---
title: "MultiAssayExperiment: Quick Start Guide"
author: "Marcel Ramos & Levi Waldron"
date: "`r format(Sys.time(), '%B %d, %Y')`"
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{Quick-start Guide}
  %\VignetteEncoding{UTF-8}
output:
  BiocStyle::html_document:
    number_sections: no
    toc: yes
    toc_depth: 4
---

```{r,include=TRUE,results="hide",message=FALSE,warning=FALSE}
library(MultiAssayExperiment)
library(S4Vectors)
```

This quick-start guide shows key features of `MultiAssayExperiment` using a
subset of the TCGA adrenocortical carcinoma (ACC) dataset. This dataset
provides five assays on 92 patients, although all five assays were not
performed for every patient:

1. **RNASeq2GeneNorm**: gene mRNA abundance by RNA-seq
2. **gistict**: GISTIC genomic copy number by gene
3. **RPPAArray**: protein abundance by Reverse Phase Protein Array
4. **Mutations**: non-silent somatic mutations by gene
5. **miRNASeqGene**: microRNA abundance by microRNA-seq.

```{r}
data(miniACC)
miniACC
```

# Component slots

## colData - information on biological units

A `DataFrame` describing the characteristics of biological units, for example
clinical data for patients. In the prepared datasets from
[The Cancer Genome Atlas][], each row is one patient and each column is a
clinical, pathological, subtype, or other variable. The `$` function provides
a shortcut for accessing or setting `colData` columns.


```{r}
colData(miniACC)[1:4, 1:4]
table(miniACC$race)
```

*Key points:*
* One row per patient
* Each row maps to zero or more observations in each experiment in the
`ExperimentList`, below.

## ExperimentList - experiment data

A base `list` or `ExperimentList` object containing the experimental datasets
for the set of samples collected. This gets converted into a class
`ExperimentList` during construction.

```{r}
experiments(miniACC)
```

*Key points:*
* One matrix-like dataset per list element (although they do not even need to
be matrix-like, see for example the `RaggedExperiment` package)
* One matrix column per assayed specimen. Each matrix column must correspond
to exactly one row of `colData`: in other words, you must know which patient or
cell line the observation came from. However, multiple columns can come from
the same patient, or there can be no data for that patient.
* Matrix rows correspond to variables, e.g. genes or genomic ranges
* `ExperimentList` elements can be genomic range-based (e.g.
`SummarizedExperiment::RangedSummarizedExperiment-class` or
`RaggedExperiment::RaggedExperiment-class`) or ID-based data (e.g.
`SummarizedExperiment::SummarizedExperiment-class`, `Biobase::eSet-class`
`base::matrix-class`, `DelayedArray::DelayedArray-class`, and derived classes)
* Any data class can be included in the `ExperimentList`, as long as it
supports: single-bracket subsetting (`[`), `dimnames`, and `dim`. Most data
classes defined in Bioconductor meet these requirements.


## sampleMap - relationship graph

`sampleMap` is a graph representation of the relationship between biological
units and experimental results. In simple cases where the column names of
`ExperimentList` data matrices match the row names of `colData`, the user won't
need to specify or think about a sample map, it can be created automatically by
the `MultiAssayExperiment` constructor.  `sampleMap` is a simple three-column
`DataFrame`:

1. `assay` column: the name of the assay, and found in the names of
`ExperimentList` list names
2. `primary` column: identifiers of patients or biological units, and found in
the row names of `colData`
3.  `colname` column: identifiers of assay results, and found in the column
names of `ExperimentList` elements
Helper functions are available for creating a map from a list. See `?listToMap`

```{r}
sampleMap(miniACC)
```


*Key points:*
* relates experimental observations (`colnames`) to `colData`
* permits experiment-specific sample naming, missing, and replicate observations

<p style="text-align: right;"> <a href="#header">back to top</a> </p>

## metadata

Metadata can be used to keep additional information about patients, assays
performed on individuals or on the entire cohort, or features such as genes,
proteins, and genomic ranges. There are many options available for storing
metadata. First, `MultiAssayExperiment` has its own metadata for describing the
entire experiment:

```{r}
metadata(miniACC)
```

Additionally, the `DataFrame` class used by `sampleMap` and `colData`, as well
as the `ExperimentList` class, similarly support metadata. Finally, many
experimental data objects that can be used in the `ExperimentList` support
metadata. These provide flexible options to users and to developers of derived
classes.

