Last updated: 2022-02-22
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Knit directory: MelanomaIMC/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 64e5fde | toobiwankenobi | 2022-02-16 | change order and naming of supp fig files |
| Rmd | 588dbb1 | toobiwankenobi | 2022-02-06 | Figure Order |
| Rmd | b20b6fb | toobiwankenobi | 2022-02-02 | update code for Supp Figures |
| Rmd | 3da15db | toobiwankenobi | 2021-11-24 | changes for revision |
| Rmd | c4e2793 | toobiwankenobi | 2021-08-04 | rearrange figure order to match pre-print |
| html | 4109ff1 | toobiwankenobi | 2021-07-07 | delete html files and adapt gitignore |
| html | 4affda4 | toobiwankenobi | 2021-04-14 | figure adaptations |
| Rmd | 3203891 | toobiwankenobi | 2021-02-19 | change celltype names |
| html | 3203891 | toobiwankenobi | 2021-02-19 | change celltype names |
| Rmd | ee1595d | toobiwankenobi | 2021-02-12 | clean repo and adapt files |
| html | ee1595d | toobiwankenobi | 2021-02-12 | clean repo and adapt files |
| html | 3f5af3f | toobiwankenobi | 2021-02-09 | add .html files |
| Rmd | afa7957 | toobiwankenobi | 2021-02-08 | minor changes on figures and figure order |
| Rmd | 20a1458 | toobiwankenobi | 2021-02-04 | adapt figure order |
| Rmd | f9bb33a | toobiwankenobi | 2021-02-04 | new Figure 5 and minor changes in figure order |
| Rmd | 73caa28 | toobiwankenobi | 2021-01-12 | minor corrections |
| Rmd | 9442cb9 | toobiwankenobi | 2020-12-22 | add all new files |
| Rmd | 1af3353 | toobiwankenobi | 2020-10-16 | add stuff |
| Rmd | a6b51cd | toobiwankenobi | 2020-10-14 | clean scripts, add new subfigures |
This script generates plots for Supplementary Figure 3. It contains data that is not stored in the SCE format.
sapply(list.files("code/helper_functions", full.names = TRUE), source)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
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intersect, setdiff, setequal, union code/helper_functions/calculateSummary.R
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visible FALSE library(SingleCellExperiment)Loading required package: SummarizedExperimentLoading required package: MatrixGenericsLoading required package: matrixStats
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colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
colWeightedMeans, colWeightedMedians, colWeightedSds,
colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
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rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
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rowWeightedSds, rowWeightedVarsLoading required package: GenomicRangesLoading required package: stats4Loading required package: BiocGenerics
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dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which.max, which.minLoading required package: S4Vectors
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collapse, desc, sliceLoading required package: GenomeInfoDbLoading required package: BiobaseWelcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Attaching package: 'Biobase'The following object is masked from 'package:MatrixGenerics':
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anyMissing, rowMedianslibrary(data.table)
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between, first, lastlibrary(ggplot2)
library(dplyr)
library(stringr)
library(ggpubr)
library(ComplexHeatmap)Loading required package: grid========================================
ComplexHeatmap version 2.10.0
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
If you use it in published research, please cite:
Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
genomic data. Bioinformatics 2016.
The new InteractiveComplexHeatmap package can directly export static
complex heatmaps into an interactive Shiny app with zero effort. Have a try!
This message can be suppressed by:
suppressPackageStartupMessages(library(ComplexHeatmap))
========================================library(circlize)========================================
circlize version 0.4.13
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: https://jokergoo.github.io/circlize_book/book/
If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
in R. Bioinformatics 2014.
