The SummarizedExperiment class
Last updated on 2024-11-19 | Edit this page
Overview
Questions
- How is information organized in SummarizedExperiment objects?
- How can that information be added, edited, and accessed?
Objectives
- Describe how both experimental data and metadata can be stored in a single object.
- Explain why this is crucial to keep data and metadata synchronised throughout analyses.
Install packages
Before we can proceed into the following sections, we install some
Bioconductor packages that we will need. First, we check that the BiocManager
package is installed before trying to use it; otherwise we install it.
Then we use the BiocManager::install() function to install
the necessary packages.
R
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(c("SummarizedExperiment"))
Motivation
Experiments are multifaceted data sets typically composed of at least two key pieces of information necessary for any analysis:
- Assay data, typically a matrix representing measurements of a set of features in a set of samples (e.g., RNA-sequencing).
- Sample metadata, typically a
data.frameof metadata representing information about samples (e.g., treatment group).
All those pieces of information must be kept synchronised – same samples, same order – for downstream analyses to accurately process the information and produce reliable results.
It is also very common for analytical workflows to analyse subsets of samples or identify outliers that need to be removed to allow for more accurate downstream analyses. In such cases, all aspects of the experiments must be subsetted to the same set of samples – in the same order – to preserve consistency in the data set and correct results.
The SummarizedExperiment – implemented in the SummarizedExperiment
package – provides a container that accommodates those essential aspects
of individual experiments into a single object coordinates data and
metadata during subsetting and reordering operations. Its flexibility
accommodating many biological data types and comprehensive set of
features make it a popular data structure re-used throughout the
Bioconductor and a key part of the Bioconductor ecosystem. For instance,
familiarity with the SummarizedExperiment is a prerequisite
for working with the DESeq2
package for differential expression analysis, and the SingleCellExperiment
extension class for single-cell analyses.
Class structure
SummarizedExperiment is a matrix-like container where
rows represent features of interest (e.g. genes, transcripts, exons,
etc.) and columns represent samples.
The objects can contain one or more assays, each represented by a matrix-like object, as long as they be of the same dimensions.
Information about the features is stored in a DataFrame
object, nested within the SummarizedExperiment object, and
accessible using the function rowData(). Each row of the
DataFrame provides information on the feature in the
corresponding row of the SummarizedExperiment object. That information
may include annotations independent of the experiment (e.g., gene
identifier) as well as quality control metrics computed from assay data
during workflows.
Similarly, information about the samples is stored in another
DataFrame object, also nested within the
SummarizedExperiment object, and accessible using the
function colData().
The following graphic displays the class geometry and highlights the vertical (column) and horizontal (row) relationships. It was obtained from the vignette of the SummarizedExperiment package.
Creating a SummarizedExperiment object
Let us first load the package:
R
library(SummarizedExperiment)
Then, let us import assay data from a file that we downloaded during the lesson setup.
The file is a simple text file in which the first column contains
made-up feature identifiers and all other columns contain simulated data
for made-up samples. As such, we can use the base R function
read.csv to parse the file into a data.frame
object.
In the example below, we indicate that the row names can be found in the first column, so that the function immediately sets the row names accordingly in the output object. Hadn’t we specified it, the function would have parsed it as a regular column and left the row names to the default integer indexing.
R
count_data <- read.csv("data/counts.csv", row.names = 1)
count_data
OUTPUT
sample_1 sample_2 sample_3 sample_4
gene_1 109 84 91 105
gene_2 111 97 98 108
gene_3 89 121 105 99
gene_4 105 109 122 101
gene_5 82 97 112 83
gene_6 89 96 90 116
gene_7 121 95 88 106
gene_8 101 101 86 103
gene_9 91 119 89 87
gene_10 81 111 81 118
gene_11 93 118 93 99
gene_12 103 111 116 103
gene_13 89 126 103 100
gene_14 101 107 111 79
gene_15 96 91 103 108
gene_16 110 102 128 103
gene_17 95 106 118 100
gene_18 99 115 114 102
gene_19 114 105 94 118
gene_20 110 88 99 102
gene_21 116 95 94 105
gene_22 114 96 107 91
gene_23 97 120 93 90
gene_24 91 84 118 97
gene_25 99 106 97 110One assay data matrix is enough to create a
SummarizedExperiment object, although without sample
metadata, only unsupervised analyses – that do not require information
about the samples – are possible.
