Last updated: 2021-02-12
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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())sapply(list.files("code/helper_functions", full.names = TRUE), source) 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
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code/helper_functions/sceChecks.R
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visible FALSE library(ComplexHeatmap)
library(SingleCellExperiment)
library(ggplot2)
library(ggrepel)
library(dplyr)
library(data.table)
library(stringr)
library(sf)
library(concaveman)
library(RANN)sce = readRDS(file = "data/data_for_analysis/sce_RNA.rds")Here we use our patch detection algorithm to detect patches of cells that express a certain chemokine. The following settings are used: - We loop through all chemokines. If a cell produces mulitple chemokines, each chemokine is treated in a separate round of patch detection. - min_clust_size: 1, even a single cell without producing neighbours is considered a patch. In the next chunk, we select only patches that at least contain 10 producing cells. - distance patch neighbours: 25µm - expansion milieu: 30µm
start = Sys.time()
cur_sce <- data.frame(colData(sce))
# loop through all chemokines
for(i in names(cur_sce[,grepl(glob2rx("C*L*"),names(cur_sce))])) {
# find clusters
sce <- findPatch(input_sce = sce,
IDs_of_interest = cur_sce[cur_sce[,names(cur_sce) == i] == 1,]$cellID,
'cellID',
'Center_X', 'Center_Y',
'ImageNumber',
distance = 25,
min_clust_size = 1,
output_colname = paste(tolower(i), "only_clust", sep = ""))
# define cells within/surrounding a cluster of chemokine producing cells
sce <- findMilieu(sce,
'cellID',
'Center_X',
'Center_Y',
'ImageNumber',
paste(tolower(i), "only_clust", sep = ""),
distance = 30,
output_colname = paste(tolower(i), "only_comm", sep = ""))
} Time difference of 5.274443 secs
[1] "patches successfully added to sce object"
Time difference of 15.56936 mins
[1] "milieus successfully added to sce object"
Time difference of 4.553741 secs
[1] "patches successfully added to sce object"
Time difference of 11.55816 mins
[1] "milieus successfully added to sce object"
Time difference of 3.889479 secs
[1] "patches successfully added to sce object"
Time difference of 11.36563 mins
[1] "milieus successfully added to sce object"
Time difference of 16.43745 secs
[1] "patches successfully added to sce object"
Time difference of 18.64624 mins
[1] "milieus successfully added to sce object"
Time difference of 8.968791 secs
[1] "patches successfully added to sce object"
Time difference of 18.86026 mins
[1] "milieus successfully added to sce object"
Time difference of 11.36862 secs
[1] "patches successfully added to sce object"
Time difference of 17.36886 mins
[1] "milieus successfully added to sce object"
Time difference of 11.9613 secs
[1] "patches successfully added to sce object"
Time difference of 25.63973 mins
[1] "milieus successfully added to sce object"
Time difference of 1.488697 secs
[1] "patches successfully added to sce object"
Time difference of 5.464931 mins
[1] "milieus successfully added to sce object"
Time difference of 16.08479 secs
[1] "patches successfully added to sce object"
Time difference of 15.67529 mins
[1] "milieus successfully added to sce object"
Time difference of 0.8059049 secs
[1] "patches successfully added to sce object"
Time difference of 3.03025 mins
[1] "milieus successfully added to sce object"
Time difference of 7.857941 secs
[1] "patches successfully added to sce object"
Time difference of 11.07932 mins
[1] "milieus successfully added to sce object"end = Sys.time()
print(end-start)Time difference of 2.595871 hoursHere we select patches which contain at least 10 producing cells. We then assign a to all milieu cells from that patch the milieu ID that was already assigned above. All smaller patches get a 0.
