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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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visible FALSE
code/helper_functions/censor_dat.R
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visible FALSE
code/helper_functions/detect_mRNA_expression.R
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visible FALSE
code/helper_functions/DistanceToClusterCenter.R
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visible FALSE
code/helper_functions/findMilieu.R code/helper_functions/findPatch.R
value ? ?
visible FALSE FALSE
code/helper_functions/getInfoFromString.R
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visible FALSE
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 code/helper_functions/read_Data.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
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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 5.514605 secs
[1] "patches successfully added to sce object"
Time difference of 17.19932 mins
[1] "milieus successfully added to sce object"
Time difference of 4.668736 secs
[1] "patches successfully added to sce object"
Time difference of 12.49801 mins
[1] "milieus successfully added to sce object"
Time difference of 3.967457 secs
[1] "patches successfully added to sce object"
Time difference of 12.58078 mins
[1] "milieus successfully added to sce object"
Time difference of 17.62502 secs
[1] "patches successfully added to sce object"
Time difference of 20.10345 mins
[1] "milieus successfully added to sce object"
Time difference of 9.109623 secs
[1] "patches successfully added to sce object"
Time difference of 21.92445 mins
[1] "milieus successfully added to sce object"
Time difference of 12.13334 secs
[1] "patches successfully added to sce object"
Time difference of 19.18648 mins
[1] "milieus successfully added to sce object"
Time difference of 12.39393 secs
[1] "patches successfully added to sce object"
Time difference of 26.49773 mins
[1] "milieus successfully added to sce object"
Time difference of 1.585672 secs
[1] "patches successfully added to sce object"
Time difference of 6.12763 mins
[1] "milieus successfully added to sce object"
Time difference of 16.86196 secs
[1] "patches successfully added to sce object"
Time difference of 17.50565 mins
[1] "milieus successfully added to sce object"
Time difference of 0.8439357 secs
[1] "patches successfully added to sce object"
Time difference of 3.367174 mins
[1] "milieus successfully added to sce object"
Time difference of 8.333804 secs
[1] "patches successfully added to sce object"
Time difference of 11.68944 mins
[1] "milieus successfully added to sce object"end = Sys.time()
print(end-start)Time difference of 2.837728 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])
}
}
# 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 4.1.2 (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 grid stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] RANN_2.6.1 concaveman_1.1.0
[3] sf_1.0-5 stringr_1.4.0
[5] data.table_1.14.2 ggrepel_0.9.1
[7] ggplot2_3.3.5 SingleCellExperiment_1.16.0
[9] SummarizedExperiment_1.24.0 Biobase_2.54.0
[11] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1
[13] IRanges_2.28.0 S4Vectors_0.32.3
[15] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
[17] matrixStats_0.61.0 ComplexHeatmap_2.10.0
[19] dplyr_1.0.7 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] bitops_1.0-7 fs_1.5.2 doParallel_1.0.16
[4] RColorBrewer_1.1-2 httr_1.4.2 rprojroot_2.0.2
[7] tools_4.1.2 bslib_0.3.1 utf8_1.2.2
[10] R6_2.5.1 KernSmooth_2.23-20 DBI_1.1.2
[13] colorspace_2.0-2 GetoptLong_1.0.5 withr_2.4.3
[16] tidyselect_1.1.1 processx_3.5.2 curl_4.3.2
[19] compiler_4.1.2 git2r_0.29.0 cli_3.1.1
[22] DelayedArray_0.20.0 sass_0.4.0 scales_1.1.1
[25] classInt_0.4-3 callr_3.7.0 proxy_0.4-26
[28] digest_0.6.29 rmarkdown_2.11 XVector_0.34.0
[31] pkgconfig_2.0.3 htmltools_0.5.2 fastmap_1.1.0
[34] rlang_1.0.0 GlobalOptions_0.1.2 rstudioapi_0.13
[37] shape_1.4.6 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 parallel_4.1.2 promises_1.2.0.1
[55] crayon_1.4.2 lattice_0.20-45 circlize_0.4.13
[58] knitr_1.37 ps_1.6.0 pillar_1.7.0
[61] rjson_0.2.21 codetools_0.2-18 glue_1.6.1
[64] evaluate_0.14 getPass_0.2-2 V8_4.0.0
[67] png_0.1-7 vctrs_0.3.8 httpuv_1.6.5
[70] foreach_1.5.2 gtable_0.3.0 purrr_0.3.4
[73] clue_0.3-60 assertthat_0.2.1 xfun_0.29
[76] e1071_1.7-9 later_1.3.0 class_7.3-20
[79] tibble_3.1.6 iterators_1.0.13 units_0.7-2
[82] cluster_2.1.2 ellipsis_0.3.2