---
title: "ivygapSE -- Bioconductor container for Ivy-GAP expression and metadata"
author: "Vincent J. Carey, stvjc at channing.harvard.edu"
date: "`r format(Sys.time(), '%B %d, %Y')`"
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{ivygapSE -- SummarizedExperiment for Ivy-GAP}
  %\VignetteEncoding{UTF-8}
output:
  BiocStyle::pdf_document:
    toc: yes
    number_sections: yes
  BiocStyle::html_document:
    highlight: pygments
    number_sections: yes
    theme: united
    toc: yes
---

# Introduction

CSV files from the Ivy-GAP project have been assembled into a SummarizedExperiment instance.
```{r loadup, echo=FALSE}
suppressPackageStartupMessages({
library(SummarizedExperiment)
library(ivygapSE)
})
```
```{r chk}
library(ivygapSE)
data(ivySE)
ivySE
```
There are several types of metadata collected with the object, including the
README.txt (use `cat(metadata(ivySE)$README, sep="\n")` to see this in R),
the URL where data were retrieved, 
a character vector (builder) with the R code for creating (much of) the SummarizedExperiment, 
and two tables of tumor-specific and
block-specific information.

# Summary information

The number of RNA-seq samples is `r ncol(ivySE)`.  The FPKM matrix has dimensions
```{r lkd}
dim(ivySE)
```

There are 42 different donors.
```{r lkse}
length(unique(metadata(ivySE)$tumorDetails$donor_id))
```
However, only 37 donors contributed tumor RNA:
```{r lkcon}
sum(metadata(ivySE)$tumorDetails$tumor_name %in% ivySE$tumor_name)
```
```{r getsbd,echo=FALSE}
subd = metadata(ivySE)$subBlockDetails
dsub = dim(subd)
```
The subBlockDetails metadata (`r dsub[1]` x `r dsub[2]`)  includes feature quantifications.  The
names of the quantification fields and their interpretation are
```{r featanno,echo=FALSE}
featanno = c("normalized_area_le"   =
"Leading Edge (LE)",
"normalized_area_lehbv" =
"Hyperplastic blood vessels in leading edge (LEhbv)",
"normalized_area_it"   =
"Infiltrating Tumor (IT)",
"normalized_area_ithbv" =
"Hyperplastic blood vessels in infiltrating tumor (IThbv)",
"normalized_area_ct"   =
"Cellular Tumor (CT)",
"normalized_area_ctpnz" =
"Perinecrotic zone (CTpnz)",
"normalized_area_cthbv" =
"Hyperplastic blood vessels in cellular tumor (CThbv)",
"normalized_area_ctpnn" =
"Pseudopalisading cells but no visible necrosis (CTpnn)",
"normalized_area_ctpan" =
"Pseudopalisading cells around necrosis (CTpan)",
"normalized_area_ctmvp" =
"Microvascular proliferation (CTmvp)",
"normalized_area_ctne" =
"Necrosis (CTne)")
featanno
```

# Exploring with the app

The `ivyGlimpse` function starts a shiny app.  Use the mouse to select
image features to scatterplot, and then select a region of the scatterplot
to stratify the blocks into two groups.  Once the selection is made,
the survival profiles of the groups are plotted, and expression
patterns for genes in selected pathways can be compared.