13_Dysfunction Stain
2021-01-19
Last updated: 2022-02-22
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Knit directory: MelanomaIMC/
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Introduction
Here, we load the data from the dysfunction stain. Images with a T cell dysfunction score were re-acquired with an extended T cell marker panel. An SCE object will be created and saved at the end and serves for further analyses (e.g. Fig 4D, Fig S10B)
Preparation
Load Libraries
library(SingleCellExperiment)Loading required package: SummarizedExperimentLoading required package: MatrixGenericsLoading required package: matrixStats
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, rowWeightedVarsLoading required package: GenomicRangesLoading required package: stats4Loading required package: BiocGenerics
Attaching package: 'BiocGenerics'The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabsThe 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.minLoading required package: S4Vectors
Attaching package: 'S4Vectors'The following objects are masked from 'package:base':
expand.grid, I, unnameLoading required package: IRangesLoading 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':
rowMediansThe following objects are masked from 'package:matrixStats':
anyMissing, rowMedianslibrary(imcRtools)Loading required package: SpatialExperimentlibrary(BiocParallel)
library(BiocNeighbors)
library(dittoSeq)Loading required package: ggplot2library(scater)Loading required package: scuttlelibrary(scales)
library(cowplot)
library(Hmisc)Loading required package: latticeLoading required package: survivalLoading required package: Formula
Attaching package: 'Hmisc'The following object is masked from 'package:Biobase':
contentsThe following objects are masked from 'package:base':
format.pval, unitslibrary(tidyverse)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──✓ tibble 3.1.6 ✓ dplyr 1.0.7
✓ tidyr 1.2.0 ✓ stringr 1.4.0
✓ readr 2.1.2 ✓ forcats 0.5.1
✓ purrr 0.3.4 ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
x readr::col_factor() masks scales::col_factor()
x dplyr::collapse() masks IRanges::collapse()
x dplyr::combine() masks Biobase::combine(), BiocGenerics::combine()
x dplyr::count() masks matrixStats::count()
x dplyr::desc() masks IRanges::desc()
x purrr::discard() masks scales::discard()
x tidyr::expand() masks S4Vectors::expand()
x dplyr::filter() masks stats::filter()
x dplyr::first() masks S4Vectors::first()
x dplyr::lag() masks stats::lag()
x ggplot2::Position() masks BiocGenerics::Position(), base::Position()
x purrr::reduce() masks GenomicRanges::reduce(), IRanges::reduce()
x dplyr::rename() masks S4Vectors::rename()
x dplyr::slice() masks IRanges::slice()
x dplyr::src() masks Hmisc::src()
x dplyr::summarize() masks Hmisc::summarize()library(ggpubr)
Attaching package: 'ggpubr'The following object is masked from 'package:cowplot':
get_legendlibrary(scran)
set.seed(12345)Load Data
sce_1 <- read_steinbock(path = "data/full_data/exhaustion_stain/",
return_as = "sce", graphs_folder = NULL, regionprops_folder = NULL,
image_file = NULL)
sce_2 <- read_steinbock(path = "data/full_data/revision_stain/",
return_as = "sce", graphs_folder = NULL, regionprops_folder = NULL,
image_file = NULL)
sce_RNA <- readRDS(file = "data/data_for_analysis/sce_RNA.rds")add metadata to sce_1
assay(sce_1,"asinh") <- asinh(counts(sce_1))
colnames(sce_1) <- paste0(sce_1$sample_id,"_",sce_1$ObjectNumber)
# add information from dysfunction score and Description
imnames <- c("E5","H9","F9","L8","N10","F2","D8","E4","J4","G11","L4","O9")
names(imnames) <- unique(sce_1$sample_id)
sce_1$Description <- imnames[sce_1$sample_id]
# Dysfunction Score
highDys <- unique(sce_RNA[,sce_RNA$dysfunction_score %in% "High Dysfunction"]$Description)
lowDys <- unique(sce_RNA[,sce_RNA$dysfunction_score %in% "Low Dysfunction"]$Description)
sce_1$dysfunction_score <- NA
sce_1[,sce_1$Description %in% highDys]$dysfunction_score <- "High Dysfunction"
sce_1[,sce_1$Description %in% lowDys]$dysfunction_score <- "Low Dysfunction"add metadata to sce_2
assay(sce_2,"asinh") <- asinh(counts(sce_2))
colnames(sce_2) <- paste0(sce_2$sample_id,"_",sce_2$ObjectNumber)
# add information from dysfunction score and Description
imnames <- c("E2","D4","D3","G9","B6","O2","M10","B3","N8","A4","I11","K2","I5","M9")
names(imnames) <- unique(sce_2$sample_id)[c(1,7:14,2:6)]
sce_2$Description <- imnames[sce_2$sample_id]
sce_2$dysfunction_score <- NA
sce_2[,sce_2$Description %in% highDys]$dysfunction_score <- "High Dysfunction"
sce_2[,sce_2$Description %in% lowDys]$dysfunction_score <- "Low Dysfunction"merge sce objects
we will also remove images E2. this images was the first image of a measurement after downtime of the machine and had substantially higher intensities that all other images.