This message can be suppressed by:
suppressPackageStartupMessages(library(circlize))
========================================library(gridExtra)
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combinelibrary(ggbeeswarm)
library(ggrepel)
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annotatelibrary(ggrastr)
library(readr)
library(cowplot)
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get_legendlibrary(corrplot)corrplot 0.92 loadedlibrary(rstatix)
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filter# load data
cells1 = fread("data/data_for_analysis/12plex_validation/overexpression/20190305/cell.csv", header = T,sep=",")
cells2 = fread("data/data_for_analysis/12plex_validation/overexpression/20190306/cell.csv", header = T,sep=",")
meta1 = fread("data/data_for_analysis/12plex_validation/overexpression/20190305/Image.csv",header = T,sep=",")
meta2 = fread("data/data_for_analysis/12plex_validation/overexpression/20190306/Image.csv",header = T,sep=",")
panel = fread( "data/data_for_analysis/12plex_validation/overexpression/20190305/20190305_A431_overexpression.csv")
# extract replicate and stain info
meta1 = meta1[,.(ImageNumber,FileName_CellImage, Group_Number, Metadata_Target)]
meta1[,':=' (replicate = getInfoFromString(FileName_CellImage,"_",1)), by=ImageNumber]
meta2 = meta2[,.(ImageNumber,FileName_CellImage, Group_Number, Metadata_Target)]
meta2[,':=' (replicate = getInfoFromString(FileName_CellImage,"_",1)), by=ImageNumber]
# sort Metal Number in same order than in the Image.csv file
panel$`Metal Number` = str_extract(panel$`Metal Tag`, "[0-9]+")
panel = panel[order(panel$`Metal Number`),]
panel$channel = c(1:nrow(panel))
cells_long1 = melt.data.table(cells1,id.vars = c("ImageNumber", "ObjectNumber"),variable.factor = F)
cells_long2 = melt.data.table(cells2,id.vars = c("ImageNumber", "ObjectNumber"),variable.factor = F)
# create unique ID with Image and ObjectNumber
cells_long1[,id := paste(ImageNumber,ObjectNumber,sep ="_")]
cells_long2[,id := paste(ImageNumber,ObjectNumber,sep ="_")]
# multiply value by 2E16 since it divided by this number in CellProfiler
cells_long1$value = cells_long1$value * 65535
cells_long2$value = cells_long2$value * 65535
# calculate counts_asinh
cells_long1[,':=' (channel = as.integer(getInfoFromFileList(variable,sep="_",strPos = 4,censorStr = "c")),
counts_asinh = asinh(value/1)),
by=id]
cells_long2[,':=' (channel = as.integer(getInfoFromFileList(variable,sep="_",strPos = 4,censorStr = "c")),
counts_asinh = asinh(value/1)),
by=id]
# take only FullStack entries and not FullStackFiltered
cells_long1[,measurement:=getInfoFromString(variable,"_",3),by=variable]
cells_long1[,signal_type:=getInfoFromString(variable,"_",2),by=variable]
cells_long2[,measurement:=getInfoFromString(variable,"_",3),by=variable]
cells_long2[,signal_type:=getInfoFromString(variable,"_",2),by=variable]
unique(cells_long1$signal_type)[1] "MeanIntensityCorrectedLS" "MeanIntensityCorrected"
[3] "MeanIntensity" unique(cells_long2$signal_type)[1] "MeanIntensityCorrectedLS" "MeanIntensityCorrected"
[3] "MeanIntensity" cells1 = merge(cells_long1, meta1,by="ImageNumber")
cells2 = merge(cells_long2, meta2,by="ImageNumber")
cells = rbind(cells1, cells2)# exclude some channels, make sure to exclude the right ones!
