### License Info
This program is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by the
Free Software Foundation, either version 3 of the License, or (at your
option) any later version.
This program is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
Public License for more details.
Please check <http://www.gnu.org/licenses/>.
The code included in the present notebook is based on this
[script](https://git-r3lab.uni.lu/computational-modelling-and-simulation/generegulationanalysis/-/blob/master/5_SBMLSearch/SourceCode/grep_disease_map.R)
developed by Yusuke Hiki
Introduction
============
The present script takes the output of our Footprint-based analysis and
matches the results againts the content of [the Covid-19 Disease
Maps](https://covid.pages.uni.lu/map_contents)
library(kableExtra)
library(stringr)
Results
=======
Reading Input files
-------------------
We first read the content ofthe Covid-19 Disease Maps which can be found
in this
[file](https://gitlab.lcsb.uni.lu/covid/models/-/blob/master/Resources/Expand%20the%20diagrams/COVID19_Disease_Map_bipartite_crosslinked.tsv):
disease_map <- read.delim("https://gitlab.lcsb.uni.lu/covid/models/-/raw/master/Resources/Expand%20the%20diagrams/COVID19_Disease_Map_bipartite_crosslinked.tsv")
We then read the output of our Footprint-based analyisis, namely
CARNIVAL's output network. This file can be found
[here](https://gitlab.lcsb.uni.lu/computational-modelling-and-simulation/footprint-based-analysis-and-causal-network-contextualisation-in-sars-cov-2-infected-a549-cell-line/-/tree/master/Carnival_Results).
carnival_results <- readRDS("InputFiles/carnival_results_withprogeny.rds")
carnival_nodes_hgnc <- unique(c(carnival_results$weightedSIF[,"Node1"], carnival_results$weightedSIF[,"Node2"]))
Matching the results
--------------------
We finally match all our genes from the carnival network with all the
genes from the different pathways included in the COVID19 Disease maps.
matching_genes <- character()
matching_pathways <- character()
for (i in 1:nrow(disease_map)){
if (!(is.na(disease_map$source_hgnc[i]))) {
source_nodes <-
str_replace(unlist(strsplit(disease_map$source_hgnc[i], split = ";")), "HGNC_SYMBOL:", "")
} else {
source_nodes <- c()
}
if (!(is.na(disease_map$target_hgnc[i]))) {
target_nodes <-
str_replace(unlist(strsplit(disease_map$target_hgnc[i], split = ";")), "HGNC_SYMBOL:", "")
} else {
target_nodes <- c()
}
all_nodes <- unique(c(source_nodes, target_nodes))
if (length(all_nodes)!= 0){
current_matching_genes <- intersect(carnival_nodes_hgnc, all_nodes)
if (length(current_matching_genes) != 0){
current_matching_pathways <- rep(disease_map$source_diagram[i], length(current_matching_genes))
matching_genes <- c(matching_genes, current_matching_genes)
matching_pathways <- c(matching_pathways, current_matching_pathways)
}
}
}
disease_map_hgnc_carnival_detected <-
data.frame(source_diagram = matching_pathways, hgnc_carnival=matching_genes) %>%
dplyr::distinct()
and We visualize the results.
