), digits = 2), round(maxval*(1.1+(0.1*length(combtab))), digits = 2)),
# breaks = seq(round(minval-(abs(0.2*minval)), digits = 2), round(maxval*(1.1+(0.1*length(combtab))), digits = 2),length.out = 5))
# pout <- pout + scale_y_continuous(limits = c(round(minval-(abs(0.2*minval)), digits = 2), round(0.1*length(combtab)*valrange, digits = 2)),
# breaks = seq(round(minval-(abs(0.2*minval)), digits = 2), round(maxval*(1.1+(0.1*length(combtab))), digits = 2),length.out = 5))
pout <- pout + scale_y_continuous(limits = c(round(minval-(abs(0.2*minval)), digits = 2), round(maxval+(0.1*length(combtab)*valrange+0.1*valrange), digits = 2)),
# breaks = seq(round(minval-(abs(0.2*minval)), digits = 2), round(maxval*(1.1+(0.1*length(combtab))), digits = 2),length.out = 5))
breaks = round(seq(round(minval-(abs(0.2*minval)), digits = 2), round(maxval*(1.1+(0.1*length(combtab))), digits = 2),length.out = 5),2))
# expand_scale(mult = c(1.2, round(maxval*(1.1+(0.1*length(combtab))), digits = 2)))
}
## Add in point coloring if desired # added 9/21/2021
if (pointcoloring != TRUE) {
pout <- pout + scale_color_manual(values = "black", breaks = "black")
}
pout
#return(pout)
}
# --------------------------------- histogram plot ---------------------------------
## Histogram function
# p1datatab = as.data.frame(colSums(counttab))
# colnames(p1datatab) = "sampreadsum"
# plot_histogram(data = p1datatab,
# fitcurve=TRUE, binwidthparam = NULL, limitx = NULL,
# labsparam = list(title = "Counts for total reads per Sample prefilter", x = "Count", y = "reads in sample"),
# pdffile = paste(outfilepath, "prefiltered_samp_readcount_hist.pdf", sep=""))
# plot_histogram <- function(data, fitcurve = FALSE, binwidthparam=NULL, labsparam, limitx = NULL, pdffile) {
plot_histogram <- function(data, groupvar = NULL, fitcurve = FALSE, binparam=NULL, labsparam, limitx = NULL, limity = NULL) {
## Amazingly - i need a catch here to make it a data.frame 2022-10-10
data <- data.frame(data)
## If we want to fit curve to our single curve
if (fitcurve == TRUE){
meantemp = mean(data[,1])
sdtemp = sd(data[,1])
}
## binning data properly
if (is.null(binparam)) {binparam = 10}
# if (binparam > 100) {binparam = 100} # 2022-03-10
## Add histograms for multiple groups
if (!is.null(groupvar)) {
## Failsafe to make sure group is a character
data$groupvar <- as.character(groupvar[,1])
}
## plot the stuff
pout <- ggplot(data, aes(x=data[,1]))
if (!is.null(groupvar)) {
pout <- pout + aes(fill=data$groupvar)
pout <- pout + geom_histogram(color = "black", bins = binparam, alpha = 0.3, position="identity")
} else {
pout <- pout + geom_histogram(fill = "blue", color = "black", bins = binparam, alpha = 0.3, position="identity")
}
if (fitcurve == TRUE){
pout <- pout + geom_freqpoly(mapping = aes(color = data$groupvar), bins = binparam, position = "identity")
# pout <- pout + stat_function(fun = function(x, mean, sd) {
# dnorm(x=x, mean = meantemp, sd = sdtemp)*binparam}, color="red")
}
if (!is.null(limitx)) {pout <- pout + coord_cartesian(xlim = limitx)}
if (!is.null(limity)) {pout <- pout + coord_cartesian(ylim = limity)}
