R/visualizePreprocessing.R
In holgerman/HT12ProcessoR: Preprocessing Illuminas HT12 v4 data
Defines functions
visualizePreprocessing
Documented in
visualizePreprocessing
#' @title Visualize expression data before and after preprocessing
#'
#' @description Visualisation of control features and expression data before and after preprocessing in several plots. Thereby, raw data before preprocessing is log2 transformed for a more meaningful comparison
#' @param ht12object A list object of class HT12prepro created with function filterAtypicalExpressed()
#' @param paramfile Path to the file specifying parameters
#' @param show_qcplots_individSamplelevel Show QC plots comparing expression values before / after preprocessing either binned on SentrixID ('FALSE') or binned on Sample ID ('TRUE')? If "from_paramfile", than the parameter will be read from the paramfile with the location of this file given in parameter paramfile.
#' @param showPlots Show plots. If FALSE, plots are only stored in slot $dokuobjects_visualizePreprocessing
#' @return A list object of class HT12prepro including additional slots $dokuobjects_visualizePreprocessing
#' @import data.table
#' @export
## debug
# paramfile = "/mnt/ifs1_projekte/genstat/02_projekte/1704_boettcher_ge_ht12/01_prepro/input_parameter_007.txt"
# ht12object = prepro_ht12
# show_qcplots_individSamplelevel = "from_paramfile"
# showPlots = T
visualizePreprocessing = function(ht12object,paramfile = NULL,show_qcplots_individSamplelevel = "from_paramfile", showPlots=T) {
### strings are imported as strings and not as factors
options(stringsAsFactors=FALSE)
myparameters = match.call()
showVennplots = F
# status checken
historie = ht12object$history$calls
if(any(grepl("filterAtypicalExpressed", historie))==F) stop("Function 'filterAtypicalExpressed()' has to be run before!")
#laden parameter
if(is.null(paramfile)==F) param <- data.frame(data.table::fread(paramfile))
sample_overview_l9 <- ht12object$chipsamples
## ----functions-----------------------------------------------------------
annotateEset <- function (e1, annotcon, attrib = sample_overview_l10instudy, showPlots = T) {
# newnames = attrib[match(names(e1), attrib$new_ID), "old_ID"]
# grep(" ", attrib$old_ID, value = T)
# newnames = stringr::str_replace_all(newnames, " ", "")
# names(e1) = newnames
e1$ilmn <- suppressWarnings(lumi::nuID2IlluminaID(row.names(e1)))[, 'Probe_Id']
e1[grep("ILMN", row.names(e1)), "ilmn"] <- row.names(e1[grep("ILMN", row.names(e1)),])
# print(table(is.na(e1$ilmn)))
e1 <- moveColFront(e1, "ilmn")
e1 <- merge(e1, annotcon[, c("ilmn", "Reporter_Group_Name", "Reporter_Group_id")], by = "ilmn", all.x = T)
e1 <- moveColFront(e1, "Reporter_Group_Name")
e1 <- moveColFront(e1, "Reporter_Group_id")
e1
}
makePrintDF <- function (e2, var_of_interest, attrib = sample_overview_l10instudy) {
# debug
# var_of_interest = "housekeeping"
# attrib = sample_overview_l10instudy
d <- e2[(e2$Reporter_Group_id == var_of_interest & is.na(e2$Reporter_Group_id) == F)|
(e2$Reporter_Group_Name == var_of_interest & is.na(e2$Reporter_Group_Name) == F), ]
# print(hh(d))
d <- t(d[, 4:(dim(d)[2])])
myid <- row.names(d)
d <- data.frame(d)
d$id <- myid
d$Sentrix.Barcode <- attrib[match_hk(d$id, attrib$new_ID), "Sentrix.Barcode"]
# print(table(d$Sentrix.Barcode, useNA = "always"))
d$Sentrix.Barcode <- attrib[match_hk(d$id, attrib$new_ID), "Sentrix.Barcode"]
# print(table(d$Sentrix.Barcode, useNA = "always"))
d$subgroup <- attrib[match_hk(d$id, attrib$new_ID), "subgroup"]
# print(mytable(d$subgroup))
dm <- melt(d, id.vars = c("id", "Sentrix.Barcode", "subgroup"))
dm$value <- as.numeric(dm$value)
names(dm)[names(dm) == "value"] <- var_of_interest
dm$fileset_id <- attrib[match_hk(d$id, attrib$new_ID), "fileset_id"]
dm$strange_batch <- attrib[match_hk(d$id, attrib$new_ID), "strangebatch"]
names(dm) <- stringr::str_replace_all(names(dm), ":", "_")
# print(ht(dm, 2))
dm
}
plotGenes <- function (var_of_interest = "housekeeping", e1m) {
pd <- e1m[, c(var_of_interest, 'Sentrix.Barcode', 'fileset_id', 'strange_batch')]
pd <- pd[order(pd$fileset_id), ]
pd$Sentrix.Barcode <- factor(pd$Sentrix.Barcode, levels = unique(pd$Sentrix.Barcode))
pd$strange_batch <- as.numeric(factor(pd$strange_batch))
# print(ht(pd))
p1 <- ggplot2::ggplot(pd, ggplot2::aes_string(x = 'Sentrix.Barcode',
y = var_of_interest,
group = 'Sentrix.Barcode',
fill = 'fileset_id',
col = 'fileset_id',
alpha = 'strange_batch')) +
ggplot2::geom_boxplot() +
ggplot2::guides(col = FALSE, alpha = F, fill = guide_legend(ncol = 4)) +
ggplot2::theme(axis.text.x = element_blank(), legend.text = element_text(size = 8)) +
ggplot2::theme(legend.position="top")
p1
}
plotGenes2 <- function (var_of_interest = "housekeeping", e1m) {
pd <- e1m[, c(var_of_interest, 'Sentrix.Barcode', 'fileset_id', 'strange_batch', "subgroup")]
pd <- pd[order(pd$fileset_id), ]
pd$Sentrix.Barcode <- factor(pd$Sentrix.Barcode, levels = unique(pd$Sentrix.Barcode))
pd$strange_batch <- as.numeric(factor(pd$strange_batch))
# print(ht(pd))
p1 <- ggplot2::ggplot(pd, ggplot2::aes_string(x = 'Sentrix.Barcode',
y = var_of_interest,
group = 'Sentrix.Barcode',
fill = 'fileset_id',
col = 'fileset_id',
alpha = 'strange_batch')) +
ggplot2::geom_boxplot() +
ggplot2::guides(col = FALSE, alpha = F, fill = guide_legend(ncol = 4)) +
ggplot2::theme(axis.text.x = element_blank(), legend.text = element_text(size = 8)) +
ggplot2::facet_grid(. ~subgroup, scales = "free_x") +
ggplot2::theme(legend.position="top")
p1
}
plotGenes2log2 <- function (var_of_interest = "housekeeping", e1m) {
pd <- e1m[, c(var_of_interest, 'Sentrix.Barcode', 'fileset_id', 'strange_batch', "subgroup")]
pd <- pd[order(pd$fileset_id), ]
pd$Sentrix.Barcode <- factor(pd$Sentrix.Barcode, levels = unique(pd$Sentrix.Barcode))
pd$strange_batch <- as.numeric(factor(pd$strange_batch))
pd[, var_of_interest] <- log2( pd[, var_of_interest])
# print(ht(pd))
p1 <- ggplot2::ggplot(pd, ggplot2::aes_string(x = 'Sentrix.Barcode',
y = var_of_interest,
group = 'Sentrix.Barcode',
fill = 'fileset_id',
col = 'fileset_id',
alpha = 'strange_batch')) +
ggplot2::geom_boxplot() +
ggplot2::guides(col = FALSE, alpha = F, fill = guide_legend(ncol = 4)) +
ggplot2::theme(axis.text.x = element_blank(), legend.text = element_text(size = 8)) +
ggplot2::facet_grid(. ~subgroup, scales = "free_x") +
ylab(paste("log2", var_of_interest))+
ggplot2::theme(legend.position="top")
p1
}
plotGenes3 <- function (var_of_interest = "housekeeping", e1m) {
pd <- e1m[, c(var_of_interest, 'Sentrix.Barcode', 'fileset_id', 'strange_batch', "subgroup", "id")]
pd$Sentrix.Barcode <- factor(pd$Sentrix.Barcode, levels = unique(pd$Sentrix.Barcode))
pd$strange_batch <- as.numeric(factor(pd$strange_batch))
pd <- pd[order(pd$fileset_id, pd$Sentrix.Barcode), ]
pd$id <- factor(pd$id, levels = unique(pd$id))
# print(ht(pd))
p1 <- ggplot2::ggplot(pd, ggplot2::aes_string(x = 'id',
y = var_of_interest,
group = 'id',
fill = 'Sentrix.Barcode',
col = 'Sentrix.Barcode',
alpha = 'strange_batch')) +
ggplot2::geom_boxplot() +
ggplot2::guides(col = FALSE, alpha = F, fill = guide_legend(ncol = 4)) +
ggplot2::theme(axis.text.x = element_blank(), legend.text = element_text(size = 8)) +
ggplot2::facet_grid(. ~subgroup, scales = "free_x")+
ggplot2::theme(legend.position="top")
p1
}
plotGenes3log2 <- function (var_of_interest = "housekeeping", e1m) {
pd <- e1m[, c(var_of_interest, 'Sentrix.Barcode', 'fileset_id', 'strange_batch', "subgroup", "id")]
pd$Sentrix.Barcode <- factor(pd$Sentrix.Barcode, levels = unique(pd$Sentrix.Barcode))
pd$strange_batch <- as.numeric(factor(pd$strange_batch))
pd[, var_of_interest] <- log2(pd[,var_of_interest])
pd <- pd[order(pd$fileset_id, pd$Sentrix.Barcode), ]
pd$id <- factor(pd$id, levels = unique(pd$id))
# print(ht(pd))
p1 <- ggplot2::ggplot(pd, ggplot2::aes_string(x = 'id',
y = var_of_interest,
group = 'id',
fill = 'Sentrix.Barcode',
col = 'Sentrix.Barcode' ,
alpha = 'strange_batch')) +
ggplot2::geom_boxplot() +
ggplot2::guides(col = FALSE, alpha = F, fill = guide_legend(ncol = 4)) +
ggplot2::theme(axis.text.x = element_blank(), legend.text = element_text(size=8)) +
ggplot2::facet_grid(. ~subgroup, scales = "free_x") + ylab(paste("log2", var_of_interest))+
ggplot2::theme(legend.position="top")
p1
}
sample_overview_l10 <- ht12object$chipsamples
mytable(sample_overview_l10$in_study)
mytable(sample_overview_l10$reasons4exclusion)
## ----load92--------------------------------------------------------------
sample_overview_l10instudy <- sample_overview_l10[ sample_overview_l10$in_study, ]
dim(sample_overview_l10)
dim(sample_overview_l10instudy)
table(table(sample_overview_l10instudy$new_ID))
if(length(table(table(sample_overview_l10instudy$new_ID))) != 1)
stop("modify code, script expects ids in column new_ID!")