# Subsetting

## Single bracket `[`

In pseudo code below, the subsetting operations work on the rows of the
following indices:
1. _i_ experimental data rows
2. _j_ the primary names or the column names (entered as a `list` or `List`)
3. _k_ assay

```
multiassayexperiment[i = rownames, j = primary or colnames, k = assay]
```

Subsetting operations always return another `MultiAssayExperiment`. For example,
the following will return any rows named "MAPK14" or "IGFBP2", and remove any
assays where no rows match:

```{r, results='hide'}
miniACC[c("MAPK14", "IGFBP2"), , ]
```

The following will keep only patients of pathological stage iv, and all their
associated assays:

```{r, results='hide'}
miniACC[, miniACC$pathologic_stage == "stage iv", ]
```

And the following will keep only the RNA-seq dataset, and only patients for
which this assay is available:
```{r, results='hide'}
miniACC[, , "RNASeq2GeneNorm"]
```

### Subsetting by genomic ranges

If any ExperimentList objects have features represented by genomic ranges
(e.g. `RangedSummarizedExperiment`, `RaggedExperiment`), then a `GRanges`
object in the first subsetting position will subset these objects as in
`GenomicRanges::findOverlaps()`.


## Double bracket `[[`

The "double bracket" method (`[[`) is a convenience function for extracting
a single element of the `MultiAssayExperiment` `ExperimentList`. It avoids
the use of `experiments(mae)[[1L]]`. For example, both of the following extract
the `ExpressionSet` object containing RNA-seq data:

```{r}
miniACC[[1L]]  #or equivalently, miniACC[["RNASeq2GeneNorm"]]
```

## Patients with complete data

`complete.cases()` shows which patients have complete data for all assays:

```{r}
summary(complete.cases(miniACC))
```

The above logical vector could be used for patient subsetting. More simply,
`intersectColumns()` will select complete cases and rearrange each
`ExperimentList` element so its columns correspond exactly to rows of
`colData` in the same order:

```{r}
accmatched = intersectColumns(miniACC)
```

Note, the column names of the assays in `accmatched` are not the same because
of assay-specific identifiers, but they have been automatically re-arranged to
correspond to the same patients. In these TCGA assays, the first three `-`
delimited positions correspond to patient, ie the first patient is
*TCGA-OR-A5J2*:

```{r}
colnames(accmatched)
```

## Row names that are common across assays

`intersectRows()` keeps only rows that are common to each assay, and aligns
them in identical order. For example, to keep only genes where data are
available for RNA-seq, GISTIC copy number, and somatic mutations:

```{r}
accmatched2 <- intersectRows(miniACC[, , c("RNASeq2GeneNorm",
                                           "gistict",
                                           "Mutations")])
rownames(accmatched2)
```

<p style="text-align: right;"> <a href="#header">back to top</a> </p>

# Extraction

## assay and assays

The `assay` and `assays` methods follow `SummarizedExperiment` convention.
The `assay` (singular) method will extract the first element of the
`ExperimentList` and will return a `matrix`.

```{r}
class(assay(miniACC))
```

The `assays` (plural) method will return a `SimpleList` of the data with each
element being a `matrix`.

```{r}
assays(miniACC)
```

*Key point:*
* Whereas the `[[` returned an assay as its original class, `assay()` and
`assays()` convert the assay data to matrix form.

<p style="text-align: right;"> <a href="#header">back to top</a> </p>

# Summary of slots and accessors

Slot in the `MultiAssayExperiment` can be accessed or set using their accessor functions:

| Slot | Accessor |
|------|----------|
| `ExperimentList` | `experiments()`|
| `colData` | `colData()` and `$` * |
| `sampleMap` | `sampleMap()` |
| `metadata` | `metadata()` |

__*__ The `$` operator on a `MultiAssayExperiment` returns a single
column of the `colData`.

# Transformation / reshaping

The `longFormat` or `wideFormat` functions will "reshape" and combine
experiments with each other and with `colData` into one `DataFrame`. These
functions provide compatibility with most of the common R/Bioconductor functions
for regression, machine learning, and visualization.