sce <- cbind(sce_1,sce_2)
rm(sce_1,sce_2)
sce <- sce[,sce$Description != "E2"]
# remove images with no dysfunction score
sce <- sce[,sce$dysfunction_score %in% c("High Dysfunction", "Low Dysfunction")]Analysis
define cut offs for the definition of CD8 T cells per sample
here we check if there are any global intensity biases for CD8
dittoRidgePlot(sce,var="CD8a", group.by= "Description",assay = "asinh")Picking joint bandwidth of 0.144
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samples E2, F2 L8, D4 and B3 should be inspected further since they have higher CD8 background. However, we will more generally chose a conservative cut off for T cell definition
for (i in unique(sce$Description)) {
p <-dittoScatterPlot(sce[,which(sce$Description == i )],x.var = "CD8a",y.var = "CXCL13",assay.x = "asinh",assay.y = "asinh", main=i,)
plot(p)
}
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CD8 cut-off at asinh = 2
for (i in unique(sce$Description)) {
p <-dittoScatterPlot(sce[,which(sce$Description == i )],x.var = "CD3",y.var = "CD8a",assay.x = "asinh",assay.y = "asinh", main=i,)
plot(p)
}
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general cut off for CD3: 1
Define Cell Types manually
sce$celltype <- "other"
sce$CXCL13 <- "negative"
sce[,which(t(assay(sce,"asinh"))[,"CD8a"] > 2 & t(assay(sce,"asinh"))[,"CD3"] > 1) ]$celltype <- "CD8_Tcell"
sce[,which(t(assay(sce,"asinh"))[,"CXCL13"] > 2 ) ]$CXCL13 <- "positive"
sce[,which(sce$celltype == "CD8_Tcell" & sce$CXCL13 == "positive")]$celltype <- "CD8_CXCL13+_Tcell"
sce[,which(sce$celltype == "other" & sce$CXCL13 == "positive")]$celltype <- "other_CXCL13+"Save SCE object
saveRDS(sce, file = "data/data_for_analysis/sce_dysfunction.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] scran_1.22.1 ggpubr_0.4.0
[3] forcats_0.5.1 stringr_1.4.0
[5] dplyr_1.0.7 purrr_0.3.4
[7] readr_2.1.2 tidyr_1.2.0
[9] tibble_3.1.6 tidyverse_1.3.1
[11] Hmisc_4.6-0 Formula_1.2-4
[13] survival_3.2-13 lattice_0.20-45
[15] cowplot_1.1.1 scales_1.1.1
[17] scater_1.22.0 scuttle_1.4.0
[19] dittoSeq_1.6.0 ggplot2_3.3.5
[21] BiocNeighbors_1.12.0 BiocParallel_1.28.3
[23] imcRtools_1.0.2 SpatialExperiment_1.4.0
[25] SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0
[27] Biobase_2.54.0 GenomicRanges_1.46.1
[29] GenomeInfoDb_1.30.1 IRanges_2.28.0
[31] S4Vectors_0.32.3 BiocGenerics_0.40.0
[33] MatrixGenerics_1.6.0 matrixStats_0.61.0
[35] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] R.methodsS3_1.8.1 bit64_4.0.5