# laden annotation probes
genesdetail <- ht12object$genesdetail
ht(genesdetail, 2)
mytable(stringr::str_sub(genesdetail$ilmn, 1, 4))
#laden expressionsets nach combat
total_nobkgd_eset_ql_combat = ht12object$total_nobkgd_eset_ql_combat
total_nobkgd_eset_ql_combat
total_nobkgd_eset= ht12object$total_nobkgd_eset
total_nobkgd_eset
# details kontrollinfo
head(annotcon)
if("Probe_Id" %in% names(annotcon)) annotcon <- reshape::rename(annotcon, c(Probe_Id = "ilmn"))
head(annotcon)
unique(annotcon$Reporter_Group_Name)
annotcon$nuid <- suppressWarnings(lumi::IlluminaID2nuID(IlluminaID = annotcon$ilmn))[, "nuID"]
annotcon[is.na(annotcon$nuid), "nuid"] <- annotcon[is.na(annotcon$nuid), "ilmn"]
ht(annotcon, 2)
## ----add-----------------------------------------------------------------
# details erccc probes
head(ercc_det)
matcher <- annotcon[annotcon$Reporter_Group_Name %in% ercc_det$ERCC.ID, ]
ercc_det$ilmn <- matcher[match_hk(ercc_det$ERCC.ID, matcher$Reporter_Group_Name), "ilmn"]
ercc_det$ercc_gruppe1 <- cut(log10(ercc_det$concentration.in.Mix.1..attomoles.ul.),
breaks = c(-3,0,1,2,3,4,5), include.lowest = T )
mytable(ercc_det$ercc_gruppe1)
ercc_det$ercc_gruppe1short <- paste0("ercc_", stringr::str_sub(sapply(stringr::str_split(ercc_det$ercc_gruppe1, ","), "[",2),1,1))
mytable(ercc_det$ercc_gruppe1short)
ht(ercc_det, 2)
ht(annotcon, 2)
toadd <- annotcon[0, ][1:dim(ercc_det)[1], ]
toadd$ilmn <- ercc_det$ilmn
matcher <- unique(annotcon[, c('nuid', "ilmn")])
toadd$nuid <- matcher[match_hk(toadd$ilmn, matcher$ilmn), "nuid"]
toadd$Reporter_Group_Name <- "ercc_det"
toadd$Reporter_Group_id <- ercc_det$ercc_gruppe1short
ht(toadd, 3)
annotcon <- rbind(annotcon, toadd)
## ----good----------------------------------------------------------------
xtabs_hk(~ sample_overview_l10$reason4exclusion + sample_overview_l10$in_study)
goodind <- sample_overview_l10instudy$new_ID
# str(goodind)
total_nobkgd_eset
total_nobkgd_eset <- total_nobkgd_eset[,goodind]
total_nobkgd_eset
total_nobkgd_eset_ql_combat # das rausnehmen nach etablierung der 2. Runde normal combat nach euklid
total_nobkgd_eset_ql_combat <- total_nobkgd_eset_ql_combat[,goodind]
total_nobkgd_eset_ql_combat
qlist33 <- venn2(rownames(exprs(total_nobkgd_eset_ql_combat)),rownames(exprs(total_nobkgd_eset)), plotte = showVennplots)
# stopifnot(identical(dim(total_nobkgd_eset_ql_combat), dim(total_nobkgd_eset)))
## ----looking, fig.width=20, fig.height=12--------------------------------
e1 <- data.frame(exprs(total_nobkgd_eset))
e1 <- annotateEset(e1, annotcon)
hh(e1)
dim(e1)
# das gnze fuer e2m
e2 <- data.frame(exprs(total_nobkgd_eset_ql_combat))
e2 <- annotateEset(e2, annotcon)
hh(e2)
dim(e2)
# ERCC anpassen um zu plotten
grep("ERCC", e1$Reporter_Group_id, value = T)
grep("ERCC", e1$Reporter_Group_Name, value = T)
e1[grep("ERCC", e1$Reporter_Group_id), c("Reporter_Group_id", "Reporter_Group_Name")] = "ERCC"
e2[grep("ERCC", e2$Reporter_Group_id), c("Reporter_Group_id", "Reporter_Group_Name")] = "ERCC"
if(show_qcplots_individSamplelevel== "from_paramfile") show_qcplots_individSamplelevel_used <- getParam2("show_qcplots_individSamplelevel", myparam = param) else show_qcplots_individSamplelevel_used = show_qcplots_individSamplelevel
stopifnot(show_qcplots_individSamplelevel_used %in% c(T, F))
show_qcplots_individSamplelevel_used
## ----erccstatus----------------------------------------------------------
ercc <- genesdetail[ genesdetail$is_ercc, "nuid"]
ercc
ercc_vorher_da <- any(ercc %in% rownames(exprs(total_nobkgd_eset)))
ercc_vorher_da
ercc_nachher_da <- any(ercc %in% rownames(exprs(total_nobkgd_eset_ql_combat)))
ercc_nachher_da
## ----indlev--------------------------------------------------------------
if(show_qcplots_individSamplelevel_used) {
plot_housekeeping_vor <- plotGenes3log2("housekeeping", e1m = makePrintDF(e1, "housekeeping")) +
ggtitle("vor Praeprozessierung")
plot_housekeeping_vor
plot_housekeeping_nach <- plotGenes3("housekeeping", e1m = makePrintDF(e2, "housekeeping")) +
ggtitle("NACH Praeprozessierung")
plot_housekeeping_nach
plot_negative_vor <- plotGenes3log2("negative", e1m = makePrintDF(e1, "negative")) +
ggtitle("vor Praeprozessierung")
plot_negative_vor
plot_negative_nach <- plotGenes3("negative", e1m = makePrintDF(e2, "negative")) +
ggtitle("NACH Praeprozessierung")
plot_negative_nach
plot_cy3_hyb_vor <- plotGenes3log2("cy3_hyb", e1m = makePrintDF(e1, "cy3_hyb")) +
ggtitle("vor Praeprozessierung")
plot_cy3_hyb_vor
plot_cy3_hyb_nach <- plotGenes3("cy3_hyb", e1m = makePrintDF(e2, "cy3_hyb")) +
ggtitle("NACH Praeprozessierung")
plot_cy3_hyb_nach
plot_low_stringency_hyb_vor <- plotGenes3log2("low_stringency_hyb", e1m = makePrintDF(e1, "low_stringency_hyb")) +
ggtitle("vor Praeprozessierung")
plot_low_stringency_hyb_vor
plot_low_stringency_hyb_nach <- plotGenes3("low_stringency_hyb", e1m = makePrintDF(e2, "low_stringency_hyb")) +
ggtitle("NACH Praeprozessierung")
plot_low_stringency_hyb_nach
plot_biotin_vor <- plotGenes3log2("biotin", e1m = makePrintDF(e1, "biotin")) +
ggtitle("vor Praeprozessierung")
plot_biotin_vor
plot_biotin_nach <- plotGenes3("biotin", e1m = makePrintDF(e2, "biotin")) +
ggtitle("NACH Praeprozessierung")
plot_biotin_nach
plot_phage_lambda_genome_high_vor <- plotGenes3log2("phage_lambda_genome_high", e1m = makePrintDF(e1, "phage_lambda_genome:high")) +
ggtitle("vor Praeprozessierung")
plot_phage_lambda_genome_high_vor
plot_phage_lambda_genome_high_nach <- plotGenes3("phage_lambda_genome_high", e1m = makePrintDF(e2, "phage_lambda_genome:high")) +
ggtitle("NACH Praeprozessierung")
plot_phage_lambda_genome_high_nach
plot_phage_lambda_genome_med_vor <- plotGenes3log2("phage_lambda_genome_med", e1m = makePrintDF(e1, "phage_lambda_genome:med")) +
ggtitle("vor Praeprozessierung")
plot_phage_lambda_genome_med_vor
plot_phage_lambda_genome_med_nach <- plotGenes3("phage_lambda_genome_med", e1m = makePrintDF(e2, "phage_lambda_genome:med")) +
ggtitle("NACH Praeprozessierung")
plot_phage_lambda_genome_med_nach
plot_phage_lambda_genome_low_vor <- plotGenes3log2("phage_lambda_genome_low", e1m = makePrintDF(e1, "phage_lambda_genome:low")) +
ggtitle("vor Praeprozessierung")
plot_phage_lambda_genome_low_vor
plot_phage_lambda_genome_low_nach <- plotGenes3("phage_lambda_genome_low", e1m = makePrintDF(e2, "phage_lambda_genome:low")) +
ggtitle("NACH Praeprozessierung")
plot_phage_lambda_genome_low_nach
plot_labeling_vor <- plotGenes3log2("labeling", e1m = makePrintDF(e1, "labeling")) +
ggtitle("vor Praeprozessierung")
plot_labeling_vor
plot_labeling_nach <- plotGenes3("labeling", e1m = makePrintDF(e2, "labeling")) +
ggtitle("NACH Praeprozessierung")
plot_labeling_nach
plot_phage_lambda_genome_pm_vor <- plotGenes3log2("phage_lambda_genome_pm", e1m = makePrintDF(e1, "phage_lambda_genome:pm")) +
ggtitle("vor Praeprozessierung")
plot_phage_lambda_genome_pm_vor
plot_phage_lambda_genome_pm_nach <- plotGenes3("phage_lambda_genome_pm", e1m = makePrintDF(e2, "phage_lambda_genome:pm")) +
ggtitle("NACH Praeprozessierung")
plot_phage_lambda_genome_pm_nach
plot_phage_lambda_genome_mm2_vor <- plotGenes3log2("phage_lambda_genome_mm2", e1m = makePrintDF(e1, "phage_lambda_genome:mm2")) +
ggtitle("vor Praeprozessierung")
plot_phage_lambda_genome_mm2_vor
plot_phage_lambda_genome_mm2_nach <- plotGenes3("phage_lambda_genome_mm2", e1m = makePrintDF(e2, "phage_lambda_genome:mm2")) +
ggtitle("NACH Praeprozessierung")
plot_phage_lambda_genome_mm2_nach
if(ercc_vorher_da) plot_ercc_vor <- plotGenes3log2("ERCC", e1m = makePrintDF(e1, "ERCC")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_vor
if(ercc_nachher_da)plot_ercc_nach <- plotGenes3("ERCC", e1m = makePrintDF(e2, "ERCC")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_nach
if(ercc_vorher_da) plot_ercc_0_vor <- plotGenes3log2("ercc_0", e1m = makePrintDF(e1, "ercc_0")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_0_vor
if(ercc_nachher_da) plot_ercc_0_nach <- plotGenes3("ercc_0", e1m = makePrintDF(e2, "ercc_0")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_0_nach
if(ercc_vorher_da) plot_ercc_1_vor <- plotGenes3log2("ercc_1", e1m = makePrintDF(e1, "ercc_1")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_1_vor
