proteomics.pathway_enrichment_analysis
FloWuenne
2023-06-19
Last updated: 2023-06-29
Checks: 6 1
Knit directory: mi_spatialomics/
This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
The R Markdown file has unstaged changes. To know which version of
the R Markdown file created these results, you’ll want to first commit
it to the Git repo. If you’re still working on the analysis, you can
ignore this warning. When you’re finished, you can run
wflow_publish to commit the R Markdown file and build the
HTML.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20230612) was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 430aac1. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .DS_Store
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: analysis/.DS_Store
Ignored: data/.DS_Store
Ignored: data/.Rapp.history
Ignored: data/140623.calcagno_et_al.seurat_object.rds
Ignored: renv/.DS_Store
Ignored: renv/library/
Ignored: renv/staging/
Untracked files:
Untracked: analysis/figures.figure_5.Rmd
Untracked: analysis/figures.supplementary_figure_X.proteomics_qc.Rmd
Untracked: code/functions.R
Untracked: figures/Supplementary_figure_X.proteomics.eps
Untracked: figures/Supplementary_figure_X.proteomics.png
Untracked: omnipathr-log/
Untracked: output/limma.full_statistics.tsv
Untracked: output/limma.mi_iz_specific_proteins.tsv
Untracked: output/proteomics.pathway_results.MIiz_MIremote.tsv
Untracked: output/proteomics.pca_res.rds
Untracked: output/proteomics.protein_missing_stats.tsv
Untracked: output/proteomics.snRNAseq_comp.tsv
Untracked: output/proteomics.vsn_norm_proteins.tsv
Untracked: references/
Unstaged changes:
Modified: analysis/data_analysis.Rmd
Modified: analysis/figures.supplementary_Figure_3.Rmd
Modified: analysis/proteomics.bulk_de_analysis.Rmd
Modified: analysis/proteomics.filter_proteomic_data.Rmd
Modified: analysis/proteomics.pathway_enrichment_analysis.Rmd
Modified: analysis/proteomics.scRNAseq_comparison.Rmd
Deleted: code/params_R.R
Modified: figures/Supplementary_figure_3.eps
Modified: figures/Supplementary_figure_3.png
Modified: output/mi_iz_specific_proteins.tsv
Modified: renv.lock
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown
(analysis/proteomics.pathway_enrichment_analysis.Rmd) and
HTML (docs/proteomics.pathway_enrichment_analysis.html)
files. If you’ve configured a remote Git repository (see
?wflow_git_remote), click on the hyperlinks in the table
below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 5f927dc | FloWuenne | 2023-06-20 | Build site. |
| Rmd | f29aea6 | FloWuenne | 2023-06-20 | Commented out Omnipath for the moment. |
| html | c1395e6 | FloWuenne | 2023-06-20 | Build site. |
| Rmd | 236130c | FloWuenne | 2023-06-20 | Updating proteomic analysis. |
| html | 236130c | FloWuenne | 2023-06-20 | Updating proteomic analysis. |
Introduction
Here, we are going to perform pathway analysis on the limma results from the proteomic analysis.
Load limma DE results
First, we load our results from the limma differential expression analysis that we calculated in DEP analysis.
