Last updated: 2022-02-22

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Knit directory: MelanomaIMC/

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html 1affd7b toobiwankenobi 2022-02-22 update .html
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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
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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

Introduction

This script generates plots for Supplementary Figure 3. It contains data that is not stored in the SCE format.

Preparations 1

Load libraries

sapply(list.files("code/helper_functions", full.names = TRUE), source)

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
        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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        code/helper_functions/scatter_function.R
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        code/helper_functions/sceChecks.R
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        code/helper_functions/validityChecks.R
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library(SingleCellExperiment)
Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats

Attaching package: 'matrixStats'
The following object is masked from 'package:dplyr':

    count

Attaching package: 'MatrixGenerics'
The following objects are masked from 'package:matrixStats':

    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
    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,
    rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
    rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
    rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
    rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
    rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
    rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
    rowWeightedSds, rowWeightedVars
Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics

Attaching package: 'BiocGenerics'
The following objects are masked from 'package:dplyr':

    combine, intersect, setdiff, union
The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':

    anyDuplicated, append, as.data.frame, basename, cbind, colnames,
    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.min
Loading required package: S4Vectors

Attaching package: 'S4Vectors'
The following objects are masked from 'package:dplyr':

    first, rename
The following objects are masked from 'package:base':

    expand.grid, I, unname
Loading required package: IRanges

Attaching package: 'IRanges'
The following objects are masked from 'package:dplyr':

    collapse, desc, slice
Loading required package: GenomeInfoDb
Loading required package: Biobase
Welcome 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':

    rowMedians
The following objects are masked from 'package:matrixStats':

    anyMissing, rowMedians
library(data.table)

Attaching package: 'data.table'
The following object is masked from 'package:SummarizedExperiment':

    shift
The following object is masked from 'package:GenomicRanges':

    shift
The following object is masked from 'package:IRanges':

    shift
The following objects are masked from 'package:S4Vectors':

    first, second
The following objects are masked from 'package:dplyr':

    between, first, last
library(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)

Attaching package: 'gridExtra'
The following object is masked from 'package:Biobase':

    combine
The following object is masked from 'package:BiocGenerics':

    combine
The following object is masked from 'package:dplyr':

    combine
library(ggbeeswarm)
library(ggrepel)
library(ggpmisc)
Loading required package: ggpp

Attaching package: 'ggpp'
The following object is masked from 'package:ggplot2':

    annotate
library(ggrastr)
library(readr)
library(cowplot)

Attaching package: 'cowplot'
The following object is masked from 'package:ggpubr':

    get_legend
library(corrplot)
corrplot 0.92 loaded
library(rstatix)

Attaching package: 'rstatix'
The following object is masked from 'package:IRanges':

    desc
The following object is masked from 'package:stats':

    filter

Load and process the data

# 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"           

Merge meta data with cells data and combine data from both replicates

cells1 = merge(cells_long1, meta1,by="ImageNumber")
cells2 = merge(cells_long2, meta2,by="ImageNumber")

cells = rbind(cells1, cells2)

Exclude DNA, Histone and panCytokeratin data and then get the info of PPIB staining

# 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",]

Supp Figure 3E

Unspecific Amplifier Binding

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)

Version Author Date
235386f toobiwankenobi 2022-02-22

Supp Figure 3D

Boxplot with uncorrected and corrected signal

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)")))

Version Author Date
235386f toobiwankenobi 2022-02-22

Preparations 2

Remove previous data

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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visible FALSE                                   
        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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        code/helper_functions/scatter_function.R
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        code/helper_functions/sceChecks.R
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        code/helper_functions/validityChecks.R
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visible FALSE                                 

Load the data

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",]

Merge meta dat with cells data and then then merge all the files to have one file with all three replicates

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)

Merge cells and panel data and exclude non-relevant channels

# 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"),]

Load data frolm 8plex validation and make it comparable

# 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)

Supp Figure 3A

Throw out the DNA, histone and panCytokeratin data and then get the info of PPIB staining

# 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")) 

Version Author Date
235386f toobiwankenobi 2022-02-22

Signal range

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  1.77

Supp Figure 3C

Influence of Antibody Incubation

dd = 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") 

Version Author Date
235386f toobiwankenobi 2022-02-22

Supp Figure 3B

Compare Positive Controls with RNAseq data

# 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).

Version Author Date
235386f toobiwankenobi 2022-02-22

Correlation

# 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