---
title: "Working with SpidermiR package"
author: " Claudia Cava, Antonio Colaprico, Alex Graudenzi, Gloria Bertoli,Tiago C. Silva,Catharina Olsen,Houtan Noushmehr, Gianluca Bontempi, Giancarlo Mauri, Isabella Castiglioni"
date: "`r Sys.Date()`"
output:
    BiocStyle::html_document:
        toc: true
        number_sections: false
        toc_depth: 2
        highlight: haddock


references:
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- id: ref6
  title: Pharmaco miR linking microRNAs and drug effects.
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- id: ref7
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  journal: Nucleic Acids Res
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  number: D1
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- id: ref9
  title: miRNAtap microRNA Targets - Aggregated Predictions.
  author: 
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    given:
  journal: Nucleic Acids Res
  volume: R package version 1.20.0
  number: 
  pages: 
  issued:
    year: 2019

vignette: >
  %\VignetteIndexEntry{Working with SpidermiR package}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(dpi = 300)
knitr::opts_chunk$set(cache=FALSE)
```

```{r, eval = TRUE, echo = FALSE,hide=TRUE, message=FALSE,warning=FALSE}
devtools::load_all()
```

# Introduction 

Biological systems are composed of multiple layers of dynamic interaction networks. These networks can be decomposed, for example, into: co-expression, physical, co-localization, genetic, pathway, and shared protein domains.

GeneMania provides us with an enormous collection of data sets for interaction network studies [@ref1]. The data can be accessed and downloaded from different database, using a web portal.  But currently, there is not a R-package to query and download these data.

An important regulatory mechanism of these network data involves microRNAs (miRNAs). miRNAs are involved in various cellular functions, such as differentiation, proliferation, and tumourigenesis. 
However, our understanding of the processes regulated by miRNAs is currently limited and the integration of miRNA data in these networks provides a comprehensive genome-scale analysis of miRNA regulatory networks.Actually, GeneMania doesn't integrate the information of miRNAs and their interactions in the network.

`SpidermiR` allows the user to query, prepare, download network data (e.g. from GeneMania), and to integrate this information with miRNA data 
with the possibility to analyze 
these downloaded data directly in one single R package. 
This techincal report gives a short overview of the essential `SpidermiR` methods and their application. 


# Installation

To install use the code below.

```{r, eval = FALSE}
if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("SpidermiR")
```

# `SpidermiRquery`: Searching network 


You can easily search GeneMania data using the `SpidermiRquery` function.


## `SpidermiRquery_species`: Searching by species
The user can query the species supported by GeneMania, using the function SpidermiRquery_species:


```{r, eval = TRUE}

org<-SpidermiRquery_species(species)
```

The list of species is shown below:
```{r, eval = TRUE, echo = FALSE}
knitr::kable(org, digits = 2,
             caption = "List of species",row.names = TRUE)
```



## `SpidermiRquery_networks_type`: Searching by network categories

The user can query the network types supported by GeneMania for a specific specie, using the function `SpidermiRquery_networks_type`. The user can select a specific specie using an index obtained by the function `SpidermiRquery_species` (e.g. organismID=org[6,] is the input for Homo_sapiens,organismID=org[9,]  is the input for Saccharomyces cerevisiae  )

```{r, eval = TRUE}
net_type<-SpidermiRquery_networks_type(organismID=org[9,])
```

The list of network categories in Saccharomyces cerevisiae is shown below:

```{r, eval = TRUE, echo = FALSE}
net_type
```


## `SpidermiRquery_spec_networks`: Searching by species, and network categories
You can filter the search by species using organism ID (above reported), and the network category.
The network category can be filtered using the following parameters: 

* **COexp** Co-expression 
* **PHint** Physical_interactions 
* **COloc**  Co-localization 
* **GENint** Genetic_interactions 
* **PATH** Pathway 
* **SHpd**  Shared_protein_domains 
* **pred**  predicted

```{r, eval = TRUE}

net_shar_prot<-SpidermiRquery_spec_networks(organismID = org[9,],
                                    network = "SHpd")
```

The databases, which data are collected, are the output of this step. An example is shown below ( for Shared protein domains in Saccharomyces_cerevisiae data are collected in INTERPRO, and PFAM):
```{r, eval = TRUE, echo = FALSE}
net_shar_prot
```



# `SpidermiRdownload`: Downloading network data
The user in this step can download the data, as previously queried.

## `SpidermiRdownload_net`: Download network
The user can download the data (previously queried) with `SpidermiRdownload_net`.

```{r, eval = TRUE}
out_net<-SpidermiRdownload_net(net_shar_prot)
```


The list of SpidermiRdownload_net is shown below:
```{r, eval = TRUE, echo = FALSE}
str(out_net)
```

## `SpidermiRdownload_miRNAprediction`: Downloading miRNA predicted data target
The user can download the predicted miRNA-gene from 4 databases:DIANA, Miranda, PicTar and TargetScan using miRNAtap [@ref9].

```{r, eval = FALSE}
mirna<-c('hsa-miR-567','hsa-miR-566')
SpidermiRdownload_miRNAprediction(mirna_list=mirna)
```
## `SpidermiRdownload_miRNAvalidate`: Downloading miRNA validated data target
The user can download the validated miRNA-gene from: miRTAR and miRwalk [@ref2] [@ref3]. 