In the example below, we create a SummarizedExperiment
object in which we store the matrix of count data under the name
‘counts’. Note that the argument ‘assays=’ (plural) can accept more than
one assay – as discussed above – which is why we encapsulate our only
assay matrix in a named list that also gives us the
opportunity to assign a name to the assay. Naming assays becomes crucial
during workflows that contain multiple assays, in order to identify and
retrieve individual assays unambiguously.
R
se <- SummarizedExperiment(
assays = list(counts = count_data)
)
se
OUTPUT
class: SummarizedExperiment
dim: 25 4
metadata(0):
assays(1): counts
rownames(25): gene_1 gene_2 ... gene_24 gene_25
rowData names(0):
colnames(4): sample_1 sample_2 sample_3 sample_4
colData names(0):In the output above, the summary view of the object reminds us that
the assay – and thus the overall SummarizedExperiment
object – contains information for 25 features in 4 samples, it contains
a single assay named ‘counts’, the features seem to be named from
‘gene_1’ to ‘gene_25’ (only the first and last ones are shown), and the
samples are named from sample_1 to sample_4.
The object does not contain any row metadata nor column metadata.
To create a more comprehensive SummarizedExperiment
object, let us import gene metadata and sample metadata for another two
files that we downloaded during the lesson setup.
The files are formatted similarly to the count data, so we use again
the base R function read.csv() to parse them into
data.frame objects.
R
sample_metadata <- read.csv("data/sample_metadata.csv", row.names = 1)
sample_metadata
OUTPUT
condition batch
sample_1 A 1
sample_2 A 2
sample_3 B 1
sample_4 B 2R
gene_metadata <- read.csv("data/gene_metadata.csv", row.names = 1)
gene_metadata
OUTPUT
chromosome
gene_1 4
gene_2 4
gene_3 5
gene_4 4
gene_5 5
gene_6 1
gene_7 2
gene_8 1
gene_9 3
gene_10 1
gene_11 1
gene_12 5
gene_13 5
gene_14 1
gene_15 3
gene_16 4
gene_17 2
gene_18 5
gene_19 1
gene_20 3
gene_21 5
gene_22 5
gene_23 1
gene_24 4
gene_25 5We can re-create the SummarizedExperiment object, this
time including the gene and sample metadata:
R
se <- SummarizedExperiment(
assays = list(counts = count_data),
colData = sample_metadata,
rowData = gene_metadata
)
se
OUTPUT
class: SummarizedExperiment
dim: 25 4
metadata(0):
assays(1): counts
rownames(25): gene_1 gene_2 ... gene_24 gene_25
rowData names(1): chromosome
colnames(4): sample_1 sample_2 sample_3 sample_4
colData names(2): condition batchComparing the output above with the previous ‘assay-only’ version of
the SummarizedExperiment object, we can see that the
rowData and colData components now contain 1
and 4 metadata, respectively.
Accessing information
A number of functions give access to the various components of
SummarizedExperiment objects.
The assays() function returns the list of assays stored
in the object. The output is always a List, event if the
object contains a single assay.
R
assays(se)
OUTPUT
List of length 1
names(1): countsThe assayNames() function returns a character vector of
the assay names. This is most useful when the object contains larger
numbers of assays, as the assays() function (see above) may
not display all of them. Knowing the names of the various assays is key
to accessing any individual assay.
R
assayNames(se)
OUTPUT
[1] "counts"The assay() function can be used to retrieve a single
assay from the object. For this, the function should be given the name
or the integer position of the desired assay. If unspecified, the
function automatically returns the first assay in the object.
