TLS identifiaction
Tobias Hoch
2020-05-28
Last updated: 2021-02-12
Checks: 6 1
Knit directory: melanoma_publication_old_data/
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Introduction
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.
Load packages and helper functions
sapply(list.files("code/helper_functions", full.names = TRUE), source) code/helper_functions/calculateSummary.R
value ?
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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visible FALSE
code/helper_functions/plotCellCounts.R
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visible FALSE
code/helper_functions/plotCellFractions.R
value ?
visible FALSE
code/helper_functions/plotDist.R
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visible FALSE
code/helper_functions/scatter_function.R
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visible FALSE
code/helper_functions/sceChecks.R
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visible FALSE
code/helper_functions/validityChecks.R
value ?
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)Load data
sce_prot = readRDS(file = "data/data_for_analysis/sce_protein.rds")
sce_rna = readRDS(file = "data/data_for_analysis/sce_RNA.rds")
image_prot <- read.csv("data/data_for_analysis/protein/Image.csv")
sce_prot$bcell_patch_score <- NULL
sce_rna$bcell_patch_score <- NULLFind patches of B cells and their milieus
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("Bcell")]$Area/pi)) 0% 25% 50% 75% 100%
1.128379 2.820948 3.385138 3.908820 9.097284 # find B cell clusters
sce_prot <- findPatch(sce_prot, sce_prot[,colData(sce_prot)$celltype %in% c("Bcell", "BnTcell")]$cellID,
'cellID', 'Center_X', 'Center_Y', 'ImageNumber',
distance = 15, min_clust_size = 20, output_colname = "bcell_patch")Time difference of 10.25391 mins
[1] "patches successfully added to sce object"# number of B cell clusters
length(unique(sce_prot$bcell_patch))[1] 207# define cells within/surrounding a cluster of B cells
sce_prot <- findMilieu(sce_prot,
'cellID', 'Center_X', 'Center_Y', 'ImageNumber', 'bcell_patch',
distance = 50, output_colname = "bcell_milieu")Time difference of 1.794294 mins
[1] "milieus successfully added to sce object"# number of chemokine communities
length(unique(sce_prot$bcell_milieu))[1] 207end = Sys.time()
print(end-start)Time difference of 12.05721 minsDefine grouping for B cell / patch densities
Cell Densities
# Protein
im_size_prot <- (image_prot$Height_cellmask * image_prot$Width_cellmask)/1000000
im_size_prot <- data.frame(im_size_prot)
im_size_prot$Description <- image_prot$Metadata_DescriptionB cell / patch grouping
# patches per image
data.frame(colData(sce_prot)) %>%
group_by(Description) %>%
filter(bcell_patch != 0) %>%
distinct(bcell_patch, .keep_all = T) %>%
summarise(n=n()) %>%
arrange(-n)# A tibble: 44 x 2
Description n
<chr> <int>
1 L8 16
2 C9 14
3 L7 13
4 O10 11
5 J9 10
6 J2 9
7 B10 8
8 E3 8
9 F9 8
10 O2 8
# … with 34 more rows# max patch size per image
max_patch <- data.frame(colData(sce_prot)) %>%
filter(bcell_patch != 0) %>%
group_by(Description, bcell_patch) %>%
summarise(n=n()) %>%
summarise(max_patch_size = max(n)) %>%
arrange(-max_patch_size)
ggplot(max_patch, aes(x=Description, y=max_patch_size)) +
geom_col() +
geom_hline(yintercept = 250)
Plot some patches
example <- findPatch(sce_prot[,sce_prot$Description %in% c("D4")], sce_prot[,sce_prot$celltype %in% c("Bcell", "BnTcell")]$cellID,
'cellID',
'Center_X', 'Center_Y',
'ImageNumber',
distance = 15,
min_clust_size = 20,
output_colname = "example_patch")Time difference of 0.3272331 secs
[1] "patches successfully added to sce object"example <- findMilieu(example,
'cellID',
'Center_X', 'Center_Y',
'ImageNumber',
'example_patch',
distance = 50,
output_colname = "example_milieu",
plot = TRUE)Time difference of 0.759469 secs
[1] "milieus successfully added to sce object"Make grouping
1 no B cells (B cell density < 10 Bcells/mm2 and no patches) 2 no B cell patches (B cell density > 10 Bcells/mm2 and no patches) 3 small B cell patches (max patch size < 250 B cells) 4 TLS (max patch size > 250 B cells)
Bcell <- data.frame(colData(sce_prot)) %>%
group_by(Description,celltype) %>%
summarise(n=n()) %>%
reshape2::dcast(Description ~ celltype, value.var = "n", fill = 0) %>%
select(Description, Bcell)
Bcell$density <- Bcell$Bcell / im_size_prot[match(Bcell$Description, im_size_prot$Description),]$im_size_prot
ggplot(Bcell) + geom_col(aes(x=Description, y=density))Warning: Removed 1 rows containing missing values (position_stack).
