Supplementary Figure 6
Florian Wuennemann
Last updated: 2024-03-21
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Knit directory: mi_spatialomics/
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Introduction
library(pheatmap)
library(data.table)
library(viridis)Loading required package: viridisLitelibrary(RColorBrewer)
library(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.4.4 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.0
✔ purrr 1.0.2 ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::between() masks data.table::between()
✖ dplyr::filter() masks stats::filter()
✖ dplyr::first() masks data.table::first()
✖ lubridate::hour() masks data.table::hour()
✖ lubridate::isoweek() masks data.table::isoweek()
✖ dplyr::lag() masks stats::lag()
✖ dplyr::last() masks data.table::last()
✖ lubridate::mday() masks data.table::mday()
✖ lubridate::minute() masks data.table::minute()
✖ lubridate::month() masks data.table::month()
✖ lubridate::quarter() masks data.table::quarter()
✖ lubridate::second() masks data.table::second()
✖ purrr::transpose() masks data.table::transpose()
✖ lubridate::wday() masks data.table::wday()
✖ lubridate::week() masks data.table::week()
✖ lubridate::yday() masks data.table::yday()
✖ lubridate::year() masks data.table::year()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(pals)
Attaching package: 'pals'
The following objects are masked from 'package:viridis':
cividis, inferno, magma, plasma, turbo, viridis
The following objects are masked from 'package:viridisLite':
cividis, inferno, magma, plasma, turbo, viridislibrary(vroom)
Attaching package: 'vroom'
The following objects are masked from 'package:readr':
as.col_spec, col_character, col_date, col_datetime, col_double,
col_factor, col_guess, col_integer, col_logical, col_number,
col_skip, col_time, cols, cols_condense, cols_only, date_names,
date_names_lang, date_names_langs, default_locale, fwf_cols,
fwf_empty, fwf_positions, fwf_widths, locale, output_column,
problems, speclibrary(plotly)
Attaching package: 'plotly'
The following object is masked from 'package:ggplot2':
last_plot
The following object is masked from 'package:stats':
filter
The following object is masked from 'package:graphics':
layoutpixel_map_color <- c("#8c3bff", "#018700", "#00acc6", "#97ff00",
"#ff7ed1", "#6b004f", "#ffa52f", "#573b00", "#005659","#ffffff")
names(pixel_map_color) <- c("Ankrd1+", "Mpo+", "aSMA+", "Pdgfra+", "Cd45+", "Ccr2+", "Trem2+ Cd68+", "Cd31+", "Tnnt2+","background")
source("./code/functions.R")
Attaching package: 'cowplot'
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stamp
here() starts at /Users/florian_wuennemann/1_Projects/MI_project/mi_spatialomicspixel_heat <- fread("../data/SeqIF/pixie_pixel_masks_0.05/pixel_channel_avg_meta_cluster.csv")
pixel_heat pixel_meta_cluster ANKRD1 aSMA CCR2 CD31 CD45
1: 1 0.6564672 0.02853554 0.16578453 0.2343214 0.12020333
2: 2 0.2760541 0.02163947 0.16486345 0.1195075 0.31608038
3: 5 0.1751630 0.67531316 0.08870606 0.3571941 0.09310648
4: 8 0.2524684 0.04044455 0.09697171 0.1752383 0.09049972