cur_sce <- data.frame(colData(sce))
for(i in names(cur_sce[,grepl(glob2rx("*only_clust"),names(cur_sce))])) {
# select cluster with more than 10 producing cells
clust <- cur_sce %>%
group_by_at(i) %>%
summarise(n=n()) %>%
distinct_at(i, .keep_all = TRUE) %>%
filter(n>10)
# remove cluster 0 (non-producing cells)
clust <- clust[clust[,i] > 0,]
# create new column
name <- str_split(i, "_")[[1]][1]
name <- paste0(name,"_pure")
cur_sce[,name] <- 0
# add cluster id to new column for all cells (producing AND non-producing) that are part of that community
if(nrow(clust) > 0){
colname_comm <- paste0(str_split(i, "_")[[1]][1],"_comm")
cur_sce[cur_sce[,colname_comm] %in% clust[,i][[1]], name] <- as.numeric(cur_sce[cur_sce[,colname_comm] %in% clust[,i][[1]], colname_comm])
}
}Warning: The `i` argument of ``[`()` can't be a matrix as of tibble 3.0.0.
Convert to a vector.
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.# add to sce
sce$ccl4_pure <- cur_sce$ccl4only_pure
sce$ccl18_pure <- cur_sce$ccl18only_pure
sce$cxcl8_pure <- cur_sce$cxcl8only_pure
sce$cxcl10_pure <- cur_sce$cxcl10only_pure
sce$cxcl12_pure <- cur_sce$cxcl12only_pure
sce$cxcl13_pure <- cur_sce$cxcl13only_pure
sce$ccl2_pure <- cur_sce$ccl2only_pure
sce$ccl22_pure <- cur_sce$ccl22only_pure
sce$cxcl9_pure <- cur_sce$cxcl9only_pure
sce$ccl8_pure <- cur_sce$ccl8only_pure
sce$ccl19_pure <- cur_sce$ccl19only_puresaveRDS(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] parallel stats4 grid stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] RANN_2.6.1 concaveman_1.1.0
[3] sf_0.9-7 stringr_1.4.0
[5] data.table_1.13.6 dplyr_1.0.2
[7] ggrepel_0.9.0 ggplot2_3.3.3
[9] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[11] Biobase_2.50.0 GenomicRanges_1.42.0
[13] GenomeInfoDb_1.26.2 IRanges_2.24.1
[15] S4Vectors_0.28.1 BiocGenerics_0.36.0
[17] MatrixGenerics_1.2.0 matrixStats_0.57.0
[19] ComplexHeatmap_2.4.3 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] jsonlite_1.7.2 GenomeInfoDbData_1.2.4 yaml_2.2.1
[4] pillar_1.4.7 lattice_0.20-41 glue_1.4.2
[7] digest_0.6.27 RColorBrewer_1.1-2 promises_1.1.1
[10] XVector_0.30.0 colorspace_2.0-0 htmltools_0.5.0
[13] httpuv_1.5.4 Matrix_1.3-2 pkgconfig_2.0.3
[16] GetoptLong_1.0.5 zlibbioc_1.36.0 purrr_0.3.4
[19] scales_1.1.1 whisker_0.4 later_1.1.0.1
[22] git2r_0.28.0 tibble_3.0.4 generics_0.1.0
[25] ellipsis_0.3.1 withr_2.3.0 magrittr_2.0.1
[28] crayon_1.3.4 evaluate_0.14 fs_1.5.0
[31] class_7.3-17 tools_4.0.3 GlobalOptions_0.1.2
[34] lifecycle_0.2.0 V8_3.4.0 munsell_0.5.0
[37] cluster_2.1.0 DelayedArray_0.16.0 compiler_4.0.3
[40] e1071_1.7-4 rlang_0.4.10 classInt_0.4-3
[43] units_0.6-7 RCurl_1.98-1.2 rstudioapi_0.13
[46] rjson_0.2.20 circlize_0.4.12 bitops_1.0-6
[49] rmarkdown_2.6 gtable_0.3.0 curl_4.3
[52] DBI_1.1.0 R6_2.5.0 knitr_1.30
[55] clue_0.3-58 rprojroot_2.0.2 KernSmooth_2.23-18
[58] shape_1.4.5 stringi_1.5.3 Rcpp_1.0.5
[61] vctrs_0.3.6 png_0.1-7 tidyselect_1.1.0
[64] xfun_0.20