cells_panel = merge(cells,panel[,.(channel,Target, `Metal Tag`, `Metal Number`)],by="channel")
cells_panel = cells_panel[Target %in% c("T1","T2","T3","T4","T5","T6","T7","T8","T9","T10","T11","T12"),]
cells_panel = cells_panel[measurement != "FullStack",]
cells_panel = cells_panel[signal_type != "MeanIntensityCorrectedLS",]
unique(cells_panel$signal_type)[1] "MeanIntensityCorrected" "MeanIntensity" cells_panel_LScorrected = cells_panel[cells_panel$signal_type == "MeanIntensityCorrectedLS",]
cells_panel_uncorrected = cells_panel[cells_panel$signal_type == "MeanIntensity",]
cells_panel_corrected = cells_panel[cells_panel$signal_type == "MeanIntensityCorrected",]results = matrix(nrow=12, ncol = 12)
rownames(results) = unique(cells_panel$Target)
colnames(results) = unique(cells_panel$Target)
results = as.data.frame(results)
cells_panel_corrected = as.data.frame(cells_panel_corrected)
# calculate percentage of unspecific binding
for(i in unique(cells_panel$Target)){
mean_target = mean(cells_panel_corrected[cells_panel_corrected["Metadata_Target"] == i & cells_panel_corrected["Target"] == i, "value"])
for(j in unique(cells_panel$Target)){
row_index = which(rownames(results) %in% i)
col_index = which(colnames(results) %in% j)
results[row_index, col_index] = mean(cells_panel_corrected[cells_panel_corrected["Metadata_Target"] == j & cells_panel_corrected["Target"] == i, "value"]) / mean_target * 100
}
}
# set crosstalk to 0 from one channel to same channel
results[results == 100] <- 0
a = Heatmap(as.matrix(results),
col = colorRamp2(c(0,1,3), c("white","blue", "red")),
row_order = order(as.numeric(gsub("T", "", unique(cells_panel_corrected$Target)))),
column_order = order(as.numeric(gsub("T", "", unique(cells_panel_corrected$Target)))),
heatmap_legend_param = list(title = "% cross-talk", size =15),
column_names_side = "top",
column_title = "Stained Channel",
row_title = "Other Channels",
row_names_side = "left",
column_names_rot = 0,
column_names_gp = gpar(fontsize = 15),
row_names_gp = gpar(fontsize = 15),
column_names_centered = T,
cell_fun = function(j, i, x, y, width, height, fill) {
grid.text(sprintf("%.1f", as.matrix(results)[i, j]), x, y, gp = gpar(fontsize = 10))
})
draw(a)cells_panel2 <- cells_panel
cells_panel2[cells_panel2$signal_type == "MeanIntensity", ]$signal_type = "uncorrected"
cells_panel2[cells_panel2$signal_type == "MeanIntensityCorrected", ]$signal_type = "spill-over corrected"
ggplot(cells_panel2[cells_panel2$Metadata_Target == "T9",], aes(x=`Metal Tag`, y=counts_asinh, fill=signal_type)) +
geom_boxplot(outlier.shape = NA, lwd=0.5) +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
text = element_text(size=18)) +
guides(fill=guide_legend(title="Signal")) +
ylab("Mean Count (asinh)") +
xlab("") +
facet_wrap(~Metadata_Target,labeller = as_labeller(c(T9 = "mRNA Probe for Channel 9 (Er168)")))rm(list = ls())
# Reload helper functions
sapply(list.files("code/helper_functions", full.names = TRUE), source) code/helper_functions/calculateSummary.R
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code/helper_functions/censor_dat.R
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code/helper_functions/detect_mRNA_expression.R
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code/helper_functions/DistanceToClusterCenter.R
