---
title: "MultiAssayExperiment: The Integrative Bioconductor Container"
author: "MultiAssay Special Interest Group"
date: "`r format(Sys.time(), '%B %d, %Y')`"
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{Coordinating Analysis of Multi-Assay Experiments}
  %\VignetteEncoding{UTF-8}
output:
  BiocStyle::html_document:
    number_sections: yes
    toc: yes
---

A built [html][] version of this vignette is available.

```{r, echo=FALSE, warning=FALSE}
suppressPackageStartupMessages(library(MultiAssayExperiment))
```

# Accessing the `MultiAssayExperiment` Application Programming Interface (API)

To see the `API` wiki document on GitHub, type:

```{r, eval=FALSE}
API()
```

A Shiny app that browses the package `API` is also available via:

```{r, eval=FALSE}
API(shiny=TRUE)
```

# Overview of the `MultiAssayExperiment` class

Here is an overview of the class and its constructors and extractors:
```{r}
empty <- MultiAssayExperiment()
empty
slotNames(empty)
```

## Components of the MultiAssayExperiment

### ExperimentList: experimental data

The `ExperimentList` slot and class is the container workhorse for the
`MultiAssayExperiment` class. It contains all the experimental data. It inherits
from class `S4Vectors::SimpleList` with one element/component per data type.

```{r}
class(experiments(empty)) # ExperimentList
```
The elements of the `ExperimentList` can contain **ID-based** and
**range-based** data. Requirements for all classes in the `ExperimentList`
are listed in the API.

See `API()` for details on using data classes not listed here. 

The following base and Bioconductor classes are supported: 

- `base::matrix`: the base class, can be used for ID-based datasets such as
gene expression summarized per-gene, microRNA, metabolomics, or microbiome
data. 

- `Biobase::ExpressionSet`: A richer representation of ID-based datasets
capable of storing additional assay-level metadata.

- `SummarizedExperiment::SummarizedExperiment`: Also provides a rich
representation of ID-based matrix-like datasets.

- `SummarizedExperiment::RangedSummarizedExperiment`: For rectangular
range-based datasets, one set of genomic ranges are assayed for multiple
samples. It can be used for gene expression, methylation, or other data
types that refer to genomic positions. 

- `MultiAssayExperiment::RangedRaggedAssay`: inherits from `GRangesList`, for
ranged-based ragged arrays, meaning that a potentially different set of
genomic ranges are assayed for each sample. A typical example would be
segmented copy number, where segmentation of copy number alterations occurs
and different genomic locations in each sample.

#### Class requirements within ExperimentList container

The datasets contained in elements of the `ExperimentList` must have:

* column names
* row names

The column names correspond to samples, and are used to match assay data to
specimen metadata stored in `pData`. 

The row names can correspond to a variety of features in the data including
but not limited to gene names, probe IDs, proteins, and named ranges. 

##### Method requirements

Classes contained in the `ExperimentList` must support the following list of
methods:

- `[`: single square bracket subsetting, with a single comma. It is assumed
that values before the comma subset rows, and values after the comma subset
columns.
- `colnames()`: corresponding to experimental samples
- `rownames()`: corresponding to features such as genes, proteins, etc.
- `dim()`: returns a vector of the number of rows and number of columns

### pData: primary data

The `MultiAssayExperiment` keeps one set of "primary" metadata that describes
the 'biological unit' which can refer to specimens, experimental subjects,
patients, etc. In this vignette, we will refer to each experimental subject as
a *patient*.

#### pData slot requirements

The `pData` dataset should be of class `DataFrame` but can accept a
`data.frame` class object that will be coerced.  

In order to relate metadata of the biological unit, the row names of the
`pData` dataset must contain patient identifiers. 

```{r}
patient.data <- data.frame(sex=c("M", "F", "M", "F"),
    age=38:41,
    row.names=c("Jack", "Jill", "Bob", "Barbara"))
patient.data
```

#### Note on the flexibility of the `DataFrame`

For many typical purposes the `DataFrame` and `data.frame` behave equivalently;
but the `Dataframe` is more flexible as it allows any vector-like data type
to be stored in its columns. The flexibility of the `DataFrame` permits, for
example, storing multiple dose-response values for a single cell line, even
if the number of doses and responses is not consistent across all cell lines.
Doses could be stored in one column of `pData` as a `SimpleList`, and
responses in another column, also as a `SimpleList`. Or, dose-response values
could be stored in a single column of `pData` as a two-column matrix for
each cell line.