#pout <- pout + labs(title = "Counts for rowMeans per Gene prefilter", x = "Count", y = "log2(RowMean)")
pout <- pout + labs(title = labsparam$title, x = labsparam$x, y = labsparam$y)
pout <- pout + theme_bw()
return(pout)
}
# --------------------------------- PCA plotter ---------------------------------
## PCA plotting
# colorvar = metatablefilt3[,desccol,drop=FALSE]
# labsparam = c(list(title = "PCA", x = "PC1", y = "PC2", color=colnames(metatablefilt3)[desccol]))
# outfile = paste(outfilepath, "pca_plots/", colnames(metatablefilt3)[desccol], "_pca_plot.pdf", sep="")
# pca_plotter(pcadata = pcadata, colorvar = colorvar, scalecolor=FALSE, labelpoints = TRUE, labsparam = labsparam, outfile = outfile)
pca_plotter <- function(pcadata, colorvar=NULL, shapevar = NULL,
scalecolor=FALSE, labelpoints = FALSE, separatelegend = FALSE, labsparam,
returnoutliers = TRUE, custompcs = NULL) {
pcdata = prcomp(pcadata, scale = TRUE)
if (is.null(custompcs)){
pc1label <- "PC1"
pc2label <- "PC2"
} else{
pc1label <- custompcs[1]
pc2label <- custompcs[2]
}
pc1 = pcdata$x[,pc1label]
pc2 = pcdata$x[,pc2label]
## Add in outlier analysis
pca_outliers <- NULL
if (returnoutliers == TRUE) {
zscore_pc <- scale(pc1)
outliercutoff <- 3
pca_outliers <- zscore_pc[zscore_pc > outliercutoff,,drop=FALSE]
colnames(pca_outliers) <- "pca_diff"
}
pcloadings = as.data.frame(pcdata$rotation)
pc1genes = rownames(pcloadings[order(pcloadings[,pc1label], decreasing=TRUE),])[1:min(ncol(pcadata), 5)]
pc2genes = rownames(pcloadings[order(pcloadings[,pc2label], decreasing=TRUE),])[1:min(ncol(pcadata), 5)]
pcproportions = summary(pcdata)$importance[2,]
xlabel = paste(labsparam$x, "_", percent(pcproportions[pc1label]), "_", paste(pc1genes, collapse = "_"), sep="")
ylabel = paste(labsparam$y, "_", percent(pcproportions[pc2label]), "_", paste(pc2genes, collapse = "_"), sep="")
pcaplotdata = cbind.data.frame(PC1 = pc1, PC2 = pc2)
if (!is.null(colorvar)) {pcaplotdata$colorvar = colorvar[,1]}
if (!is.null(shapevar)) {pcaplotdata$shapevar = shapevar[,1]}
if (labelpoints == TRUE) {
pout <- ggplot(data = pcaplotdata, aes(x=pcaplotdata[,1], y = pcaplotdata[,2], label=rownames(pcaplotdata)))
} else {
pout <- ggplot(data = pcaplotdata, aes(x=pcaplotdata[,1], y = pcaplotdata[,2]))
}
# if (!is.null(colorvar)) {
# pout <- pout + aes(color=colorvar)
# pout <- pout + geom_point(size = 2.5, shape = 16)
# }
# if (!is.null(shapevar)) {
# pout <- pout + aes(shape=shapevar)
# pout <- pout + geom_point(size = 2.5)
# }
if (!is.null(colorvar)) {pout <- pout + aes(color=colorvar)}
if (!is.null(shapevar)) {pout <- pout + aes(shape=shapevar)}
if (!is.null(shapevar)) {
pout <- pout + geom_point(alpha = 0.7)
} else {
pout <- pout + geom_point(alpha = 0.7, shape = 16)
}
if (labelpoints == TRUE) {pout <- pout + geom_text_repel(box.padding = unit(0.5, "lines"), point.padding = unit(0.5, "lines"),
max.overlaps = 1000)}
if (returnoutliers == TRUE){
pcaplotoutliers <- pcaplotdata[rownames(pca_outliers),]
# pout <- pout + geom_text_repel(data = pcaplotoutliers, aes(x=pcaplotoutliers[,1], y = pcaplotoutliers[,2],
# color=pcaplotoutliers[,3], label=rownames(pcaplotoutliers)),
# box.padding = unit(0.5, "lines"), point.padding = unit(0.5, "lines"))
if (!is.null(colorvar)) {