## `longFormat`

In _long_ format a single column provides all assay results, with additional
optional `colData` columns whose values are repeated as necessary.
Here *assay* is the name of the ExperimentList element, *primary* is the patient
identifier (rowname of colData), *rowname* is the assay rowname (in this case
genes), *colname* is the assay-specific identifier (column name), *value* is the
numeric measurement (gene expression, copy number, presence of a non-silent
mutation, etc), and following these are the *vital_status* and *days_to_death*
colData columns that have been added:

```{r}
longFormat(miniACC[c("TP53", "CTNNB1"), , ],
           colDataCols = c("vital_status", "days_to_death"))
```

## `wideFormat`

In _wide_ format, each feature from each assay goes in a separate column, with
one row per primary identifier (patient). Here, each variable becomes a new
column:

```{r}
wideFormat(miniACC[c("TP53", "CTNNB1"), , ],
           colDataCols = c("vital_status", "days_to_death"))
```

# MultiAssayExperiment class construction and concatenation

## MultiAssayExperiment constructor function
The `MultiAssayExperiment` constructor function can take three arguments:

1. `experiments` - An `ExperimentList` or `list` of data
2. `colData` - A `DataFrame` describing the patients (or cell lines, or other
biological units)
3. `sampleMap` - A `DataFrame` of `assay`, `primary`, and `colname` identifiers

The miniACC object can be reconstructed as follows:
```{r}
MultiAssayExperiment(experiments=experiments(miniACC),
    colData=colData(miniACC),
    sampleMap=sampleMap(miniACC),
    metadata=metadata(miniACC))
```


## `prepMultiAssay` - Constructor function helper

The `prepMultiAssay` function allows the user to diagnose typical problems
when creating a `MultiAssayExperiment` object. See `?prepMultiAssay` for more
details.

## `c` - concatenate to MultiAssayExperiment

The `c` function allows the user to concatenate an additional experiment to an
existing `MultiAssayExperiment`. The optional `sampleMap` argument allows
concatenating an assay whose column names do not match the row names of
`colData`. For convenience, the _mapFrom_ argument allows the user to map from
a particular experiment **provided** that the **order** of the colnames is in
the **same**. A `warning` will be issued to make the user aware of this
assumption. For example, to concatenate a matrix of log2-transformed RNA-seq
results:

```{r}
miniACC2 <- c(miniACC, log2rnaseq = log2(assays(miniACC)$RNASeq2GeneNorm), mapFrom=1L)
assays(miniACC2)
```

<p style="text-align: right;"> <a href="#header">back to top</a> </p>

# Examples

## UpsetR "Venn" diagram

We see that 43 samples have all 5 assays, 32 are missing reverse-phase protein
(RPPAArray), 2 are missing Mutations, 1 is missing gistict, 12 have only
mutations and gistict, etc:

```{r}
library(UpSetR)
upsetSamples(miniACC)
```

## Kaplan-meier plot stratified by a clinical variable

The colData can provide clinical data for things like a Kaplan-Meier plot for
overall survival stratified by nodal stage. To simplify things, first add a "y"
column to the colData, containing the `Surv` object for survival analysis:

_**Note**_: `survfit` method does not work well with `DataFrame`. To bypass
the error, here we covert `colData` to a `data.frame`.

```{r}
suppressPackageStartupMessages({
  library(survival)
  library(survminer)
})
coldat <- as.data.frame(colData(miniACC))
coldat$y <- Surv(miniACC$days_to_death, miniACC$vital_status)
colData(miniACC) <- DataFrame(coldat)
```

And remove any patients missing overall survival information:
```{r}
miniACC <- miniACC[, complete.cases(coldat$y), ]
coldat <- as(colData(miniACC), "data.frame")
```

```{r}
fit <- survfit(y ~ pathology_N_stage, data = coldat)
ggsurvplot(fit, data = coldat, risk.table = TRUE)
```

## Multivariate Cox regression including RNA-seq, copy number, and pathology

Choose the *EZH2* gene for demonstration. This subsetting will drop assays with
no row named EZH2:

```{r}
wideacc <- wideFormat(miniACC["EZH2", , ],
    colDataCols = c("vital_status", "days_to_death", "pathology_N_stage"))
wideacc$y <- Surv(wideacc$days_to_death, wideacc$vital_status)
head(wideacc)
```

Perform a multivariate Cox regression with *EZH2* copy number (gistict),
log2-transformed *EZH2* expression (RNASeq2GeneNorm), and nodal status
(pathology_N_stage) as predictors:

```{r}
coxph(Surv(days_to_death, vital_status) ~ gistict_EZH2 +
          log2(RNASeq2GeneNorm_EZH2) + pathology_N_stage,  data=wideacc)
```

We see that *EZH2* expression is significantly associated with overal survival
(p < 0.001), but *EZH2* copy number and nodal status are not. This analysis
could easily be extended to the whole genome for discovery of prognostic
features by repeated univariate regressions over columns, penalized multivariate
regression, etc.

For further detail, see the main MultiAssayExperiment vignette.

# Session info

```{r}
sessionInfo()
```

<p style="text-align: right;"> <a href="#header">back to top</a> </p>

[The Cancer Genome Atlas]: https://cancergenome.nih.gov/