im_w_patches <- data.frame(colData(sce_prot)) %>%
group_by(Description) %>%
filter(bcell_patch != 0) %>%
distinct(bcell_patch, .keep_all = T) %>%
summarise(n=n()) %>%
arrange(-n)Bcell$Bcell_patch_score <- ""
Bcell$Bcell_patch_score <- ifelse(Bcell$density < 10,"no B cells", Bcell$Bcell_patch_score)
Bcell$Bcell_patch_score <- ifelse(Bcell$density > 10,"no B cell patches", Bcell$Bcell_patch_score)
Bcell[Bcell$Description %in% max_patch[max_patch$max_patch_size<250,]$Description,]$Bcell_patch_score <- "small B cell patches"
Bcell[Bcell$Description %in% max_patch[max_patch$max_patch_size>=250,]$Description,]$Bcell_patch_score <- "TLS"
Bcell %>%
group_by(Bcell_patch_score) %>%
summarise(n=n())# A tibble: 4 x 2
Bcell_patch_score n
<chr> <int>
1 no B cell patches 36
2 no B cells 87
3 small B cell patches 16
4 TLS 28Bcell$Bcell_patch_score <- factor(Bcell$Bcell_patch_score, levels = c("no B cells", "no B cell patches", "small B cell patches", "TLS"))
# add to sce
cur_df <- data.frame(colData(sce_prot))
cur_df <- left_join(cur_df, Bcell[,c("Description", "Bcell_patch_score")])
sce_prot$bcell_patch_score <- cur_df$Bcell_patch_score
# add to rna sce
cur_df <- data.frame(colData(sce_rna))
cur_df <- left_join(cur_df, Bcell[,c("Description", "Bcell_patch_score")])
sce_rna$bcell_patch_score <- cur_df$Bcell_patch_scoreAnalysis
B cell densities of the different groups
ggplot(Bcell,aes(x=Bcell_patch_score, y = log10(density+1))) +
geom_boxplot() +
geom_quasirandom() +
ylab("B cell density (log10)")Warning: Removed 1 rows containing non-finite values (stat_boxplot).Warning: Removed 1 rows containing missing values (position_quasirandom).
Number of Patients in different groups
data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL") %>%
distinct(PatientID, .keep_all = T) %>%
group_by(bcell_patch_score) %>%
summarise(patients = n())# A tibble: 4 x 2
bcell_patch_score patients
<fct> <int>
1 no B cells 30
2 no B cell patches 16
3 small B cell patches 12
4 TLS 11Save 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 4.0.3 (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 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] RANN_2.6.1 ggbeeswarm_0.6.0
[3] sf_0.9-7 data.table_1.13.6
[5] concaveman_1.1.0 cytomapper_1.3.1
[7] EBImage_4.32.0 forcats_0.5.0
[9] stringr_1.4.0 dplyr_1.0.2
[11] purrr_0.3.4 readr_1.4.0
[13] tidyr_1.1.2 tibble_3.0.4
[15] tidyverse_1.3.0 ggridges_0.5.3
[17] cowplot_1.1.1 reshape2_1.4.4
[19] CATALYST_1.12.2 igraph_1.2.6
[21] viridis_0.5.1 viridisLite_0.3.0
[23] scater_1.16.2 ggplot2_3.3.3
[25] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[27] Biobase_2.50.0 GenomicRanges_1.42.0
[29] GenomeInfoDb_1.26.2 IRanges_2.24.1
[31] S4Vectors_0.28.1 BiocGenerics_0.36.0
[33] MatrixGenerics_1.2.0 matrixStats_0.57.0
[35] workflowr_1.6.2
loaded via a namespace (and not attached):
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[3] tidyselect_1.1.0 htmlwidgets_1.5.3
[5] grid_4.0.3 BiocParallel_1.22.0
[7] Rtsne_0.15 flowCore_2.0.1
[9] munsell_0.5.0 units_0.6-7
[11] codetools_0.2-18 withr_2.3.0
[13] colorspace_2.0-0 knitr_1.30
[15] rstudioapi_0.13 labeling_0.4.2
[17] git2r_0.28.0 GenomeInfoDbData_1.2.4
[19] farver_2.0.3 flowWorkspace_4.0.6
[21] rprojroot_2.0.2 vctrs_0.3.6
[23] generics_0.1.0 TH.data_1.0-10
[25] xfun_0.20 R6_2.5.0
[27] clue_0.3-58 rsvd_1.0.3
[29] locfit_1.5-9.4 bitops_1.0-6
[31] DelayedArray_0.16.0 assertthat_0.2.1
[33] promises_1.1.1 scales_1.1.1
[35] multcomp_1.4-15 beeswarm_0.2.3
[37] gtable_0.3.0 RProtoBufLib_2.0.0
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[43] splines_4.0.3 hexbin_1.28.2
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[51] RBGL_1.64.0 tools_4.0.3
[53] ellipsis_0.3.1 raster_3.4-5
[55] RColorBrewer_1.1-2 Rcpp_1.0.5
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[89] V8_3.4.0 KernSmooth_2.23-18
[91] crayon_1.3.4 htmltools_0.5.0
[93] later_1.1.0.1 tiff_0.1-6
[95] RcppParallel_5.0.2 lubridate_1.7.9.2
[97] DBI_1.1.0 dbplyr_2.0.0
[99] ComplexHeatmap_2.4.3 MASS_7.3-53
[101] Matrix_1.3-2 car_3.0-10
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[105] sp_1.4-5 foreign_0.8-81
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[149] irlba_2.3.3