pout <- pout + geom_text_repel(data = pcaplotoutliers, aes(x=pcaplotoutliers[,1], y = pcaplotoutliers[,2],
color=pcaplotoutliers[,3], label=rownames(pcaplotoutliers)),
box.padding = unit(0.5, "lines"), point.padding = unit(0.5, "lines"))
} else {
pout <- pout + geom_text_repel(data = pcaplotoutliers, aes(x=pcaplotoutliers[,1], y = pcaplotoutliers[,2],
label=rownames(pcaplotoutliers)),
box.padding = unit(0.5, "lines"), point.padding = unit(0.5, "lines"))
}
}
pout <- pout + labs(title = labsparam$title, x = xlabel, y = ylabel, color=labsparam$color, shape=labsparam$shape)
if (scalecolor == TRUE) {pout <- pout + scale_color_gradient(low = "yellow", high = "blue")}
if (separatelegend == TRUE) {
legend <- g_legend(pout)
pout <- pout + theme_pubr(legend = "none")
return(list(pca_plot = pout, pca_legend = legend, pca_outliers = pca_outliers, pcdata = pcdata))
} else {
pout <- pout + theme_pubr(legend = "top")
return(list(pca_out = pout, pca_outliers = pca_outliers, pcdata = pcdata))
}
}
# --------------------------------- scatter plot ---------------------------------
## Scatter Plot
# input table has first two columns as x and y, any additional shapevar or colorvar must be a specified separate column
# datalabels work by giving a list of samples that match rownames that you want to label, NEED ROWNAMES IF YOU WANNA LABEL
# indata = data.frame(col1, col2, col3, col4)
# datalabels = rownames(intab[,col3 = LABELTHESE])
# colorvar = indata[,col3,drop=FALSE]
# shapevar = indata[,col4,drop=FALSE]
# labsparam = list(title = "raw to UQ norm count comparison", x = "raw", y = "norm")
# scatter_plotter(indata, colorvar, shapevar, datalabels, labsparam, plotstats)
scatter_plotter <- function(indata, colorvar=NULL, shapevar = NULL, sizevar = NULL,
datalabels = NULL, labsparam, plotstats = FALSE, addjitterparam = NULL){
if (!is.null(colorvar)) {indata$colorvar = colorvar[,1]}
if (!is.null(shapevar)) {indata$shapevar = shapevar[,1]}
if (!is.null(sizevar)) {indata$sizevar = sizevar[,1]}
plotdata <- na.omit(indata)
pout <- ggplot(data = plotdata, aes(x=plotdata[,1], y = plotdata[,2]))
# if (!is.null(colorvar)) {
# pout <- pout + aes(color=plotdata$colorvar,fill=plotdata$colorvar)
# if (sum(areColors(plotdata$colorvar)) == length(plotdata$colorvar) & !is.numeric(plotdata$colorvar)) { ## If your colorvar are literally colors and NOT number - then use them
# pout <- pout + scale_color_identity()
# }
# } ##1/13/2021
if (!is.null(colorvar)) {
pout <- pout + aes(color = colorvar,fill = colorvar)
if (sum(areColors(plotdata$colorvar)) == length(plotdata$colorvar) & !is.numeric(plotdata$colorvar)) { ## If your colorvar are literally colors and NOT number - then use them
pout <- pout + scale_color_identity()
pout <- pout + scale_fill_identity()
}
} ##1/13/2021
if (!is.null(shapevar)) {pout <- pout + aes(shape=plotdata$shapevar)}
if (!is.null(sizevar)) {pout <- pout + aes(size=plotdata$sizevar)}
# pout <- pout + geom_point(alpha = 0.7, shape = 16)
if (!is.null(addjitterparam)){
if (!is.null(shapevar)) {