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code/helper_functions/findMilieu.R code/helper_functions/findPatch.R
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code/helper_functions/getInfoFromString.R
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code/helper_functions/getSpotnumber.R
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code/helper_functions/plotCellCounts.R
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code/helper_functions/plotCellFractions.R
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code/helper_functions/plotDist.R code/helper_functions/read_Data.R
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visible FALSE cells1 = fread("data/data_for_analysis/12plex_validation/HeLa/20190208/cell.csv", header = T,sep=",")
cells3 = fread("data/data_for_analysis/12plex_validation/HeLa/20190215/cell.csv", header = T,sep=",")
cells4 = fread("data/data_for_analysis/12plex_validation/HeLa/20190222/cell.csv", header = T,sep=",")
meta1 = fread("data/data_for_analysis/12plex_validation/HeLa/20190208/Image.csv",header = T,sep=",")
meta3 = fread("data/data_for_analysis/12plex_validation/HeLa/20190215/Image.csv",header = T,sep=",")
meta4 = fread("data/data_for_analysis/12plex_validation/HeLa/20190222/Image.csv",header = T,sep=",")
meta1 = meta1[,.(ImageNumber,FileName_CellImage)]
meta3 = meta3[,.(ImageNumber,FileName_CellImage)]
meta4 = meta4[,.(ImageNumber,FileName_CellImage)]
meta1[,':=' (replicate = getInfoFromString(FileName_CellImage,"_",1),stain = getInfoFromString(FileName_CellImage,"_",6),
secondary_stain = getInfoFromString(FileName_CellImage,"_",7)),by=ImageNumber]
meta3[,':=' (replicate = getInfoFromString(FileName_CellImage,"_",1),stain = getInfoFromString(FileName_CellImage,"_",6),
secondary_stain = getInfoFromString(FileName_CellImage,"_",7)),by=ImageNumber]
meta4[,':=' (replicate = getInfoFromString(FileName_CellImage,"_",1),stain = getInfoFromString(FileName_CellImage,"_",7),
secondary_stain = getInfoFromString(FileName_CellImage,"_",8),
another_stain = getInfoFromString(FileName_CellImage,"_",6)),by=ImageNumber]
# rename positive stain
meta1[meta1$stain=="positive" & meta1$secondary_stain == "noAb",]$stain = "positive without Ab"
meta1[meta1$stain=="positive" & meta1$secondary_stain == "withAb",]$stain = "positive with Ab"
meta3[meta3$stain=="positive" & meta3$secondary_stain == "noAb",]$stain = "positive without Ab"
meta3[meta3$stain=="positive" & meta3$secondary_stain == "withAb",]$stain = "positive with Ab"
meta3[meta3$stain=="negative" & meta3$secondary_stain == "s0",]$stain = "negative 1st"
meta3[meta3$stain=="negative" & meta3$secondary_stain == "new",]$stain = "negative"
meta4[meta4$stain=="noAb",]$stain = "positive without Ab"
meta4[meta4$stain=="withAb",]$stain = "positive with Ab"
meta4[meta4$another_stain=="negative",]$stain = "negative"
meta4[meta4$another_stain=="T12",]$stain = "T12"
# change stain name
meta3[meta3$stain=="T7new",]$stain = "T7"
meta3[meta3$stain=="T5new",]$stain = "T5"
meta3[meta3$stain=="T11new",]$stain = "T11"
meta3[meta3$replicate =="20190221",]$replicate = "20190215"
panel = fread( "data/data_for_analysis/12plex_validation/HeLa/20190212_HeLa_12plex_validation.csv")