disease_map_hgnc_carnival_detected %>%
kbl(col.names = c("Covid19 DM Diagram", "Carnival nodes")) %>%
kable_styling()
<table class="table" style="margin-left: auto; margin-right: auto;">
<thead>
<tr>
<th style="text-align:left;">
Covid19 DM Diagram
</th>
<th style="text-align:left;">
Carnival nodes
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
C19DMap:Interferon 1 pathway
</td>
<td style="text-align:left;">
IRF3
</td>
</tr>
<tr>
<td style="text-align:left;">
C19DMap:Interferon 1 pathway
</td>
<td style="text-align:left;">
TBK1
</td>
</tr>
<tr>
<td style="text-align:left;">
C19DMap:Interferon 1 pathway
</td>
<td style="text-align:left;">
IKBKE
</td>
</tr>
<tr>
<td style="text-align:left;">
C19DMap:TGFbeta signalling
</td>
<td style="text-align:left;">
MAPK3
</td>
</tr>
<tr>
<td style="text-align:left;">
C19DMap:TGFbeta signalling
</td>
<td style="text-align:left;">
SMAD1
</td>
</tr>
<tr>
<td style="text-align:left;">
C19DMap:PAMP signalling
</td>
<td style="text-align:left;">
TICAM1
</td>
</tr>
<tr>
<td style="text-align:left;">
C19DMap:PAMP signalling
</td>
<td style="text-align:left;">
IKBKE
</td>
</tr>
<tr>
<td style="text-align:left;">
C19DMap:PAMP signalling
</td>
<td style="text-align:left;">
IRF3
</td>
</tr>
<tr>
<td style="text-align:left;">
C19DMap:PAMP signalling
</td>
<td style="text-align:left;">
TBK1
</td>
</tr>
<tr>
<td style="text-align:left;">
C19DMap:Pyrimidine deprivation
</td>
<td style="text-align:left;">
IRF3
</td>
</tr>
<tr>
<td style="text-align:left;">
C19DMap:Pyrimidine deprivation
</td>
<td style="text-align:left;">
TBK1
</td>
</tr>
<tr>
<td style="text-align:left;">
C19DMap:Orf3a protein interactions
</td>
<td style="text-align:left;">
TICAM1
</td>
</tr>
<tr>
<td style="text-align:left;">
C19DMap:Endoplasmatic Reticulum stress
</td>
<td style="text-align:left;">
ATF6
</td>
</tr>
<tr>
<td style="text-align:left;">
C19DMap:Endoplasmatic Reticulum stress
</td>
<td style="text-align:left;">
ATF4
</td>
</tr>
<tr>
<td style="text-align:left;">
C19DMap:Endoplasmatic Reticulum stress
</td>
<td style="text-align:left;">
MBTPS1
</td>
</tr>
<tr>
<td style="text-align:left;">
C19DMap:Interferon lambda pathway
</td>
<td style="text-align:left;">
IRF3
</td>
</tr>
<tr>
<td style="text-align:left;">
C19DMap:Interferon lambda pathway
</td>
<td style="text-align:left;">
TBK1
</td>
</tr>
</tbody>
</table>
write.table(x=disease_map_hgnc_carnival_detected,
file = "MatchingGenes/carnival_diagrams.tsv", sep = "\t",
row.names = FALSE, quote = FALSE)
Session Info Details
====================
## R version 4.0.4 (2021-02-15)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19042)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_Germany.1252 LC_CTYPE=English_Germany.1252
## [3] LC_MONETARY=English_Germany.1252 LC_NUMERIC=C
## [5] LC_TIME=English_Germany.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] stringr_1.4.0 kableExtra_1.3.4
##
## loaded via a namespace (and not attached):
## [1] highr_0.9 compiler_4.0.4 pillar_1.7.0 tools_4.0.4
## [5] digest_0.6.27 evaluate_0.14 lifecycle_1.0.1 tibble_3.1.6
## [9] viridisLite_0.4.0 pkgconfig_2.0.3 rlang_1.0.1 DBI_1.1.2
## [13] cli_3.2.0 rstudioapi_0.13 yaml_2.2.1 xfun_0.24
## [17] httr_1.4.2 dplyr_1.0.8 xml2_1.3.2 knitr_1.33
## [21] generics_0.1.2 vctrs_0.3.8 systemfonts_1.0.2 webshot_0.5.2
## [25] tidyselect_1.1.2 svglite_2.1.0 glue_1.4.2 R6_2.5.1
## [29] fansi_1.0.2 rmarkdown_2.9 purrr_0.3.4 magrittr_2.0.1
## [33] scales_1.1.1 htmltools_0.5.1.1 ellipsis_0.3.2 assertthat_0.2.1
## [37] rvest_1.0.0 colorspace_2.0-1 utf8_1.2.2 stringi_1.6.2
## [41] munsell_0.5.0 crayon_1.5.0