figures.supplementary_figure_X.proteomics_qc
FloWuenne
2023-06-26
Last updated: 2024-03-21
Checks: 7 0
Knit directory: mi_spatialomics/
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This supplementary figure contains QC plots for the proteomic data analysis.
## load data
metadata_counts <- fread("./output/proteomics/proteomics.protein_missing_stats.tsv")Subfigure A - Total proteins identified per group
## Summarize observations across group
metadata_counts_stat <- data_summary(metadata_counts,
varname = "wo_cont", groupnames = c("group"))Loading required package: plyr------------------------------------------------------------------------------You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)------------------------------------------------------------------------------
Attaching package: 'plyr'The following objects are masked from 'package:plotly':
arrange, mutate, rename, summariseThe following object is masked from 'package:here':
hereThe following objects are masked from 'package:dplyr':
arrange, count, desc, failwith, id, mutate, rename, summarise,
summarizeThe following object is masked from 'package:purrr':
compactavg_proteins <- mean(metadata_counts$wo_cont)
avg_proteins <- sd(metadata_counts$wo_cont)
metadata_counts_stat$group <- factor(metadata_counts_stat$group,
levels = c("MI_IZ","MI_remote","control"))
## Barplot to show the number of proteins detected per group (excluding contaminants)
n_prot_plot <- ggplot(metadata_counts_stat,aes(group,wo_cont)) +
geom_bar(aes(fill = group),stat="identity", color="black",
position=position_dodge()) +
geom_point(data = metadata_counts, aes(group,wo_cont),
fill = "white",pch = 21, color= "black",size = 4) +
geom_errorbar(aes(ymin=wo_cont, ymax=wo_cont+sd), width=.2,
position=position_dodge(.9)) +
coord_flip() +
scale_fill_manual(values = proteome_palette,
labels = c("Control","MI_remote","MI_IZ")) +
labs(x = "",
y = "Proteins detected") +
theme(legend.position = "none")
n_prot_plot
Subfigure B - % missing proteins vs number of nuclei collected
metadata_counts$group <- factor(metadata_counts$group,
levels = c("control","MI_remote","MI_IZ"))
missingness_plot <- ggplot(metadata_counts,aes(missingness,n_nuclei, label = sample)) +
geom_point(pch = 21, size = 5,aes(fill = group)) +
scale_fill_manual(values = proteome_palette,
labels = c("Control","MI_remote","MI_IZ")) +
geom_label_repel() +
labs(x = "% missing values",
y = "# nuclei in tissue collected",
fill = "Group") +
theme(legend.position = "top")
missingness_plot
Subfigure C - % variance explained by PCs
pca_res <- readRDS("./output/proteomics/proteomics.pca_res.rds")
sdev <- pca_res$sdev[1:5]
var_explained <- data.frame(PC = paste0("PC",1:5),
var_expl = (sdev)^2/sum((sdev)^2)
)
pca_var <- ggplot(var_explained,aes(PC,var_expl)) +
geom_bar(stat = "identity", color = "black", fill = "grey30") +
geom_text(aes(label = paste(100*round(var_expl,2),sep = "")), vjust=1.4, color = "white", size =6) +
labs(x = "Principal components",
y = "% Variance explained")
pca_var
Subfigure D - Upset plot for shared proteins between conditions