### sampleMap: relating pData to multiple assays

The `sampleMap` is a `DataFrame` that relates the "primary" data
(`pData`) to the experimental assays:

```{r}
class(sampleMap(empty)) # DataFrame
```

The `sampleMap` provides an unambiguous map from every experimental
observation to *one and only one* row in `pData`. It is, however, permissible
for a row of `pData` to be associated with multiple experimental observations
or no observations at all.  In other words, there is a "many-to-one" mapping
from experimental observations to rows of `pData`, and a "one-to-any-number"
mapping from rows of `pData` to experimental observations.

#### sampleMap structure

The `sampleMap` has three columns, with the following column names:

1. **assay** provides the names of the different experiments / assays
performed. These are user-defined, with the only requirement that the names
of the `ExperimentList`, where the experimental assays are stored, must be
contained in this column.

2. **primary** provides the "primary" sample names. All values in this column
must also be present in the rownames of `pData(MultiAssayExperiment)`.
In this example, allowable values in this column are "Jack", "Jill",
"Barbara", and "Bob".

3. **colname** provides the sample names used by experimental datasets, which
in practice are often different than the primary sample names. For each assay,
all column names must be found in this column. Otherwise, those assays would
be orphaned: it would be impossible to match them up to samples in the overall
experiment. As mentioned above, duplicated values are allowed, to represent
replicates with the same overall experiment-level annotation.

This design is motivated by the following situations:

1. It allows flexibility for any amount of technical replication and biological
replication (such as tumor and matched normal for a single patient) of
individual assays.
2. It allows missing observations (such as RNA-seq performed only for some of
the patients).
3. It allows the use of different identifiers to be used for patients /
specimens and for each assay. These different identifiers are matched
unambiguously, and consistency between them is maintained during subsetting
and re-ordering.

##### Instances where `sampleMap` isn't provided

If each assay uses the same colnames (i.e., if the same sample identifiers are
used for each experiment), a simple list of these datasets is sufficient for
the `MultiAssayExperiment` constructor function. It is not necessary for
them to have the same rownames or colnames:

```{r}
exprss1 <- matrix(rnorm(16), ncol = 4,
        dimnames = list(sprintf("ENST00000%i", sample(288754:290000, 4)),
                c("Jack", "Jill", "Bob", "Bobby")))
exprss2 <- matrix(rnorm(12), ncol = 3,
        dimnames = list(sprintf("ENST00000%i", sample(288754:290000, 4)),
                c("Jack", "Jane", "Bob")))
doubleExp <- list("methyl 2k"  = exprss1, "methyl 3k" = exprss2)
simpleMultiAssay <- MultiAssayExperiment(experiments=doubleExp)
simpleMultiAssay
```

In the above example, the user did not provide the `pData` argument so the
constructor function filled it with an empty `DataFrame`:

```{r}
pData(simpleMultiAssay)
```

But the `pData` can be provided. Here, note that any assay sample (column)
that cannot be mapped to a corresponding row in the provided `pData` gets
dropped. This is part of ensuring internal validity of the
`MultiAssayExperiment`.

```{r}
simpleMultiAssay2 <- MultiAssayExperiment(experiments=doubleExp,
                                          pData=patient.data)
simpleMultiAssay2
pData(simpleMultiAssay2)
```

### metadata

Can be of *ANY* class, for storing study-wide metadata, such as citation
information. For an empty `MultiAssayExperiment` object, it is NULL. 

```{r}
class(metadata(empty)) # NULL (class "ANY")
```

# Creating a `MultiAssayExperiment` object: a rich example

In this section we demonstrate all core supported data classes, using different
sample ID conventions for each assay, with primary pData. The some 
supported classes such as, `matrix`, `ExpressionSet`, 
`SummarizedExperiment`, `RangedSummarizedExperiment`, and `RangedRaggedAssay`.