pout <- pout + geom_point(alpha = 0.7, position = position_jitter(width = addjitterparam))
} else {
pout <- pout + geom_point(alpha = 0.7, shape = 16, position = position_jitter(width = addjitterparam))
}
} else { ## Have to keep shapevar in their even if with jiutterparam
if (!is.null(shapevar)) {
pout <- pout + geom_point(alpha = 0.7)
} else {
pout <- pout + geom_point(alpha = 0.7, shape = 16)
}
}
if (!is.null(datalabels)) {
## pout <- pout + geom_label_repel(data = labeledplottab, mapping = aes(x=labeledplottab[,1], y = labeledplottab[,2],
## # color=labeledplottab$colorvar, size=labeledplottab$sizevar,
## label=rownames(labeledplottab)),
## box.padding = unit(0.5, "lines"), point.padding = unit(0.5, "lines"))
#pout <- pout + aes(label=rownames(labeledplottab))
# labeledplottab <- plotdata[datalabels,]
# pout <- pout + geom_label_repel(data = labeledplottab, mapping = aes(x=labeledplottab[,1], y = labeledplottab[,2],
# color = NULL, size = NULL, shape = NULL, label = rownames(labeledplottab)),
# box.padding = unit(1, "lines"), point.padding = unit(1, "lines"))
plotdata$labelvar <- ""
plotdata[datalabels,"labelvar"] <- datalabels
pout <- pout + aes(label=plotdata$labelvar)
pout <- pout + geom_label_repel(mapping = aes(size = NULL, label = plotdata$labelvar, fill = NULL),
box.padding = unit(0.5, "lines"), point.padding = unit(0.5, "lines"),
size = 2, max.overlaps = 10000)
}
pout
if (plotstats != FALSE) {
if (plotstats == TRUE) {corrmethod = "spearman"} else {corrmethod = plotstats}
# pout <- pout + geom_smooth(method = lm, color="black")
# pout <- pout + geom_text(label = lm_eqn(plotdata[,c(1:2)]), parse=TRUE,
# mapping = aes(x = 0.8*max(plotdata[,1]), y = 0.9*max(plotdata[,2])), size = 8)
#
pout <- pout + geom_smooth(method = "gam", formula = y ~ x, color="black", data = plotdata[,c(1,2)],
inherit.aes = FALSE, aes(x = plotdata[,1], y = plotdata[,2], color = NULL, shape = NULL),)
pout <- pout + stat_cor(data = plotdata[,c(1,2)], inherit.aes = FALSE, aes(x = plotdata[,1], y = plotdata[,2], color = NULL, shape = NULL),
method = corrmethod, label.x = 0.6*min(plotdata[,1]), label.y = 1.1*max(plotdata[,2]), size = 3)
}
pout <- pout + labs(title = labsparam$title, x = labsparam$x, y = labsparam$y, color = labsparam$color, shape = labsparam$shape, size = labsparam$size)
pout <- pout + theme_pubr()
pout
return(pout)
}
# --------------------------------- scatter plot with BIG add on rasterizing ---------------------------------
# scatter_BIG_plotter <- function(indata, colorvar=NULL, shapevar = NULL, sizevar = NULL,
# datalabels = NULL, labsparam, plotstats = FALSE, addjitterparam = NULL){
#
# if (!is.null(colorvar)) {indata$colorvar = colorvar[,1]}
# if (!is.null(shapevar)) {indata$shapevar = shapevar[,1]}
# if (!is.null(sizevar)) {indata$sizevar = sizevar[,1]}
#
# plotdata <- na.omit(indata)
#
# pout <- ggplot(data = plotdata, aes(x=plotdata[,1], y = plotdata[,2]))
# # if (!is.null(colorvar)) {
# # pout <- pout + aes(color=plotdata$colorvar,fill=plotdata$colorvar)
# # if (sum(areColors(plotdata$colorvar)) == length(plotdata$colorvar) & !is.numeric(plotdata$colorvar)) { ## If your colorvar are