5: 11 0.2810251 0.03931616 0.29839863 0.2179582 1.37854076
6: 12 0.2042928 0.02445315 0.98214607 0.1210940 0.39986051
7: 17 0.2493411 0.02978874 0.23478253 0.1286853 0.26765573
8: 18 0.1980813 0.03114820 0.11136213 0.5636297 0.07064971
9: 20 0.3814408 0.03187345 0.10676994 0.1285132 0.10595270
10: 23 0.3647658 0.04919484 0.34145857 0.2864004 0.22677640
CD68 MPO PDGFRa TNNT2 TREM2 count
1: 0.02963032 0.14417472 0.05818441 0.3331969 0.07812194 307505563
2: 0.07763542 0.93435865 0.07390264 0.2107106 0.11017212 26537966
3: 0.02850467 0.07914696 0.11918324 0.2620314 0.05507709 20357034
4: 0.04440526 0.10189771 0.50140056 0.3540510 0.07616204 127310325
5: 0.12912089 0.26835699 0.12163833 0.2498097 0.16524068 4581183
6: 0.23309418 0.21003429 0.16730817 0.1769699 0.39281374 20030320
7: 0.75303281 0.12003899 0.12104188 0.2046672 1.06433816 26214802
8: 0.02691090 0.07606489 0.06639025 0.3491986 0.05912748 330116307
9: 0.02739381 0.06758424 0.04609704 0.6888311 0.04905438 1938275265
10: 0.06095365 0.17504628 0.10001979 0.3157357 0.12774674 142733133
pixel_meta_cluster_rename
1: Ankrd1+
2: Mpo+
3: aSMA+
4: Pdgfra+
5: Cd45+
6: Ccr2+
7: Trem2_Cd68+
8: Cd31+
9: Tnnt2+
10: backgroundpixel_heat <- pixel_heat %>%
subset(pixel_meta_cluster_rename != "background")mat_rownames <- pixel_heat$pixel_meta_cluster_rename
mat_rownames <- gsub("_","+ ",mat_rownames)
mat_rownames <- paste(mat_rownames,"pixels", sep = " ")
mat_dat <- pixel_heat %>%
dplyr::select(-c(pixel_meta_cluster,count,pixel_meta_cluster_rename))cap = 3 #hierarchical clustering cap
hclust_coln = "pixel_meta_cluster_rename"
rwb_cols = colorRampPalette(c("royalblue4","white","red4"))(99)
mat_dat = scale(mat_dat)
mat_dat = pmin(mat_dat, cap)
rownames(mat_dat) <- mat_rownames
# Determine breaks
range = max(abs(mat_dat))
breaks = seq(-range, range, length.out=100)
mat_col = data.frame(pixel_cluster = as.factor(mat_rownames))
rownames(mat_col) <- mat_rownames
mat_colors = pixel_map_color[1:length(mat_rownames)]
names(mat_colors) = mat_rownames
mat_colors = list(pixel_cluster = mat_colors)
# Make heatmap
pheatmap(mat_dat,
color = rwb_cols,
border_color = "black",
breaks = breaks,
cluster_rows = TRUE,
cluster_cols = TRUE,
treeheight_col = 25,
treeheight_row = 25,
#treeheight_col = 0,
show_rownames = TRUE,
annotation_row = mat_col,
annotation_colors = mat_colors,
annotation_names_row = FALSE,
annotation_legend = FALSE,
legend = TRUE,
#legend_breaks = c(-3,-2,-1,0,1,2,3),
#legend_labels = c("-3","-2","-1","0","1","2","3"),
main = "",
filename = "./output/seqIF/supplementary_figure_6.pixie_pixel_cluster_heatmap.pdf",
fontsize = 20,
width = 8,
height = 6)Pixel cluster changes over time
## read in pixel cluster counts
pixel_counts = fread("../data/SeqIF/pixie_pixel_masks_0.05/pixel_counts.all_samples.csv")
pixel_counts <- pixel_counts %>%
mutate("Pixel_cluster" = if_else(Pixel_cluster %in% c("Cd68+","Trem2+"),"Trem2+ Cd68+",Pixel_cluster)) %>%
subset(!Pixel_cluster %in% c("Cd68+","Trem2+"))
pixel_cluster_counts_stats <- pixel_counts %>%
subset(Pixel_cluster != "background") %>%
separate("Sample_ID", into = c("time","sample")) %>%