## Create toy datasets demonstrating all supported data types

We have three matrix-like datasets. First, let's represent expression data as
an `ExpressionSet`:

```{r, message=FALSE}
library(Biobase)
(arraydat <- matrix(seq(101, 108), ncol=4,
                    dimnames=list(c("ENST00000294241", "ENST00000355076"),
                                  c("array1", "array2", "array3", "array4"))))
arraypdat <- as(data.frame(slope53=rnorm(4),
                           row.names=c("array1", "array2", "array3",
                                       "array4")), "AnnotatedDataFrame")
exprdat <- ExpressionSet(assayData=arraydat, phenoData=arraypdat)
exprdat
```

The following map matches `pData` sample names to `exprdata` sample
names. Note that row orders aren't initially matched up, and this is OK.

```{r}
(exprmap <- data.frame(primary=rownames(patient.data)[c(1, 2, 4, 3)],
                       assay=c("array1", "array2", "array3", "array4"),
                       stringsAsFactors = FALSE))
```

Now methylation data, which we will represent as a `matrix`. It uses
gene identifiers also, but measures a partially overlapping set of genes.
Now, let's store this as a simple matrix which can contains a replicate
for one of the patients.

```{r}
(methyldat <-
   matrix(1:10, ncol=5,
          dimnames=list(c("ENST00000355076", "ENST00000383706"),
                        c("methyl1", "methyl2", "methyl3",
                          "methyl4", "methyl5"))))
```

The following map matches `pData` sample names to `methyldat` sample
names.

```{r}
(methylmap <- data.frame(primary = c("Jack", "Jack", "Jill", "Barbara", "Bob"),
              assay = c("methyl1", "methyl2", "methyl3", "methyl4", "methyl5"),
              stringsAsFactors = FALSE))
```

Now we have a microRNA platform, which has no common identifiers with the
other datasets, and which we also represent as a `matrix`. It
is also missing data for "Jill". We will use the same sample naming
convention as we did for arrays.

```{r}
(microdat <- matrix(201:212, ncol=3,
                    dimnames=list(c("hsa-miR-21", "hsa-miR-191",
                                    "hsa-miR-148a", "hsa-miR148b"),
                                  c("micro1", "micro2", "micro3"))))
```

And the following map matches pData sample names to microdat
sample names.

```{r}
(micromap <- data.frame(primary = c("Jack", "Barbara", "Bob"),
                        assay = c("micro1", "micro2", "micro3"),
                        stringsAsFactors = FALSE))
```

Let's include a `RangedRaggedAssay`, which is defined in this package and
extends `GRangesList`. This is intended for data such as segmented copy
number, which provides genomic ranges that may be different for each sample.
We start with a `GRangesList`, which will later be converted automatically
by the `MultiAssayExperiment` constructor function.

```{r}
suppressPackageStartupMessages(library(GenomicRanges))
## completely encompasses ENST00000355076
gr1 <-
  GRanges(seqnames = "chr3", ranges = IRanges(58000000, 59502360),
          strand = "+", score = 5L, GC = 0.45)
## first is within ENST0000035076
gr2 <-
  GRanges(seqnames = c("chr3", "chr3"),
          ranges = IRanges(c(58493000, 3), width=9000),
          strand = c("+", "-"), score = 3:4, GC = c(0.3, 0.5))
gr3 <-
  GRanges(seqnames = c("chr1", "chr2"),
          ranges = IRanges(c(1, 4), c(3, 9)),
          strand = c("-", "-"), score = c(6L, 2L), GC = c(0.4, 0.1))
grl <- GRangesList("gr1" = gr1, "gr2" = gr2, "gr3" = gr3)
names(grl) <- c("snparray1", "snparray2", "snparray3")
grl
```

The following `data.frame` matches `pData` samples to the
`GRangesList`:

```{r}
(rangemap <- data.frame(primary = c("Jack", "Jill", "Jill"),
                        assay = c("snparray1", "snparray2", "snparray3"),
                        stringsAsFactors = FALSE))
```

Finally, we create a dataset of class `RangedSummarizedExperiment`:

```{r}
library(SummarizedExperiment)
nrows <- 5; ncols <- 4
counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
rowRanges <- GRanges(rep(c("chr1", "chr2"), c(2, nrows - 2)),
                     IRanges(floor(runif(nrows, 1e5, 1e6)), width=100),
                     strand=sample(c("+", "-"), nrows, TRUE),
                     feature_id=sprintf("ID\\%03d", 1:nrows))
names(rowRanges) <- letters[1:5]
colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 2),
                     row.names= c("mysnparray1", "mysnparray2",
                                  "mysnparray3", "mysnparray4"))
rse <- SummarizedExperiment(assays=SimpleList(counts=counts),
                            rowRanges=rowRanges, colData=colData)
```

