---
title: "Quick guide"
author: "Nicolò Rossi"
date: "28/3/2020"
output: 
    html_document:
        df_print: paged
vignette: >
    %\VignetteIndexEntry{Quick guide}
    %\VignetteEngine{knitr::rmarkdown}
    %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(CIMICE)
library(dplyr)
library(igraph)
library(Matrix)
library(purrr)
```

## Reference for quick CIMICE analysis

In this short tutorial it is shown how to execute CIMICE's analysis

### Read input

To load the input use `CIMICE::read` family functions or the pair `CIMICE::make_dataset`, `CIMICE::update_df` in the following manner: 

```{r cars, results=FALSE}
# read from file
read_CAPRI(system.file("extdata", "example.CAPRI", package = "CIMICE", mustWork = TRUE))
```

```{r}
# from a string
read_CAPRI_string("
s\\g A B C D
S1 0 0 0 1
S2 1 0 0 0
S3 1 0 0 0
S4 1 0 0 1
S5 1 1 0 1
S6 1 1 0 1
S7 1 0 1 1
S8 1 1 0 1
")
```

```{r}
# using CIMICE::make_dataset and CIMICE::update_df
# genes
make_dataset(A,B,C,D) %>% 
    # samples
    update_df("S1", 0, 0, 0, 1) %>%
    update_df("S2", 1, 0, 0, 0) %>%
    update_df("S3", 1, 0, 0, 0) %>%
    update_df("S4", 1, 0, 0, 1) %>%
    update_df("S5", 1, 1, 0, 1) %>%
    update_df("S6", 1, 1, 0, 1) %>%
    update_df("S7", 1, 0, 1, 1) %>%
    update_df("S8", 1, 1, 0, 1)
```

This last dataset will be use in this example, under the name of `example_dataset()`. Input dataset analysis and feature selection should be done at this point.

## Preprocess dataset

This early phase reorganizes the dataset to simplify further analysis.

```{r}
preproc <- dataset_preprocessing(example_dataset())
samples <- preproc[["samples"]]
freqs   <- preproc[["freqs"]]
labels  <- preproc[["labels"]]
genes   <- preproc[["genes"]]
```

## Build topology

The default method to organize genotypes in a graph in CIMICE is based on the "subset" relation among them.

```{r}
g <- graph_non_transitive_subset_topology(samples,labels)
```

```{r, echo=FALSE}
V(g)$vertex.size <- rep(10, length(V(g)))
plot(g, vertex.size=rep(55, length(V(g))))
```

## Compute Weights

CIMICE provides a strategy to estimate transition probabilities among genotypes. This phase is divided in four different subphases that can be executed together
in this way:

```{r}
W <- compute_weights_default(g, freqs)
```

## Visualize output:

CIMICE supports many different output options, the recommended one is based on "visNetwork".

```{r}
out <- quick_run(example_dataset()) # quick_run summarizes all the previous steps
draw_visNetwork(out)
```