# sort Metal Number in same order than in the Image.csv file!!
panel$`Metal Number` = str_extract(panel$`Metal Tag`, "[0-9]+")
panel = panel[order(panel$`Metal Number`),]
panel$channel = c(1:nrow(panel))
# melt table
cells1_long = melt.data.table(cells1,id.vars = c("ImageNumber", "ObjectNumber"),variable.factor = F)
cells3_long = melt.data.table(cells3,id.vars = c("ImageNumber", "ObjectNumber"),variable.factor = F)
cells4_long = melt.data.table(cells4,id.vars = c("ImageNumber", "ObjectNumber"),variable.factor = F)
# multiply value by 2E16 since it divided by this number in CellProfiler
cells1_long$value = cells1_long$value * 65535
cells3_long$value = cells3_long$value * 65535
cells4_long$value = cells4_long$value * 65535
cells1_long[,id := paste(ImageNumber,ObjectNumber,sep ="_")]
cells1_long[,':=' (channel = as.integer(getInfoFromFileList(variable,sep="_",strPos = 4,censorStr = "c")),
counts_asinh = asinh(value/1)),
by=id]
cells3_long[,id := paste(ImageNumber,ObjectNumber,sep ="_")]
cells3_long[,':=' (channel = as.integer(getInfoFromFileList(variable,sep="_",strPos = 4,censorStr = "c")),
counts_asinh = asinh(value/1)),
by=id]
cells4_long[,id := paste(ImageNumber,ObjectNumber,sep ="_")]
cells4_long[,':=' (channel = as.integer(getInfoFromFileList(variable,sep="_",strPos = 4,censorStr = "c")),
counts_asinh = asinh(value/1)),
by=id]
# take only FullStack entries and not FullStackFiltered (not possible with RNA measurement, only with Ab's)
cells1_long[,measurement:=getInfoFromString(variable,"_",3),by=variable]
cells3_long[,measurement:=getInfoFromString(variable,"_",3),by=variable]
cells4_long[,measurement:=getInfoFromString(variable,"_",3),by=variable]
cells1_long = cells1_long[measurement=="FullStack",]
cells3_long = cells3_long[measurement=="FullStack",]
cells4_long = cells4_long[measurement=="FullStack",]cells1_long = merge(cells1_long,meta1,by="ImageNumber")
cells3_long = merge(cells3_long,meta3,by="ImageNumber")
cells4_long = merge(cells4_long,meta4,by="ImageNumber")
# select only 1 negative measuremenet in 3th measurement
cells3_long = cells3_long[cells3_long$stain != "negative 1st", ]
# remove additional columns which are not needed
cells1_long = cells1_long[,!("secondary_stain")]
cells3_long = cells3_long[,!("secondary_stain")]
cells4_long = cells4_long[,!("secondary_stain")]
cells4_long = cells4_long[,!("another_stain")]
cells = rbind(cells1_long, cells3_long)
cells = rbind(cells, cells4_long)# exclude certain channels, make sure to exclude the right ones!
cells_panel <- merge(cells,panel[,.(channel,Target)],by="channel")
cells_panel <- cells_panel[Target %in% c("T1","T2","T3","T4","T5","T6","T7","T8","T9","T10","T11","T12"),]# import data from 8plex validation
cells_panel_8plex = readRDS(file = "data/data_for_analysis/12plex_validation/HeLa/2018_data_8plex_validation_DS.rds")
# make both data.frames same so they can be merged
cells_panel = cells_panel[, !("measurement")]
cells_panel_8plex = cells_panel_8plex[, !("AreaShape_Area")]