And we map the `pData` samples to the `RangedSummarizedExperiment`: 

```{r}
(rangemap2 <-
   data.frame(primary = c("Jack", "Jill", "Bob", "Barbara"),
              assay = c("mysnparray1", "mysnparray2", "mysnparray3",
                        "mysnparray4"), stringsAsFactors = FALSE))
```

## `sampleMap` creation

The `MultiAssayExperiment` constructor function can create the `sampleMap`
automatically if a single naming convention is used, but in this example
it cannot because we used platform-specific sample identifiers
(e.g. mysnparray1, etc). So we must provide an ID map that matches the
samples of each experiment back to the `pData`, as a three-column
`data.frame` or `DataFrame` with three columns named "assay", primary", and
"colname". Here we start with a list:

```{r}
listmap <- list(exprmap, methylmap, micromap, rangemap, rangemap2)
names(listmap) <- c("Affy", "Methyl 450k", "Mirna", "CNV gistic", "CNV gistic2")
listmap
```

and use the convenience function `listToMap` to convert the list of
`data.frame` objects to a valid object for the `sampleMap`:

```{r}
dfmap <- listToMap(listmap)
dfmap
```

Note, `dfmap` can be reverted to a list with another provided function:

```{r, eval=FALSE}
mapToList(dfmap, "assay")
```

## Experimental data as a `list()`

Create an named list of experiments for the `MultiAssayExperiment` function.
All of these names must be found within in the third column of `dfmap`:

```{r}
objlist <- list("Affy" = exprdat, "Methyl 450k" = methyldat,
                "Mirna" = microdat, "CNV gistic" = grl, "CNV gistic2" = rse)
```

## Creation of the `MultiAssayExperiment` class object

We recommend using the `MultiAssayExperiment` constructor function:

```{r}
myMultiAssay <- MultiAssayExperiment(objlist, patient.data, dfmap)
myMultiAssay
```

The following extractor functions can be used to get extract data from
the object:

```{r}
experiments(myMultiAssay)
pData(myMultiAssay)
sampleMap(myMultiAssay)
metadata(myMultiAssay)
```

Note that the `ExperimentList` class extends the `SimpleList` class to add some
validity checks specific to `MultiAssayExperiment`.  It can be used like
a list.

## Helper function to create a `MultiAssayExperiment` object

The `PrepMultiAssay` function helps diagnose common problems when creating a
`MultiAssayExperiment` object. It provides error messages and/or warnings in
instances where names (either `colnames` or `ExperimentList` element names) are
inconsistent with those found in the sampleMap. Input arguments are the same
as those in the `MultiAssayExperiment` (i.e., `ExperimentList`, `pData`,
`sampleMap`). The resulting output of the `PrepMultiAssay` function is a list
of inputs including a "drops" element for names that were not able to be
matched.

Instances where `ExperimentList` is created without names will prompt an error
from `PrepMultiAssay`. Named `ExperimentList` elements are essential for checks
in `MultiAssayExperiment`.

```{r}
objlist3 <- objlist
(names(objlist3) <- NULL)
try(PrepMultiAssay(objlist3, patient.data, dfmap)$ExperimentList)
```

Non-matching names may also be present in the `ExperimentList` elements and the
"assay" column of the `sampleMap`. If names only differ by case and are
identical and unique, names will be standardized to lower case and replaced.

```{r}
names(objlist3) <- toupper(names(objlist))
names(objlist3)
unique(dfmap[, "assay"])
PrepMultiAssay(objlist3, patient.data, dfmap)$ExperimentList
```

When `colnames` in the `ExperimentList` cannot be matched back to the primary
data (`pData`), these will be dropped and added to the drops element. 

```{r}
exampleMap <- sampleMap(simpleMultiAssay2)
sapply(doubleExp, colnames)
exampleMap
PrepMultiAssay(doubleExp, patient.data, exampleMap)$drops
```

A similar operation is performed for checking "primary" `sampleMap` names and
`pData` rownames. In this example, we add a row corresponding to "Joe" that
does not have a match in the experimental data.

```{r}
exMap <- rbind(dfmap,
               DataFrame(assay = "New methyl",
                         primary = "Joe",
                         colname = "Joe"))
PrepMultiAssay(objlist, patient.data, exMap)$drops
```

To create a `MultiAssayExperiment` from the results of the `PrepMultiAssay`
function, take each corresponding element from the resulting list and enter
them as arguments to the `MultiAssayExperiment` constructor function.

```{r}
prepped <- PrepMultiAssay(objlist, patient.data, exMap)
preppedMulti <- MultiAssayExperiment(prepped$ExperimentList, prepped$pData,
                                     prepped$sampleMap)
preppedMulti
```

## Helper functions to create `Bioconductor` classes from raw data

Recent updates to the `GenomicRanges` and `SummarizedExperiment` packages
allow the user to create standard _Bioconductor_ classes from raw data. Raw
data read in as either `data.frame` or `DataFrame` can be converted to
`GRangesList` or `SummarizedExperiment` classes depending on the type of data.