limma_res <- fread("./output/limma.full_statistics.tsv")
## groups : "MI_IZ_vs_control" "MI_remote_vs_control" "MI_IZ_vs_MI_remote"
## MI_IZ vs MI_remote
mi_signature <- subset(limma_res,analysis == "MI_IZ_vs_MI_remote")
mi_signature <- mi_signature %>%
dplyr::select(t,gene) %>%
filter(!is.na(t)) %>%
arrange(desc(t)) %>%
column_to_rownames(var = "gene") %>%
as.matrix()Pathway activity analysis using decoupler
Load sigmd get sets
We will use Msigdb databases to perform pathway analysis
mh_gsea <- import_gmt(gmtfile = "./references/mh.all.v2023.1.Mm.symbols.gmt")
|
| | 0%
|
|= | 2%
|
|=== | 4%
|
|==== | 6%
|
|====== | 8%
|
|======= | 10%
|
|======== | 12%
|
|========== | 14%
|
|=========== | 16%
|
|============= | 18%
|
|============== | 20%
|
|=============== | 22%
|
|================= | 24%
|
|================== | 26%
|
|==================== | 28%
|
|===================== | 30%
|
|====================== | 32%
|
|======================== | 34%
|
|========================= | 36%
|
|=========================== | 38%
|
|============================ | 40%
|
|============================= | 42%
|
|=============================== | 44%
|
|================================ | 46%
|
|================================== | 48%
|
|=================================== | 50%
|
|==================================== | 52%
|
|====================================== | 54%
|
|======================================= | 56%
|
|========================================= | 58%
|
|========================================== | 60%
|
|=========================================== | 62%
|
|============================================= | 64%
|
|============================================== | 66%
|
|================================================ | 68%
|
|================================================= | 70%
|
|================================================== | 72%
|
|==================================================== | 74%
|
|===================================================== | 76%
|
|======================================================= | 78%
|
|======================================================== | 80%
|
|========================================================= | 82%
|
|=========================================================== | 84%
|
|============================================================ | 86%
|
|============================================================== | 88%
|
|=============================================================== | 90%
|
|================================================================ | 92%
|
|================================================================== | 94%
|
|=================================================================== | 96%
|
|===================================================================== | 98%
|
|======================================================================| 100%m2_all_gsea <- import_gmt(gmtfile = "./references/m2.all.v2023.1.Mm.symbols.gmt")
|
| | 0%
|
| | 1%
|
|= | 1%
|
|= | 2%
|
|== | 2%
|
|== | 3%
|
|== | 4%
|
|=== | 4%
|
|=== | 5%
|
|==== | 5%
|
|==== | 6%
|
|===== | 6%
|
|===== | 7%
|
|===== | 8%
|
|====== | 8%
|
|====== | 9%
|
|======= | 9%
|
|======= | 10%
|
|======= | 11%
|
|======== | 11%
|
|======== | 12%
|
|========= | 12%
|
|========= | 13%
|
|========= | 14%
|
|========== | 14%
|
|========== | 15%
|
|=========== | 15%
|
|=========== | 16%
|
|============ | 16%
|
|============ | 17%
|
|============ | 18%
|
|============= | 18%
|
|============= | 19%
|
|============== | 19%
|
|============== | 20%
|
|============== | 21%
|
|=============== | 21%
|
|=============== | 22%
|
|================ | 22%
|
|================ | 23%
|
|================ | 24%
|
|================= | 24%
|
|================= | 25%
|
|================== | 25%
|
|================== | 26%
|
|=================== | 26%
|
|=================== | 27%
|
|=================== | 28%
|
|==================== | 28%
|
|==================== | 29%
|
|===================== | 29%
|
|===================== | 30%
|
|===================== | 31%
|
|====================== | 31%
|
|====================== | 32%
|
|======================= | 32%
|
|======================= | 33%
|
|======================= | 34%
|
|======================== | 34%
|
|======================== | 35%
|
|========================= | 35%
|
|========================= | 36%
|
|========================== | 36%
|
|========================== | 37%
|
|========================== | 38%
|
|=========================== | 38%
|
|=========================== | 39%
|
|============================ | 39%
|
|============================ | 40%
|
|============================ | 41%
|
|============================= | 41%
|
|============================= | 42%
|
|============================== | 42%
|
|============================== | 43%
|
|============================== | 44%
|
|=============================== | 44%
|
|=============================== | 45%
|
|================================ | 45%
|
|================================ | 46%
|
|================================= | 46%
|
|================================= | 47%
|
|================================= | 48%
|
|================================== | 48%
|
|================================== | 49%
|
|=================================== | 49%
|
|=================================== | 50%
|
|=================================== | 51%
|
|==================================== | 51%
|
|==================================== | 52%
|
|===================================== | 52%
|
|===================================== | 53%
|
|===================================== | 54%
|
|====================================== | 54%
|
|====================================== | 55%