# rename the stains in the 8plex data
unique(cells_panel_8plex$stain) [1] "C1" "C2" "C3" "C4" "C5" "C6" "C7" "C8"
[9] "all" "oldMix" "neg" cells_panel_8plex = cells_panel_8plex[!(stain %in% ("oldMix")), ]
cells_panel_8plex[stain == "C1", ]$stain = "T1"
cells_panel_8plex[stain == "C2", ]$stain = "T4"
cells_panel_8plex[stain == "C3", ]$stain = "T3"
cells_panel_8plex[stain == "C4", ]$stain = "T8"
cells_panel_8plex[stain == "C5", ]$stain = "T12"
cells_panel_8plex[stain == "C6", ]$stain = "T2"
cells_panel_8plex[stain == "C7", ]$stain = "T9"
cells_panel_8plex[stain == "C8", ]$stain = "T6"
cells_panel_8plex[stain == "all", ]$stain = "positive with Ab"
cells_panel_8plex[stain == "neg", ]$stain = "negative"
cells_panel_8plex[Target == "C1", ]$Target = "T1"
cells_panel_8plex[Target == "C2", ]$Target = "T4"
cells_panel_8plex[Target == "C3", ]$Target = "T3"
cells_panel_8plex[Target == "C4", ]$Target = "T8"
cells_panel_8plex[Target == "C5", ]$Target = "T12"
cells_panel_8plex[Target == "C6", ]$Target = "T2"
cells_panel_8plex[Target == "C7", ]$Target = "T9"
cells_panel_8plex[Target == "C8", ]$Target = "T6"
# check
unique(cells_panel_8plex$stain) [1] "T1" "T4" "T3" "T8"
[5] "T12" "T2" "T9" "T6"
[9] "positive with Ab" "negative" unique(cells_panel_8plex$Target)[1] "T1" "T4" "T3" "T8" "T12" "T2" "T9" "T6" all(colnames(cells_panel) == colnames(cells_panel_8plex))[1] TRUE# merge both files
cells_panel_12plex = rbind(cells_panel, cells_panel_8plex)# subset
cells_panel_12plex_sub = cells_panel_12plex[stain == Target,]
# relevel factor
cells_panel_12plex_sub$Target <- factor(cells_panel_12plex_sub$Target, levels = c("T1","T2","T3","T4","T5","T6","T7","T8","T9","T10","T11","T12"))
# check out difference in signal intensity for PPIB in the different channels
ggplot(data = cells_panel_12plex_sub[Target %in%c("T1","T2","T3","T4","T5","T6","T7","T8","T9","T10","T11","T12"),], aes(x=as.factor(Target),y=counts_asinh)) +
geom_boxplot(fill="deepskyblue1")+
theme_minimal()+
theme(text = element_text(size=25), axis.text.x = element_text(angle = 90, hjust = 1)) +
ylab("Mean Intensity Count per Cell [asinh]") +
xlab("Target Channels") +
geom_hline(yintercept = mean(cells_panel_12plex_sub$counts_asinh), linetype = 2) +
scale_x_discrete(breaks=c("T1", "T2", "T3","T4","T5","T6","T7","T8", "T9", "T10","T11", "T12"),
labels= c("T1 PPIB","T2 PPIB","T3 PPIB", "T4 PPIB", "T5 PPIB","T6 PPIB","T7 PPIB","T8 PPIB","T9 PPIB","T10 PPIB", "T11 PPIB", "T12 PPIB")) cells_panel_12plex_sub %>%
group_by(stain) %>%
summarize(mean_per_channel = mean(counts_asinh)) %>%
mutate(sd = sd(mean_per_channel), mean = mean(mean_per_channel), min = min(mean_per_channel), max = max(mean_per_channel))# A tibble: 12 × 6
stain mean_per_channel sd mean min max
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 T1 1.58 0.195 1.48 1.14 1.77
2 T10 1.44 0.195 1.48 1.14 1.77
3 T11 1.14 0.195 1.48 1.14 1.77
4 T12 1.68 0.195 1.48 1.14 1.77
5 T2 1.68 0.195 1.48 1.14 1.77
6 T3 1.38 0.195 1.48 1.14 1.77
7 T4 1.64 0.195 1.48 1.14 1.77
