Last updated: 2022-02-22
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This script generates plots for Supplementary Figure 8.
sapply(list.files("code/helper_functions", full.names = TRUE), source)
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between, first, lastlibrary(ggplot2)
library(broom)
library(dplyr)
library(tidyverse)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──✓ tibble 3.1.6 ✓ purrr 0.3.4
✓ tidyr 1.2.0 ✓ stringr 1.4.0
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collapse, desc, sliceLoading required package: GenomeInfoDbLoading required package: BiobaseWelcome to Bioconductor
Vignettes contain introductory material; view with
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dcast, meltlibrary(circlize)========================================
circlize version 0.4.13
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: https://jokergoo.github.io/circlize_book/book/
If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
in R. Bioinformatics 2014.
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========================================library(rstatix)
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filterlibrary(corrplot)corrplot 0.92 loadedlibrary(ggpubr)
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get_legend# SCE object
sce_rna = readRDS(file = "data/data_for_analysis/sce_RNA.rds")
sce_rna <- sce_rna[,sce_rna$Location != "CTRL"]
targets <- metadata(sce_rna)$chemokines_morethan600_withcontrolsce_rna$MM_location <- ifelse(sce_rna$MM_location %in% c("skin", "skin_undefine"), "skin_undefined", sce_rna$MM_location)
groups <- data.frame(colData(sce_rna)) %>%
distinct(ImageNumber, .keep_all = T) %>%
group_by(MM_location) %>%
distinct(PatientID, .keep_all = T) %>%
summarise(n=n()) %>%
filter(n>=10) %>%
arrange(-n)fractions_per_image <- data.frame(colData(sce_rna)) %>%
group_by(ImageNumber, MM_location, expressor, celltype) %>%
summarise(n = n()) %>%
group_by(ImageNumber) %>%
mutate(fraction_per_image = n / sum(n)) %>%
group_by(ImageNumber, expressor) %>%
mutate(group_fraction = sum(fraction_per_image)) %>%
ungroup() %>%
filter(expressor %in% targets & MM_location %in% groups$MM_location)`summarise()` has grouped output by 'ImageNumber', 'MM_location', 'expressor'.
You can override using the `.groups` argument.# fraction of expressor cells per image
fraction_expressor_per_image <- fractions_per_image %>%
distinct(ImageNumber, MM_location, expressor, .keep_all = T) %>%
reshape2::dcast(ImageNumber + MM_location ~ expressor, value.var = "group_fraction", fill = 0) %>%
reshape2::melt(id.vars = c("ImageNumber", "MM_location"), variable.name = "expressor",
value.name = "fraction_per_image")
# fraction of celltype expressing a certain combi per image
celltype_fractions <- fractions_per_image %>%
distinct(ImageNumber, celltype, expressor, .keep_all = T) %>%
reshape2::dcast(ImageNumber + MM_location + expressor ~ celltype, value.var = "fraction_per_image", fill = 0) %>%
reshape2::melt(id.vars = c("ImageNumber", "MM_location", "expressor"),
variable.name = "celltype", value.name = "fraction_per_image") %>%
reshape2::dcast(ImageNumber + MM_location + celltype ~ expressor, value.var = "fraction_per_image", fill = 0) %>%
reshape2::melt(id.vars = c("ImageNumber", "MM_location", "celltype"),
variable.name = "expressor", value.name = "fraction_per_image") %>%
group_by(MM_location, expressor, celltype) %>%
summarise(sum_fraction = sum(fraction_per_image)) %>% # sum-up fractions over all images
group_by(MM_location, expressor) %>%
mutate(proportions = sum_fraction / sum(sum_fraction)) # calculate proportions for each expressor`summarise()` has grouped output by 'MM_location', 'expressor'. You can override
using the `.groups` argument.# calculate signif of expressor-fractions per MM_location
fraction_expressor_per_image %>%
group_by(expressor) %>%
wilcox_test(fraction_per_image ~ MM_location) %>%
adjust_pvalue(method = "BH") %>%
add_significance("p.adj",cutpoints = c(0, 1e-04, 0.001, 0.01, 0.1, 1)) %>%
arrange(p.adj)# A tibble: 14 × 10
expressor .y. group1 group2 n1 n2 statistic p p.adj
<fct> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl>
1 CCL19 fraction_per… LN skin_… 49 52 1739 0.00129 0.0181
2 CXCL13 fraction_per… LN skin_… 49 52 1634. 0.0138 0.0966
3 CCL22 fraction_per… LN skin_… 49 52 1557 0.0548 0.145
4 CXCL12_CCL2 fraction_per… LN skin_… 49 52 1535 0.062 0.145
5 CXCL8 fraction_per… LN skin_… 49 52 1580 0.0378 0.145
