---
title: "Introduction_4_process_pgxseg"
author: "Hangjia Zhao"
date: "`r Sys.Date()`"
output: 
  BiocStyle::html_document:
  toc: true
vignette: >
  %\VignetteIndexEntry{Introduction_4_process_pgxseg}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

[Progenetix](https://progenetix.org/) is an open data resource that provides curated individual cancer copy number variation (CNV) profiles along with associated metadata sourced from published oncogenomic studies and various data repositories. Progenetix uses the ".pgxseg" data format to store variant data, which encompasses CNV (Copy Number Variation) and SNV (Single Nucleotide Variant), as well as the metadata of associated samples. This vignette describes how to work with local ".pgxseg" files using this package. For more details about the ".pgxseg" file format, please refer to the the [documentation](https://docs.progenetix.org/file-formats/?#pgxseg-sample-variant-files).

# Load library 

```{r setup}
library(pgxRpi)
library(GenomicRanges) # for pgxfreq object
```

## `pgxSegprocess` function 

This function extracts segment variants, CNV frequency, and metadata from local "pgxseg" files. Additionally, it supports survival data visualization if survival data is available within the file.

The parameters of this function used in this tutorial:

* `file`: A string specifying the path and name of the "pgxseg" file where the data is to be read. 
* `group_id`: A string specifying which id is used for grouping in KM plot or CNV frequency calculation. Default is `"group_id"`.
* `show_KM_plot`: A logical value determining whether to return the Kaplan-Meier plot based on metadata. Default is `FALSE`.
* `return_metadata`: A logical value determining whether to return metadata. Default is `FALSE`.
* `return_seg`: A logical value determining whether to return segment data. Default is `FALSE`.
* `return_frequency`: A logical value determining whether to return CNV frequency data. The frequency calculation is based on segments in segment data and specified group id in metadata. Default is `FALSE`.
* `cnv_column_idx`: Index of the column specifying the CNV state used for calculating CNV frequency. The index must be at least 6, with the default set to `6`. The CNV states should either contain "DUP" for duplications and "DEL" for deletions, or level-specific CNV states represented using Experimental Factor Ontology (EFO) codes.
* `assembly`: A string specifying the genome assembly version to apply to CNV frequency calculation and plotting. Allowed options are "hg19" and "hg38". Default is `"hg38"`.
* `...` Other parameters relevant to KM plot. These include `pval`, `pval.coord`, `pval.method`, `conf.int`, `linetype`, and `palette` (see ggsurvplot function from *survminer* package)

# Extract segment data 

```{r}
# specify the location of the example file
file_name <- system.file("extdata", "example.pgxseg",package = 'pgxRpi')

# extract segment data
seg <- pgxSegprocess(file=file_name,return_seg = TRUE)
```

The segment data looks like this

```{r}
head(seg)
```

# Extract metadata 

```{r}
meta <- pgxSegprocess(file=file_name,return_metadata = TRUE)
```

The metadata looks like this

```{r}
head(meta)
```

# Visualize survival data in metadata 

The KM plot is generated using samples that have available follow-up state and follow-up time information. By default, the grouping is based on the 'group_id' column in the metadata.

```{r, fig.width=7, fig.height=5}
pgxSegprocess(file=file_name,show_KM_plot = TRUE)
```

You can try different grouping by `group_id` parameter

```{r, fig.width=7, fig.height=5}
pgxSegprocess(file=file_name,show_KM_plot = TRUE,group_id = 'histological_diagnosis_id')
```

You can specify more parameters to modify this plot (see parameter `...` in documentation)

```{r, fig.width=7, fig.height=5}
pgxSegprocess(file=file_name,show_KM_plot = TRUE,pval=TRUE,palette='npg')
```

# Calculate CNV frequency 

The CNV frequency is calculated from segments of samples with the same group id. The group id is specified in `group_id` parameter. More details about CNV frequency see the vignette *Introduction_3_access_cnv_frequency*.  

```{r}
# Default is "group_id" in metadata
frequency <- pgxSegprocess(file=file_name,return_frequency = TRUE) 
# Use different ids for grouping
frequency_2 <- pgxSegprocess(file=file_name,return_frequency = TRUE, 
                             group_id ='icdo_morphology_id')
frequency
```

The returned object is same as the CNV frequency object with "pgxfreq" format returned by `pgxLoader` function. The CNV frequency is calculated from groups which exist in both metadata and segment data. It is noted that not all groups in metadata must exist in segment data (e.g. some samples don't have CNV calls).

```{r}
head(frequency[["pgx:icdot-C16.9"]])
```

The associated metadata in CNV frequency objects looks like this 

```{r}
mcols(frequency)
```

```{r}
mcols(frequency_2)
```

You can visualize the CNV frequency of the interesting group using `pgxFreqplot` function. For more details on this function, see the vignette *Introduction_3_access_cnv_frequency*.

```{r, fig.width=7, fig.height=5}
pgxFreqplot(frequency, filters="pgx:icdot-C16.9")
```

```{r, fig.width=7, fig.height=5}
pgxFreqplot(frequency, filters="pgx:icdot-C16.9",chrom = c(1,8,14), layout = c(3,1))
```

```{r,fig.width=6, fig.height=6}
pgxFreqplot(frequency, filters=c("pgx:icdot-C16.9","pgx:icdot-C73.9"),circos = TRUE)
```

# Extract all data 

If you want to extract different types of data, such as segment variants, metadata, CNV frequency and visualize the survival data simultaneously, you can set the corresponding parameters to TRUE. The returned data will be an object that includes all specified data. Note that in this case, the CNV frequency and KM plot will use the same `group_id`.

```{r, fig.width=7, fig.height=5}
info <- pgxSegprocess(file=file_name,show_KM_plot = TRUE, return_seg = TRUE, 
                      return_metadata = TRUE, return_frequency = TRUE)
```

```{r}
names(info)
```

# Session Info

```{r echo = FALSE}
sessionInfo()
```


