05 cytotoxic T cell quantification
Daniel
July 2020
Last updated: 2021-02-12
Checks: 6 1
Knit directory: melanoma_publication_old_data/
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knitr::opts_chunk$set(echo = TRUE, message= FALSE)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())Load libraries
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
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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
value ?
visible FALSE library(LSD)
library(SingleCellExperiment)
library(ggplot2)
library(scater)
library(viridis)
library(igraph)
library(CATALYST)
library(reshape2)
library(cowplot)
library(ggridges)
library(pheatmap)
library(tidyverse)Load Data
Cytotoxic T cell Scoring
T Cell Density
We want to calculate the T cell density. we calculate this per mm2. We use the count for cytotxic T cells as defined under “03_cell_type_definition” script
cur_df <- data.frame(celltype = sce_prot$celltype,
ImageNumber = sce_prot$ImageNumber)
# count cell types per images
cellcount <- (t(table(cur_df)))
# here we get the imagesize from the image metadata
im_size <- (image_mat$Height_cellmask * image_mat$Width_cellmask)/1000000
# data frame
cellcount <- data.frame(cellcount)
# we calculate the density for each celltype for 1 mm2
cellcount$density <- cellcount$Freq/im_size[cellcount$ImageNumber]
cellcount <- cellcount[cellcount$celltype == "Tcytotoxic",]
# there are roughly 60 images with 50 or less cytotoxic T cells
hist(cellcount$density,breaks = 300)
# add cyotoxic T cell density to sce
cellcount$ImageNumber <- as.integer(cellcount$ImageNumber)
cur_sce <- data.frame(colData(sce_prot))
cur_sce <- left_join(cur_sce, cellcount[,c("ImageNumber", "density")])
sce_prot$cyotoxic_density_image <- cur_sce$densityT cell density score per image
- absent: up to 40 cytotoxic T cells/mm2
- low: 40-100 cytotoxic T cells/mm2
- med: 100-400 cytotoxic T cells/mm2
- high: >400 cytotoxic T cells/mm2
# define a vector with all ImgeNumbers
T_density_scores <- c(1:length(unique(cellcount$ImageNumber)))
T_density_scores <- rep("unassigned",length(unique(cellcount$ImageNumber)))
T_absent <- which(cellcount$density <= 40)
T_low <- which(cellcount$density > 40 & cellcount[cellcount$celltype == "Tcytotoxic",]$density <= 100)
T_med <- which(cellcount$density > 100 & cellcount$density <= 400)
T_high <- which(cellcount$density > 400)
T_density_scores[T_absent] <- "absent"
T_density_scores[T_low] <- "low"
T_density_scores[T_med] <- "med"
T_density_scores[T_high] <- "high"
# now we add the information to the single cell experiment
sce_prot$Tcell_density_score_image <- T_density_scores[sce_prot$ImageNumber]
sce_prot$Tcell_density_score_image <- factor(sce_prot$Tcell_density_score_image, levels = c("absent", "low", "med", "high"))
# number of samples per group
data.frame(colData(sce_prot)) %>%
distinct(Description, .keep_all = T) %>%
group_by(Tcell_density_score_image) %>%
summarise(n=n())# A tibble: 4 x 2
Tcell_density_score_image n
<fct> <int>
1 absent 51
2 low 25
3 med 46
4 high 45E_I_D score compared to T_frac_score_per_ImageNumber
cur_df <- data.frame(T_density = sce_prot$Tcell_density_score_image,
E_I_D = sce_prot$E_I_D)
table(cur_df) E_I_D
T_density D E E/D I I/E
absent 36924 116043 18583 50857 30611
low 8541 53869 4971 25955 34393
med 0 77110 0 94479 69812
high 0 59707 0 142200 137295Add Scores to RNA data set
sce_rna$infiltration <- NULL
sce_rna$T_frac_score_per_BlockID <- NULL
sce_rna$T_frac_score_per_ImageNumber <- NULL
sce_rna$T_frac_score_per_PatientID <- NULL
sce_rna$cyotoxic_density_image <- NULL
description_data <- data.frame(colData(sce_prot)) %>%
distinct(Description, .keep_all = TRUE)
col_rna <- data.frame(colData(sce_rna))
# left_join
col_rna <- left_join(col_rna, description_data[,c("Description", "Tcell_density_score_image", "cyotoxic_density_image")])
