lunaphore.heatmaps_pixie.figure3
FloWuenne
2023-08-21
Last updated: 2023-08-24
Checks: 6 1
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 1.1.2 ✔ readr 2.1.4
✔ forcats 1.0.0 ✔ stringr 1.5.0
✔ ggplot2 3.4.2 ✔ tibble 3.2.1
✔ lubridate 1.9.2 ✔ tidyr 1.3.0
✔ purrr 1.0.1 ── 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, specpixel_map_color <- c("#0173B2", "#DE8F05", "#029E73", "#D55E00", "#CC78BC",
"#CA9161", "#FBAFE4", "#949494", "#ECE133", "#56B4E9")
cell_cluster_color <- glasbey()
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$pixel_meta_cluster_rename
mat_rownames <- gsub("_","+ ",mat_rownames)
mat_rownames <- paste(mat_rownames,"pixels", sep = " ")
mat_dat <- avg_pixel_cluster %>%
dplyr::select(-c(pixel_meta_cluster,count,pixel_meta_cluster_rename))cap = 3 #hierarchical clustering cap
hclust_coln = "pixel_meta_cluster_rename"
rwb_cols = colorRampPalette(c("royalblue4","white","red4"))(99)
mat_dat = scale(mat_dat)
mat_dat = pmin(mat_dat, cap)
rownames(mat_dat) <- mat_rownames
# Determine breaks
range = max(abs(mat_dat))
breaks = seq(-range, range, length.out=100)
mat_col = data.frame(pixel_cluster = as.factor(mat_rownames))
rownames(mat_col) <- mat_rownames
mat_colors = pixel_map_color[1:length(mat_rownames)]
names(mat_colors) = mat_rownames
mat_colors = list(pixel_cluster = mat_colors)
# Make heatmap
pheatmap(mat_dat,
color = rwb_cols,
border_color = "black",
breaks = breaks,
cluster_rows = TRUE,
cluster_cols = TRUE,
treeheight_col = 25,
treeheight_row = 25,
#treeheight_col = 0,
show_rownames = TRUE,
annotation_row = mat_col,
annotation_colors = mat_colors,
annotation_names_row = FALSE,
annotation_legend = FALSE,
legend = TRUE,
#legend_breaks = c(-3,-2,-1,0,1,2,3),
#legend_labels = c("-3","-2","-1","0","1","2","3"),
main = "",
filename = "./output/figure3.pixie_pixel_cluster_heatmap.png",
fontsize = 20,
width = 8,
height = 6)
dev.off()null device
1 Pixel cluster changes over time
## read in pixel cluster counts
pixel_counts = fread("/Users/florian_wuennemann/1_Projects/MI_project/Lunaphore/pixie/masked_subset/subset_0.05_wseg/pixel_counts.all_samples.csv")pixel_cluster_counts_stats <- pixel_counts %>%
subset(Pixel_cluster != "background") %>%
separate("Sample_ID", into = c("time","sample")) %>%
group_by(time,Pixel_cluster) %>%
summarise("n_pixel" = sum(Count)) %>%
mutate("percent" = n_pixel / sum(n_pixel)) %>%
ungroup()`summarise()` has grouped output by 'time'. You can override using the
`.groups` argument.pixel_cluster_counts_stats$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$time)#ggplot(cells_over_time, aes(x=time, y=percent, fill=cell_meta_cluster)) +
#geom_bar(stat = "identity", position = "stack",color = "black")
pixel_number_distribution <- ggplot(pixel_cluster_counts_stats,
aes(x=time_cont, y=percent)) +
geom_area(aes(fill = Pixel_cluster), color = "black") +
theme(legend.position = "none",
axis.line = element_blank()) +
scale_fill_manual(values = pixel_map_color) +
scale_x_discrete(expand = c(0,0.1),
name ="Time",
limits=c("Control","4h","24h","48h")) +
labs(y = "% cells")
pixel_number_distribution
save_plot(pixel_number_distribution,
file = "./plots/Figure3.pixel_clusters_overtimes.pdf",
base_height = 3.5,
base_asp = 1)Cell cluster map
Cell cluster tissue maps