8 T5 1.21 0.195 1.48 1.14 1.77
9 T6 1.37 0.195 1.48 1.14 1.77
10 T7 1.77 0.195 1.48 1.14 1.77
11 T8 1.48 0.195 1.48 1.14 1.77
12 T9 1.39 0.195 1.48 1.14 1.77dd = cells_panel[stain == "positive without Ab" | stain == "positive with Ab",]
# relevel factor
dd$Target <- factor(dd$Target, levels = c("T1","T2","T3","T4","T5","T6","T7","T8","T9","T10","T11","T12"))
dd$stain <- ifelse(dd$stain == "positive with Ab", "+ Antibodies", "- Antibodies")
# plot
ggplot(data=dd, aes(x=Target, y=counts_asinh, fill = stain)) +
geom_boxplot() +
scale_x_discrete(breaks=c("T1", "T2", "T3","T4","T5","T6","T7","T8", "T9", "T11","T10", "T12"),
labels= c("T1 POLR2A","T2 PPIB","T3 UBC", "T4 HPRT1", "T5 TUB","T6 RPL28","T7 RPL5","T8 B2M","T9 ACTB","T10 LDHA", "T11 RPLP0", "T12 GAPDH")) +
theme_minimal()+
theme(text = element_text(size=25), axis.text.x = element_text(angle = 90, hjust = 1)) +
ylab("Mean Intensity Count per Cell [asinh]") +
xlab("Target Channels") +
scale_fill_discrete(name = "Protocol Type") # add gene name
cells_panel_12plex$gene <- ""
cells_panel_12plex[cells_panel_12plex$Target == "T1",]$gene <- "POLR2A"
cells_panel_12plex[cells_panel_12plex$Target == "T2",]$gene <- "PPIB"
cells_panel_12plex[cells_panel_12plex$Target == "T3",]$gene <- "UBC"
cells_panel_12plex[cells_panel_12plex$Target == "T4",]$gene <- "HPRT1"
cells_panel_12plex[cells_panel_12plex$Target == "T5",]$gene <- "TUB"
cells_panel_12plex[cells_panel_12plex$Target == "T6",]$gene <- "RPL28"
cells_panel_12plex[cells_panel_12plex$Target == "T7",]$gene <- "RPL5"
cells_panel_12plex[cells_panel_12plex$Target == "T8",]$gene <- "B2M"
cells_panel_12plex[cells_panel_12plex$Target == "T9",]$gene <- "ACTB"
cells_panel_12plex[cells_panel_12plex$Target == "T10",]$gene <- "LDHA"
cells_panel_12plex[cells_panel_12plex$Target == "T11",]$gene <- "RPLP0"
cells_panel_12plex[cells_panel_12plex$Target == "T12",]$gene <- "GAPDH"
median_per_gene <- cells_panel_12plex %>%
filter(stain == "positive with Ab") %>%
filter(replicate %in% c("20190208", "20190215", "20190222")) %>%
group_by(gene) %>%
summarise(expression_RNAScope = median(counts_asinh))
# data from human protein atlas
rna_seq <- data.frame(median_per_gene$gene)
colnames(rna_seq) <- "gene"
# load scRNA-seq data (downloaded from HPA, https://www.proteinatlas.org/about/download, Human Protein Atlas version 21.0)
HPA_data <- read.delim("data/data_for_analysis/HPA_rna_celline.tsv")
# HeLa cell line with genes-of-interest
HPA_data_sub <- HPA_data %>%
filter(Cell.line == "HeLa") %>%
filter(Gene.name %in% unique(cells_panel_12plex$gene))
colnames(HPA_data_sub) <- c("geneID", "gene", "cellline", "TPM", "pTPM", "nTPM")
# merge
sum <- left_join(median_per_gene, HPA_data_sub)Joining, by = "gene"# plot
ggplot(sum, aes(x=log2(nTPM), y=expression_RNAScope, label=gene)) +
geom_label_repel(size=7, fill="deepskyblue1", alpha=1,
min.segment.length = unit(0, 'lines'),
force_pull = 0) +
geom_point(size=3) +
geom_smooth(method = "lm") +
xlab("Normalized Expression RNA-Seq nTPM [log2]") +
