---
title: "Matching Footprint-based analysis results to harmonised Drug-Targets"
author: "Alberto Valdeolivas: alvadeolivas@gmail.com; Date:"
date: "04/04/2022"
always_allow_html: true
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

### 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/.


# Introduction

The present script takes the output of our Footprint-based analysis and matches
the results againts a file containing an harmonised list  of drug-targets from DrugBank, 
clinical trials and also INDRA and AILANI results. 

```{r, warning=FALSE, message=FALSE}
library(DT)
library(dplyr)
```

# Results

## Reading Input files

We first read the harmonised list of drug-target interactions

```{r, warning=FALSE, message=FALSE}
drug_target <- read.delim("InputFiles/harmonised_drugs_mirnas_ups.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). 

```{r, warning=FALSE, message=FALSE}
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.

```{r, warning=FALSE, message=FALSE}
match_drug_target <- drug_target %>% 
  dplyr::filter(target_symbol %in% carnival_nodes_hgnc)
```

and We visualize the results. 

```{r, warning=FALSE, message=FALSE}
datatable(match_drug_target)
```

```{r, warning=FALSE, message=FALSE}
write.table(x=match_drug_target, file = "MatchingDrugs/carnival_drugs.tsv", sep = "\t",
  row.names = FALSE, quote = FALSE)
```

# Session Info Details

```{r, echo=FALSE, eval=TRUE}
sessionInfo()
```