05_RNA_chemokine_expressing_cells
Tobias Hoch
2020-07-28
Last updated: 2021-02-18
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Knit directory: melanoma_publication_old_data/
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Introduction
Here, we define chemokine-expressing cells
Preparations
knitr::opts_chunk$set(echo = TRUE, message= FALSE)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())Load libraries
source("code/helper_functions/detect_mRNA_expression.R")
source("code/helper_functions/validityChecks.R")
library(SingleCellExperiment)
library(dplyr)
library(ggplot2)
library(scater)
library(CATALYST)
library(reshape2)
library(LSD)
library(data.table)
library(ComplexHeatmap)
library(corrplot)
library(pheatmap)
library(grid)
library(gridExtra)
library(tidyr)
library(colorRamps)Load the single cell experiment object and the image metadata
sce <- readRDS(file = "data/data_for_analysis/sce_RNA.rds")Detect Chemokine Expressing Cells
Detection of chemokine expressing cells
for the detection of chemokine expressing cells we make use of the fact that we also measured a negative control (DapB).
# get the names of the chemokine channels without the negative control channel
chemokine_channels = rownames(sce[which(grepl("T\\d+_",rownames(sce)) & ! grepl("DapB",rownames(sce))),])
# run function to define chemokine expressing cells
output_list <- compute_difference(sce,
cellID = "cellID",
assay_name = "asinh",
threshold = 0.01,
mRNA_channels = chemokine_channels,
negative_control = "T6_DapB",
return_calc_metrics = TRUE)
# overwrite SCE object
sce <- output_list$output_scePlot results from chemokine detection
# check difference between DapB and signal (histogram)
for(i in chemokine_channels){
# subset whole data set for visualization purposes
diff_chemo <- output_list[[i]]
diff_chemo_sub <- diff_chemo[sample(nrow(diff_chemo), nrow(diff_chemo)*0.1), ]
a = ggplot(data = diff_chemo_sub, aes(x=diff)) +
geom_histogram(binwidth = 0.01) +
xlab(paste("DapB mRNA signal subtracted from", i, sep = " ")) + ggtitle("Raw distribution") +
theme(plot.title = element_text(hjust = 0.5, size = 35),
axis.title = element_text(size = 25),
axis.text = element_text(size = 15))
b = ggplot(data = diff_chemo_sub, aes(x=scaled_diff)) +
geom_histogram(binwidth = 0.1, aes(fill =
ifelse(padj <= 0.01 & scaled_diff > 0, 'p<0.01', 'n.s.'))) +
xlab(paste("DapB mRNA signal subtracted from", i, sep = " ")) + ggtitle("Scaled distribution") +
labs(fill = "legend") +
xlim(-5,7) +
scale_fill_manual(values = c("black", "deepskyblue1")) +
theme(plot.title = element_text(hjust = 0.5, size = 35),
axis.title = element_text(size = 25),
legend.text = element_text(size = 20),
legend.title = element_text(size=20),
axis.text = element_text(size = 15))
# significant cells defined by subtraction
c = ggplot(data=diff_chemo_sub, aes(x=mean_negative_control, y=mean_chemokine)) +
geom_point(alpha=0.2, aes(col =
ifelse(padj <= 0.01 & scaled_diff > 0, 'p<0.01', 'n.s.'))) +
scale_color_manual(values = c("black", "deepskyblue1")) +
xlim(0,5.5) + ylim(0,5.5) +
ylab(paste("Mean expression of", i, sep=" ")) +
xlab("Mean DapB mRNAexpression") +
ggtitle(paste("DapB mRNA vs.", i, sep = " ")) +
theme(plot.title = element_text(hjust = 0.5, size = 35),
axis.title = element_text(size = 25),
legend.position = "none",
axis.text = element_text(size = 15))
#png(file = paste("~/Daniel_volume/Rout_RNA/chemokine_detection_method_comparison/",i,"difference_distribution_BH_0.01.png", sep="_"), height = 1000, width = 1600)
grid.arrange(a,b,c, nrow = 3, ncol=1)
#dev.off()
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Basic Stats
Basic numbers on the chemokine expressing cells
chemokines <- data.frame(colData(sce))
chemokines <- chemokines[, chemokine_channels]
# calculate the amount of cells that are positive for 1, 2 and multiple combinations. exclude column containing the ids (12)
single_combinations = chemokines[rowSums(chemokines[,-1]) == 1,-1]
# number of single chemokines positive cells
nrow(single_combinations)[1] 45168double_combinations = chemokines[rowSums(chemokines[,-1]) == 2,-1]
# number of single chemokines positive cells
nrow(double_combinations)[1] 8656multiple_combinations = chemokines[rowSums(chemokines[,-1]) >= 3,-1]
# number of cells that express 3 or more chemokines
nrow(multiple_combinations)[1] 2427# number of double positives per chemokine
double_counts <- colSums(double_combinations)
# frequency matrix and corrplot for frequency matrix
double_combinations[double_combinations == 0] <- NA
count_matrix = psych::pairwiseCount(x=double_combinations)
# normalize the frequency matrix by the amount of double combinations that occur for each chemokine
frequency_matrix <- count_matrix
for (i in colnames(count_matrix)){
frequency_matrix[,i] <- frequency_matrix[,i]/double_counts[i]
}Frequency double-positive cells
The next plot shows the frequencies of all double positive cell occurences. e.g. of all T4_CXCL10 expressing cells that also express another chemokine more than 50% express T9_CXCL9.