The function to create a `GRangesList` from a `data.frame`, called
`makeGRangesListFromDataFrame` can be found in the `GenomicRanges` package.
`makeSummarizedExperimentFromDataFrame` is available in the
`SummarizedExperiment` package. It is also possible to create a
`RangedSummarizedExperiment` class object from raw data when ranged data is
available.

A simple example can be obtained from the function documentation in
`GenomicRanges`:

```{r}
library(GenomicRanges)
grldf <- structure(
    list(chr = structure(c(1L, 1L, 1L, 1L, 1L),
                         class = "factor", .Label = "chr1"), 
         start = 11:15, end = 12:16,
         strand = structure(c(4L, 1L, 4L, 3L, 2L),
                            .Label = c("-", ".", "*", "+"),
                            class = "factor"), 
         score = 1:5, specimen =
             structure(c(1L, 1L, 2L, 2L, 3L),
                       .Label = c("a", "b", "c"),
                       class = "factor"),
         gene_symbols = structure(1:5, .Label = c("GENEa", "GENEb",
                                                  "GENEc", "GENEd",
                                                  "GENEe"),
                                  class = "factor")),
    .Names = c("chr", "start", "end", "strand", "score", "specimen",
               "gene_symbols"),
    row.names = c(NA, -5L), class = "data.frame")
makeGRangesListFromDataFrame(grldf, split.field = "specimen",
                             names.field = "gene_symbols")
```

In the `SummarizedExperiment` package: 

```{r}
library(SummarizedExperiment)
sedf <- structure(list(chr = structure(c(1L, 1L, 1L, 1L, 1L),
                                        class = "factor", .Label = "chr2"), 
                        start = 11:15, end = 12:16,
                        strand = structure(c(4L, 1L, 4L, 3L, 2L),
                                           .Label = c("-", ".", "*", "+"),
                                           class = "factor"), 
                        expr0 = 3:7, expr1 = 8:12, expr2 = 12:16),
                   .Names = c("chr", "start", "end", "strand",
                              "expr0", "expr1", "expr2"),
                   row.names = c("GENEe", "GENEd", "GENEc", "GENEb", "GENEa"),
                   class = "data.frame")
sedf
makeSummarizedExperimentFromDataFrame(sedf)
```

# `RangedRaggedAssay` class

Note that the `GRangesList` got converted to a `RangedRaggedAssay`, a class
intended for data such as segmented copy number that is provides different
genomic ranges for each sample. `RangedRaggedAssay` is defined by this
package and inherits from `GRangesList`:

```{r}
methods(class="RangedRaggedAssay")
getMethod("dimnames", "RangedRaggedAssay")
```

It has some additional methods that are required for any data class contained
in a `MultiAssayExperiment`:

```{r}
class(experiments(myMultiAssay)[[4]])
rownames(experiments(myMultiAssay)[[4]])
colnames(experiments(myMultiAssay)[[4]])
```

One of the requirements for the `assay` method (specifically for this
`RangedRaggedAssay` `ExperimentList` element) is that the metadata have a
`score` column from which to obtain values for the resulting assay matrix.
Here we have added ficticious values to such column contained within list
elements. See `assay,RangedRaggedAssay,ANY-method` documentation.

```{r}
assay(experiments(myMultiAssay)[[4]], background = 2)
```

## Updated `assay` method

The `assay` method uses the "inner" metadata columns to obtain a 
score value from which to create the assay matrix.

```{r}
rra <- experiments(myMultiAssay)[[4]]
mcols(rra[[1]])
assay(rra, background = 2)
```

# Integrated subsetting across experiments

The core functionality of `MultiAssayExperiment` is to allow subsetting by
assay, rownames, and colnames, across all experiments simultaneously while
guaranteeing continued matching of samples.