literally colors and NOT number - then use them
# # pout <- pout + scale_color_identity()
# # }
# # } ##1/13/2021
# if (!is.null(colorvar)) {
# pout <- pout + aes(color = colorvar,fill = colorvar)
# if (sum(areColors(plotdata$colorvar)) == length(plotdata$colorvar) & !is.numeric(plotdata$colorvar)) { ## If your colorvar are literally colors and NOT number - then use them
# pout <- pout + scale_color_identity()
# pout <- pout + scale_fill_identity()
# }
# } ##1/13/2021
# if (!is.null(shapevar)) {pout <- pout + aes(shape=plotdata$shapevar)}
# if (!is.null(sizevar)) {pout <- pout + aes(size=plotdata$sizevar)}
# # pout <- pout + geom_point(alpha = 0.7, shape = 16)
# if (!is.null(addjitterparam)){
# if (!is.null(shapevar)) {
# pout <- pout + geom_point(alpha = 0.7, position = position_jitter(width = addjitterparam))
# } else {
# pout <- pout + geom_point(alpha = 0.7, shape = 16, position = position_jitter(width = addjitterparam))
# }
# } else { ## Have to keep shapevar in their even if with jiutterparam
# if (!is.null(shapevar)) {
# pout <- pout + geom_point(alpha = 0.7)
# } else {
# pout <- pout + geom_point(alpha = 0.7, shape = 16)
# }
# }
# if (!is.null(datalabels)) {
#
# ## pout <- pout + geom_label_repel(data = labeledplottab, mapping = aes(x=labeledplottab[,1], y = labeledplottab[,2],
# ## # color=labeledplottab$colorvar, size=labeledplottab$sizevar,
# ## label=rownames(labeledplottab)),
# ## box.padding = unit(0.5, "lines"), point.padding = unit(0.5, "lines"))
# #pout <- pout + aes(label=rownames(labeledplottab))
#
# # labeledplottab <- plotdata[datalabels,]
# # pout <- pout + geom_label_repel(data = labeledplottab, mapping = aes(x=labeledplottab[,1], y = labeledplottab[,2],
# # color = NULL, size = NULL, shape = NULL, label = rownames(labeledplottab)),
# # box.padding = unit(1, "lines"), point.padding = unit(1, "lines"))
#
# plotdata$labelvar <- ""
# plotdata[datalabels,"labelvar"] <- datalabels
# pout <- pout + aes(label=plotdata$labelvar)
# pout <- pout + geom_label_repel(mapping = aes(size = NULL, label = plotdata$labelvar, fill = NULL),
# box.padding = unit(0.5, "lines"), point.padding = unit(0.5, "lines"),
# size = 2, max.overlaps = 10000)
#
# }
# pout
#
#
# if (plotstats != FALSE) {
# if (plotstats == TRUE) {corrmethod = "spearman"} else {corrmethod = plotstats}
# # pout <- pout + geom_smooth(method = lm, color="black")
# # pout <- pout + geom_text(label = lm_eqn(plotdata[,c(1:2)]), parse=TRUE,
# # mapping = aes(x = 0.8*max(plotdata[,1]), y = 0.9*max(plotdata[,2])), size = 8)
# #
# pout <- pout + geom_smooth(method = "gam", formula = y ~ x, color="black", data = plotdata[,c(1,2)],
# inherit.aes = FALSE, aes(x = plotdata[,1], y = plotdata[,2], color = NULL, shape = NULL),)
# pout <- pout + stat_cor(data = plotdata[,c(1,2)], inherit.aes = FALSE, aes(x = plotdata[,1], y = plotdata[,2], color = NULL, shape = NULL),
# method = corrmethod, label.x = 0.6*min(plotdata[,1]), label.y = 1.1*max(plotdata[,2]), size = 3)
# }
# pout <- pout + labs(title = labsparam$title, x = labsparam$x, y = labsparam$y, color = labsparam$color, shape = labsparam$shape, size = labsparam$size)
