---
title: "Structure and content of RAVmodel"
author: "Sehyun Oh"
date: "`r format(Sys.time(), '%B %d, %Y')`"
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{Introduction on RAVmodel}
  %\VignetteEncoding{UTF-8}
output:
  BiocStyle::html_document:
    number_sections: yes
    toc: yes
    toc_depth: 4
---

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

# Setup

## Install and load package
```{r eval = FALSE}
if (!require("BiocManager"))
    install.packages("BiocManager")
BiocManager::install("GenomicSuperSignature")
```

```{r results="hide", message=FALSE, warning=FALSE}
library(GenomicSuperSignature)
```


## Download RAVmodel
You can download GenomicSuperSignature from Google Cloud bucket using
`GenomicSuperSignature::getModel` function. Currently available models are 
built from top 20 PCs of 536 studies (containing 44,890 samples) containing 
13,934 common genes from each of 536 study's top 90% varying genes based on 
their study-level standard deviation. There are two versions of this RAVmodel
annotated with different gene sets for GSEA: MSigDB C2 (`C2`) and three 
priors from PLIER package (`PLIERpriors`). In this vignette, we are showing 
the `C2` annotated model.

Note that the first interactive run of this code, you will be asked to allow
R to create a cache directory. The model file will be stored there and 
subsequent calls to `getModel` will read from the cache.

```{r load_model}
RAVmodel <- getModel("C2", load=TRUE)
```


# Content of RAVmodel
`RAVindex` is a matrix containing genes in rows and RAVs in columns. `colData` 
slot provides the information on each RAVs, such as GSEA annotation and 
studies involved in each cluster. `metadata` slot stores model construction 
information. `trainingData` slot contains the information on individual 
studies in training dataset, such as MeSH terms assigned to each study.

```{r}
RAVmodel
```

<center>
<img src="https://raw.githubusercontent.com/shbrief/GenomicSuperSignaturePaper/master/inst/images/GSig_RAVmodel.png" width="90%" height="90%"/>
</center>

## RAVindex
*R*eplicable *A*xis of *V*ariation (RAV) index is the main component of
GenomicSuperSignature. It serves as an index connecting new datasets and the 
existing database. You can access it through `GenomicSuperSignature::RAVindex` 
(equivalent of `SummarizedExperiment::assay`). Rows are genes and columns are 
RAVs.

Here, RAVmodel consists of 13,934 genes and 4,764 RAVs.

```{r}
class(RAVindex(RAVmodel))
dim(RAVindex(RAVmodel))
RAVindex(RAVmodel)[1:4, 1:4]
```


## Metadata for RAVmodel
Metadata slot of RAVmodel contains information related to the model building.

```{r}
names(metadata(RAVmodel))
```

* `cluster` : cluster membership of each PCs from the training dataset      
* `size` : an integer vector with the length of clusters, containing the number
of PCs in each cluster   
* `k` : the number of all clusters in the given RAVmodel   
* `n` : the number of top PCs kept from each study in the training dataset
* `geneSets` : the name of gene sets used for GSEA annotation   
* `MeSH_freq` : the frequency of MeSH terms associated with the training 
dataset. MeSH terms like 'Humans' and 'RNA-seq' are top ranked (which is very 
expected) because the training dataset of this model is Human RNA sequencing
data. 
* `updateNote` : a brief note on the given model's specification
* `version` : the version of the given model

```{r}
head(metadata(RAVmodel)$cluster)
head(metadata(RAVmodel)$size)
metadata(RAVmodel)$k
metadata(RAVmodel)$n
geneSets(RAVmodel)
head(metadata(RAVmodel)$MeSH_freq)
updateNote(RAVmodel)  
metadata(RAVmodel)$version
```


## Studies in each RAV
You can find which studies are in each cluster using `studies` method. Output is
a list with the length of clusters, where each element is a character vector 
containing the name of studies in each cluster.

```{r}
length(studies(RAVmodel))
studies(RAVmodel)[1:3]
```

You can check which PC from different studies are in RAVs using `PCinRAV`.

```{r}
PCinRAV(RAVmodel, 2)
```


## Silhouette width for each RAV
Silhouette width ranges from -1 to 1 for each cluster. Typically, it is 
interpreted as follows:   
  - Values close to 1 suggest that the observation is well matched to the 
  assigned cluster   
  - Values close to 0 suggest that the observation is borderline matched 
  between two clusters   
  - Values close to -1 suggest that the observations may be assigned to the 
  wrong cluster   
  
For RAVmodel, the average silhouette width of each cluster is a quality control
measure and suggested as a secondary reference to choose proper RAVs, 
following validation score.

```{r}
x <- silhouetteWidth(RAVmodel)
head(x)   # average silhouette width of the first 6 RAVs
```


## GSEA on each RAV
Pre-processed GSEA results on each RAV are stored in RAVmodel and can be 
accessed through `gsea` function.

```{r}
class(gsea(RAVmodel))
class(gsea(RAVmodel)[[1]])
length(gsea(RAVmodel))
gsea(RAVmodel)[1]
```


## MeSH terms for each study
You can find MeSH terms associated with each study using `mesh` method. 
Output is a list with the length of studies used for training. Each element of
this output list is a data frame containing the assigned MeSH terms and the 
detail of them. The last column `bagOfWords` is the frequency of the MeSH term 
in the whole training dataset.

```{r}
length(mesh(RAVmodel))
mesh(RAVmodel)[1]
```


## PCA summary for each study
PCA summary of each study can be accessed through `PCAsummary` method. Output 
is a list with the length of studies, where each element is a matrix containing 
PCA summary results: standard deviation (SD), variance explained by each PC 
(Variance), and the cumulative variance explained (Cumulative).

```{r}
length(PCAsummary(RAVmodel))
PCAsummary(RAVmodel)[1]
```


# Session Info

<details>
```{r}
sessionInfo()
```
</details>