## Subsetting samples / columns

Experimental samples are stored in the rows of `pData` but the columns of
elements of `ExperimentList`, so when we refer to subsetting by columns, we
are referring to columns of the experimental assays. Subsetting by samples /
columns will be more obvious after recalling the `pData`:

```{r}
pData(myMultiAssay)
```

Subsetting by samples identifies the selected samples in rows of the pData
DataFrame, then selects all columns of the `ExperimentList` corresponding to
these rows. Here we use an integer to keep the first two rows of pData, and
all experimental assays associated to those two primary samples:

```{r}
subsetByColumn(myMultiAssay, 1:2)
```

Note that the above operation keeps different numbers of columns / samples
from each assay, reflecting the reality that some samples may not have been
assayed in all experiments, and may have replicates in some.

Subsetting the primary identifiers using a character vector corresponding to
some rownames of `pData` returns the same result:

```{r}
subsetByColumn(myMultiAssay, c("Jack", "Jill"))
```

Columns can be subset using a logical vector. Here the dollar sign operator
(`$`) accesses one of the columns in `pData`.

```{r}
malesMultiAssay <- subsetByColumn(myMultiAssay, myMultiAssay$sex == "M")
pData(malesMultiAssay)
```

Note that selecting male patients from all assays could have been accomplished
equivalently using the square bracket:

```{r}
myMultiAssay[, myMultiAssay$sex == "M", ]
```

Finally, for special use cases you can exert detail control of which samples
to select using a `list` or `CharacterList`, which is just a convenient form
of a list containing character vectors.

```{r}
allsamples <- colnames(myMultiAssay)
allsamples
```

Now let's get rid of the Methyl 450k arrays 3 to 5, a couple different but
equivalent ways:

```{r}
allsamples[["Methyl 450k"]] <- allsamples[["Methyl 450k"]][-3:-5]
myMultiAssay[, as.list(allsamples), ]
```

## Subsetting assays

You can select certain assays / experiments using subset, by providing a
character, logical, or integer vector.  An example using character:

```{r}
subsetByAssay(myMultiAssay, c("Affy", "CNV gistic"))
```

Examples using logical and integer:

```{r}
is.cnv = grepl("CNV", names(experiments(myMultiAssay)))
is.cnv
subsetByAssay(myMultiAssay, is.cnv)
subsetByAssay(myMultiAssay, which(is.cnv))
```

`subsetByRow`, `subsetByColumn`, and `subsetByAssay` are endogenous operations,
in that it always returns another `MultiAssayExperiment` object.

Use `assay(myMultiAssay)` to retrieve the experimental data in an ordinary
`list` of numeric matrices from the different data classes (in progress).

## Subsetting rows (features) by IDs, integers, or logicals

Rows of the assays correspond to assay features or measurements, such as genes.
Regardless of whether the assay is ID-based (e.g., matrix, ExpressionSet) or
range-based (e.g., RangedSummarizedExperiment, RangedRaggedAssay), they can be
subset using any of the following:

- a **character vector** of IDs that will be matched to rownames in each assay

- an **integer vector** that will select rows of this position from each assay.
This probably doesn't make sense unless every `ExperimentList` element
represents the same measurements in the same order and will generate an error
if any of the integer elements exceeds the number of rows in any
`ExperimentList` element. The most likely use of integer subsetting would be
as a `head` function, for example to look at the first 6 rows of each assay.

- a **logical vector** that will be passed directly to the row subsetting
operation for each assay.

- a **list** or **CharacterList** of the same length as ExperimentList. Each
element of the subsetting list will be passed on exactly to subset rows of the
corresponding element of the `ExperimentList`.

Again, this operation always returns a `MultiAssayExperiment` class, unless
"drop=TRUE" is passed to the `[` backet subset, with any `ExperimentList`
element not containing the feature having zero rows.

