lunaphore.heatmaps_pixie.figure3
FloWuenne
2023-08-21
Last updated: 2023-12-06
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Knit directory: mi_spatialomics/
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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 2.1.4
✔ 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("#0173B2", "#DE8F05", "#029E73", "#D55E00", "#CC78BC",
"#CA9161", "#FBAFE4", "#949494", "#ECE133", "#56B4E9")
cell_cluster_colors <- c("#6b004f","#ff7ed1",
"#018700","#d60000",
"#97ff00","#ffa52f",
"#d55e00","#8c3bff",
"#0000dd","#ff00ff")
names(cell_cluster_colors) <- c("Fibroblasts","Neutrophils",
"Mono / Macros Ccr2+","Smooth muscle cells",
"Macrophages Trem2+","Cardiomyocytes Ankrd1+",
"Endothelial cells","Other Leukocytes",
"Cardiomyocytes","Macrophages Trem2-")
source("./code/functions.R")
Attaching package: 'cowplot'
The following object is masked from 'package:lubridate':
stamp
here() starts at /Users/florian_wuennemann/1_Projects/MI_project/mi_spatialomicsPixel cluster
Pixel cluster maps
avg_pixel_cluster <- fread("/Users/florian_wuennemann/1_Projects/MI_project/Lunaphore/pixie/masked_subset/subset_0.05_wseg/pixel_channel_avg_meta_cluster.csv")
avg_pixel_cluster <- avg_pixel_cluster %>%
subset(pixel_meta_cluster_rename != "background")mat_rownames <- avg_pixel_cluster$pixel_meta_cluster_rename
mat_rownames <- gsub("_","+ ",mat_rownames)
mat_rownames <- paste(mat_rownames,"pixels", sep = " ")
mat_dat <- avg_pixel_cluster %>%
dplyr::select(-c(pixel_meta_cluster,count,pixel_meta_cluster_rename))cap = 3 #hierarchical clustering cap
hclust_coln = "pixel_meta_cluster_rename"
rwb_cols = colorRampPalette(c("royalblue4","white","red4"))(99)
mat_dat = scale(mat_dat)
mat_dat = pmin(mat_dat, cap)
rownames(mat_dat) <- mat_rownames
# Determine breaks
range = max(abs(mat_dat))
breaks = seq(-range, range, length.out=100)
mat_col = data.frame(pixel_cluster = as.factor(mat_rownames))
rownames(mat_col) <- mat_rownames
mat_colors = pixel_map_color[1:length(mat_rownames)]
names(mat_colors) = mat_rownames
mat_colors = list(pixel_cluster = mat_colors)
# Make heatmap
pheatmap(mat_dat,
color = rwb_cols,
border_color = "black",
breaks = breaks,
cluster_rows = TRUE,
cluster_cols = TRUE,
treeheight_col = 25,
treeheight_row = 25,
#treeheight_col = 0,
show_rownames = TRUE,
annotation_row = mat_col,
annotation_colors = mat_colors,
annotation_names_row = FALSE,
annotation_legend = FALSE,
legend = TRUE,
#legend_breaks = c(-3,-2,-1,0,1,2,3),
#legend_labels = c("-3","-2","-1","0","1","2","3"),
main = "",
filename = "./output/seqIF/figure3.pixie_pixel_cluster_heatmap.png",
fontsize = 20,
width = 8,
height = 6)
dev.off()null device
1 Pixel cluster changes over time
## read in pixel cluster counts
pixel_counts = fread("/Users/florian_wuennemann/1_Projects/MI_project/Lunaphore/pixie/masked_subset/subset_0.05_wseg/pixel_counts.all_samples.csv")pixel_cluster_counts_stats <- pixel_counts %>%
subset(Pixel_cluster != "background") %>%
separate("Sample_ID", into = c("time","sample")) %>%
group_by(time,Pixel_cluster) %>%
summarise("n_pixel" = sum(Count)) %>%
mutate("percent" = n_pixel / sum(n_pixel)) %>%
ungroup()`summarise()` has grouped output by 'time'. You can override using the
`.groups` argument.pixel_cluster_counts_stats$time <-factor(pixel_cluster_counts_stats$time,
levels = c("Control","4h","24h","48h"))
pixel_cluster_counts_stats$time_cont <- as.numeric(pixel_cluster_counts_stats$time)#ggplot(cells_over_time, aes(x=time, y=percent, fill=cell_meta_cluster)) +
#geom_bar(stat = "identity", position = "stack",color = "black")
pixel_number_distribution <- ggplot(pixel_cluster_counts_stats,
aes(x=time_cont, y=percent)) +
