---
title: "iPRISM User Guide"
output: 
  html_document:
    df_print: paged
    toc: yes
  pdf_document:
    toc: yes
  prettydoc::html_pretty:
    highlight: github
    theme: cayman
vignette: >
  %\VignetteIndexEntry{iPRISM User Guide}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

```{r setup}
# Load the package
library(iPRISM)
```

## Introduction

Welcome to the vignette for the **PRISM** package. This document provides an overview of the package's functionalities, including basic usage examples and detailed explanations of the main functions. The **PRISM** package includes the core function for the paper named *PRISM: Predicting Response to cancer Immunotherapy through Systematic Modeling*.

## Example 1: Correlation Plot with Cell Types and Pathways

The `cor_plot` function generates a correlation plot between cell types and pathways, displaying correlation coefficients as a heatmap and significant correlations as scatter points.

```{r}
# Read cell line and pathway information
data(data.path, package = "iPRISM")
data(data.cell, package = "iPRISM")

# Draw the plot
cor_plot(data1 = data.path, data2 = data.cell, sig.name1 = "path", sig.name2 = "cell")
```

## Example 2: Enrichment Analysis using Multiplex Networks

The `get_gsea_path` function constructs a multiplex network, performs random walk restart, and calculates gene scores. It then transforms the scores and applies GSEA using the provided gene sets.

```{r}
# Load example data
data(Seeds, package = "iPRISM")
data(ppi, package = "iPRISM")
data(path_list, package = "iPRISM")

# Shrink pathway list to the top 2 pathways
path_list <- path_list[1:5]

# Perform GSEA
result <- get_gsea_path(seed = Seeds, network = ppi, pathlist = path_list, gsea.nperm = 100)
print(result)
```

## Example 3: Fit Logistic Regression Model

The `get_logiModel` function fits a logistic regression model as the paper highlighted, with an option for stepwise model selection.

```{r}
# Load example data
data(data_sig, package = "iPRISM")

# Fit logistic regression model
b <- get_logiModel(data.sig = data_sig, pred.value = pred_value, step = TRUE)
summary(b)
```