R
head(assay(se, "counts"))
OUTPUT
sample_1 sample_2 sample_3 sample_4
gene_1 109 84 91 105
gene_2 111 97 98 108
gene_3 89 121 105 99
gene_4 105 109 122 101
gene_5 82 97 112 83
gene_6 89 96 90 116The colData() and rowData() functions can
be used to retrieve sample metadata and row metadata, respectively.
R
colData(se)
OUTPUT
DataFrame with 4 rows and 2 columns
condition batch
<character> <integer>
sample_1 A 1
sample_2 A 2
sample_3 B 1
sample_4 B 2R
rowData(se)
OUTPUT
DataFrame with 25 rows and 1 column
chromosome
<integer>
gene_1 4
gene_2 4
gene_3 5
gene_4 4
gene_5 5
... ...
gene_21 5
gene_22 5
gene_23 1
gene_24 4
gene_25 5Separately, the $ operator can be used to access a
single column of sample metadata. A useful feature of this operator is
the autocompletion that is triggered automatically in RStudio or using
the tabulation key in terminal applications.
R
se$batch
OUTPUT
[1] 1 2 1 2Notably, there is no operator for accessing a single column of
feature metadata. For this, users need to first access the full
DataFrame returned by rowData() before
accessing a column using the standard $ or [[
operators, e.g.
R
rowData(se)[["chromosome"]]
OUTPUT
[1] 4 4 5 4 5 1 2 1 3 1 1 5 5 1 3 4 2 5 1 3 5 5 1 4 5Adding and editing information
Information can be added to SummarizedExperiment after
their creation. In fact, this is the basis for workflows that compute
normalised assay values – adding those to the list of assays –, and
quality control metrics for either features or samples – adding those to
the rowData and colData components, as
appropriate – progressively growing the amount of information stored
within the overall object.
Most of the functions for accessing information, described in the previous section, have a counterpart function for adding new values or editing existing ones. Note that editing is merely the result of adding values under a name already in use, which has the effect of replacing existing values.
In the example below, we add an assay named ‘logcounts’ which is the result of applying a log-transformation to the ‘counts’ assay after adding a pseucocount of one:
R
assay(se, "logcounts") <- log1p(assay(se, "counts"))
se
OUTPUT
class: SummarizedExperiment
dim: 25 4
metadata(0):
assays(2): counts logcounts
rownames(25): gene_1 gene_2 ... gene_24 gene_25
rowData names(1): chromosome
colnames(4): sample_1 sample_2 sample_3 sample_4
colData names(2): condition batchIn the output above, we see that the object now contains two assays: the ‘counts’ assay included in the object when it was first created, and the ‘logcounts’ assay added just now.
Similarly, the colData() and rowData()
functions – as well as the $ operator – can be used to add
and edit values in the corresponding components.
In the example below, we compute the sum of counts for each sample, and store the result in the sample metadata table under the new name ‘sum_counts’.
R
colData(se)[["sum_counts"]] <- colSums(assay(se, "counts"))
colData(se)
OUTPUT
DataFrame with 4 rows and 3 columns
condition batch sum_counts
<character> <integer> <numeric>
sample_1 A 1 2506
sample_2 A 2 2600
sample_3 B 1 2550
sample_4 B 2 2533In this next example, we compute the average count for each feature, and store the result in the feature metadata table under the new name ‘mean_counts’.
R
rowData(se)[["mean_counts"]] <- rowSums(assay(se, "counts"))
rowData(se)
OUTPUT
DataFrame with 25 rows and 2 columns
chromosome mean_counts
<integer> <numeric>
gene_1 4 389
gene_2 4 414
gene_3 5 414
gene_4 4 437
gene_5 5 374
... ... ...
gene_21 5 410
gene_22 5 408
gene_23 1 400
gene_24 4 390
gene_25 5 412Key Points
- The
SummarizedExperimentclass provides a single container for storing both assay data and metadata. - Assay data and metadata are kept synchronised through subsetting and reordering operations.
- A comprehensive set of functions are available to access, add, and
edit information stored in the various components of the
SummarizedExperimentobjects.