if(ercc_nachher_da) plot_ercc_1_nach <- plotGenes3("ercc_1", e1m = makePrintDF(e2, "ercc_1")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_1_nach
if(ercc_vorher_da) plot_ercc_2_vor <- plotGenes3log2("ercc_2", e1m = makePrintDF(e1, "ercc_2")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_2_vor
if(ercc_nachher_da) plot_ercc_2_nach <- plotGenes3("ercc_2", e1m = makePrintDF(e2, "ercc_2")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_2_nach
if(ercc_vorher_da) plot_ercc_3_vor <- plotGenes3log2("ercc_3", e1m = makePrintDF(e1, "ercc_3")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_3_vor
if(ercc_nachher_da) plot_ercc_3_nach <- plotGenes3("ercc_3", e1m = makePrintDF(e2, "ercc_3")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_3_nach
if(ercc_vorher_da) plot_ercc_4_vor <- plotGenes3log2("ercc_4", e1m = makePrintDF(e1, "ercc_4")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_4_vor
if(ercc_nachher_da) plot_ercc_4_nach <- plotGenes3("ercc_4", e1m = makePrintDF(e2, "ercc_4")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_4_nach
if(ercc_vorher_da) plot_ercc_5_vor <- plotGenes3log2("ercc_5", e1m = makePrintDF(e1, "ercc_5")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_5_vor
if(ercc_nachher_da) plot_ercc_5_nach <- plotGenes3("ercc_5", e1m = makePrintDF(e2, "ercc_5")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_5_nach
}
## ----plotsentrix---------------------------------------------------------
if(show_qcplots_individSamplelevel_used==F) {
plot_housekeeping_vor <- plotGenes2log2("housekeeping", e1m = makePrintDF(e1, "housekeeping")) +
ggtitle("vor Praeprozessierung")
plot_housekeeping_vor
plot_housekeeping_nach <- plotGenes2("housekeeping", e1m = makePrintDF(e2, "housekeeping")) +
ggtitle("NACH Praeprozessierung")
plot_housekeeping_nach
plot_negative_vor <- plotGenes2log2("negative", e1m = makePrintDF(e1, "negative")) +
ggtitle("vor Praeprozessierung")
plot_negative_vor
plot_negative_nach <- plotGenes2("negative", e1m = makePrintDF(e2, "negative")) +
ggtitle("NACH Praeprozessierung")
plot_negative_nach
plot_cy3_hyb_vor <- plotGenes2log2("cy3_hyb", e1m = makePrintDF(e1, "cy3_hyb")) +
ggtitle("vor Praeprozessierung")
plot_cy3_hyb_vor
plot_cy3_hyb_nach <- plotGenes2("cy3_hyb", e1m = makePrintDF(e2, "cy3_hyb")) +
ggtitle("NACH Praeprozessierung")
plot_cy3_hyb_nach
plot_low_stringency_hyb_vor <- plotGenes2log2("low_stringency_hyb", e1m = makePrintDF(e1, "low_stringency_hyb")) +
ggtitle("vor Praeprozessierung")
plot_low_stringency_hyb_vor
plot_low_stringency_hyb_nach <- plotGenes2("low_stringency_hyb", e1m = makePrintDF(e2, "low_stringency_hyb")) +
ggtitle("NACH Praeprozessierung")
plot_low_stringency_hyb_nach
plot_biotin_vor <- plotGenes2log2("biotin", e1m = makePrintDF(e1, "biotin")) +
ggtitle("vor Praeprozessierung")
plot_biotin_vor
plot_biotin_nach <- plotGenes2("biotin", e1m = makePrintDF(e2, "biotin")) +
ggtitle("NACH Praeprozessierung")
plot_biotin_nach
plot_phage_lambda_genome_high_vor <- plotGenes2log2("phage_lambda_genome_high", e1m = makePrintDF(e1, "phage_lambda_genome:high")) +
ggtitle("vor Praeprozessierung")
plot_phage_lambda_genome_high_vor
plot_phage_lambda_genome_high_nach <- plotGenes2("phage_lambda_genome_high", e1m = makePrintDF(e2, "phage_lambda_genome:high")) +
ggtitle("NACH Praeprozessierung")
plot_phage_lambda_genome_high_nach
plot_phage_lambda_genome_med_vor <- plotGenes2log2("phage_lambda_genome_med", e1m = makePrintDF(e1, "phage_lambda_genome:med")) +
ggtitle("vor Praeprozessierung")
plot_phage_lambda_genome_med_vor
plot_phage_lambda_genome_med_nach <- plotGenes2("phage_lambda_genome_med", e1m = makePrintDF(e2, "phage_lambda_genome:med")) +
ggtitle("NACH Praeprozessierung")
plot_phage_lambda_genome_med_nach
plot_phage_lambda_genome_low_vor <- plotGenes2log2("phage_lambda_genome_low", e1m = makePrintDF(e1, "phage_lambda_genome:low")) +
ggtitle("vor Praeprozessierung")
plot_phage_lambda_genome_low_vor
plot_phage_lambda_genome_low_nach <- plotGenes2("phage_lambda_genome_low", e1m = makePrintDF(e2, "phage_lambda_genome:low")) +
ggtitle("NACH Praeprozessierung")
plot_phage_lambda_genome_low_nach
plot_labeling_vor <- plotGenes2log2("labeling", e1m = makePrintDF(e1, "labeling")) + ggtitle("vor Praeprozessierung")
plot_labeling_vor
plot_labeling_nach <- plotGenes2("labeling", e1m = makePrintDF(e2, "labeling")) + ggtitle("NACH Praeprozessierung")
plot_labeling_nach
plot_phage_lambda_genome_pm_vor <- plotGenes2log2("phage_lambda_genome_pm", e1m = makePrintDF(e1, "phage_lambda_genome:pm")) +
ggtitle("vor Praeprozessierung")
plot_phage_lambda_genome_pm_vor
plot_phage_lambda_genome_pm_nach <- plotGenes2("phage_lambda_genome_pm", e1m = makePrintDF(e2, "phage_lambda_genome:pm")) +
ggtitle("NACH Praeprozessierung")
plot_phage_lambda_genome_pm_nach
plot_phage_lambda_genome_mm2_vor <- plotGenes2log2("phage_lambda_genome_mm2", e1m = makePrintDF(e1, "phage_lambda_genome:mm2")) +
ggtitle("vor Praeprozessierung")
plot_phage_lambda_genome_mm2_vor
plot_phage_lambda_genome_mm2_nach <- plotGenes2("phage_lambda_genome_mm2", e1m = makePrintDF(e2, "phage_lambda_genome:mm2")) +
ggtitle("NACH Praeprozessierung")
plot_phage_lambda_genome_mm2_nach
if(ercc_vorher_da) plot_ercc_vor <- plotGenes2log2("ERCC", e1m = makePrintDF(e1, "ERCC")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_vor
if(ercc_nachher_da) plot_ercc_nach <- plotGenes2("ERCC", e1m = makePrintDF(e2, "ERCC")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_nach
if(ercc_vorher_da) plot_ercc_0_vor <- plotGenes2log2("ercc_0", e1m = makePrintDF(e1, "ercc_0")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_0_vor
if(ercc_nachher_da) plot_ercc_0_nach <- plotGenes2("ercc_0", e1m = makePrintDF(e2, "ercc_0")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_0_nach
if(ercc_vorher_da)plot_ercc_1_vor <- plotGenes2log2("ercc_1", e1m = makePrintDF(e1, "ercc_1")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_1_vor
if(ercc_nachher_da) plot_ercc_1_nach <- plotGenes2("ercc_1", e1m = makePrintDF(e2, "ercc_1")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_1_nach
if(ercc_vorher_da) plot_ercc_2_vor <- plotGenes2log2("ercc_2", e1m = makePrintDF(e1, "ercc_2")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_2_vor
if(ercc_nachher_da) plot_ercc_2_nach <- plotGenes2("ercc_2", e1m = makePrintDF(e2, "ercc_2")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_2_nach
if(ercc_vorher_da) plot_ercc_3_vor <- plotGenes2log2("ercc_3", e1m = makePrintDF(e1, "ercc_3")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_3_vor
if(ercc_nachher_da) plot_ercc_3_nach <- plotGenes2("ercc_3", e1m = makePrintDF(e2, "ercc_3")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_3_nach
if(ercc_vorher_da) plot_ercc_4_vor <- plotGenes2log2("ercc_4", e1m = makePrintDF(e1, "ercc_4")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_4_vor
if(ercc_nachher_da) plot_ercc_4_nach <- plotGenes2("ercc_4", e1m = makePrintDF(e2, "ercc_4")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_4_nach
if(ercc_vorher_da) plot_ercc_5_vor <- plotGenes2log2("ercc_5", e1m = makePrintDF(e1, "ercc_5")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_5_vor
if(ercc_nachher_da) plot_ercc_5_nach <- plotGenes2("ercc_5", e1m = makePrintDF(e2, "ercc_5")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_5_nach
}
## ----repamong------------------------------------------------------------
# categspalte bauen
hh(e1)
makePrintAllDF <- function (e1, attrib = sample_overview_l10instudy) {
# unique(e1[order(e1$Reporter_Group_Name),1:2])
e1$plotcateg <- e1$Reporter_Group_id
e1$plotcateg[is.na(e1$plotcateg) == F & e1$plotcateg == "phage_lambda_genome"] <- "biotin"
e1$plotcateg[is.na(e1$plotcateg) == F & e1$plotcateg == "thrB"] <- "labeling"
e1$plotcateg[is.na(e1$plotcateg) == F & e1$plotcateg == "permuted_negative"] <- "negative"
e1$plotcateg[is.na(e1$plotcateg) == F & e1$plotcateg == "phage_lambda_genome:high"] <- "hybrid_high"
e1$plotcateg[is.na(e1$plotcateg) == F & e1$plotcateg == "phage_lambda_genome:med"] <- "hybrid_med"
e1$plotcateg[is.na(e1$plotcateg) == F & e1$plotcateg == "phage_lambda_genome:low"] <- "hybrid_low"
