proteomics.pathway_enrichment_analysis
FloWuenne
2023-06-19
Last updated: 2023-07-23
Checks: 7 0
Knit directory: mi_spatialomics/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | ed31d81 | FloWuenne | 2023-07-02 | Finalized proteomics analysis. |
| html | ed31d81 | FloWuenne | 2023-07-02 | Finalized proteomics analysis. |
| 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")
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|======================================================================| 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()
| Version | Author | Date |
|---|---|---|
| ed31d81 | FloWuenne | 2023-07-02 |
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