group_by(sample,time,Pixel_cluster) %>%
summarise("n_pixel" = sum(Count)) %>%
mutate("percent" = n_pixel / sum(n_pixel)) %>%
ungroup()`summarise()` has grouped output by 'sample', 'time'. You can override using
the `.groups` argument.pixel_cluster_counts_stats$time <-factor(pixel_cluster_counts_stats$time,
levels = c("Control","4h","24h","48h"))
pixel_cluster_counts_stats$Pixel_cluster <- factor(pixel_cluster_counts_stats$Pixel_cluster,
levels = c("Tnnt2+", "Ankrd1+","Cd31+","Pdgfra+", "Mpo+","aSMA+", "Ccr2+","Trem2+ Cd68+","Cd45+"))pixel_number_distribution <- ggplot(pixel_cluster_counts_stats,
aes(x=time, y=percent * 100)) +
# geom_bar(stat = "identity", position = "stack", aes(fill = Pixel_cluster), color = "black") +
geom_bar(position='dodge', stat='summary', fun='mean', color = "black",
aes(fill = Pixel_cluster)) +
# Add points for each fill group on top of bars
geom_point(aes(group=Pixel_cluster), size=2.5, shape=21, fill="white", color="black") +
theme(legend.position = "none",
axis.line = element_blank()) +
scale_fill_manual(values = pixel_map_color) +
scale_x_discrete(expand = c(0,0.1),
name ="Time",
limits=c("Control","4h","24h","48h")) +
labs(y = "% cells") +
facet_wrap(~ Pixel_cluster, scales = "free_y", nrow = 2)
pixel_number_distribution
save_plot(pixel_number_distribution,
file = "./output/seqIF/supplementary_figure_6.pixie_clusters_overtime.pdf",
base_height = 4,
base_asp = 2.5)
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.1.2
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Berlin
tzcode source: internal
attached base packages:
[1] stats graphics grDevices datasets utils methods base
other attached packages:
[1] here_1.0.1 ggsci_3.0.0 cowplot_1.1.2 plotly_4.10.4
[5] vroom_1.6.5 pals_1.8 lubridate_1.9.3 forcats_1.0.0
[9] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 readr_2.1.5
[13] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.4 tidyverse_2.0.0
[17] RColorBrewer_1.1-3 viridis_0.6.4 viridisLite_0.4.2 data.table_1.14.10
[21] pheatmap_1.0.12 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] tidyselect_1.2.0 farver_2.1.1 fastmap_1.1.1
[4] lazyeval_0.2.2 promises_1.2.1 digest_0.6.34
[7] timechange_0.2.0 lifecycle_1.0.4 processx_3.8.3
[10] magrittr_2.0.3 compiler_4.3.1 rlang_1.1.3
[13] sass_0.4.8 tools_4.3.1 utf8_1.2.4
[16] yaml_2.3.8 knitr_1.45 labeling_0.4.3
[19] htmlwidgets_1.6.4 bit_4.0.5 mapproj_1.2.11
[22] withr_2.5.2 grid_4.3.1 fansi_1.0.6
[25] git2r_0.33.0 colorspace_2.1-0 scales_1.3.0
[28] dichromat_2.0-0.1 cli_3.6.2 rmarkdown_2.25
[31] crayon_1.5.2 ragg_1.2.7 generics_0.1.3
[34] rstudioapi_0.15.0 httr_1.4.7 tzdb_0.4.0
[37] cachem_1.0.8 maps_3.4.2 BiocManager_1.30.22
[40] vctrs_0.6.5 jsonlite_1.8.8 callr_3.7.3
[43] hms_1.1.3 bit64_4.0.5 systemfonts_1.0.5
[46] jquerylib_0.1.4 glue_1.7.0 ps_1.7.6
[49] stringi_1.8.3 gtable_0.3.4 later_1.3.2
[52] munsell_0.5.0 pillar_1.9.0 htmltools_0.5.7
[55] R6_2.5.1 textshaping_0.3.7 rprojroot_2.0.4
[58] evaluate_0.23 highr_0.10 renv_1.0.3
[61] httpuv_1.6.14 bslib_0.6.1 Rcpp_1.0.12
[64] gridExtra_2.3 whisker_0.4.1 xfun_0.41
[67] fs_1.6.3 getPass_0.2-4 pkgconfig_2.0.3