For example, return a MultiAssayExperiment where `Affy` and `Methyl 450k`
contain only "ENST0000035076"" row, and "Mirna" and "CNV gistic" have zero
rows (`drop` argument is set to `FALSE` by default in `subsetBy*`):

```{r}
featSubsetted0 <- subsetByRow(myMultiAssay, "ENST00000355076")
class(featSubsetted0)
class(experiments(featSubsetted0))
experiments(featSubsetted0)
```

In the following, `Affy` ExpressionSet keeps both rows but with their
order reversed, and `Methyl 450k` keeps only its second row.

```{r}
featSubsetted <-
  subsetByRow(myMultiAssay, c("ENST00000355076", "ENST00000294241"))
exprs(experiments(myMultiAssay)[[1]])
exprs(experiments(featSubsetted)[[1]])
```

## Subsetting rows (features) by `GenomicRanges`

For `MultiAssayExperiment` objects containing range-based objects (currently
`RangedSummarizedExperiment` and `RangedRaggedAssay`), these can be subset
using a `GRanges` object, for example:

```{r}
gr <- GRanges(seqnames = c("chr1"), strand = c("-", "+", "-"),
              ranges = IRanges(start = c(1, 4, 6), width = 3))
```

Now do the subsetting. The function doing the work here is
`IRanges::subsetByOverlaps` - see its arguments for flexible types of
subsetting by range. The first three arguments here are for `subset`, the
rest passed on to `IRanges::subsetByOverlaps` through "...":

```{r}
subsetted <- subsetByRow(myMultiAssay, gr, maxgap = 2L, type = "within")
experiments(subsetted)
```

## Subsetting by square bracket `[`

The bracket method for the `MultiAssayExperiment` is equivalent but more
compact than the `subsetBy*()` methods. The three positions within the bracket
operator indicate rows, columns, and assays, respectively (pseudocode):

```{r, eval=FALSE}
myMultiAssay[rows, columns, assays]
```

For example, to select the gene "ENST00000355076":

```{r}
myMultiAssay["ENST00000355076", , ]
```

The above operation works across all types of assays, whether ID-based
(e.g. matrix, ExpressionSet, SummarizedExperiment) or range-based
(e.g. RangedSummarizedExperiment, RangedRaggedAssay). Note that when using
the bracket method `[`, the drop argument is *TRUE* by default.

You can subset by rows, columns, and assays in a single bracket operation,
and they will be performed in that order (rows, then columns, then assays):

```{r}
myMultiAssay["ENST00000355076", 1:2, c("Affy", "Methyl 450k")]
```

## Subsetting by character, integer, and logical

By columns - character, integer, and logical are all allowed, for example:
```{r}
myMultiAssay[, "Jack", ]
myMultiAssay[, 1, ]
myMultiAssay[, c(TRUE, FALSE, FALSE, FALSE), ]
```

By assay - character, integer, and logical are allowed:
```{r}
myMultiAssay[, , "Mirna"]
myMultiAssay[, , 3]
myMultiAssay[, , c(FALSE, FALSE, TRUE, FALSE, FALSE)]
```

## the "drop" argument

Specify `drop=FALSE` to keep assays with zero rows or zero columns, e.g.:

```{r}
myMultiAssay["ENST00000355076", , , drop=FALSE]
```

Using the default `drop=TRUE`, assays with no rows or no columns are removed:

```{r}
myMultiAssay["ENST00000355076", , , drop=TRUE]
```

# rownames and colnames

rownames and colnames return a `CharacterList` of rownames and colnames across
all the assays.  A `CharacterList` is just an alternative to `list` when each
element contains a character vector, that provides a nice show method:

```{r}
rownames(myMultiAssay)
colnames(myMultiAssay)
```

# Requirements for support of additional data classes

Any data classes in the `ExperimentList` object must support the following
methods:

* `colnames()`
* `rownames()`
* `[`
* `dim()`


Here is what happens if one of the methods doesn't:
```{r}
objlist2 <- objlist
objlist2[[2]] <- as.vector(objlist2[[2]])
invalid.obj <- try(MultiAssayExperiment(objlist2, patient.data, dfmap))
invalid.obj
```

# Methods for MultiAssayExperiment

The following methods are defined for `MultiAssayExperiment`:
```{r}
methods(class="MultiAssayExperiment")
```

# Wishlist

* `c()` function for adding new assays to existing `MultiAssayExperiment`
    + e.g. c(myMultiAssay, neweset)
    + require that sample names in the new object match pData sample names
    + require that sample names in the new object already exist in pData
* Figure out how to support a "long-and-skinny" SQL database
* "mergeDups" function to merge duplicate samples in any assay
    + For matrix-like objects, it is clear how to do this. Default would be
      simple mean of the columns, but could allow user-specified functions.
    + For GRangesList, it's not obvious how to merge duplicates.
      Just concatenate?

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

[html]: http://rpubs.com/lwaldron/multiassayexperiment
