Last updated: 2022-02-22
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Knit directory: MelanomaIMC/
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This script detects interaction networks of a given celltype (here: B cells) and defines these networks as clusters. Once a cluster is defined, an algorithm screens the neighbourhood of those clusters to identify cells within/surrounding a cluster. These cells are defined as the community of a cluster.
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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visible FALSE FALSE
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 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(SingleCellExperiment)
library(ggplot2)
library(scater)
library(viridis)
library(igraph)
library(CATALYST)
library(reshape2)
library(cowplot)
library(ggridges)
library(tidyverse)
library(viridis)
library(dplyr)
library(cytomapper)
library(concaveman)
library(data.table)
library(sf)
library(ggbeeswarm)
library(RANN)sce_prot = readRDS(file = "data/data_for_analysis/sce_protein.rds")
sce_rna = readRDS(file = "data/data_for_analysis/sce_RNA.rds")A B cell cluster is defined by at least 20 adjacent B cells (Bcell and BnTcell, max distance of 15µm between them). A milieu is defined by all cells within a cluster and the proximity (enlarging distance = 15µm)
sce_prot$bcell_patch <- NULL
sce_prot$bcell_milieu <- NULL
sce_prot$bcell_patch_score <- NULL
start = Sys.time()
# quantiles of cell radius
quantile(sqrt(sce_prot[,sce_prot$celltype %in% c("B cell")]$Area/pi)) 0% 25% 50% 75% 100%
1.128379 2.820948 3.385138 3.908820 9.097284 # find B cell clusters
sce_prot <- findPatch(sce_prot, sce_prot[,colData(sce_prot)$celltype %in% c("B cell", "BnT cell")]$cellID,
'cellID', 'Center_X', 'Center_Y', 'ImageNumber',
distance = 15, min_clust_size = 10, output_colname = "bcell_patch")Time difference of 10.19127 mins
[1] "patches successfully added to sce object"# number of B cell clusters
length(unique(sce_prot$bcell_patch))[1] 375# define cells within/surrounding a cluster of B cells
sce_prot <- findMilieu(sce_prot,
'cellID', 'Center_X', 'Center_Y', 'ImageNumber', 'bcell_patch',
distance = 30, output_colname = "bcell_milieu")Time difference of 2.777045 mins
[1] "milieus successfully added to sce object"# number of chemokine communities
length(unique(sce_prot$bcell_milieu))[1] 375end = Sys.time()
print(end-start)Time difference of 12.97945 minsexample <- findPatch(sce_prot[,sce_prot$Description %in% c("D4")], sce_prot[,sce_prot$celltype %in% c("B cell", "BnT cell")]$cellID,
'cellID',
'Center_X', 'Center_Y',
'ImageNumber',
distance = 15,
min_clust_size = 10,
output_colname = "example_patch")Time difference of 0.4569645 secs
[1] "patches successfully added to sce object"example <- findMilieu(example,
'cellID',
'Center_X', 'Center_Y',
'ImageNumber',
'example_patch',
distance = 30,
output_colname = "example_milieu",
plot = TRUE)
| Version | Author | Date |
|---|---|---|
| 5418dcd | toobiwankenobi | 2022-02-22 |
Time difference of 1.115749 secs
[1] "milieus successfully added to sce object"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] RANN_2.6.1 ggbeeswarm_0.6.0
[3] sf_1.0-5 data.table_1.14.2
[5] concaveman_1.1.0 cytomapper_1.6.0
[7] EBImage_4.36.0 forcats_0.5.1
[9] stringr_1.4.0 purrr_0.3.4