e1$plotcateg[is.na(e1$plotcateg) == F & e1$plotcateg == "phage_lambda_genome:mm2"] <- "string_mm"
e1$plotcateg[is.na(e1$plotcateg) == F & e1$plotcateg == "phage_lambda_genome:pm"] <- "string_pm"
plotcategories <- unique(na.omit(e1$plotcateg))
plotcategories
e1c <- data.table::data.table(e1[is.na(e1$plotcateg) == F, names(e1) %nin% c('Reporter_Group_id',
'Reporter_Group_Name',
"subgroup",
'ilmn')])
myplot <- melt(e1c,id.vars = "plotcateg")
myplot[, subgroup := attrib[match_hk(variable, attrib$new_ID), "subgroup"]]
myplot[, mytable(subgroup, doprint = F)]
myplot
}
e1a <- makePrintAllDF(e1)
e1a
plot_allcateg_vor <- ggplot2::ggplot(data.frame(e1a),
ggplot2::aes(plotcateg,
log2(value),
fill = plotcateg) ) +
ggplot2::geom_violin(scale = 'width') +
ggplot2::theme(axis.text.x = element_text(angle = -30, hjust = 0, size = 12), axis.text.y = element_text(size = 12)) +
ggplot2::guides(fill = F) +
ggtitle("log2 expression values BEFORE preprocess") +
ggplot2::facet_grid( subgroup ~., scales = "free_x")
plot_allcateg_vor
e2a <- makePrintAllDF(e2)
e2a
plot_allcateg_nach <- ggplot2::ggplot(data.frame(e2a),
ggplot2::aes(plotcateg,
value,
fill = plotcateg) ) +
ggplot2::geom_violin(scale = 'width') +
ggplot2::theme(axis.text.x = element_text(angle = -30, hjust = 0, size = 12), axis.text.y = element_text(size = 12)) +
ggplot2::guides(fill = F) +
ggtitle("expression values AFTER preprocess") +
ggplot2::facet_grid( subgroup ~., scales = "free_x")
plot_allcateg_nach
# einzeln plotten housekeeping
head(ilmnAnnot014allgInfos)
makeIndPlot <- function(e1, gruppe = "housekeeping", attrib = sample_overview_l10instudy) {
e1_house <- data.table::data.table(e1[is.na(e1$Reporter_Group_id) == F & e1$Reporter_Group_id %in% gruppe, ])
messvar <- names(e1_house)
messvar <- messvar[messvar %in% attrib$new_ID]
e1_house <- melt(e1_house,id.vars = c( 'Reporter_Group_id', 'Reporter_Group_Name',"ilmn"), measure.vars = messvar)
e1_house$Reporter_Group_id[e1_house$Reporter_Group_id == "phage_lambda_genome:mm2"] = "string_mm"
e1_house$Reporter_Group_id[e1_house$Reporter_Group_id == "phage_lambda_genome:pm"] = "string_pm"
# ersetzen ilmn durch gene falls bekannt
e1_house[, ilmn := ifelse(ilmn %in% paste0("ILMN_", ilmnAnnot014allgInfos$ilmn),
ilmnAnnot014allgInfos[match_hk(e1_house$ilmn,
paste0("ILMN_",ilmnAnnot014allgInfos$ilmn)),
"SymbolReannotated_orgHsEg"], ilmn)]
ordered_ilmn <- e1_house[order(Reporter_Group_id), unique(ilmn)]
e1_house[, ilmn := factor(ilmn, levels = ordered_ilmn)]
e1_house[, subgroup := attrib[match_hk(variable, attrib$new_ID), "subgroup"]]
e1_house[, mytable(subgroup, doprint = F)]
e1_house
}
e1_detail <- makeIndPlot(e1, gruppe = c("housekeeping", "phage_lambda_genome:mm2", "phage_lambda_genome:pm"))
plot_details_vor <- ggplot2::ggplot(e1_detail,
ggplot2::aes(ilmn, log2(value),
fill = Reporter_Group_id)) +
ggplot2::geom_violin(scale = 'width') +
ggplot2::theme(axis.text.x = element_text(angle = -30, hjust = 0, size = 12), axis.text.y = element_text(size = 12)) +
ggtitle("expression values BEFORE preprocess") +
ggplot2::facet_grid( subgroup ~., scales = "free_x")
plot_details_vor
e2_detail <- makeIndPlot(e2, gruppe = c("housekeeping", "phage_lambda_genome:mm2", "phage_lambda_genome:pm"))
plot_details_nach <- ggplot2::ggplot(e2_detail,
ggplot2::aes(ilmn, value, fill = Reporter_Group_id )) +
ggplot2::geom_violin(scale = 'width') +
ggplot2::theme(axis.text.x = element_text(angle = -30, hjust = 0, size = 12), axis.text.y = element_text(size = 12)) +
ggtitle("expression values AFTER preprocess") +
ggplot2::facet_grid( subgroup ~., scales = "free_x")
plot_details_nach
## ----pdfplot-------------------------------------------------------------
vglPlotte = function(plot_biotin_vor, plot_biotin_nach, mywidth = c(1,1)) {
ylimit = c(min(c(plot_biotin_vor$data[,1], plot_biotin_nach$data[,1]), na.rm = T), max(c(plot_biotin_vor$data[,1], plot_biotin_nach$data[,1]), na.rm = T))
suppressMessages(gridExtra::grid.arrange(gridExtra::arrangeGrob((plot_biotin_vor + ggplot2::theme(legend.position="top")+ggplot2::guides(fill=guide_legend(ncol=8)) + ggplot2::ggtitle('BEFORE preprocessing')+ ggplot2::ylim(ylimit)), (plot_biotin_nach + ggplot2::scale_y_continuous(labels = function(x) signif(x,5))+ ggplot2::ggtitle('AFTER preprocessing') + ggplot2::theme(legend.position="top")+ggplot2::guides(fill=guide_legend(ncol=8))+ ggplot2::ylim(ylimit)), ncol=2, widths=mywidth)))}
vglPlotte2 = function(plot_biotin_vor, plot_biotin_nach, mywidth = c(1,2)) {
suppressMessages(gridExtra::grid.arrange(gridExtra::arrangeGrob((plot_biotin_vor + ggplot2::ggtitle('BEFORE preprocessing')+ggplot2::guides(fill=guide_legend(ncol=6))+ ggplot2::theme(legend.position="top")), (plot_biotin_nach+ ggplot2::ggtitle('AFTER preprocessing')+ggplot2::guides(fill=guide_legend(ncol=6))+ ggplot2::theme(legend.position="top")), ncol=2, widths=mywidth)))}
if(showPlots==T) {
vglPlotte(plot_biotin_vor, plot_biotin_nach)
vglPlotte(plot_cy3_hyb_vor, plot_cy3_hyb_nach)
vglPlotte(plot_housekeeping_vor, plot_housekeeping_nach)
vglPlotte(plot_labeling_vor, plot_labeling_nach)
vglPlotte(plot_negative_vor, plot_negative_nach)
vglPlotte(plot_low_stringency_hyb_vor, plot_low_stringency_hyb_nach)
vglPlotte(plot_phage_lambda_genome_mm2_vor, plot_phage_lambda_genome_mm2_nach)
vglPlotte(plot_phage_lambda_genome_pm_vor, plot_phage_lambda_genome_pm_nach)
vglPlotte(plot_phage_lambda_genome_low_vor, plot_phage_lambda_genome_low_nach)
vglPlotte(plot_phage_lambda_genome_med_vor, plot_phage_lambda_genome_med_nach)
vglPlotte(plot_phage_lambda_genome_high_vor, plot_phage_lambda_genome_high_nach)
if(ercc_vorher_da & ercc_nachher_da){
vglPlotte(plot_ercc_vor, plot_ercc_nach)
vglPlotte(plot_ercc_0_vor, plot_ercc_0_nach)
vglPlotte(plot_ercc_1_vor, plot_ercc_1_nach)
vglPlotte(plot_ercc_2_vor, plot_ercc_2_nach)
vglPlotte(plot_ercc_3_vor, plot_ercc_3_nach)
vglPlotte(plot_ercc_4_vor, plot_ercc_4_nach)
vglPlotte(plot_ercc_5_vor, plot_ercc_5_nach)
}
ylimits = c(5,17)
ybreaks = seq(5,17,1)
vglPlotte2(plot_allcateg_vor + ggplot2::scale_y_continuous(lim=ylimits, breaks = ybreaks), plot_allcateg_nach + scale_y_continuous(lim=ylimits, breaks =ybreaks), mywidth = c(1,1))
vglPlotte2(plot_details_vor +ggplot2::scale_y_continuous(lim=ylimits, breaks = ybreaks), plot_details_nach + scale_y_continuous(lim=ylimits, breaks =ybreaks), mywidth = c(1,1))
}
#
# plot_housekeeping_vor
# plot_housekeeping_nach
# plot_negative_vor
# plot_negative_nach
# plot_cy3_hyb_vor
# plot_cy3_hyb_nach
# plot_low_stringency_hyb_vor
# plot_low_stringency_hyb_nach
# plot_biotin_vor
# plot_biotin_nach
# plot_phage_lambda_genome_high_vor
# plot_phage_lambda_genome_high_nach
# plot_phage_lambda_genome_med_vor
# plot_phage_lambda_genome_med_nach
# plot_phage_lambda_genome_low_vor
# plot_phage_lambda_genome_low_nach
# plot_labeling_vor
# plot_labeling_nach
# plot_phage_lambda_genome_pm_vor
# plot_phage_lambda_genome_pm_nach
# plot_phage_lambda_genome_mm2_vor
# plot_phage_lambda_genome_mm2_nach
# if(ercc_vorher_da) plot_ercc_vor
# if(ercc_nachher_da)plot_ercc_nach
# if(ercc_vorher_da) plot_ercc_0_vor
# if(ercc_nachher_da) plot_ercc_0_nach
# if(ercc_vorher_da) plot_ercc_1_vor
# if(ercc_nachher_da) plot_ercc_1_nach
# if(ercc_vorher_da) plot_ercc_2_vor
# if(ercc_nachher_da) plot_ercc_2_nach
# if(ercc_vorher_da) plot_ercc_3_vor
# if(ercc_nachher_da) plot_ercc_3_nach
# if(ercc_vorher_da) plot_ercc_4_vor
# if(ercc_nachher_da) plot_ercc_4_nach
# if(ercc_vorher_da) plot_ercc_5_vor
# if(ercc_nachher_da) plot_ercc_5_nach
# plot_goodexpression_nach
# plot_allcateg_vor
# plot_allcateg_nach
# plot_details_vor
# plot_details_nach
plots <- grep('^plot_', ls(), value = T)
fordoku <- c(plots, "ercc_vorher_da", "ercc_nachher_da")
stopifnot(sum(duplicated(fordoku))==0)
ht12object$dokuobjects_visualizePreprocessing = lapply(fordoku, function(x) get(x))
names(ht12object$dokuobjects_visualizePreprocessing) = fordoku
ht12object$history = rbind(ht12object$history, data.frame(calls = paste(Sys.time(), deparse(myparameters))))
ht12object$history
ht12object
}
holgerman/HT12ProcessoR documentation built on June 5, 2021, 9:18 a.m.