|
|======================================= | 55%
|
|======================================= | 56%
|
|======================================== | 56%
|
|======================================== | 57%
|
|======================================== | 58%
|
|========================================= | 58%
|
|========================================= | 59%
|
|========================================== | 59%
|
|========================================== | 60%
|
|========================================== | 61%
|
|=========================================== | 61%
|
|=========================================== | 62%
|
|============================================ | 62%
|
|============================================ | 63%
|
|============================================ | 64%
|
|============================================= | 64%
|
|============================================= | 65%
|
|============================================== | 65%
|
|============================================== | 66%
|
|=============================================== | 66%
|
|=============================================== | 67%
|
|=============================================== | 68%
|
|================================================ | 68%
|
|================================================ | 69%
|
|================================================= | 69%
|
|================================================= | 70%
|
|================================================= | 71%
|
|================================================== | 71%
|
|================================================== | 72%
|
|=================================================== | 72%
|
|=================================================== | 73%
|
|=================================================== | 74%
|
|==================================================== | 74%
|
|==================================================== | 75%
|
|===================================================== | 75%
|
|===================================================== | 76%
|
|====================================================== | 76%
|
|====================================================== | 77%
|
|====================================================== | 78%
|
|======================================================= | 78%
|
|======================================================= | 79%
|
|======================================================== | 79%
|
|======================================================== | 80%
|
|======================================================== | 81%
|
|========================================================= | 81%
|
|========================================================= | 82%
|
|========================================================== | 82%
|
|========================================================== | 83%
|
|========================================================== | 84%
|
|=========================================================== | 84%
|
|=========================================================== | 85%
|
|============================================================ | 85%
|
|============================================================ | 86%
|
|============================================================= | 86%
|
|============================================================= | 87%
|
|============================================================= | 88%
|
|============================================================== | 88%
|
|============================================================== | 89%
|
|=============================================================== | 89%
|
|=============================================================== | 90%
|
|=============================================================== | 91%
|
|================================================================ | 91%
|
|================================================================ | 92%
|
|================================================================= | 92%
|
|================================================================= | 93%
|
|================================================================= | 94%
|
|================================================================== | 94%
|
|================================================================== | 95%
|
|=================================================================== | 95%
|
|=================================================================== | 96%
|
|==================================================================== | 96%
|
|==================================================================== | 97%
|
|==================================================================== | 98%
|
|===================================================================== | 98%
|
|===================================================================== | 99%
|
|======================================================================| 99%
|
|======================================================================| 100%mh_gsea_net <- rename_net(mh_gsea, term, gene, .mor= NULL)
saveRDS(mh_gsea_net,"references/mh.all.v2023.1.Mm.symbols.sets.rds")
mh_gsea_sets <- extract_sets(mh_gsea_net)MI_IZ vs MI_remote pathways
We will focus on the comparison between the MI_IZ region versus the MI_remote region, as this comparison should capture the local differences of the endocardial layer close to the infarct versus those far away. As we have seen in the PCA and differential expression analysis, there are also not a lot of strong differences between MI_remote and MI_control, meaning that most changes we would identify comparing to the control, will also be captured in the MI_IZ vs MI_remote comparison.
