---
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_document2:
number_sections: yes
toc: true
---
```{r, echo=FALSE, warning=FALSE}
suppressPackageStartupMessages({
library(MultiAssayExperiment)
library(GenomicRanges)
})
```
# Installation
```{r, eval = FALSE}
source("https://bioconductor.org/biocLite.R")
BiocInstaller::biocLite("MultiAssayExperiment")
```
Loading the packages:
```{r, eval = FALSE}
library(MultiAssayExperiment)
library(GenomicRanges)
```
# 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.
The following base and Bioconductor classes are known to work as elements of
the ExperimentList:
- `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.
- `RaggedExperiment::RaggedExperiment`: For range-based datasets, such as
copy number and mutation data, the `RaggedExperiment` class can be used
to represent measurements by genomic positions.
#### Class requirements within `ExperimentList` container
See the [API section](#application-programming-interface-api) for details on
requirements for using other data classes. In general, data classes meeting
minimum requirements, including support for square bracket `[` subsetting and
`dimnames()` will work by default.
The datasets contained in elements of the `ExperimentList` can have:
* column names (required)
* row names (optional)
The column names correspond to samples, and are used to match assay data to
specimen metadata stored in `colData`.
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.
Note that the existence of "row" names does *not* mean the data must be
rectangular or matrix-like.
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.
- `dimnames()` : corresponding to features (such as genes, proteins, etc.)
and experimental samples
- `dim()`: returns a vector of the number of rows and number of columns
### `colData`: 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*.
#### `colData` slot requirements
The `colData` 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
`colData` 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 `colData` as a `SimpleList`, and
responses in another column, also as a `SimpleList`. Or, dose-response values
could be stored in a single column of `colData` as a two-column matrix for
each cell line.
### `sampleMap`: relating `colData` to multiple assays
The `sampleMap` is a `DataFrame` that relates the "primary" data
(`colData`) 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 `colData`. It is, however, permissible
for a row of `colData` 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 `colData`, and a "one-to-any-number"
mapping from rows of `colData` 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 `colData(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 `colData` argument so the
constructor function filled it with an empty `DataFrame`:
```{r}
colData(simpleMultiAssay)
```
But the `colData` can be provided. Here, note that any assay sample (column)
that cannot be mapped to a corresponding row in the provided `colData` gets
dropped. This is part of ensuring internal validity of the
`MultiAssayExperiment`.
```{r}
simpleMultiAssay2 <- MultiAssayExperiment(experiments=doubleExp,
colData=patient.data)
simpleMultiAssay2
colData(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")
```
back to top
# 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 `colData`. The some
supported classes such as, `matrix`, `ExpressionSet`,
`SummarizedExperiment`, and `RangedSummarizedExperiment`.
## 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 `colData` 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 `colData` 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 `colData` sample names to `microdat` sample names.
```{r}
(micromap <- data.frame(primary = c("Jack", "Barbara", "Bob"),
assay = c("micro1", "micro2", "micro3"),
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 `colData` samples to the `RangedSummarizedExperiment`:
```{r}
(rangemap <-
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 `colData`, 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)
names(listmap) <- c("Affy", "Methyl 450k", "Mirna", "CNV gistic")
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" = 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)
colData(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`, `colData`,
`sampleMap`). The resulting output of the `prepMultiAssay` function is a list
of inputs including a "metadata$drops" element for names that were not able to be
matched.
Instances where `ExperimentList` is created without names will prompt an error
from `prepMultiAssay`. Named `ExperimentList` elements are essential for checks
in `MultiAssayExperiment`.
```{r}
objlist3 <- objlist
(names(objlist3) <- NULL)
try(prepMultiAssay(objlist3, patient.data, dfmap)$experiments)
```
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)$experiments
```
When `colnames` in the `ExperimentList` cannot be matched back to the primary
data (`colData`), these will be dropped and added to the drops element.
```{r}
exampleMap <- sampleMap(simpleMultiAssay2)
sapply(doubleExp, colnames)
exampleMap
prepMultiAssay(doubleExp, patient.data, exampleMap)$metadata$drops
```
A similar operation is performed for checking "primary" `sampleMap` names and
`colData` rownames. In this example, we add a row corresponding to "Joe" that
does not have a match in the experimental data.
```{r}
exMap <- rbind(dfmap,
DataFrame(assay = "New methyl",
primary = "Joe",
colname = "Joe"))
invisible(prepMultiAssay(objlist, patient.data, exMap))
```
To create a `MultiAssayExperiment` from the results of the `prepMultiAssay`
function, take each corresponding element from the resulting list and enter
them as arguments to the `MultiAssayExperiment` constructor function.