R Package Documentation
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Defines functions visualizePreprocessing
Documented in visualizePreprocessing
#' @title Visualize expression data before and after preprocessing
#'
#' @description Visualisation of control features and expression data before and after preprocessing in several plots. Thereby, raw data before preprocessing is log2 transformed for a more meaningful comparison
#' @param ht12object A list object of class HT12prepro created with function filterAtypicalExpressed()
#' @param paramfile Path to the file specifying parameters
#' @param show_qcplots_individSamplelevel Show QC plots comparing expression values before / after preprocessing either binned on SentrixID ('FALSE') or binned on Sample ID ('TRUE')? If "from_paramfile", than the parameter will be read from the paramfile with the location of this file given in parameter paramfile.
#' @param showPlots Show plots. If FALSE, plots are only stored in slot $dokuobjects_visualizePreprocessing
#' @return A list object of class HT12prepro including additional slots $dokuobjects_visualizePreprocessing
#' @import data.table
#' @export
## debug
# paramfile = "/mnt/ifs1_projekte/genstat/02_projekte/1704_boettcher_ge_ht12/01_prepro/input_parameter_007.txt"
# ht12object = prepro_ht12
# show_qcplots_individSamplelevel = "from_paramfile"
# showPlots = T
visualizePreprocessing = function(ht12object,paramfile = NULL,show_qcplots_individSamplelevel = "from_paramfile", showPlots=T) {
### strings are imported as strings and not as factors
options(stringsAsFactors=FALSE)
myparameters = match.call()
showVennplots = F
# status checken
historie = ht12object$history$calls
if(any(grepl("filterAtypicalExpressed", historie))==F) stop("Function 'filterAtypicalExpressed()' has to be run before!")
#laden parameter
if(is.null(paramfile)==F) param <- data.frame(data.table::fread(paramfile))
sample_overview_l9 <- ht12object$chipsamples
## ----functions-----------------------------------------------------------
annotateEset <- function (e1, annotcon, attrib = sample_overview_l10instudy, showPlots = T) {
# newnames = attrib[match(names(e1), attrib$new_ID), "old_ID"]
# grep(" ", attrib$old_ID, value = T)
# newnames = stringr::str_replace_all(newnames, " ", "")
# names(e1) = newnames
e1$ilmn <- suppressWarnings(lumi::nuID2IlluminaID(row.names(e1)))[, 'Probe_Id']
e1[grep("ILMN", row.names(e1)), "ilmn"] <- row.names(e1[grep("ILMN", row.names(e1)),])
# print(table(is.na(e1$ilmn)))
e1 <- moveColFront(e1, "ilmn")
e1 <- merge(e1, annotcon[, c("ilmn", "Reporter_Group_Name", "Reporter_Group_id")], by = "ilmn", all.x = T)
e1 <- moveColFront(e1, "Reporter_Group_Name")
e1 <- moveColFront(e1, "Reporter_Group_id")
e1
}
makePrintDF <- function (e2, var_of_interest, attrib = sample_overview_l10instudy) {
# debug
# var_of_interest = "housekeeping"
# attrib = sample_overview_l10instudy
d <- e2[(e2$Reporter_Group_id == var_of_interest & is.na(e2$Reporter_Group_id) == F)|
(e2$Reporter_Group_Name == var_of_interest & is.na(e2$Reporter_Group_Name) == F), ]
# print(hh(d))
d <- t(d[, 4:(dim(d)[2])])
myid <- row.names(d)
d <- data.frame(d)
d$id <- myid
d$Sentrix.Barcode <- attrib[match_hk(d$id, attrib$new_ID), "Sentrix.Barcode"]
# print(table(d$Sentrix.Barcode, useNA = "always"))
d$Sentrix.Barcode <- attrib[match_hk(d$id, attrib$new_ID), "Sentrix.Barcode"]
# print(table(d$Sentrix.Barcode, useNA = "always"))
d$subgroup <- attrib[match_hk(d$id, attrib$new_ID), "subgroup"]
# print(mytable(d$subgroup))
dm <- melt(d, id.vars = c("id", "Sentrix.Barcode", "subgroup"))
dm$value <- as.numeric(dm$value)
names(dm)[names(dm) == "value"] <- var_of_interest
dm$fileset_id <- attrib[match_hk(d$id, attrib$new_ID), "fileset_id"]
dm$strange_batch <- attrib[match_hk(d$id, attrib$new_ID), "strangebatch"]
names(dm) <- stringr::str_replace_all(names(dm), ":", "_")
# print(ht(dm, 2))
dm
}
plotGenes <- function (var_of_interest = "housekeeping", e1m) {
pd <- e1m[, c(var_of_interest, 'Sentrix.Barcode', 'fileset_id', 'strange_batch')]
pd <- pd[order(pd$fileset_id), ]
pd$Sentrix.Barcode <- factor(pd$Sentrix.Barcode, levels = unique(pd$Sentrix.Barcode))
pd$strange_batch <- as.numeric(factor(pd$strange_batch))
# print(ht(pd))
p1 <- ggplot2::ggplot(pd, ggplot2::aes_string(x = 'Sentrix.Barcode',
y = var_of_interest,
group = 'Sentrix.Barcode',
fill = 'fileset_id',
col = 'fileset_id',
alpha = 'strange_batch')) +
ggplot2::geom_boxplot() +
ggplot2::guides(col = FALSE, alpha = F, fill = guide_legend(ncol = 4)) +
ggplot2::theme(axis.text.x = element_blank(), legend.text = element_text(size = 8)) +
ggplot2::theme(legend.position="top")
p1
}
plotGenes2 <- function (var_of_interest = "housekeeping", e1m) {
pd <- e1m[, c(var_of_interest, 'Sentrix.Barcode', 'fileset_id', 'strange_batch', "subgroup")]
pd <- pd[order(pd$fileset_id), ]
pd$Sentrix.Barcode <- factor(pd$Sentrix.Barcode, levels = unique(pd$Sentrix.Barcode))
pd$strange_batch <- as.numeric(factor(pd$strange_batch))
# print(ht(pd))
p1 <- ggplot2::ggplot(pd, ggplot2::aes_string(x = 'Sentrix.Barcode',
y = var_of_interest,
group = 'Sentrix.Barcode',
fill = 'fileset_id',
col = 'fileset_id',
alpha = 'strange_batch')) +
ggplot2::geom_boxplot() +
ggplot2::guides(col = FALSE, alpha = F, fill = guide_legend(ncol = 4)) +
ggplot2::theme(axis.text.x = element_blank(), legend.text = element_text(size = 8)) +
ggplot2::facet_grid(. ~subgroup, scales = "free_x") +
ggplot2::theme(legend.position="top")
p1
}
plotGenes2log2 <- function (var_of_interest = "housekeeping", e1m) {
pd <- e1m[, c(var_of_interest, 'Sentrix.Barcode', 'fileset_id', 'strange_batch', "subgroup")]
pd <- pd[order(pd$fileset_id), ]
pd$Sentrix.Barcode <- factor(pd$Sentrix.Barcode, levels = unique(pd$Sentrix.Barcode))
pd$strange_batch <- as.numeric(factor(pd$strange_batch))
pd[, var_of_interest] <- log2( pd[, var_of_interest])
# print(ht(pd))
p1 <- ggplot2::ggplot(pd, ggplot2::aes_string(x = 'Sentrix.Barcode',
y = var_of_interest,
group = 'Sentrix.Barcode',
fill = 'fileset_id',
col = 'fileset_id',
alpha = 'strange_batch')) +
ggplot2::geom_boxplot() +
ggplot2::guides(col = FALSE, alpha = F, fill = guide_legend(ncol = 4)) +
ggplot2::theme(axis.text.x = element_blank(), legend.text = element_text(size = 8)) +
ggplot2::facet_grid(. ~subgroup, scales = "free_x") +
ylab(paste("log2", var_of_interest))+
ggplot2::theme(legend.position="top")
p1
}
plotGenes3 <- function (var_of_interest = "housekeeping", e1m) {
pd <- e1m[, c(var_of_interest, 'Sentrix.Barcode', 'fileset_id', 'strange_batch', "subgroup", "id")]
pd$Sentrix.Barcode <- factor(pd$Sentrix.Barcode, levels = unique(pd$Sentrix.Barcode))
pd$strange_batch <- as.numeric(factor(pd$strange_batch))
pd <- pd[order(pd$fileset_id, pd$Sentrix.Barcode), ]
pd$id <- factor(pd$id, levels = unique(pd$id))
# print(ht(pd))
p1 <- ggplot2::ggplot(pd, ggplot2::aes_string(x = 'id',
y = var_of_interest,
group = 'id',
fill = 'Sentrix.Barcode',
col = 'Sentrix.Barcode',
alpha = 'strange_batch')) +
ggplot2::geom_boxplot() +
ggplot2::guides(col = FALSE, alpha = F, fill = guide_legend(ncol = 4)) +
ggplot2::theme(axis.text.x = element_blank(), legend.text = element_text(size = 8)) +
ggplot2::facet_grid(. ~subgroup, scales = "free_x")+
ggplot2::theme(legend.position="top")
p1
}
plotGenes3log2 <- function (var_of_interest = "housekeeping", e1m) {
pd <- e1m[, c(var_of_interest, 'Sentrix.Barcode', 'fileset_id', 'strange_batch', "subgroup", "id")]
pd$Sentrix.Barcode <- factor(pd$Sentrix.Barcode, levels = unique(pd$Sentrix.Barcode))
pd$strange_batch <- as.numeric(factor(pd$strange_batch))
pd[, var_of_interest] <- log2(pd[,var_of_interest])
pd <- pd[order(pd$fileset_id, pd$Sentrix.Barcode), ]
pd$id <- factor(pd$id, levels = unique(pd$id))
# print(ht(pd))
p1 <- ggplot2::ggplot(pd, ggplot2::aes_string(x = 'id',
y = var_of_interest,
group = 'id',
fill = 'Sentrix.Barcode',
col = 'Sentrix.Barcode' ,
alpha = 'strange_batch')) +
ggplot2::geom_boxplot() +
ggplot2::guides(col = FALSE, alpha = F, fill = guide_legend(ncol = 4)) +
ggplot2::theme(axis.text.x = element_blank(), legend.text = element_text(size=8)) +
ggplot2::facet_grid(. ~subgroup, scales = "free_x") + ylab(paste("log2", var_of_interest))+
ggplot2::theme(legend.position="top")
p1
}
sample_overview_l10 <- ht12object$chipsamples
mytable(sample_overview_l10$in_study)
mytable(sample_overview_l10$reasons4exclusion)
## ----load92--------------------------------------------------------------
sample_overview_l10instudy <- sample_overview_l10[ sample_overview_l10$in_study, ]
dim(sample_overview_l10)
dim(sample_overview_l10instudy)
table(table(sample_overview_l10instudy$new_ID))
if(length(table(table(sample_overview_l10instudy$new_ID))) != 1)
stop("modify code, script expects ids in column new_ID!")