corrplot(frequency_matrix, is.corr = FALSE, tl.col = 'black', method = 'pie', type = 'full',
tl.srt = 45, tl.cex = 0.8, tl.offset = 0.5, cl.length = 2, cl.cex = 1, cl.align.text = "l", cl.ratio = 0.3,
diag=TRUE, order = "hclust")
Corrplot of Frequency matrix
Now we normalize the numbers of double positives by the numbers of all respective positive chemokines. this shows that usually between 20-40 percent of chemokine expressing cells are double positive expressors.
single_counts <- colSums(single_combinations)
frequency_matrix <- count_matrix
for (i in colnames(count_matrix)){
frequency_matrix[,i] <- frequency_matrix[,i]/single_counts[i]
}
corrplot(frequency_matrix, is.corr = FALSE, tl.col = 'black', method = 'pie', type = 'full',
tl.srt = 45, tl.cex = 0.8, tl.offset = 0.5, cl.length = 2, cl.cex = 1, cl.align.text = "l", cl.ratio = 0.3,
cl.lim = c(0,1), diag=TRUE, order = "hclust")
SCE object
Add data to SCE object
# general chemokine producer tag for every cell (logical binary)
sce$chemokine <- ifelse(rowSums(data.frame(colData(sce)[,chemokine_channels])) > 0, TRUE, FALSE)
# rename colData entry names
idx <- match(chemokine_channels, colnames(colData(sce)))
for(i in idx){
colnames(colData(sce))[i] <- strsplit(colnames(colData(sce))[i], split = "_")[[1]][2]
}Add expressor info and colour_vector (with control samples)
cur_df <- colData(sce)
cur_df <- as_tibble(cur_df)
cur_df <- cur_df[,grepl("CCL|CXCL|Dap",colnames(cur_df))]
for(i in colnames(cur_df)){
cur_df[[i]] <- ifelse(cur_df[[i]]== 1,i,"NA")
}
cur_df <- cur_df %>%
unite(expressor,sep = "_",na.rm =TRUE,remove=FALSE)
cur_df$expressor <- gsub("NA_","",cur_df$expressor)
cur_df$expressor <- gsub("_NA","",cur_df$expressor)
# summary table of all combinations
summary_cur_df <- table(cur_df$expressor)
# order table according to abundance of combinations
summary_cur_df<- summary_cur_df[order(-as.numeric(summary_cur_df))]
# combinations with more than 600 occurrences
targets <- names(which(summary_cur_df > 600))
targets <- targets[!targets == "NA"]
# add expressor info to sce object
sce$expressor <- cur_df$expressor
# add target names to metadata
metadata(sce)$chemokines_morethan600_withcontrol <- targetsSave SCE object
saveRDS(object = sce,file = "data/data_for_analysis/sce_RNA.rds")
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04 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=C
[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 parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] colorRamps_2.3 tidyr_1.1.2
[3] gridExtra_2.3 pheatmap_1.0.12