```{r}
prepped <- prepMultiAssay(objlist, patient.data, exMap)
preppedMulti <- MultiAssayExperiment(prepped$experiments, prepped$colData,
prepped$sampleMap, prepped$metadata)
preppedMulti
```
Alternatively, use the `do.call` function to easily create a `MultiAssayExperiment`
from the output of `prepMultiAssay` function:
```{r}
do.call(MultiAssayExperiment, prepped)
```
## 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)
grlls <- list(chr = rep("chr1", nrows), start = seq(11, 15),
end = seq(12, 16), strand = c("+", "-", "+", "*", "*"),
score = seq(1, 5), specimen = c("a", "a", "b", "b", "c"),
gene_symbols = paste0("GENE", letters[seq_len(nrows)]))
grldf <- as.data.frame(grlls, stringsAsFactors = FALSE)
makeGRangesListFromDataFrame(grldf, split.field = "specimen",
names.field = "gene_symbols")
```
In the `SummarizedExperiment` package:
```{r}
library(SummarizedExperiment)
sels <- list(chr = rep("chr2", nrows), start = seq(11, 15),
end = seq(12, 16), strand = c("+", "-", "+", "*", "*"),
expr0 = seq(3, 7), expr1 = seq(8, 12), expr2 = seq(12, 16))
sedf <- as.data.frame(sels, row.names = paste0("GENE",
letters[rev(seq_len(nrows))]),
stringsAsFactors = FALSE)
sedf
makeSummarizedExperimentFromDataFrame(sedf)
```
back to top
# Integrated subsetting across experiments
`MultiAssayExperiment` allows subsetting by rows, columns, and assays,
rownames, and colnames, across all experiments simultaneously while
guaranteeing continued matching of samples.
Subsetting can be done most compactly by the square bracket method, or more
verbosely and potentially more flexibly by the `subsetBy*()` methods.
## Subsetting by square bracket `[`
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`). 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).
The following selects the `ENST00000355076` gene across all samples, then the
first two samples of each assay, and finally the Affy and Methyl 450k 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]
```
## More on subsetting by columns
Experimental samples are stored in the rows of `colData` 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 `colData`:
```{r}
colData(myMultiAssay)
```
Subsetting by samples identifies the selected samples in rows of the colData
DataFrame, then selects all columns of the `ExperimentList` corresponding to
these rows. Here we use an integer to keep the first two rows of colData, and
all experimental assays associated to those two primary samples:
```{r}
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.
Columns can be subset using a logical vector. Here the dollar sign operator
(`$`) accesses one of the columns in `colData`.
```{r}
malesMultiAssay <- myMultiAssay[, myMultiAssay$sex == "M"]
colData(malesMultiAssay)
```
Finally, for special use cases you can exert detailed control of row or column
subsetting, by using a `list` or `CharacterList` to subset. The following
creates a `CharacterList` of the column names of each assay:
```{r}
allsamples <- colnames(myMultiAssay)
allsamples
```
Now let's get rid of three Methyl 450k arrays, those in positions 3, 4, and 5:
```{r}
allsamples[["Methyl 450k"]] <- allsamples[["Methyl 450k"]][-3:-5]
myMultiAssay[, as.list(allsamples), ]
subsetByColumn(myMultiAssay, as.list(allsamples)) #equivalent
```
## Subsetting assays
You can select certain assays / experiments using subset, by providing a
character, logical, or integer vector. An example using character:
```{r}
myMultiAssay[, , c("Affy", "CNV gistic")]
```
You can subset assays also using logical or integer vectors:
```{r}
is.cnv = grepl("CNV", names(experiments(myMultiAssay)))
is.cnv
myMultiAssay[, , is.cnv] #logical subsetting
myMultiAssay[, , which(is.cnv)] #integer subsetting
```
## 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`), 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")
featSubsetted0 <- myMultiAssay["ENST00000355076", , ] #equivalent
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`), these can be subset
using a `GRanges` object, for example:
```{r}
gr <- GRanges(seqnames = c("chr1", "chr1", "chr2"), strand = c("+", "+", "+"),
ranges = IRanges(start = c(330602, 443625, 934533),
end = c(330701, 443724, 934632)))
```
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)
rowRanges(subsetted[[4]])
```
Square bracket subsetting can still be used here, but passing on arguments to
`IRanges::subsetByOverlaps` through "..." is simpler using `subsetByRow()`.
## Subsetting is endogenous
`subsetByRow`, `subsetByColumn`, `subsetByAssay`, and square bracket subsetting
are all "endogenous" operations, in that they always returns another
`MultiAssayExperiment` object.