# laden annotation probes
genesdetail <- ht12object$genesdetail
ht(genesdetail, 2)
mytable(stringr::str_sub(genesdetail$ilmn, 1, 4))
#laden expressionsets nach combat
total_nobkgd_eset_ql_combat = ht12object$total_nobkgd_eset_ql_combat
total_nobkgd_eset_ql_combat
total_nobkgd_eset= ht12object$total_nobkgd_eset
total_nobkgd_eset
# details kontrollinfo
head(annotcon)
if("Probe_Id" %in% names(annotcon)) annotcon <- reshape::rename(annotcon, c(Probe_Id = "ilmn"))
head(annotcon)
unique(annotcon$Reporter_Group_Name)
annotcon$nuid <- suppressWarnings(lumi::IlluminaID2nuID(IlluminaID = annotcon$ilmn))[, "nuID"]
annotcon[is.na(annotcon$nuid), "nuid"] <- annotcon[is.na(annotcon$nuid), "ilmn"]
ht(annotcon, 2)
## ----add-----------------------------------------------------------------
# details erccc probes
head(ercc_det)
matcher <- annotcon[annotcon$Reporter_Group_Name %in% ercc_det$ERCC.ID, ]
ercc_det$ilmn <- matcher[match_hk(ercc_det$ERCC.ID, matcher$Reporter_Group_Name), "ilmn"]
ercc_det$ercc_gruppe1 <- cut(log10(ercc_det$concentration.in.Mix.1..attomoles.ul.),
breaks = c(-3,0,1,2,3,4,5), include.lowest = T )
mytable(ercc_det$ercc_gruppe1)
ercc_det$ercc_gruppe1short <- paste0("ercc_", stringr::str_sub(sapply(stringr::str_split(ercc_det$ercc_gruppe1, ","), "[",2),1,1))
mytable(ercc_det$ercc_gruppe1short)
ht(ercc_det, 2)
ht(annotcon, 2)
toadd <- annotcon[0, ][1:dim(ercc_det)[1], ]
toadd$ilmn <- ercc_det$ilmn
matcher <- unique(annotcon[, c('nuid', "ilmn")])
toadd$nuid <- matcher[match_hk(toadd$ilmn, matcher$ilmn), "nuid"]
toadd$Reporter_Group_Name <- "ercc_det"
toadd$Reporter_Group_id <- ercc_det$ercc_gruppe1short
ht(toadd, 3)
annotcon <- rbind(annotcon, toadd)
## ----good----------------------------------------------------------------
xtabs_hk(~ sample_overview_l10$reason4exclusion + sample_overview_l10$in_study)
goodind <- sample_overview_l10instudy$new_ID
# str(goodind)
total_nobkgd_eset
total_nobkgd_eset <- total_nobkgd_eset[,goodind]
total_nobkgd_eset
total_nobkgd_eset_ql_combat # das rausnehmen nach etablierung der 2. Runde normal combat nach euklid
total_nobkgd_eset_ql_combat <- total_nobkgd_eset_ql_combat[,goodind]
total_nobkgd_eset_ql_combat
qlist33 <- venn2(rownames(exprs(total_nobkgd_eset_ql_combat)),rownames(exprs(total_nobkgd_eset)), plotte = showVennplots)
# stopifnot(identical(dim(total_nobkgd_eset_ql_combat), dim(total_nobkgd_eset)))
## ----looking, fig.width=20, fig.height=12--------------------------------
e1 <- data.frame(exprs(total_nobkgd_eset))
e1 <- annotateEset(e1, annotcon)
hh(e1)
dim(e1)
# das gnze fuer e2m
e2 <- data.frame(exprs(total_nobkgd_eset_ql_combat))
e2 <- annotateEset(e2, annotcon)
hh(e2)
dim(e2)
# ERCC anpassen um zu plotten
grep("ERCC", e1$Reporter_Group_id, value = T)
grep("ERCC", e1$Reporter_Group_Name, value = T)
e1[grep("ERCC", e1$Reporter_Group_id), c("Reporter_Group_id", "Reporter_Group_Name")] = "ERCC"
e2[grep("ERCC", e2$Reporter_Group_id), c("Reporter_Group_id", "Reporter_Group_Name")] = "ERCC"
if(show_qcplots_individSamplelevel== "from_paramfile") show_qcplots_individSamplelevel_used <- getParam2("show_qcplots_individSamplelevel", myparam = param) else show_qcplots_individSamplelevel_used = show_qcplots_individSamplelevel
stopifnot(show_qcplots_individSamplelevel_used %in% c(T, F))
show_qcplots_individSamplelevel_used
## ----erccstatus----------------------------------------------------------
ercc <- genesdetail[ genesdetail$is_ercc, "nuid"]
ercc
ercc_vorher_da <- any(ercc %in% rownames(exprs(total_nobkgd_eset)))
ercc_vorher_da
ercc_nachher_da <- any(ercc %in% rownames(exprs(total_nobkgd_eset_ql_combat)))
ercc_nachher_da
## ----indlev--------------------------------------------------------------
if(show_qcplots_individSamplelevel_used) {
plot_housekeeping_vor <- plotGenes3log2("housekeeping", e1m = makePrintDF(e1, "housekeeping")) +
ggtitle("vor Praeprozessierung")
plot_housekeeping_vor
plot_housekeeping_nach <- plotGenes3("housekeeping", e1m = makePrintDF(e2, "housekeeping")) +
ggtitle("NACH Praeprozessierung")
plot_housekeeping_nach
plot_negative_vor <- plotGenes3log2("negative", e1m = makePrintDF(e1, "negative")) +
ggtitle("vor Praeprozessierung")
plot_negative_vor
plot_negative_nach <- plotGenes3("negative", e1m = makePrintDF(e2, "negative")) +
ggtitle("NACH Praeprozessierung")
plot_negative_nach
plot_cy3_hyb_vor <- plotGenes3log2("cy3_hyb", e1m = makePrintDF(e1, "cy3_hyb")) +
ggtitle("vor Praeprozessierung")
plot_cy3_hyb_vor
plot_cy3_hyb_nach <- plotGenes3("cy3_hyb", e1m = makePrintDF(e2, "cy3_hyb")) +
ggtitle("NACH Praeprozessierung")
plot_cy3_hyb_nach
plot_low_stringency_hyb_vor <- plotGenes3log2("low_stringency_hyb", e1m = makePrintDF(e1, "low_stringency_hyb")) +
ggtitle("vor Praeprozessierung")
plot_low_stringency_hyb_vor
plot_low_stringency_hyb_nach <- plotGenes3("low_stringency_hyb", e1m = makePrintDF(e2, "low_stringency_hyb")) +
ggtitle("NACH Praeprozessierung")
plot_low_stringency_hyb_nach
plot_biotin_vor <- plotGenes3log2("biotin", e1m = makePrintDF(e1, "biotin")) +
ggtitle("vor Praeprozessierung")
plot_biotin_vor
plot_biotin_nach <- plotGenes3("biotin", e1m = makePrintDF(e2, "biotin")) +
ggtitle("NACH Praeprozessierung")
plot_biotin_nach
plot_phage_lambda_genome_high_vor <- plotGenes3log2("phage_lambda_genome_high", e1m = makePrintDF(e1, "phage_lambda_genome:high")) +
ggtitle("vor Praeprozessierung")
plot_phage_lambda_genome_high_vor
plot_phage_lambda_genome_high_nach <- plotGenes3("phage_lambda_genome_high", e1m = makePrintDF(e2, "phage_lambda_genome:high")) +
ggtitle("NACH Praeprozessierung")
plot_phage_lambda_genome_high_nach
plot_phage_lambda_genome_med_vor <- plotGenes3log2("phage_lambda_genome_med", e1m = makePrintDF(e1, "phage_lambda_genome:med")) +
ggtitle("vor Praeprozessierung")
plot_phage_lambda_genome_med_vor
plot_phage_lambda_genome_med_nach <- plotGenes3("phage_lambda_genome_med", e1m = makePrintDF(e2, "phage_lambda_genome:med")) +
ggtitle("NACH Praeprozessierung")
plot_phage_lambda_genome_med_nach
plot_phage_lambda_genome_low_vor <- plotGenes3log2("phage_lambda_genome_low", e1m = makePrintDF(e1, "phage_lambda_genome:low")) +
ggtitle("vor Praeprozessierung")
plot_phage_lambda_genome_low_vor
plot_phage_lambda_genome_low_nach <- plotGenes3("phage_lambda_genome_low", e1m = makePrintDF(e2, "phage_lambda_genome:low")) +
ggtitle("NACH Praeprozessierung")
plot_phage_lambda_genome_low_nach
plot_labeling_vor <- plotGenes3log2("labeling", e1m = makePrintDF(e1, "labeling")) +
ggtitle("vor Praeprozessierung")
plot_labeling_vor
plot_labeling_nach <- plotGenes3("labeling", e1m = makePrintDF(e2, "labeling")) +
ggtitle("NACH Praeprozessierung")
plot_labeling_nach
plot_phage_lambda_genome_pm_vor <- plotGenes3log2("phage_lambda_genome_pm", e1m = makePrintDF(e1, "phage_lambda_genome:pm")) +
ggtitle("vor Praeprozessierung")
plot_phage_lambda_genome_pm_vor
plot_phage_lambda_genome_pm_nach <- plotGenes3("phage_lambda_genome_pm", e1m = makePrintDF(e2, "phage_lambda_genome:pm")) +
ggtitle("NACH Praeprozessierung")
plot_phage_lambda_genome_pm_nach
plot_phage_lambda_genome_mm2_vor <- plotGenes3log2("phage_lambda_genome_mm2", e1m = makePrintDF(e1, "phage_lambda_genome:mm2")) +
ggtitle("vor Praeprozessierung")
plot_phage_lambda_genome_mm2_vor
plot_phage_lambda_genome_mm2_nach <- plotGenes3("phage_lambda_genome_mm2", e1m = makePrintDF(e2, "phage_lambda_genome:mm2")) +
ggtitle("NACH Praeprozessierung")
plot_phage_lambda_genome_mm2_nach
if(ercc_vorher_da) plot_ercc_vor <- plotGenes3log2("ERCC", e1m = makePrintDF(e1, "ERCC")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_vor
if(ercc_nachher_da)plot_ercc_nach <- plotGenes3("ERCC", e1m = makePrintDF(e2, "ERCC")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_nach
if(ercc_vorher_da) plot_ercc_0_vor <- plotGenes3log2("ercc_0", e1m = makePrintDF(e1, "ercc_0")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_0_vor
if(ercc_nachher_da) plot_ercc_0_nach <- plotGenes3("ercc_0", e1m = makePrintDF(e2, "ercc_0")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_0_nach
if(ercc_vorher_da) plot_ercc_1_vor <- plotGenes3log2("ercc_1", e1m = makePrintDF(e1, "ercc_1")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_1_vor
