---
title: "A Quick Start Guide to R Package 'TGS'"
# author: "Saptarshi Pyne"
# date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Chap 1: A Quick Start Guide}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

Install R package 'TGS'.
```{r, eval = FALSE}
install.packages('TGS')
```

Attach R package 'TGS'.
```{r setup}
library(TGS)
```
Let us assume that you have a time-series gene expression data.
It is comprised of multiple time series. Each time series 
contains the expressions of $10$ genes across $21$ time 
points. The data is saved in a file named 
'input_data_10.tsv'. The file is saved inside directory
'/home/saptarshi/datasets/'.  

First, assign absolute path to the input directory.
```{r, eval = FALSE}
## Assign absolute path to the input directory.
input_dir <- '/home/saptarshi/datasets'
```

Then reconstruct time-varying gene regulatory networks 
(GRNs) using algorithm 'TGS'.
```{r, eval = FALSE}
## Assign the name of the desired output directory.
## The output directory will be created automatically.
output_dir <- '/home/saptarshi/My_TGS_output'

## Run algorithm 'TGS'.
## It is assumed that your data is continuous.
## In case, your data is discrete, simply
## make the following changes:
## (a) is.discrete = TRUE,
## (b) num.discr.levels = <number of discrete
## levels each gene has>,
## (c) discr.algo = ''.
##
TGS::LearnTgs(
  isfile = 0,
  json.file = '',
  input.dirname = input_dir,
  input.data.filename = 'input_data_10.tsv',
  num.timepts = 21,
  true.net.filename = '',
  input.wt.data.filename = '',
  is.discrete = FALSE,
  num.discr.levels = 2,
  discr.algo = 'discretizeData.2L.Tesla',
  mi.estimator = 'mi.pca.cmi',
  apply.aracne = FALSE,
  clr.algo = 'CLR',
  max.fanin = 14,
  allow.self.loop = TRUE,
  scoring.func = 'BIC',
  output.dirname = output_dir
)
```

You may also reconstruct time-varying GRNs
using algorithm 'TGS+'. The only difference is that the input
argument `apply.aracne` is set to `TRUE`.
```{r, eval = FALSE}
## Assign the name of the desired output directory.
## The output directory will be created automatically.
output_dir <- '/home/saptarshi/My_TGS_plus_output'

## Run algorithm 'TGS'
TGS::LearnTgs(
  isfile = 0,
  json.file = '',
  input.dirname = input_dir,
  input.data.filename = 'input_data_10.tsv',
  num.timepts = 21,
  true.net.filename = '',
  input.wt.data.filename = '',
  is.discrete = FALSE,
  num.discr.levels = 2,
  discr.algo = 'discretizeData.2L.Tesla',
  mi.estimator = 'mi.pca.cmi',
  apply.aracne = TRUE,
  clr.algo = 'CLR',
  max.fanin = 14,
  allow.self.loop = TRUE,
  scoring.func = 'BIC',
  output.dirname = output_dir
)
```

Once the reconstruction is complete, please go to the
output directory. There should be a file named
'unrolled.DBN.adj.matrix.list.RData'. This file 
contains the reconstructed time-varying GRNs. 
Load this file in an R session.
```{r, eval = FALSE}
## Loads a list named 'unrolled.DBN.adj.matrix.list'
load('unrolled.DBN.adj.matrix.list.RData')
```

Print the reconstructed GRN of the $7^{th}$ time 
interval.
```{r, eval = FALSE}
print(unrolled.DBN.adj.matrix.list[[7]])
```