6 CXCL9_CCL19 fraction_per… LN skin_… 49 52 1523 0.0487 0.145
7 CCL18 fraction_per… LN skin_… 49 52 1501 0.124 0.217
8 CXCL10_CCL2 fraction_per… LN skin_… 49 52 1054 0.121 0.217
9 CXCL10 fraction_per… LN skin_… 49 52 1095 0.225 0.315
10 CXCL12 fraction_per… LN skin_… 49 52 1457 0.215 0.315
11 CCL4 fraction_per… LN skin_… 49 52 1382 0.465 0.592
12 CCL2 fraction_per… LN skin_… 49 52 1326. 0.724 0.845
13 CXCL10_CXCL9 fraction_per… LN skin_… 49 52 1270 0.981 0.981
14 CXCL9 fraction_per… LN skin_… 49 52 1288 0.926 0.981
# … with 1 more variable: p.adj.signif <chr>plot_list <- list()
for(i in groups$MM_location) {
a <- fraction_expressor_per_image %>%
filter(MM_location == i) %>%
group_by(ImageNumber, expressor) %>%
ggplot(., aes(y=as.factor(expressor), x=fraction_per_image)) +
geom_boxplot() +
geom_point(alpha=0.2) +
theme_bw() +
theme(axis.title.y = element_blank(),
axis.text.y = element_text(hjust=0.5)) +
xlab("Cell Fraction per Image") +
coord_cartesian(xlim = c(0,0.05))
b <- celltype_fractions %>%
filter(MM_location == i) %>%
ggplot(., aes(y=expressor, x=-proportions, fill=celltype)) +
geom_bar(stat = "identity") +
theme_bw() +
theme(axis.text.y = element_blank(),
axis.title.y = element_blank()) +
guides(fill=guide_legend(title = "Cell Type",nrow=2,byrow=TRUE)) +
xlab("Producing Cell Types") +
scale_fill_manual(values = unname(metadata(sce_rna)$colour_vectors$celltype),
breaks = names(metadata(sce_rna)$colour_vectors$celltype),
labels = names(metadata(sce_rna)$colour_vectors$celltype)) +
scale_x_continuous(breaks=c(-1.00,-0.75,-0.5, -0.25, 0.00),
labels=c("100%", "75%", "50%", "25%", "0%"))
leg <- get_legend(b)
grid.arrange(b+theme(legend.position = "none"),a,nrow=1,
widths = c(.75,1),
top = i)
}grid.arrange(leg)# top abundant chemokines
cur_rna <- data.frame(colData(sce_rna))
# sum
rna_sum <- cur_rna %>%
group_by(Description, expressor) %>%
summarise(n = n()) %>%
reshape2::dcast(Description ~ expressor, value.var = "n", fill = 0) `summarise()` has grouped output by 'Description'. You can override using the
`.groups` argument.# only keep highly abundant chemokines
rna_sum <- rna_sum[,colnames(rna_sum) %in% targets]
# correlation
cor <- cor(rna_sum, rna_sum, method = "pearson")
corrplot(cor,
order = "FPC",
addCoef.col = "black",
method = "circle",
tl.col="black",
tl.cex = 1.5)
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] ggpubr_0.4.0 corrplot_0.92
[3] rstatix_0.7.0 circlize_0.4.13
[5] reshape2_1.4.4 scater_1.22.0
[7] scuttle_1.4.0 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 gridExtra_2.3
[19] ggbeeswarm_0.6.0 cowplot_1.1.1
[21] forcats_0.5.1 stringr_1.4.0
[23] purrr_0.3.4 readr_2.1.2
[25] tidyr_1.2.0 tibble_3.1.6
[27] tidyverse_1.3.1 broom_0.7.12
[29] ggplot2_3.3.5 data.table_1.14.2
[31] dplyr_1.0.7 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] colorspace_2.0-2 ggsignif_0.6.3
[3] ellipsis_0.3.2 rprojroot_2.0.2
[5] XVector_0.34.0 GlobalOptions_0.1.2
[7] BiocNeighbors_1.12.0 fs_1.5.2
[9] rstudioapi_0.13 farver_2.1.0
[11] ggrepel_0.9.1 fansi_1.0.2
[13] lubridate_1.8.0 xml2_1.3.3
[15] sparseMatrixStats_1.6.0 knitr_1.37
[17] jsonlite_1.7.3 dbplyr_2.1.1
[19] compiler_4.1.2 httr_1.4.2
[21] backports_1.4.1 assertthat_0.2.1
[23] Matrix_1.4-0 fastmap_1.1.0
[25] cli_3.1.1 later_1.3.0
[27] BiocSingular_1.10.0 htmltools_0.5.2
[29] tools_4.1.2 rsvd_1.0.5
[31] gtable_0.3.0 glue_1.6.1
[33] GenomeInfoDbData_1.2.7 Rcpp_1.0.8
[35] carData_3.0-5 cellranger_1.1.0
[37] jquerylib_0.1.4 vctrs_0.3.8
[39] DelayedMatrixStats_1.16.0 xfun_0.29
[41] ps_1.6.0 beachmat_2.10.0
[43] rvest_1.0.2 lifecycle_1.0.1
[45] irlba_2.3.5 getPass_0.2-2
[47] zlibbioc_1.40.0 scales_1.1.1
[49] hms_1.1.1 promises_1.2.0.1
[51] parallel_4.1.2 yaml_2.2.2
[53] sass_0.4.0 stringi_1.7.6
[55] highr_0.9 ScaledMatrix_1.2.0
[57] BiocParallel_1.28.3 shape_1.4.6
[59] rlang_1.0.0 pkgconfig_2.0.3
[61] bitops_1.0-7 evaluate_0.14
[63] lattice_0.20-45 labeling_0.4.2
[65] processx_3.5.2 tidyselect_1.1.1
[67] plyr_1.8.6 magrittr_2.0.2
[69] R6_2.5.1 generics_0.1.2
[71] DelayedArray_0.20.0 DBI_1.1.2
[73] pillar_1.7.0 haven_2.4.3
[75] whisker_0.4 withr_2.4.3
[77] abind_1.4-5 RCurl_1.98-1.5
[79] car_3.0-12 modelr_0.1.8
[81] crayon_1.4.2 utf8_1.2.2
[83] tzdb_0.2.0 rmarkdown_2.11
[85] viridis_0.6.2 grid_4.1.2
[87] readxl_1.3.1 callr_3.7.0
[89] git2r_0.29.0 reprex_2.0.1
[91] digest_0.6.29 httpuv_1.6.5
[93] munsell_0.5.0 beeswarm_0.4.0
[95] viridisLite_0.4.0 vipor_0.4.5
[97] bslib_0.3.1