if(ercc_nachher_da) plot_ercc_1_nach <- plotGenes3("ercc_1", e1m = makePrintDF(e2, "ercc_1")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_1_nach
if(ercc_vorher_da) plot_ercc_2_vor <- plotGenes3log2("ercc_2", e1m = makePrintDF(e1, "ercc_2")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_2_vor
if(ercc_nachher_da) plot_ercc_2_nach <- plotGenes3("ercc_2", e1m = makePrintDF(e2, "ercc_2")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_2_nach
if(ercc_vorher_da) plot_ercc_3_vor <- plotGenes3log2("ercc_3", e1m = makePrintDF(e1, "ercc_3")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_3_vor
if(ercc_nachher_da) plot_ercc_3_nach <- plotGenes3("ercc_3", e1m = makePrintDF(e2, "ercc_3")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_3_nach
if(ercc_vorher_da) plot_ercc_4_vor <- plotGenes3log2("ercc_4", e1m = makePrintDF(e1, "ercc_4")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_4_vor
if(ercc_nachher_da) plot_ercc_4_nach <- plotGenes3("ercc_4", e1m = makePrintDF(e2, "ercc_4")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_4_nach
if(ercc_vorher_da) plot_ercc_5_vor <- plotGenes3log2("ercc_5", e1m = makePrintDF(e1, "ercc_5")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_5_vor
if(ercc_nachher_da) plot_ercc_5_nach <- plotGenes3("ercc_5", e1m = makePrintDF(e2, "ercc_5")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_5_nach
}
## ----plotsentrix---------------------------------------------------------
if(show_qcplots_individSamplelevel_used==F) {
plot_housekeeping_vor <- plotGenes2log2("housekeeping", e1m = makePrintDF(e1, "housekeeping")) +
ggtitle("vor Praeprozessierung")
plot_housekeeping_vor
plot_housekeeping_nach <- plotGenes2("housekeeping", e1m = makePrintDF(e2, "housekeeping")) +
ggtitle("NACH Praeprozessierung")
plot_housekeeping_nach
plot_negative_vor <- plotGenes2log2("negative", e1m = makePrintDF(e1, "negative")) +
ggtitle("vor Praeprozessierung")
plot_negative_vor
plot_negative_nach <- plotGenes2("negative", e1m = makePrintDF(e2, "negative")) +
ggtitle("NACH Praeprozessierung")
plot_negative_nach
plot_cy3_hyb_vor <- plotGenes2log2("cy3_hyb", e1m = makePrintDF(e1, "cy3_hyb")) +
ggtitle("vor Praeprozessierung")
plot_cy3_hyb_vor
plot_cy3_hyb_nach <- plotGenes2("cy3_hyb", e1m = makePrintDF(e2, "cy3_hyb")) +
ggtitle("NACH Praeprozessierung")
plot_cy3_hyb_nach
plot_low_stringency_hyb_vor <- plotGenes2log2("low_stringency_hyb", e1m = makePrintDF(e1, "low_stringency_hyb")) +
ggtitle("vor Praeprozessierung")
plot_low_stringency_hyb_vor
plot_low_stringency_hyb_nach <- plotGenes2("low_stringency_hyb", e1m = makePrintDF(e2, "low_stringency_hyb")) +
ggtitle("NACH Praeprozessierung")
plot_low_stringency_hyb_nach
plot_biotin_vor <- plotGenes2log2("biotin", e1m = makePrintDF(e1, "biotin")) +
ggtitle("vor Praeprozessierung")
plot_biotin_vor
plot_biotin_nach <- plotGenes2("biotin", e1m = makePrintDF(e2, "biotin")) +
ggtitle("NACH Praeprozessierung")
plot_biotin_nach
plot_phage_lambda_genome_high_vor <- plotGenes2log2("phage_lambda_genome_high", e1m = makePrintDF(e1, "phage_lambda_genome:high")) +
ggtitle("vor Praeprozessierung")
plot_phage_lambda_genome_high_vor
plot_phage_lambda_genome_high_nach <- plotGenes2("phage_lambda_genome_high", e1m = makePrintDF(e2, "phage_lambda_genome:high")) +
ggtitle("NACH Praeprozessierung")
plot_phage_lambda_genome_high_nach
plot_phage_lambda_genome_med_vor <- plotGenes2log2("phage_lambda_genome_med", e1m = makePrintDF(e1, "phage_lambda_genome:med")) +
ggtitle("vor Praeprozessierung")
plot_phage_lambda_genome_med_vor
plot_phage_lambda_genome_med_nach <- plotGenes2("phage_lambda_genome_med", e1m = makePrintDF(e2, "phage_lambda_genome:med")) +
ggtitle("NACH Praeprozessierung")
plot_phage_lambda_genome_med_nach
plot_phage_lambda_genome_low_vor <- plotGenes2log2("phage_lambda_genome_low", e1m = makePrintDF(e1, "phage_lambda_genome:low")) +
ggtitle("vor Praeprozessierung")
plot_phage_lambda_genome_low_vor
plot_phage_lambda_genome_low_nach <- plotGenes2("phage_lambda_genome_low", e1m = makePrintDF(e2, "phage_lambda_genome:low")) +
ggtitle("NACH Praeprozessierung")
plot_phage_lambda_genome_low_nach
plot_labeling_vor <- plotGenes2log2("labeling", e1m = makePrintDF(e1, "labeling")) + ggtitle("vor Praeprozessierung")
plot_labeling_vor
plot_labeling_nach <- plotGenes2("labeling", e1m = makePrintDF(e2, "labeling")) + ggtitle("NACH Praeprozessierung")
plot_labeling_nach
plot_phage_lambda_genome_pm_vor <- plotGenes2log2("phage_lambda_genome_pm", e1m = makePrintDF(e1, "phage_lambda_genome:pm")) +
ggtitle("vor Praeprozessierung")
plot_phage_lambda_genome_pm_vor
plot_phage_lambda_genome_pm_nach <- plotGenes2("phage_lambda_genome_pm", e1m = makePrintDF(e2, "phage_lambda_genome:pm")) +
ggtitle("NACH Praeprozessierung")
plot_phage_lambda_genome_pm_nach
plot_phage_lambda_genome_mm2_vor <- plotGenes2log2("phage_lambda_genome_mm2", e1m = makePrintDF(e1, "phage_lambda_genome:mm2")) +
ggtitle("vor Praeprozessierung")
plot_phage_lambda_genome_mm2_vor
plot_phage_lambda_genome_mm2_nach <- plotGenes2("phage_lambda_genome_mm2", e1m = makePrintDF(e2, "phage_lambda_genome:mm2")) +
ggtitle("NACH Praeprozessierung")
plot_phage_lambda_genome_mm2_nach
if(ercc_vorher_da) plot_ercc_vor <- plotGenes2log2("ERCC", e1m = makePrintDF(e1, "ERCC")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_vor
if(ercc_nachher_da) plot_ercc_nach <- plotGenes2("ERCC", e1m = makePrintDF(e2, "ERCC")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_nach
if(ercc_vorher_da) plot_ercc_0_vor <- plotGenes2log2("ercc_0", e1m = makePrintDF(e1, "ercc_0")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_0_vor
if(ercc_nachher_da) plot_ercc_0_nach <- plotGenes2("ercc_0", e1m = makePrintDF(e2, "ercc_0")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_0_nach
if(ercc_vorher_da)plot_ercc_1_vor <- plotGenes2log2("ercc_1", e1m = makePrintDF(e1, "ercc_1")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_1_vor
if(ercc_nachher_da) plot_ercc_1_nach <- plotGenes2("ercc_1", e1m = makePrintDF(e2, "ercc_1")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_1_nach
if(ercc_vorher_da) plot_ercc_2_vor <- plotGenes2log2("ercc_2", e1m = makePrintDF(e1, "ercc_2")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_2_vor
if(ercc_nachher_da) plot_ercc_2_nach <- plotGenes2("ercc_2", e1m = makePrintDF(e2, "ercc_2")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_2_nach
if(ercc_vorher_da) plot_ercc_3_vor <- plotGenes2log2("ercc_3", e1m = makePrintDF(e1, "ercc_3")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_3_vor
if(ercc_nachher_da) plot_ercc_3_nach <- plotGenes2("ercc_3", e1m = makePrintDF(e2, "ercc_3")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_3_nach
if(ercc_vorher_da) plot_ercc_4_vor <- plotGenes2log2("ercc_4", e1m = makePrintDF(e1, "ercc_4")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_4_vor
if(ercc_nachher_da) plot_ercc_4_nach <- plotGenes2("ercc_4", e1m = makePrintDF(e2, "ercc_4")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_4_nach
if(ercc_vorher_da) plot_ercc_5_vor <- plotGenes2log2("ercc_5", e1m = makePrintDF(e1, "ercc_5")) +
ggtitle("vor Praeprozessierung")
if(ercc_vorher_da) plot_ercc_5_vor
if(ercc_nachher_da) plot_ercc_5_nach <- plotGenes2("ercc_5", e1m = makePrintDF(e2, "ercc_5")) +
ggtitle("NACH Praeprozessierung")
if(ercc_nachher_da) plot_ercc_5_nach
}
## ----repamong------------------------------------------------------------
# categspalte bauen
hh(e1)
makePrintAllDF <- function (e1, attrib = sample_overview_l10instudy) {
# unique(e1[order(e1$Reporter_Group_Name),1:2])
e1$plotcateg <- e1$Reporter_Group_id
e1$plotcateg[is.na(e1$plotcateg) == F & e1$plotcateg == "phage_lambda_genome"] <- "biotin"
e1$plotcateg[is.na(e1$plotcateg) == F & e1$plotcateg == "thrB"] <- "labeling"
e1$plotcateg[is.na(e1$plotcateg) == F & e1$plotcateg == "permuted_negative"] <- "negative"
e1$plotcateg[is.na(e1$plotcateg) == F & e1$plotcateg == "phage_lambda_genome:high"] <- "hybrid_high"
e1$plotcateg[is.na(e1$plotcateg) == F & e1$plotcateg == "phage_lambda_genome:med"] <- "hybrid_med"