ylab("Median Expression RNAScope [asinh]") +
stat_cor(method = "pearson",
aes(label = paste0("atop(", ..r.label.., ",", ..p.label.. ,")")),
size = 10, cor.coef.name = "R", label.sep="\n", label.y.npc = "top") +
theme_minimal() +
theme(text = element_text(size=20)) `geom_smooth()` using formula 'y ~ x'Warning: Removed 1 rows containing non-finite values (stat_smooth).Warning: Removed 1 rows containing non-finite values (stat_cor).# correlation of the two technologies
sum_sub <- sum[sum$gene != "POLR2A",]
cor.test(sum_sub$expression_RNAScope, log2(sum_sub$nTPM), method = c("pearson"))
Pearson's product-moment correlation
data: sum_sub$expression_RNAScope and log2(sum_sub$nTPM)
t = 4.6597, df = 9, p-value = 0.001186
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.4861349 0.9576603
sample estimates:
cor
0.8408121
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS
Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] rstatix_0.7.0 corrplot_0.92
[3] cowplot_1.1.1 readr_2.1.2
[5] ggrastr_1.0.1 ggpmisc_0.4.5
[7] ggpp_0.4.3 ggrepel_0.9.1
[9] ggbeeswarm_0.6.0 gridExtra_2.3
[11] circlize_0.4.13 ComplexHeatmap_2.10.0
[13] ggpubr_0.4.0 stringr_1.4.0
[15] ggplot2_3.3.5 data.table_1.14.2
[17] SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0
[19] Biobase_2.54.0 GenomicRanges_1.46.1
[21] GenomeInfoDb_1.30.1 IRanges_2.28.0
[23] S4Vectors_0.32.3 BiocGenerics_0.40.0
[25] MatrixGenerics_1.6.0 matrixStats_0.61.0
[27] dplyr_1.0.7 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] colorspace_2.0-2 ggsignif_0.6.3 rjson_0.2.21
[4] ellipsis_0.3.2 rprojroot_2.0.2 XVector_0.34.0
[7] GlobalOptions_0.1.2 fs_1.5.2 clue_0.3-60
[10] rstudioapi_0.13 farver_2.1.0 MatrixModels_0.5-0
[13] bit64_4.0.5 fansi_1.0.2 splines_4.1.2
[16] codetools_0.2-18 doParallel_1.0.16 knitr_1.37
[19] jsonlite_1.7.3 broom_0.7.12 cluster_2.1.2
[22] png_0.1-7 compiler_4.1.2 httr_1.4.2
[25] backports_1.4.1 assertthat_0.2.1 Matrix_1.4-0
[28] fastmap_1.1.0 cli_3.1.1 later_1.3.0
[31] htmltools_0.5.2 quantreg_5.87 tools_4.1.2
[34] gtable_0.3.0 glue_1.6.1 GenomeInfoDbData_1.2.7
[37] Rcpp_1.0.8 carData_3.0-5 jquerylib_0.1.4
[40] vctrs_0.3.8 nlme_3.1-155 iterators_1.0.13
[43] xfun_0.29 ps_1.6.0 lifecycle_1.0.1
[46] getPass_0.2-2 zlibbioc_1.40.0 scales_1.1.1
[49] hms_1.1.1 promises_1.2.0.1 parallel_4.1.2
[52] SparseM_1.81 RColorBrewer_1.1-2 yaml_2.2.2
[55] sass_0.4.0 stringi_1.7.6 highr_0.9
[58] foreach_1.5.2 shape_1.4.6 rlang_1.0.0
[61] pkgconfig_2.0.3 bitops_1.0-7 evaluate_0.14
[64] lattice_0.20-45 purrr_0.3.4 labeling_0.4.2
[67] bit_4.0.4 processx_3.5.2 tidyselect_1.1.1
[70] magrittr_2.0.2 R6_2.5.1 magick_2.7.3
[73] generics_0.1.2 DelayedArray_0.20.0 DBI_1.1.2
[76] mgcv_1.8-38 pillar_1.7.0 whisker_0.4
[79] withr_2.4.3 abind_1.4-5 RCurl_1.98-1.5
[82] tibble_3.1.6 crayon_1.4.2 car_3.0-12
[85] utf8_1.2.2 tzdb_0.2.0 rmarkdown_2.11
[88] GetoptLong_1.0.5 callr_3.7.0 git2r_0.29.0
[91] digest_0.6.29 tidyr_1.2.0 httpuv_1.6.5
[94] munsell_0.5.0 beeswarm_0.4.0 vipor_0.4.5
[97] bslib_0.3.1