# add to sce (attention: cytotoxic density is calculated on protein data set!)
sce_rna$Tcell_density_score_image <- col_rna$Tcell_density_score_image
sce_rna$cyotoxic_density_image <- col_rna$densitySave updated SCE
saveRDS(sce_prot,file = "data/data_for_analysis/sce_protein.rds")
saveRDS(sce_rna,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] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] forcats_0.5.0 stringr_1.4.0
[3] dplyr_1.0.2 purrr_0.3.4
[5] readr_1.4.0 tidyr_1.1.2
[7] tibble_3.0.4 tidyverse_1.3.0
[9] pheatmap_1.0.12 ggridges_0.5.3
[11] cowplot_1.1.1 reshape2_1.4.4
[13] CATALYST_1.12.2 igraph_1.2.6
[15] viridis_0.5.1 viridisLite_0.3.0
[17] scater_1.16.2 ggplot2_3.3.3
[19] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[21] Biobase_2.50.0 GenomicRanges_1.42.0
[23] GenomeInfoDb_1.26.2 IRanges_2.24.1
[25] S4Vectors_0.28.1 BiocGenerics_0.36.0
[27] MatrixGenerics_1.2.0 matrixStats_0.57.0
[29] LSD_4.1-0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] backports_1.2.1 readxl_1.3.1
[3] circlize_0.4.12 drc_3.0-1
[5] plyr_1.8.6 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] fansi_0.4.1 magrittr_2.0.1
[15] CytoML_2.0.5 cluster_2.1.0
[17] openxlsx_4.2.3 ComplexHeatmap_2.4.3
[19] modelr_0.1.8 RcppParallel_5.0.2
[21] sandwich_3.0-0 flowWorkspace_4.0.6
[23] cytolib_2.0.3 jpeg_0.1-8.1
[25] colorspace_2.0-0 rvest_0.3.6
[27] ggrepel_0.9.0 haven_2.3.1
[29] xfun_0.20 crayon_1.3.4
[31] RCurl_1.98-1.2 jsonlite_1.7.2
[33] hexbin_1.28.2 graph_1.66.0
[35] survival_3.2-7 zoo_1.8-8
[37] glue_1.4.2 gtable_0.3.0
[39] nnls_1.4 zlibbioc_1.36.0
[41] XVector_0.30.0 GetoptLong_1.0.5
[43] DelayedArray_0.16.0 ggcyto_1.16.0
[45] car_3.0-10 BiocSingular_1.4.0
[47] Rgraphviz_2.32.0 shape_1.4.5
[49] abind_1.4-5 scales_1.1.1
[51] mvtnorm_1.1-1 DBI_1.1.0
[53] Rcpp_1.0.5 plotrix_3.7-8
[55] clue_0.3-58 foreign_0.8-81
[57] rsvd_1.0.3 FlowSOM_1.20.0
[59] tsne_0.1-3 httr_1.4.2
[61] RColorBrewer_1.1-2 ellipsis_0.3.1
[63] pkgconfig_2.0.3 XML_3.99-0.5
[65] dbplyr_2.0.0 utf8_1.1.4
[67] tidyselect_1.1.0 rlang_0.4.10
[69] later_1.1.0.1 munsell_0.5.0
[71] cellranger_1.1.0 tools_4.0.3
[73] cli_2.2.0 generics_0.1.0
[75] broom_0.7.3 evaluate_0.14
[77] yaml_2.2.1 knitr_1.30
[79] fs_1.5.0 zip_2.1.1
[81] RBGL_1.64.0 whisker_0.4
[83] xml2_1.3.2 compiler_4.0.3
[85] rstudioapi_0.13 beeswarm_0.2.3
[87] curl_4.3 png_0.1-7
[89] reprex_0.3.0 stringi_1.5.3
[91] lattice_0.20-41 Matrix_1.3-2
[93] vctrs_0.3.6 pillar_1.4.7
[95] lifecycle_0.2.0 GlobalOptions_0.1.2
[97] BiocNeighbors_1.6.0 data.table_1.13.6
[99] bitops_1.0-6 irlba_2.3.3
[101] httpuv_1.5.4 R6_2.5.0
[103] latticeExtra_0.6-29 promises_1.1.1
[105] gridExtra_2.3 RProtoBufLib_2.0.0
[107] rio_0.5.16 vipor_0.4.5
[109] codetools_0.2-18 assertthat_0.2.1
[111] MASS_7.3-53 gtools_3.8.2
[113] rprojroot_2.0.2 rjson_0.2.20
[115] withr_2.3.0 multcomp_1.4-15
[117] GenomeInfoDbData_1.2.4 hms_0.5.3
[119] ncdfFlow_2.34.0 grid_4.0.3
[121] rmarkdown_2.6 DelayedMatrixStats_1.10.1
[123] carData_3.0-4 Rtsne_0.15
[125] git2r_0.28.0 lubridate_1.7.9.2
[127] base64enc_0.1-3 ggbeeswarm_0.6.0