geom_area(aes(fill = Pixel_cluster), color = "black") +
theme(legend.position = "none",
axis.line = element_blank()) +
scale_fill_manual(values = pixel_map_color) +
scale_x_discrete(expand = c(0,0.1),
name ="Time",
limits=c("Control","4h","24h","48h")) +
labs(y = "% cells")
pixel_number_distribution
| Version | Author | Date |
|---|---|---|
| 5dee03d | FloWuenne | 2023-09-04 |
save_plot(pixel_number_distribution,
file = "./plots/Figure3.pixel_clusters_overtimes.pdf",
base_height = 3.5,
base_asp = 1)Cell cluster map
Cell cluster tissue maps
avg_cell_cluster <- fread("/Users/florian_wuennemann/1_Projects/MI_project/Lunaphore/pixie/masked_subset/cell_masks_0.05/cell_meta_cluster_count_avg.csv")
colnames(avg_cell_cluster) <- gsub("pixel_meta_cluster_rename_","",colnames(avg_cell_cluster))
avg_cell_cluster <- avg_cell_cluster %>%
subset(cell_meta_cluster_rename != "background") %>%
subset(cell_meta_cluster_rename != "out_of_mask") %>%
dplyr::select(-c(background,count))mat_rownames <- avg_cell_cluster$cell_meta_cluster_rename
mat_rownames <- gsub("_","+ ",mat_rownames)
mat_dat <- avg_cell_cluster %>%
dplyr::select(-c(cell_meta_cluster,cell_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)
## Set color palette
mat_col = data.frame(cell_cluster = as.factor(mat_rownames))
rownames(mat_col) <- mat_rownames
mat_colors = cell_cluster_colors[1:length(mat_rownames)]
names(mat_colors) = mat_rownames
#mat_colors = list(pixel_cluster = mat_colors)
mat_colors = list(cell_cluster = cell_cluster_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/figure3.pixie_cell_cluster_heatmap.pdf",
fontsize = 20,
width = 8,
height = 6)Cell cluster changes over time
cells_over_time <- fread("/Users/florian_wuennemann/1_Projects/MI_project/Lunaphore/pixie/masked_subset/cell_masks_0.05/cell_table_size_normalized_cell_labels.csv")
cell_groups_sample <- cells_over_time %>%
separate("fov", into = c("time","sample"), remove = FALSE) %>%
group_by(fov,time,cell_meta_cluster) %>%
tally()
n_cell_plot <- ggplot(cell_groups_sample,aes(cell_meta_cluster,log10(n),color = fov,
label = fov)) +
geom_point(size = 3,aes(shape = time)) +
coord_flip()
ggplotly(n_cell_plot)## Plot distribution of all cell types for a given sample
unique(cells_over_time$cell_meta_cluster) [1] "out_of_mask" "Fibroblasts" "Mono / Macros Ccr2+"
[4] "Cardiomyocytes" "Neutrophils" "background"
[7] "Cardiomyocytes Ankrd1+" "Endothelial cells" "Macrophages Trem2+"
[10] "Smooth muscle cells" "Macrophages Trem2-" "Other Leukocytes" unique(cells_over_time$fov)[1] "24h_86" "24h_83" "Control_13" "Control_12" "4h_97"
[6] "4h_96" "48h_79" "48h_76" "Control_14"cells_over_time_sub <- subset(cells_over_time,fov == "48h_79")
spatial_plot <- ggplot(cells_over_time_sub,aes(X_centroid,Y_centroid,color = cell_meta_cluster)) +
geom_point(size = 0.01) +
facet_wrap(~ cell_meta_cluster) +
labs(title= unique(cells_over_time_sub$fov))
spatial_plot
| Version | Author | Date |
|---|---|---|
| 2dcd178 | FloWuenne | 2023-12-06 |
## Plot distribution of 1 cell type over time
# cells_over_time_sub <- subset(cells_over_time,fov %in% c("Control_14","4h_97","24h_86","48h_76"))
cells_over_time_sub <- cells_over_time
pt_size = 0.001
# cells_over_time_sub$fov <- factor(cells_over_time_sub$fov,
# levels = c("Control_14","4h_97","24h_86","48h_76"))
spatial_plot_monos_macros_ccr2 <- ggplot(cells_over_time_sub,aes(X_centroid,Y_centroid)) +
geom_point(data = subset(cells_over_time_sub,cell_meta_cluster == "Cardiomyocytes"),size = pt_size,
color = "darkgrey") +
geom_point(data = subset(cells_over_time_sub,cell_meta_cluster == "Cardiomyocytes Ankrd1+"),size = pt_size,
color = "blue") +