[11] readr_2.1.2 tidyr_1.2.0
[13] tibble_3.1.6 tidyverse_1.3.1
[15] ggridges_0.5.3 cowplot_1.1.1
[17] reshape2_1.4.4 CATALYST_1.18.1
[19] igraph_1.2.11 viridis_0.6.2
[21] viridisLite_0.4.0 scater_1.22.0
[23] scuttle_1.4.0 ggplot2_3.3.5
[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] dplyr_1.0.7 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] scattermore_0.7 flowWorkspace_4.6.0
[3] knitr_1.37 irlba_2.3.5
[5] multcomp_1.4-18 DelayedArray_0.20.0
[7] RCurl_1.98-1.5 doParallel_1.0.16
[9] generics_0.1.2 flowCore_2.6.0
[11] ScaledMatrix_1.2.0 terra_1.5-17
[13] callr_3.7.0 TH.data_1.1-0
[15] proxy_0.4-26 ggpointdensity_0.1.0
[17] tzdb_0.2.0 xml2_1.3.3
[19] lubridate_1.8.0 httpuv_1.6.5
[21] assertthat_0.2.1 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 Rgraphviz_2.38.0
[31] DBI_1.1.2 htmlwidgets_1.5.4
[33] ellipsis_0.3.2 ggcyto_1.22.0
[35] ggnewscale_0.4.5 ggpubr_0.4.0
[37] backports_1.4.1 V8_4.0.0
[39] cytolib_2.6.1 svgPanZoom_0.3.4
[41] RcppParallel_5.1.5 sparseMatrixStats_1.6.0
[43] vctrs_0.3.8 abind_1.4-5
[45] withr_2.4.3 ggforce_0.3.3
[47] aws.signature_0.6.0 svglite_2.0.0
[49] cluster_2.1.2 crayon_1.4.2
[51] drc_3.0-1 units_0.7-2
[53] pkgconfig_2.0.3 tweenr_1.0.2
[55] vipor_0.4.5 rlang_1.0.0
[57] lifecycle_1.0.1 sandwich_3.0-1
[59] modelr_0.1.8 rsvd_1.0.5
[61] cellranger_1.1.0 rprojroot_2.0.2
[63] polyclip_1.10-0 graph_1.72.0
[65] tiff_0.1-11 Matrix_1.4-0
[67] raster_3.5-15 carData_3.0-5
[69] Rhdf5lib_1.16.0 zoo_1.8-9
[71] reprex_2.0.1 base64enc_0.1-3
[73] beeswarm_0.4.0 whisker_0.4
[75] GlobalOptions_0.1.2 processx_3.5.2
[77] pheatmap_1.0.12 png_0.1-7
[79] rjson_0.2.21 bitops_1.0-7
[81] shinydashboard_0.7.2 getPass_0.2-2
[83] KernSmooth_2.23-20 rhdf5filters_1.6.0
[85] ConsensusClusterPlus_1.58.0 DelayedMatrixStats_1.16.0
[87] classInt_0.4-3 shape_1.4.6
[89] jpeg_0.1-9 rstatix_0.7.0
[91] ggsignif_0.6.3 aws.s3_0.3.21
[93] beachmat_2.10.0 scales_1.1.1
[95] magrittr_2.0.2 plyr_1.8.6
[97] hexbin_1.28.2 zlibbioc_1.40.0
[99] compiler_4.1.2 RColorBrewer_1.1-2
[101] plotrix_3.8-2 clue_0.3-60
[103] cli_3.1.1 XVector_0.34.0
[105] ncdfFlow_2.40.0 ps_1.6.0
[107] FlowSOM_2.2.0 MASS_7.3-55
[109] tidyselect_1.1.1 stringi_1.7.6
[111] RProtoBufLib_2.6.0 highr_0.9
[113] yaml_2.2.2 BiocSingular_1.10.0
[115] locfit_1.5-9.4 latticeExtra_0.6-29
[117] ggrepel_0.9.1 grid_4.1.2
[119] sass_0.4.0 tools_4.1.2
[121] parallel_4.1.2 CytoML_2.6.0
[123] circlize_0.4.13 rstudioapi_0.13
[125] foreach_1.5.2 git2r_0.29.0
[127] gridExtra_2.3 farver_2.1.0
[129] Rtsne_0.15 digest_0.6.29
[131] shiny_1.7.1 Rcpp_1.0.8
[133] car_3.0-12 broom_0.7.12
[135] later_1.3.0 httr_1.4.2
[137] ComplexHeatmap_2.10.0 colorspace_2.0-2
[139] rvest_1.0.2 XML_3.99-0.8
[141] fs_1.5.2 splines_4.1.2
[143] RBGL_1.70.0 sp_1.4-6
[145] systemfonts_1.0.3 xtable_1.8-4
[147] jsonlite_1.7.3 R6_2.5.1
[149] pillar_1.7.0 htmltools_0.5.2
[151] mime_0.12 nnls_1.4
[153] glue_1.6.1 fastmap_1.1.0
[155] BiocParallel_1.28.3 BiocNeighbors_1.12.0
[157] fftwtools_0.9-11 class_7.3-20
[159] codetools_0.2-18 mvtnorm_1.1-3
[161] utf8_1.2.2 lattice_0.20-45
[163] bslib_0.3.1 curl_4.3.2
[165] colorRamps_2.3 gtools_3.9.2
[167] survival_3.2-13 rmarkdown_2.11
[169] munsell_0.5.0 e1071_1.7-9
[171] rhdf5_2.38.0 GetoptLong_1.0.5
[173] GenomeInfoDbData_1.2.7 iterators_1.0.13
[175] HDF5Array_1.22.1 haven_2.4.3
[177] gtable_0.3.0