12_dysfunction_score
2021-01-19
Last updated: 2022-02-10
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Knit directory: MelanomaIMC/
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Introduction
This script generates plots for Figure 4.
Preparations
knitr::opts_chunk$set(echo = TRUE, message= FALSE)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())Load libraries
First, we will load the libraries needed for this part of the analysis.
sapply(list.files("code/helper_functions", full.names = TRUE), source) code/helper_functions/calculateSummary.R
value ?
visible FALSE
code/helper_functions/censor_dat.R
value ?
visible FALSE
code/helper_functions/detect_mRNA_expression.R
value ?
visible FALSE
code/helper_functions/DistanceToClusterCenter.R
value ?
visible FALSE
code/helper_functions/findMilieu.R code/helper_functions/findPatch.R
value ? ?
visible FALSE FALSE
code/helper_functions/getInfoFromString.R
value ?
visible FALSE
code/helper_functions/getSpotnumber.R
value ?
visible FALSE
code/helper_functions/plotCellCounts.R
value ?
visible FALSE
code/helper_functions/plotCellFractions.R
value ?
visible FALSE
code/helper_functions/plotDist.R code/helper_functions/read_Data.R
value ? ?
visible FALSE FALSE
code/helper_functions/scatter_function.R
value ?
visible FALSE
code/helper_functions/sceChecks.R
value ?
visible FALSE
code/helper_functions/validityChecks.R
value ?
visible FALSE library(SingleCellExperiment)
library(reshape2)
library(tidyverse)
library(dplyr)Read the data
sce_rna = readRDS(file = "data/data_for_analysis/sce_RNA.rds")
sce_prot = readRDS(file = "data/data_for_analysis/sce_protein.rds")
sce_rna$dysfunction_score <- NULL
sce_rna$dysfunction_density <- NULL
sce_prot$dysfunction_score <- NULL
sce_prot$dysfunction_density <- NULLDerive Scoring
We divide the images with a high CD8 cell density into two groups: Low Dysfunction (CD8+CXCL13+ < 5%) and High Dysfunction (CD8+CXCL13+ >= 5%). Margin punches from LN are not considered.
# paste density scoring and exhaustion scoring
dysfunction <- data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL") %>% # remove controls
mutate(MM_location_punch = paste(MM_location, Location, sep = "_")) %>%
filter(MM_location_punch != "LN_M") %>% # remove LN margin samples
filter(Tcell_density_score_image %in% c("high")) %>%
mutate(celltype2 = paste(celltype, CXCL13, sep = "_")) %>% # add CXCL13 info to celltype
filter(celltype2 %in% c("CD8+ T cell_1", "CD8+ T cell_0")) %>%
group_by(Description, celltype2) %>%
summarise(n=n()) %>%
reshape2::dcast(Description ~ celltype2, value.var = "n", fill = 0) %>%
reshape2::melt(id.vars = c("Description"), variable.name = "celltype2", value.name = "n") %>%
group_by(Description) %>%
mutate(fraction = n / sum(n)) %>%
filter(celltype2 == "CD8+ T cell_1") %>%
ungroup() %>%
mutate(dysfunction_score = ifelse(fraction >= median(fraction), "High Dysfunction", "Low Dysfunction")) %>%
select(Description, dysfunction_score, fraction)
cur_rna <- data.frame(colData(sce_rna))
cur_rna <- left_join(cur_rna, dysfunction)
sce_rna$dysfunction_score <- cur_rna$dysfunction_score
sce_rna$dysfunction_density <- paste(sce_rna$Tcell_density_score_image, sce_rna$dysfunction_score, sep = " - ")
cur_prot <- data.frame(colData(sce_prot))
cur_prot <- left_join(cur_prot, dysfunction)
sce_prot$dysfunction_score <- cur_prot$dysfunction_score
sce_prot$dysfunction_density <- paste(sce_prot$Tcell_density_score_image, sce_prot$dysfunction_score, sep = " - ")Number of Patients in different groups
data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL" & is.na(dysfunction_score) == FALSE) %>%
distinct(PatientID, .keep_all = T) %>%
group_by(dysfunction_score) %>%
summarise(patients = n())# A tibble: 2 × 2
dysfunction_score patients
<chr> <int>
1 High Dysfunction 8
2 Low Dysfunction 14Fraction of Dysfunctional Cells per Image
ggplot(dysfunction, aes(x=reorder(Description,-fraction), y=fraction)) +
geom_bar(stat="identity") +
geom_hline(yintercept = 0.05) +
xlab("Description") +
ylab("Fraction of CXCL13+ CD8+ T cells")
median(dysfunction$fraction)[1] 0.0581435save 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 4.1.2 (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] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] forcats_0.5.1 stringr_1.4.0
[3] purrr_0.3.4 readr_2.1.2
[5] tidyr_1.2.0 tibble_3.1.6
[7] ggplot2_3.3.5 tidyverse_1.3.1
[9] reshape2_1.4.4 SingleCellExperiment_1.16.0
[11] SummarizedExperiment_1.24.0 Biobase_2.54.0
[13] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1
[15] IRanges_2.28.0 S4Vectors_0.32.3
[17] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
[19] matrixStats_0.61.0 dplyr_1.0.7
[21] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] bitops_1.0-7 fs_1.5.2 lubridate_1.8.0
[4] httr_1.4.2 rprojroot_2.0.2 tools_4.1.2
[7] backports_1.4.1 bslib_0.3.1 utf8_1.2.2
[10] R6_2.5.1 DBI_1.1.2 colorspace_2.0-2
[13] withr_2.4.3 tidyselect_1.1.1 processx_3.5.2
[16] compiler_4.1.2 git2r_0.29.0 cli_3.1.1
[19] rvest_1.0.2 xml2_1.3.3 DelayedArray_0.20.0
[22] labeling_0.4.2 sass_0.4.0 scales_1.1.1
[25] callr_3.7.0 digest_0.6.29 rmarkdown_2.11
[28] XVector_0.34.0 pkgconfig_2.0.3 htmltools_0.5.2
[31] highr_0.9 dbplyr_2.1.1 fastmap_1.1.0
[34] rlang_1.0.0 readxl_1.3.1 rstudioapi_0.13
[37] farver_2.1.0 jquerylib_0.1.4 generics_0.1.2
[40] jsonlite_1.7.3 RCurl_1.98-1.5 magrittr_2.0.2
[43] GenomeInfoDbData_1.2.7 Matrix_1.4-0 Rcpp_1.0.8
[46] munsell_0.5.0 fansi_1.0.2 lifecycle_1.0.1
[49] stringi_1.7.6 whisker_0.4 yaml_2.2.2
[52] zlibbioc_1.40.0 plyr_1.8.6 grid_4.1.2
[55] promises_1.2.0.1 crayon_1.4.2 lattice_0.20-45
[58] haven_2.4.3 hms_1.1.1 knitr_1.37
[61] ps_1.6.0 pillar_1.7.0 reprex_2.0.1
[64] glue_1.6.1 evaluate_0.14 getPass_0.2-2
[67] modelr_0.1.8 vctrs_0.3.8 tzdb_0.2.0
[70] httpuv_1.6.5 cellranger_1.1.0 gtable_0.3.0
[73] assertthat_0.2.1 xfun_0.29 broom_0.7.12
[76] later_1.3.0 ellipsis_0.3.2