e1$plotcateg[is.na(e1$plotcateg) == F & e1$plotcateg == "phage_lambda_genome:low"] <- "hybrid_low"
e1$plotcateg[is.na(e1$plotcateg) == F & e1$plotcateg == "phage_lambda_genome:mm2"] <- "string_mm"
e1$plotcateg[is.na(e1$plotcateg) == F & e1$plotcateg == "phage_lambda_genome:pm"] <- "string_pm"
plotcategories <- unique(na.omit(e1$plotcateg))
plotcategories
e1c <- data.table::data.table(e1[is.na(e1$plotcateg) == F, names(e1) %nin% c('Reporter_Group_id',
'Reporter_Group_Name',
"subgroup",
'ilmn')])
myplot <- melt(e1c,id.vars = "plotcateg")
myplot[, subgroup := attrib[match_hk(variable, attrib$new_ID), "subgroup"]]
myplot[, mytable(subgroup, doprint = F)]
myplot
}
e1a <- makePrintAllDF(e1)
e1a
plot_allcateg_vor <- ggplot2::ggplot(data.frame(e1a),
ggplot2::aes(plotcateg,
log2(value),
fill = plotcateg) ) +
ggplot2::geom_violin(scale = 'width') +
ggplot2::theme(axis.text.x = element_text(angle = -30, hjust = 0, size = 12), axis.text.y = element_text(size = 12)) +
ggplot2::guides(fill = F) +
ggtitle("log2 expression values BEFORE preprocess") +
ggplot2::facet_grid( subgroup ~., scales = "free_x")
plot_allcateg_vor
e2a <- makePrintAllDF(e2)
e2a
plot_allcateg_nach <- ggplot2::ggplot(data.frame(e2a),
ggplot2::aes(plotcateg,
value,
fill = plotcateg) ) +
ggplot2::geom_violin(scale = 'width') +
ggplot2::theme(axis.text.x = element_text(angle = -30, hjust = 0, size = 12), axis.text.y = element_text(size = 12)) +
ggplot2::guides(fill = F) +
ggtitle("expression values AFTER preprocess") +
ggplot2::facet_grid( subgroup ~., scales = "free_x")
plot_allcateg_nach
# einzeln plotten housekeeping
head(ilmnAnnot014allgInfos)
makeIndPlot <- function(e1, gruppe = "housekeeping", attrib = sample_overview_l10instudy) {
e1_house <- data.table::data.table(e1[is.na(e1$Reporter_Group_id) == F & e1$Reporter_Group_id %in% gruppe, ])
messvar <- names(e1_house)
messvar <- messvar[messvar %in% attrib$new_ID]
e1_house <- melt(e1_house,id.vars = c( 'Reporter_Group_id', 'Reporter_Group_Name',"ilmn"), measure.vars = messvar)
e1_house$Reporter_Group_id[e1_house$Reporter_Group_id == "phage_lambda_genome:mm2"] = "string_mm"
e1_house$Reporter_Group_id[e1_house$Reporter_Group_id == "phage_lambda_genome:pm"] = "string_pm"
# ersetzen ilmn durch gene falls bekannt
e1_house[, ilmn := ifelse(ilmn %in% paste0("ILMN_", ilmnAnnot014allgInfos$ilmn),
ilmnAnnot014allgInfos[match_hk(e1_house$ilmn,
paste0("ILMN_",ilmnAnnot014allgInfos$ilmn)),
"SymbolReannotated_orgHsEg"], ilmn)]
ordered_ilmn <- e1_house[order(Reporter_Group_id), unique(ilmn)]
e1_house[, ilmn := factor(ilmn, levels = ordered_ilmn)]
e1_house[, subgroup := attrib[match_hk(variable, attrib$new_ID), "subgroup"]]
e1_house[, mytable(subgroup, doprint = F)]
e1_house
}
e1_detail <- makeIndPlot(e1, gruppe = c("housekeeping", "phage_lambda_genome:mm2", "phage_lambda_genome:pm"))
plot_details_vor <- ggplot2::ggplot(e1_detail,
ggplot2::aes(ilmn, log2(value),
fill = Reporter_Group_id)) +
ggplot2::geom_violin(scale = 'width') +
ggplot2::theme(axis.text.x = element_text(angle = -30, hjust = 0, size = 12), axis.text.y = element_text(size = 12)) +
ggtitle("expression values BEFORE preprocess") +
ggplot2::facet_grid( subgroup ~., scales = "free_x")
plot_details_vor
e2_detail <- makeIndPlot(e2, gruppe = c("housekeeping", "phage_lambda_genome:mm2", "phage_lambda_genome:pm"))
plot_details_nach <- ggplot2::ggplot(e2_detail,
ggplot2::aes(ilmn, value, fill = Reporter_Group_id )) +
ggplot2::geom_violin(scale = 'width') +
ggplot2::theme(axis.text.x = element_text(angle = -30, hjust = 0, size = 12), axis.text.y = element_text(size = 12)) +
ggtitle("expression values AFTER preprocess") +
ggplot2::facet_grid( subgroup ~., scales = "free_x")
plot_details_nach
## ----pdfplot-------------------------------------------------------------
vglPlotte = function(plot_biotin_vor, plot_biotin_nach, mywidth = c(1,1)) {
ylimit = c(min(c(plot_biotin_vor$data[,1], plot_biotin_nach$data[,1]), na.rm = T), max(c(plot_biotin_vor$data[,1], plot_biotin_nach$data[,1]), na.rm = T))
suppressMessages(gridExtra::grid.arrange(gridExtra::arrangeGrob((plot_biotin_vor + ggplot2::theme(legend.position="top")+ggplot2::guides(fill=guide_legend(ncol=8)) + ggplot2::ggtitle('BEFORE preprocessing')+ ggplot2::ylim(ylimit)), (plot_biotin_nach + ggplot2::scale_y_continuous(labels = function(x) signif(x,5))+ ggplot2::ggtitle('AFTER preprocessing') + ggplot2::theme(legend.position="top")+ggplot2::guides(fill=guide_legend(ncol=8))+ ggplot2::ylim(ylimit)), ncol=2, widths=mywidth)))}
vglPlotte2 = function(plot_biotin_vor, plot_biotin_nach, mywidth = c(1,2)) {
suppressMessages(gridExtra::grid.arrange(gridExtra::arrangeGrob((plot_biotin_vor + ggplot2::ggtitle('BEFORE preprocessing')+ggplot2::guides(fill=guide_legend(ncol=6))+ ggplot2::theme(legend.position="top")), (plot_biotin_nach+ ggplot2::ggtitle('AFTER preprocessing')+ggplot2::guides(fill=guide_legend(ncol=6))+ ggplot2::theme(legend.position="top")), ncol=2, widths=mywidth)))}
if(showPlots==T) {
vglPlotte(plot_biotin_vor, plot_biotin_nach)
vglPlotte(plot_cy3_hyb_vor, plot_cy3_hyb_nach)
vglPlotte(plot_housekeeping_vor, plot_housekeeping_nach)
vglPlotte(plot_labeling_vor, plot_labeling_nach)
vglPlotte(plot_negative_vor, plot_negative_nach)
vglPlotte(plot_low_stringency_hyb_vor, plot_low_stringency_hyb_nach)
vglPlotte(plot_phage_lambda_genome_mm2_vor, plot_phage_lambda_genome_mm2_nach)
vglPlotte(plot_phage_lambda_genome_pm_vor, plot_phage_lambda_genome_pm_nach)
vglPlotte(plot_phage_lambda_genome_low_vor, plot_phage_lambda_genome_low_nach)
vglPlotte(plot_phage_lambda_genome_med_vor, plot_phage_lambda_genome_med_nach)
vglPlotte(plot_phage_lambda_genome_high_vor, plot_phage_lambda_genome_high_nach)
if(ercc_vorher_da & ercc_nachher_da){
vglPlotte(plot_ercc_vor, plot_ercc_nach)
vglPlotte(plot_ercc_0_vor, plot_ercc_0_nach)
vglPlotte(plot_ercc_1_vor, plot_ercc_1_nach)
vglPlotte(plot_ercc_2_vor, plot_ercc_2_nach)
vglPlotte(plot_ercc_3_vor, plot_ercc_3_nach)
vglPlotte(plot_ercc_4_vor, plot_ercc_4_nach)
vglPlotte(plot_ercc_5_vor, plot_ercc_5_nach)
}
ylimits = c(5,17)
ybreaks = seq(5,17,1)
vglPlotte2(plot_allcateg_vor + ggplot2::scale_y_continuous(lim=ylimits, breaks = ybreaks), plot_allcateg_nach + scale_y_continuous(lim=ylimits, breaks =ybreaks), mywidth = c(1,1))
vglPlotte2(plot_details_vor +ggplot2::scale_y_continuous(lim=ylimits, breaks = ybreaks), plot_details_nach + scale_y_continuous(lim=ylimits, breaks =ybreaks), mywidth = c(1,1))
}
#
# plot_housekeeping_vor
# plot_housekeeping_nach
# plot_negative_vor
# plot_negative_nach
# plot_cy3_hyb_vor
# plot_cy3_hyb_nach
# plot_low_stringency_hyb_vor
# plot_low_stringency_hyb_nach
# plot_biotin_vor
# plot_biotin_nach
# plot_phage_lambda_genome_high_vor
# plot_phage_lambda_genome_high_nach
# plot_phage_lambda_genome_med_vor
# plot_phage_lambda_genome_med_nach
# plot_phage_lambda_genome_low_vor
# plot_phage_lambda_genome_low_nach
# plot_labeling_vor
# plot_labeling_nach
# plot_phage_lambda_genome_pm_vor
# plot_phage_lambda_genome_pm_nach
# plot_phage_lambda_genome_mm2_vor
# plot_phage_lambda_genome_mm2_nach
# if(ercc_vorher_da) plot_ercc_vor
# if(ercc_nachher_da)plot_ercc_nach
# if(ercc_vorher_da) plot_ercc_0_vor
# if(ercc_nachher_da) plot_ercc_0_nach
# if(ercc_vorher_da) plot_ercc_1_vor
# if(ercc_nachher_da) plot_ercc_1_nach
# if(ercc_vorher_da) plot_ercc_2_vor
# if(ercc_nachher_da) plot_ercc_2_nach
# if(ercc_vorher_da) plot_ercc_3_vor
# if(ercc_nachher_da) plot_ercc_3_nach
# if(ercc_vorher_da) plot_ercc_4_vor
# if(ercc_nachher_da) plot_ercc_4_nach
# if(ercc_vorher_da) plot_ercc_5_vor
# if(ercc_nachher_da) plot_ercc_5_nach
# plot_goodexpression_nach
# plot_allcateg_vor
# plot_allcateg_nach
# plot_details_vor
# plot_details_nach
plots <- grep('^plot_', ls(), value = T)
fordoku <- c(plots, "ercc_vorher_da", "ercc_nachher_da")
stopifnot(sum(duplicated(fordoku))==0)
ht12object$dokuobjects_visualizePreprocessing = lapply(fordoku, function(x) get(x))
names(ht12object$dokuobjects_visualizePreprocessing) = fordoku
ht12object$history = rbind(ht12object$history, data.frame(calls = paste(Sys.time(), deparse(myparameters))))
ht12object$history
ht12object
}
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