Msigdb
## Run decoupler based on limma statistics
mi_ulm <- run_ulm(mat=mi_signature, .target = gene , .source = term, .mor= NULL,
net=mh_gsea, minsize = 3)
sig_pathways_mi <- subset(mi_ulm,p_value <= 0.05) %>%
arrange(desc(score)) %>%
dplyr::select(-statistic,-condition)
write.table(mi_ulm,
file = "./output/proteomics.pathway_results.MIiz_MIremote.tsv",
sep = "\t",
col.names = TRUE,
row.names = FALSE,
quote = FALSE)# Plot
ggplot(sig_pathways_mi, aes(x = reorder(source, score), y = score)) +
geom_bar(aes(fill = score), stat = "identity") +
scale_fill_gradient2(low = proteome_palette[['MI_remote']], high = proteome_palette[['MI_IZ']],
mid = "whitesmoke", midpoint = 0) +
theme_minimal() +
theme(axis.title = element_text(face = "bold", size = 12),
axis.text.x =
element_text(angle = 45, hjust = 1, size =10, face= "bold"),
axis.text.y = element_text(size =10, face= "bold"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
xlab("Pathways") +
coord_flip()
pathway <- 'HALLMARK_COAGULATION'
df <- mh_gsea_net %>%
filter(source == pathway) %>%
arrange(target)
inter <- sort(intersect(rownames(mi_signature),df$target))
sessionInfo()R version 4.2.3 (2023-03-15)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.4.1
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] stats4 stats graphics grDevices datasets utils methods
[8] base
other attached packages:
[1] ggsci_3.0.0 cowplot_1.1.1 lubridate_1.9.2
[4] forcats_1.0.0 stringr_1.5.0 dplyr_1.1.2
[7] purrr_1.0.1 readr_2.1.4 tidyr_1.3.0
[10] tibble_3.2.1 ggplot2_3.4.2 tidyverse_2.0.0
[13] pheatmap_1.0.12 data.table_1.14.8 GSEABase_1.60.0
[16] graph_1.76.0 annotate_1.76.0 XML_3.99-0.14
[19] AnnotationDbi_1.60.2 IRanges_2.32.0 S4Vectors_0.36.2
[22] Biobase_2.58.0 BiocGenerics_0.44.0 here_1.0.1
[25] OmnipathR_3.9.6 decoupleR_2.5.2 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] colorspace_2.1-0 rprojroot_2.0.3 XVector_0.38.0
[4] fs_1.6.2 rstudioapi_0.14 farver_2.1.1
[7] bit64_4.0.5 fansi_1.0.4 xml2_1.3.4
[10] cachem_1.0.8 knitr_1.42 jsonlite_1.8.4
[13] png_0.1-8 BiocManager_1.30.21 compiler_4.2.3
[16] httr_1.4.6 backports_1.4.1 Matrix_1.5-3
[19] fastmap_1.1.1 cli_3.6.1 later_1.3.1
[22] htmltools_0.5.5 prettyunits_1.1.1 tools_4.2.3
[25] igraph_1.4.3 gtable_0.3.3 glue_1.6.2
[28] GenomeInfoDbData_1.2.9 reshape2_1.4.4 rappdirs_0.3.3
[31] Rcpp_1.0.10 cellranger_1.1.0 jquerylib_0.1.4
[34] vctrs_0.6.2 Biostrings_2.66.0 xfun_0.39
[37] ps_1.7.4 rvest_1.0.3 timechange_0.2.0
[40] lifecycle_1.0.3 renv_0.17.3 getPass_0.2-2
[43] zlibbioc_1.44.0 scales_1.2.1 hms_1.1.3
[46] promises_1.2.0.1 parallel_4.2.3 RColorBrewer_1.1-3
[49] yaml_2.3.7 curl_5.0.0 memoise_2.0.1
[52] sass_0.4.6 stringi_1.7.12 RSQLite_2.3.1
[55] highr_0.10 checkmate_2.2.0 GenomeInfoDb_1.34.9
[58] rlang_1.1.1 pkgconfig_2.0.3 bitops_1.0-7
[61] evaluate_0.21 lattice_0.20-45 labeling_0.4.2
[64] bit_4.0.5 processx_3.8.0 tidyselect_1.2.0
[67] parallelly_1.36.0 plyr_1.8.8 logger_0.2.2
[70] magrittr_2.0.3 R6_2.5.1 generics_0.1.3
[73] DBI_1.1.3 pillar_1.9.0 whisker_0.4.1
[76] withr_2.5.0 KEGGREST_1.38.0 RCurl_1.98-1.12
[79] crayon_1.5.2 utf8_1.2.3 tzdb_0.4.0
[82] rmarkdown_2.21 progress_1.2.2 grid_4.2.3
[85] readxl_1.4.2 blob_1.2.4 callr_3.7.3
[88] git2r_0.32.0 digest_0.6.31 xtable_1.8-4
[91] httpuv_1.6.11 munsell_0.5.0 bslib_0.4.2