library(ggplot2)
library(ComplexUpset)
## Load limma results and subset for significantly differential proteins between conditions
limma_res <- fread("./output/proteomics/proteomics.limma.full_statistics.tsv")
limma_res_sig <- subset(limma_res,adj.P.Val <= 0.05)
limma_res_sig <- limma_res_sig %>%
select(gene,analysis) %>%
mutate(value = 1) %>%
# Reshape the data frame so that each gene has 1 row entry for each comparison
pivot_wider(names_from = analysis,
values_from = value,
values_fill = list(value = 0)) %>%
# Sum rows by gene
group_by(gene) %>%
summarise_all(sum) %>%
ungroup()
analysis_groups <- c("MI_IZ_vs_control","MI_remote_vs_control","MI_IZ_vs_MI_remote")
upset_plot <- upset(limma_res_sig, analysis_groups , name='Comparison',
width_ratio=0.1,
set_sizes=(
upset_set_size()
+ theme(axis.text.x=element_text(angle=90))
))
upset_plot
Assemble final figure
supp_figure_X <- n_prot_plot + missingness_plot + pca_var + wrap_elements(upset_plot)
supp_figure_X <- supp_figure_X +
plot_layout(nrow = 2,ncol =2 , widths = c(1,2)) +
plot_annotation(tag_levels = 'a') &
theme(plot.tag = element_text(size = 25)) &
theme(plot.background = element_rect(fill = "white"))
supp_figure_X
save_plot(filename = "./figures/Supplementary_figure_X.proteomics.png",
plot = supp_figure_X,
base_height = 8)
save_plot(filename = "./figures/Supplementary_figure_X.proteomics.eps",
plot = supp_figure_X,
base_height = 8)
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] ComplexUpset_1.3.3 plyr_1.8.9 RColorBrewer_1.1-3
[4] ggsci_3.0.0 cowplot_1.1.2 ggbeeswarm_0.7.2
[7] ggsignif_0.6.4 heatmaply_1.5.0 plotly_4.10.4
[10] viridis_0.6.4 viridisLite_0.4.2 ggVennDiagram_1.5.0
[13] patchwork_1.2.0 ggrepel_0.9.5 here_1.0.1
[16] data.table_1.14.10 lubridate_1.9.3 forcats_1.0.0
[19] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2
[22] readr_2.1.5 tidyr_1.3.0 tibble_3.2.1
[25] ggplot2_3.4.4 tidyverse_2.0.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] tidyselect_1.2.0 farver_2.1.1 vipor_0.4.7
[4] fastmap_1.1.1 TSP_1.2-4 lazyeval_0.2.2
[7] promises_1.2.1 digest_0.6.34 timechange_0.2.0
[10] lifecycle_1.0.4 processx_3.8.3 magrittr_2.0.3
[13] compiler_4.3.1 rlang_1.1.3 sass_0.4.8
[16] tools_4.3.1 utf8_1.2.4 yaml_2.3.8
[19] knitr_1.45 labeling_0.4.3 htmlwidgets_1.6.4
[22] registry_0.5-1 ca_0.71.1 withr_2.5.2
[25] grid_4.3.1 fansi_1.0.6 git2r_0.33.0
[28] colorspace_2.1-0 scales_1.3.0 iterators_1.0.14
[31] cli_3.6.2 rmarkdown_2.25 ragg_1.2.7
[34] generics_0.1.3 rstudioapi_0.15.0 httr_1.4.7
[37] tzdb_0.4.0 cachem_1.0.8 assertthat_0.2.1
[40] BiocManager_1.30.22 vctrs_0.6.5 webshot_0.5.5
[43] jsonlite_1.8.8 seriation_1.5.4 callr_3.7.3
[46] hms_1.1.3 beeswarm_0.4.0 systemfonts_1.0.5
[49] dendextend_1.17.1 foreach_1.5.2 jquerylib_0.1.4
[52] glue_1.7.0 codetools_0.2-19 ps_1.7.6
[55] stringi_1.8.3 gtable_0.3.4 later_1.3.2
[58] munsell_0.5.0 pillar_1.9.0 htmltools_0.5.7
[61] R6_2.5.1 textshaping_0.3.7 rprojroot_2.0.4
[64] evaluate_0.23 highr_0.10 renv_1.0.3
[67] httpuv_1.6.14 bslib_0.6.1 Rcpp_1.0.12
[70] gridExtra_2.3 whisker_0.4.1 xfun_0.41
[73] fs_1.6.3 getPass_0.2-4 pkgconfig_2.0.3