[3] knitr_1.37 irlba_2.3.5
[5] DelayedArray_0.20.0 R.utils_2.11.0
[7] data.table_1.14.2 rpart_4.1.16
[9] RCurl_1.98-1.5 generics_0.1.2
[11] ScaledMatrix_1.2.0 callr_3.7.0
[13] terra_1.5-17 proxy_0.4-26
[15] bit_4.0.4 tzdb_0.2.0
[17] xml2_1.3.3 lubridate_1.8.0
[19] httpuv_1.6.5 assertthat_0.2.1
[21] viridis_0.6.2 xfun_0.29
[23] hms_1.1.1 jquerylib_0.1.4
[25] evaluate_0.14 promises_1.2.0.1
[27] fansi_1.0.2 dbplyr_2.1.1
[29] readxl_1.3.1 igraph_1.2.11
[31] DBI_1.1.2 htmlwidgets_1.5.4
[33] ellipsis_0.3.2 backports_1.4.1
[35] svgPanZoom_0.3.4 sparseMatrixStats_1.6.0
[37] vctrs_0.3.8 abind_1.4-5
[39] withr_2.4.3 ggforce_0.3.3
[41] cytomapper_1.6.0 checkmate_2.0.0
[43] vroom_1.5.7 svglite_2.0.0
[45] cluster_2.1.2 crayon_1.4.2
[47] labeling_0.4.2 edgeR_3.36.0
[49] pkgconfig_2.0.3 units_0.7-2
[51] tweenr_1.0.2 vipor_0.4.5
[53] nnet_7.3-17 rlang_1.0.0
[55] lifecycle_1.0.1 modelr_0.1.8
[57] rsvd_1.0.5 cellranger_1.1.0
[59] rprojroot_2.0.2 polyclip_1.10-0
[61] tiff_0.1-11 Matrix_1.4-0
[63] raster_3.5-15 carData_3.0-5
[65] Rhdf5lib_1.16.0 reprex_2.0.1
[67] base64enc_0.1-3 beeswarm_0.4.0
[69] RTriangle_1.6-0.10 whisker_0.4
[71] ggridges_0.5.3 processx_3.5.2
[73] pheatmap_1.0.12 png_0.1-7
[75] viridisLite_0.4.0 rjson_0.2.21
[77] bitops_1.0-7 shinydashboard_0.7.2
[79] getPass_0.2-2 R.oo_1.24.0
[81] KernSmooth_2.23-20 rhdf5filters_1.6.0
[83] DelayedMatrixStats_1.16.0 classInt_0.4-3
[85] jpeg_0.1-9 rstatix_0.7.0
[87] ggsignif_0.6.3 beachmat_2.10.0
[89] magrittr_2.0.2 plyr_1.8.6
[91] zlibbioc_1.40.0 compiler_4.1.2
[93] dqrng_0.3.0 concaveman_1.1.0
[95] RColorBrewer_1.1-2 cli_3.1.1
[97] XVector_0.34.0 ps_1.6.0
[99] htmlTable_2.4.0 MASS_7.3-55
[101] tidyselect_1.1.1 stringi_1.7.6
[103] highr_0.9 yaml_2.2.2
[105] BiocSingular_1.10.0 locfit_1.5-9.4
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[117] git2r_0.29.0 metapod_1.2.0
[119] gridExtra_2.3 farver_2.1.0
[121] ggraph_2.0.5 DropletUtils_1.14.2
[123] digest_0.6.29 shiny_1.7.1
[125] Rcpp_1.0.8 car_3.0-12
[127] broom_0.7.12 later_1.3.0
[129] httr_1.4.2 sf_1.0-5
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[141] tidygraph_1.2.0 R6_2.5.1
[143] pillar_1.7.0 htmltools_0.5.2
[145] mime_0.12 glue_1.6.1
[147] fastmap_1.1.0 DT_0.20
[149] fftwtools_0.9-11 class_7.3-20
[151] codetools_0.2-18 utf8_1.2.2
[153] bslib_0.3.1 ggbeeswarm_0.6.0
[155] magick_2.7.3 limma_3.50.0
[157] rmarkdown_2.11 munsell_0.5.0
[159] e1071_1.7-9 rhdf5_2.38.0
[161] GenomeInfoDbData_1.2.7 HDF5Array_1.22.1
[163] haven_2.4.3 gtable_0.3.0