# pout <- pout + theme_pubr()
# pout
#
# return(pout)
# }
# --------------------------------- linechart plot ---------------------------------
## linechart Plot
# input table has first two columns as x and y, any additional shapevar or colorvar must be a specified separate column.
# datalabels work by giving a list of samples that match rownames that you want to label
# indata = data.frame(col1, col2, col3, col4)
# datalabels = rownames(intab[,col3 = LABELTHESE])
# colorvar = indata[,col3,drop=FALSE]
# shapevar = indata[,col4,drop=FALSE]
# labsparam = list(title = "raw to UQ norm count comparison", x = "raw", y = "norm")
# scatter_plotter(indata, colorvar, shapevar, datalabels, labsparam, plotstats)
linechart_plotter <- function(indata, groupvar = NULL, colorvar=NULL, shapevar = NULL,
datalabels = NULL, showpoints = TRUE, labsparam){
plotdata <- indata
if (!is.null(groupvar)) {plotdata$groupvar = groupvar[,1]}
if (!is.null(colorvar)) {plotdata$colorvar = colorvar[,1]}
if (!is.null(shapevar)) {plotdata$shapevar = shapevar[,1]}
pout <- ggplot(data = plotdata, aes(x=plotdata[,1], y = plotdata[,2]))
if (!is.null(colorvar)) {pout <- pout + aes(color=plotdata$colorvar)}
if (!is.null(shapevar)) {pout <- pout + aes(shape=plotdata$shapevar)}
if (!is.null(groupvar)) {pout <- pout + aes(group=plotdata$groupvar)}
if (showpoints == TRUE) { pout <- pout + geom_point(alpha = 0.3, shape = 16)}
pout <- pout + geom_path()
if (!is.null(datalabels)) {
labeledplottab <- plotdata[datalabels,]
pout <- pout + geom_text_repel(data = labeledplottab, aes(x=labeledplottab[,1], y = labeledplottab[,2],
color=labeledplottab$colorvar, label=rownames(labeledplottab)),
box.padding = unit(0.5, "lines"), point.padding = unit(0.5, "lines"))
}
pout <- pout + labs(title = labsparam$title, x = labsparam$x, y = labsparam$y, color = labsparam$color, shape = labsparam$shape, size = labsparam$size)
pout <- pout + theme_pubr()
return(pout)
}
## Volcano Scatter Plot
# intable = table with log2fc as 2nd columns and pvalue as 1st
# pvalcutoff = value for pval cutoff
# log2fccutoff = value for log2fc cutoff
# labelparam = param for labeling plots, true or false - will label ALL GOI for now
# pout = create_volcano_plot(intable, pvalcutoff = 0.01, log2fccutoff = 1, labelparam = FALSE)
create_volcano_plot <- function(intable, pvalcutoff = 0.01, log2fccutoff = 1, labeledgenes = NULL, nameparam = "Volcano Plot") {
plottab = intable
plottab[is.na(plottab[,2]),2] <- 1 ## Failsafe to have any pvalues of NA be set as 1
## Failsafe for the log2fc - setting all NA to 0
plottab[is.na(plottab[,1]),1] <- 0
plottab$color = "notsig"
plottab[plottab[,2] < pvalcutoff & plottab[,1,drop=FALSE] > 0, "color"] <- "sigpvalup" # pink
plottab[plottab[,1,drop=FALSE] > log2fccutoff, "color"] <- "siglog2fcup" # red
plottab[plottab[,1,drop=FALSE] > log2fccutoff & plottab[,2] < pvalcutoff, "color"] <- "sigbothup" # darkred
plottab[plottab[,2] < pvalcutoff & plottab[,1,drop=FALSE] < 0, "color"] <- "sigpvaldown" # light blue