avg_cell_cluster <- fread("/Users/florian_wuennemann/1_Projects/MI_project/Lunaphore/pixie/masked_subset/cell_masks_0.05/cell_meta_cluster_channel_avg.csv")
avg_cell_cluster <- avg_cell_cluster %>%
subset(cell_meta_cluster_rename != "background")mat_rownames <- avg_cell_cluster$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)
mat_col = data.frame(pixel_cluster = as.factor(mat_rownames))
rownames(mat_col) <- mat_rownames
mat_colors = cell_cluster_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/figure3.pixie_cell_cluster_heatmap.png",
fontsize = 20,
width = 8,
height = 6)
dev.off()null device
1 Cell cluster changes over time
cells_over_time <- fread("./data/pixie.cell_table_size_normalized_cell_labels.csv")
cells_over_time <- cells_over_time %>%
mutate("cell_type_labels" = if_else(grepl("Myeloid",cell_meta_cluster),"Myeloid cells",cell_meta_cluster))
cells_over_time <- cells_over_time %>%
subset(cell_type_labels != "background") %>%
separate("fov", into = c("time","sample")) %>%
group_by(time,cell_type_labels) %>%
tally() %>%
ungroup()cells_over_time <- cells_over_time %>%
group_by(time) %>%
mutate("percent" = n / sum(n)) %>%
ungroup()cells_over_time$time <-factor(cells_over_time$time,
levels = c("Control","4h","24h","48h"))
cells_over_time$time_cont <- as.numeric(cells_over_time$time)#ggplot(cells_over_time, aes(x=time, y=percent, fill=cell_meta_cluster)) +
#geom_bar(stat = "identity", position = "stack",color = "black")
mat_colors = cell_cluster_color[1:length(mat_rownames)]
cell_number_distribution <- ggplot(cells_over_time, aes(x=time_cont, y=percent)) +
geom_area(aes(fill = cell_type_labels), color = "black") +
theme(legend.position = "top",
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,
file = "./plots/Figure3.cell_types_overtimes.pdf",
base_height = 3.5,
base_asp = 1)
sessionInfo()R version 4.2.3 (2023-03-15)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.5
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
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 vroom_1.6.3
[5] pals_1.7 lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
[9] dplyr_1.1.2 purrr_1.0.1 readr_2.1.4 tidyr_1.3.0
[13] tibble_3.2.1 ggplot2_3.4.2 tidyverse_2.0.0 RColorBrewer_1.1-3
[17] viridis_0.6.4 viridisLite_0.4.2 data.table_1.14.8 pheatmap_1.0.12
[21] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] httr_1.4.6 sass_0.4.7 maps_3.4.1
[4] bit64_4.0.5 jsonlite_1.8.7 bslib_0.5.0
[7] getPass_0.2-2 BiocManager_1.30.21.1 highr_0.10
[10] renv_1.0.0 yaml_2.3.7 pillar_1.9.0
[13] glue_1.6.2 digest_0.6.33 promises_1.2.0.1
[16] colorspace_2.1-0 htmltools_0.5.5 httpuv_1.6.11
[19] pkgconfig_2.0.3 scales_1.2.1 processx_3.8.2
[22] whisker_0.4.1 later_1.3.1 tzdb_0.4.0
[25] timechange_0.2.0 git2r_0.32.0 generics_0.1.3
[28] farver_2.1.1 cachem_1.0.8 withr_2.5.0
[31] cli_3.6.1 magrittr_2.0.3 crayon_1.5.2
[34] evaluate_0.21 ps_1.7.5 fs_1.6.3
[37] fansi_1.0.4 textshaping_0.3.6 tools_4.2.3
[40] hms_1.1.3 lifecycle_1.0.3 munsell_0.5.0
[43] callr_3.7.3 compiler_4.2.3 jquerylib_0.1.4
[46] systemfonts_1.0.4 rlang_1.1.1 grid_4.2.3
[49] dichromat_2.0-0.1 rstudioapi_0.15.0 labeling_0.4.2
[52] rmarkdown_2.23 gtable_0.3.3 R6_2.5.1
[55] gridExtra_2.3 knitr_1.43 fastmap_1.1.1
[58] bit_4.0.5 utf8_1.2.3 rprojroot_2.0.3
[61] ragg_1.2.5 stringi_1.7.12 Rcpp_1.0.11
[64] vctrs_0.6.3 mapproj_1.2.11 tidyselect_1.2.0
[67] xfun_0.39