[5] corrplot_0.84 ComplexHeatmap_2.4.3
[7] data.table_1.13.6 LSD_4.1-0
[9] reshape2_1.4.4 CATALYST_1.12.2
[11] scater_1.16.2 ggplot2_3.3.3
[13] dplyr_1.0.2 SingleCellExperiment_1.12.0
[15] SummarizedExperiment_1.20.0 Biobase_2.50.0
[17] GenomicRanges_1.42.0 GenomeInfoDb_1.26.2
[19] IRanges_2.24.1 S4Vectors_0.28.1
[21] BiocGenerics_0.36.0 MatrixGenerics_1.2.0
[23] matrixStats_0.57.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 circlize_0.4.12
[3] drc_3.0-1 plyr_1.8.6
[5] igraph_1.2.6 ConsensusClusterPlus_1.52.0
[7] splines_4.0.3 flowCore_2.0.1
[9] BiocParallel_1.22.0 TH.data_1.0-10
[11] digest_0.6.27 htmltools_0.5.0
[13] viridis_0.5.1 magrittr_2.0.1
[15] CytoML_2.0.5 cluster_2.1.0
[17] openxlsx_4.2.3 RcppParallel_5.0.2
[19] sandwich_3.0-0 flowWorkspace_4.0.6
[21] cytolib_2.0.3 jpeg_0.1-8.1
[23] colorspace_2.0-0 ggrepel_0.9.0
[25] haven_2.3.1 xfun_0.20
[27] crayon_1.3.4 RCurl_1.98-1.2
[29] jsonlite_1.7.2 hexbin_1.28.2
[31] graph_1.66.0 survival_3.2-7
[33] zoo_1.8-8 glue_1.4.2
[35] gtable_0.3.0 nnls_1.4
[37] zlibbioc_1.36.0 XVector_0.30.0
[39] GetoptLong_1.0.5 DelayedArray_0.16.0
[41] ggcyto_1.16.0 car_3.0-10
[43] BiocSingular_1.4.0 Rgraphviz_2.32.0
[45] shape_1.4.5 abind_1.4-5
[47] scales_1.1.1 mvtnorm_1.1-1
[49] Rcpp_1.0.5 plotrix_3.7-8
[51] viridisLite_0.3.0 tmvnsim_1.0-2
[53] clue_0.3-58 foreign_0.8-81
[55] rsvd_1.0.3 FlowSOM_1.20.0
[57] tsne_0.1-3 RColorBrewer_1.1-2
[59] ellipsis_0.3.1 farver_2.0.3
[61] pkgconfig_2.0.3 XML_3.99-0.5
[63] labeling_0.4.2 tidyselect_1.1.0
[65] rlang_0.4.10 later_1.1.0.1
[67] munsell_0.5.0 cellranger_1.1.0
[69] tools_4.0.3 generics_0.1.0
[71] ggridges_0.5.3 evaluate_0.14
[73] stringr_1.4.0 yaml_2.2.1
[75] knitr_1.30 fs_1.5.0
[77] zip_2.1.1 purrr_0.3.4
[79] nlme_3.1-151 RBGL_1.64.0
[81] whisker_0.4 xml2_1.3.2
[83] compiler_4.0.3 rstudioapi_0.13
[85] beeswarm_0.2.3 curl_4.3
[87] png_0.1-7 tibble_3.0.4
[89] stringi_1.5.3 forcats_0.5.0
[91] lattice_0.20-41 Matrix_1.3-2
[93] psych_2.0.12 vctrs_0.3.6
[95] pillar_1.4.7 lifecycle_0.2.0
[97] GlobalOptions_0.1.2 BiocNeighbors_1.6.0
[99] cowplot_1.1.1 bitops_1.0-6
[101] irlba_2.3.3 httpuv_1.5.4
[103] R6_2.5.0 latticeExtra_0.6-29
[105] promises_1.1.1 RProtoBufLib_2.0.0
[107] rio_0.5.16 vipor_0.4.5
[109] codetools_0.2-18 MASS_7.3-53
[111] gtools_3.8.2 rprojroot_2.0.2
[113] rjson_0.2.20 withr_2.3.0
[115] mnormt_2.0.2 multcomp_1.4-15
[117] GenomeInfoDbData_1.2.4 hms_0.5.3
[119] ncdfFlow_2.34.0 rmarkdown_2.6
[121] DelayedMatrixStats_1.10.1 carData_3.0-4
[123] Rtsne_0.15 git2r_0.28.0
[125] base64enc_0.1-3 ggbeeswarm_0.6.0