plottab[plottab[,1,drop=FALSE] < -log2fccutoff, "color"] <- "siglog2fcdown" # blue
plottab[plottab[,1,drop=FALSE] < -log2fccutoff & plottab[,2] < pvalcutoff, "color"] <- "sigbothdown" # dark blue
plottab[,2] = -log10(plottab[,2])
plottabin = as.data.frame(plottab)
# great, pink, red, darkred, light blue, blue, dark blue
colorparam = c("grey", "#ff9999", "#ff0000", "#990000", "#b2b2ff", "#1919ff", "#0000b2")
names(colorparam) = c("notsig","sigpvalup", "siglog2fcup", "sigbothup", "sigpvaldown", "siglog2fcdown", "sigbothdown" )
if (is.logical(labeledgenes) && labeledgenes == TRUE) {
maxnumlabeledgenes = 40
labeledgenes = rownames(plottabin[plottabin[,"color"] %in% c("sigbothup", "sigbothdown"),])
labelmetric = plottabin[,1, drop=FALSE] * plottabin[,2, drop=FALSE]
labeledgenes = rownames(labelmetric[order(abs(labelmetric), decreasing = TRUE),,drop=FALSE][1:40,,drop=FALSE])
}
labeltab = plottabin[rownames(plottabin) %in% labeledgenes,]
pout <- ggplot(plottabin, aes(x = plottabin[,1], y = plottabin[,2], color = plottabin[,3]))
pout <- pout + geom_point(size = 0.5, shape = 16)
pout <- pout + scale_color_manual(values = colorparam)
pout <- pout + labs(x = "log2fc", y = "-log10(pvalue)",
title = nameparam, color = "significance")
if (!is.null(labeledgenes)) {pout <- pout + geom_label_repel(
data = labeltab,
aes(x = labeltab[,1], y=labeltab[,2], label = rownames(labeltab), color=labeltab[,3]),
size=2, segment.size = 0.2, fontface="bold", segment.color = "black",
box.padding = unit(0.1, "lines"), point.padding = unit(0.1, "lines"),
max.overlaps = 10000
)}
return(pout)
}
## WRITE HEATMAP FUNCTION
#counttab ## the counttable
#subsetnum = FALSE ## set to a number if you want to select by the N most varied genes (good for subsetting)
#metatable ## The metatable
#annotationlist ## The annotation object created beforehand (NEED TO AUTOMATE THIS MORE)
#colclusterparam = FALSE ## if not FALSE - will cluster the columns
#rowclusterparam = FALSE ## if not FALSE - will cluster the rows
#pdfoutfile ## the path to the outfile that the pdf will be saved to
# clustermethod = "ward.D2"
create_heatmap <- function(counttab,
subsetnum = FALSE,
scale_data = TRUE,
colmetatable = NULL, colannotationlist = NULL,
rowmetatable = NULL, rowannotationlist = NULL,
colclusterparam = FALSE, rowclusterparam = FALSE,
separate_legend = FALSE, heatmapcolorparam = NULL,
addborders = FALSE, columnsplitparam = NULL, rowsplitparam = NULL,
titleparam = c(column_title = "Samples", row_title = "Genes", figure_title = NULL, legend_title = "zscore"),
show_column_dend_param = TRUE, show_row_dend_param = TRUE ## 2023-01-30
# pdfoutfile
) {
colclusterparamIN = colclusterparam
rowclusterparamIN = rowclusterparam
## Filter to the N most varied genes if applicable
if (subsetnum !=FALSE) {
counttab_variance = sort(apply(counttab,1,var), decreasing=TRUE)
counttab = counttab[names(counttab_variance[1:subsetnum]),,drop=FALSE]