geom_point(data = subset(cells_over_time_sub,cell_meta_cluster == "Neutrophils" | cell_meta_cluster == "Mono / Macros Ccr2+"),size = pt_size,
color = "magenta", alpha = 0.5) +
# geom_point(data = subset(cells_over_time_sub,cell_meta_cluster == "Mono / Macros Ccr2+"),size = pt_size,
# color = "yellow") +
facet_wrap(~ fov)
spatial_plot_monos_macros_ccr2
| Version | Author | Date |
|---|---|---|
| 2dcd178 | FloWuenne | 2023-12-06 |
## Color palette
cells_over_time_sub <- cells_over_time %>%
# mutate("cell_type_labels" = if_else(grepl("Macro|Neutro",cell_meta_cluster),"Myeloid cells",cell_meta_cluster))
mutate("cell_type_labels" = cell_meta_cluster)
cells_over_time_sub <- cells_over_time_sub %>%
subset(!cell_type_labels %in% c("background","out_of_mask")) %>%
separate("fov", into = c("time","sample")) %>%
group_by(time,cell_type_labels) %>%
tally() %>%
ungroup()
cells_over_time_sub <- cells_over_time_sub %>%
group_by(time) %>%
mutate("percent" = n / sum(n)) %>%
ungroup()cells_over_time_sub$time <-factor(cells_over_time_sub$time,
levels = c("Control","4h","24h","48h"))
cells_over_time_sub$time_cont <- as.numeric(cells_over_time_sub$time)
cell_number_distribution_bar <- ggplot(cells_over_time_sub, aes(x=time_cont, y=percent, fill= cell_type_labels)) +
#geom_area(aes(fill = cell_type_labels), color = "black") +
geom_bar(stat = "identity", position = "fill", color = "black") +
theme(legend.position = "right",
legend.title = element_blank(),
axis.line = element_blank(),
legend.text=element_text(size=18),
axis.text = element_text(size=18)) +
labs(y = "% cells") +
scale_fill_manual(values = cell_cluster_colors) +
scale_x_discrete(expand = c(0,0.1),
name ="Time",
limits=c("Control","4h","24h","48h")) +
guides(fill=guide_legend(nrow=10,byrow=TRUE))
cell_number_distribution_bar
| Version | Author | Date |
|---|---|---|
| 2dcd178 | FloWuenne | 2023-12-06 |
## Continuous plot
# mat_colors = cell_cluster_color[1:length(mat_rownames)]
# cell_number_distribution <- ggplot(cells_over_time_sub, aes(x=time_cont, y=percent)) +
# geom_area(aes(fill = cell_type_labels), color = "black") +
# theme(legend.position = "none",
# axis.line = element_blank()) +
# #scale_fill_manual(values = mat_colors) +
# scale_x_discrete(expand = c(0,0.1),
# name ="Time",
# limits=c("Control","4h","24h","48h")) +
# labs(y = "% cells")
# cell_number_distribution
save_plot(cell_number_distribution_bar,
file = "./plots/Figure3.cell_types_overtimes.pdf",
base_height = 6,
base_asp = 1.3)
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_1.1.1 plotly_4.10.3
[5] vroom_1.6.4 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_2.1.4
[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_1.14.8
[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_0.6.33
[7] timechange_0.2.0 lifecycle_1.0.4 ellipsis_0.3.2
[10] processx_3.8.2 magrittr_2.0.3 compiler_4.3.1
[13] rlang_1.1.2 sass_0.4.7 tools_4.3.1
[16] utf8_1.2.4 yaml_2.3.7 knitr_1.45
[19] labeling_0.4.3 htmlwidgets_1.6.3 bit_4.0.5
[22] mapproj_1.2.11 withr_2.5.2 grid_4.3.1
[25] fansi_1.0.5 git2r_0.33.0 colorspace_2.1-0
[28] scales_1.3.0 dichromat_2.0-0.1 cli_3.6.1
[31] rmarkdown_2.25 crayon_1.5.2 ragg_1.2.6
[34] generics_0.1.3 rstudioapi_0.15.0 httr_1.4.7
[37] tzdb_0.4.0 cachem_1.0.8 maps_3.4.1.1
[40] BiocManager_1.30.22 vctrs_0.6.5 jsonlite_1.8.8
[43] callr_3.7.3 hms_1.1.3 bit64_4.0.5
[46] crosstalk_1.2.1 systemfonts_1.0.5 jquerylib_0.1.4
[49] glue_1.6.2 ps_1.7.5 stringi_1.8.2
[52] gtable_0.3.4 later_1.3.1 munsell_0.5.0
[55] pillar_1.9.0 htmltools_0.5.7 R6_2.5.1
[58] textshaping_0.3.7 rprojroot_2.0.4 evaluate_0.23
[61] highr_0.10 renv_1.0.3 httpuv_1.6.12
[64] bslib_0.6.1 Rcpp_1.0.11 gridExtra_2.3
[67] whisker_0.4.1 xfun_0.41 fs_1.6.3
[70] getPass_0.2-2 pkgconfig_2.0.3