## Double-bracket subsetting to select experiments
A double-bracket subset operation refers to an experiment, and will return
the object contained within an `ExperimentList` element. It is **not**
endogenous. For example, the first `ExperimentList` element is called "Affy"
and contains an `ExpressionSet`:
```{r}
names(myMultiAssay)
myMultiAssay[[1]]
myMultiAssay[["Affy"]]
```
back to top
# Helpers for data clean-up and management
## `complete.cases`
The `complete.cases` function returns a logical vector identifying which
primary units have data for all experiments. Recall that `myMultiAssay`
provides data for four individuals:
```{r}
colData(myMultiAssay)
```
Of these, only Jack has data for all 5 experiments:
```{r}
complete.cases(myMultiAssay)
```
But all four have coplete cases for Affy and Methyl 450k:
```{r}
complete.cases(myMultiAssay[, , 1:2])
```
This output can be used to select individuals with complete data:
```{r}
myMultiAssay[, complete.cases(myMultiAssay), ]
```
## `duplicated`
The `duplicated` function returns a list of `LogicalList`s, identifying
replicate samples for each individual within each assay:
```{r}
duplicated(myMultiAssay)
```
# Functions to clean up a `MultiAssayExperiment`
A set of helper functions are available for clean-up operations. These include
`intersectRows`, `intersectColumns`,`mergeReplicates`, `complete.cases`,
and `duplicated`.
The `intersectRows` function takes all common rownames across experiments and
returns a `MultiAssayExperiment` with those rows.
```{r}
(noMatches <- intersectRows(myMultiAssay))
```
`complete.cases` returns a logical vector of `colData` rows that correspond
to biological units that have data in all elements of the `ExperimentList`
```{r}
complete.cases(myMultiAssay)
colData(myMultiAssay)[complete.cases(myMultiAssay), ]
```
A call to `intersectColumns` returns another `MultiAssayExperiment` where the
columns of each element of the `ExperimentList` correspond exactly to the rows
of `colData`.
```{r}
intersectColumns(myMultiAssay)
```
`duplicated` identifies `primary` column values or biological units that have
multiple observations per `assay`. It returns a `list` of `LogicalList`s
that indicate what biological units have one or more duplicated measurements.
This output is used for merging replicates by default.
```{r}
duplicated(myMultiAssay)
```
The `mergeReplicates` function allows the user to specify a function (default:
`mean`) for combining replicate columns in each assay element. This can be
combined with `intersectColumns` to create a `MultiAssayExperiment` object with
one measurement in each experiment per biological unit.
```{r}
mergeReplicates(intersectColumns(myMultiAssay))
```
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# Extractor functions
Extractor functions convert a `MultiAssayExperiment` into other forms that are
convenient for analyzing. These would normally be called after any desired
subsetting has been performed.
## `longFormat` & `wideFormat`
Produces *long* (default) or *wide* `DataFrame` objects. The following
produces a long `DataFrame` (the default) for the first two assays:
```{r}
longFormat(myMultiAssay[, , 1:2])
```
This is especially useful for performing regression against patient or sample
data from `colData` using the `pDataCols` argument:
```{r}
longFormat(myMultiAssay[, , 1:2], colDataCols="age")
```
The "wide" format is useful for calculating correlations or performing
regression against different genomic features. Wide format is in general not
possible with replicate measurements, so we demonstrate on the cleaned
`MultiAssayExperiment` for the first 5 columns:
```{r}
maemerge <- mergeReplicates(intersectColumns(myMultiAssay))
wideFormat(maemerge, shape="wide", colDataCols="sex")[, 1:5]
```
## `assay` / `assays`
The `assay` (singular) function takes a particular experiment and returns
a matrix. By default, it will return the _first_ experiment as a matrix.
```{r}
assay(myMultiAssay)
```
The `assays` (plural) function returns a `SimpleList` of data matrices from the
`ExperimentList`:
```{r}
assays(myMultiAssay)
```
# Dimension names: `rownames` and `colnames`
`rownames` and `colnames` return a `CharacterList` of row names and column names
across all the assays. A `CharacterList` is an efficient alternative to
`list` used when each element contains a character vector. It also provides a
nice show method:
```{r}
rownames(myMultiAssay)
colnames(myMultiAssay)
```
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# Requirements for support of additional data classes
Any data classes in the `ExperimentList` object must support the following
methods:
* `dimnames`
* `[`
* `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
```
# Application Programming Interface (API)
For more information on the formal API of `MultiAssayExperiment`, please see
the `API` wiki document on GitHub. From within an R session, type:
```{r, eval=FALSE}
API()
```
to produce the webpage for the API or use the "shiny" argument to get a `shiny`
app that browses the package, such as:
```{r, eval=FALSE}
API(shiny=TRUE)
```
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# Methods for MultiAssayExperiment
The following methods are defined for `MultiAssayExperiment`:
```{r}
methods(class="MultiAssayExperiment")
```
# Soon to be documented/developed
* `c()` function for adding new assays to existing `MultiAssayExperiment`
+ e.g. c(myMultiAssay, neweset)
+ require that sample names in the new object match colData sample names
+ require that sample names in the new object already exist in colData
* "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?
# Wishlist
* Figure out how to support a "long-and-skinny" SQL database
# sessionInfo()
```{r}
sessionInfo()
```
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