```{r, eval = FALSE}
list<-SpidermiRdownload_miRNAvalidate(validated)
```


## `SpidermiRdownload_miRNAextra_cir`:Download Extracellular Circulating microRNAs
The user can download extracellular circulating miRNAs from miRandola database

```{r, eval = FALSE}
list_circ<-SpidermiRdownload_miRNAextra_cir(miRNAextra_cir)
```




# `SpidermiRprepare`: Preparing the data

## `SpidermiRprepare_NET`: Prepare matrix of gene network  with Ensembl Gene ID, and gene symbols
`SpidermiRprepare_NET` reads network data from `SpidermiRdownload_net` and enables user to prepare them for downstream analysis. In particular, it prepares matrix of gene network mapping Ensembl Gene ID to gene symbols. Gene symbols are needed to integrate miRNAdata. 


```{r, eval = TRUE}

geneSymb_net<-SpidermiRprepare_NET(organismID = org[9,],
                                    data = out_net)
```

The network with gene symbols ID is shown below:
```{r, eval = TRUE, echo = FALSE}
knitr::kable(geneSymb_net[[1]][1:5,c(1,2,3,5,8)], digits = 2,
             caption = "shared protein domain",row.names = FALSE)
```

# `SpidermiRanalyze`: : Analyze data from network data





## `SpidermiRanalyze_direct_net`: Searching by biomarkers of interest with direct interaction

Starting from a set of biomarkers of interest (BI), genes, miRNA or both, given by the user, this function finds sub-networks including all direct interactions involving at least one of the BI.

```{r, eval = TRUE}
biomark_of_interest<-c("hsa-let-7a","CDC34","hsa-miR-27a","PEX7","EPT1","FOX","hsa-miR-5a")
miRNA_NET <-data.frame(V1=c('hsa-let-7a','CASP3','BRCA','hsa-miR-7a','hsa-miR-5a','SMAD','SOX'),V2=c('CASP3','TAMOXIFEN','MYC','PTEN','FOX','HIF1','P53'),stringsAsFactors=FALSE)
GIdirect_net<-SpidermiRanalyze_direct_net(data=miRNA_NET,BI=biomark_of_interest)
```

The data frame of `SpidermiRanalyze_direct_net`, GIdirect_net, is shown below:
```{r, eval = TRUE, echo = FALSE}
str(GIdirect_net)
```

## `SpidermiRanalyze_direct_subnetwork`: Network composed by only the nodes in a set of biomarkers of interest

Starting from  BI, this function finds sub-networks including all direct interactions involving only BI.

```{r, eval = FALSE}

subnet<-SpidermiRanalyze_direct_subnetwork(data=miRNA_NET,BI=biomark_of_interest)

```



## `SpidermiRanalyze_subnetwork_neigh`: Network composed by the nodes in the list of BI and all the edges among this brunch of nodes. 

Starting from  BI, this function finds sub-networks including all direct and indirect interactions involving at least one of BI.


```{r, eval = FALSE}

GIdirect_net_neigh<-SpidermiRanalyze_subnetwork_neigh(data=miRNA_NET,BI=biomark_of_interest)
```



## `SpidermiRanalyze_degree_centrality`: Ranking degree centrality genes

This function finds the number of direct neighbours of a node in a network and allows the selection of those nodes with a number of direct neighbours higher than a selected cut-off.

```{r, eval = FALSE}
top10_cent_gene<-SpidermiRanalyze_degree_centrality(miRNA_NET)
```









# `Features databases SpidermiR`: 

Features of databases integrated in `SpidermiR` are:




```{r, eval = TRUE,echo = FALSE}
B<-matrix( c("Gene network", "Validated miRNA-target","", "Predicted miRNA-target","","","", "Extracellular Circulating microRNAs", "Pharmaco-miR","",
      
             "GeneMania", "miRwalk","miRTarBase", "DIANA", "Miranda", "PicTar","TargetScan","miRandola","DGIdb","MATADOR",
             
             "Current","miRwalk2","miRTarBase 7","DIANA- 5.0","N/A","N/A","TargetScan7.1","miRandola v 02/2017","N/A","N/A",
             2017,2015,2017,2013,2010,"N/A","2016",2017,2018,"N/A",
             "http://genemania.org/data/current/","http://zmf.umm.uni-heidelberg.de/apps/zmf/mirwalk2/downloads/vtm/hsa-vtm-gene.rdata.zip","http://mirtarbase.mbc.nctu.edu.tw/cache/download/7.0/miRTarBase_SE_WR.xls","https://bioconductor.org/packages/release/bioc/html/miRNAtap.html","https://bioconductor.org/packages/release/bioc/html/miRNAtap.html","https://bioconductor.org/packages/release/bioc/html/miRNAtap.html","https://bioconductor.org/packages/release/bioc/html/miRNAtap.html","http://mirandola.iit.cnr.it/download/miRandola_version_02_2017.txt","http://dgidb.org/data/interactions.tsv", "http://matador.embl.de/media/download/matador.tsv.gz"
             
             ), nrow=10, ncol=5)
colnames(B)<-c("CATEGORY","EXTERNAL DATABASE","VERSION","LAST UPDATE","LINK")

```



```{r, eval = TRUE, echo = FALSE}
knitr::kable(B, digits = 2,
             caption = "Features",row.names = FALSE)
```



******

Session Information
******
```{r sessionInfo}
sessionInfo()
```

# References