Next steps
Last updated on 2026-09-09 | Edit this page
Overview
Questions
- What is a
SummarizedExperiment? - What is Bioconductor?
Objectives
- Introduce the Bioconductor project.
- Introduce the notion of data containers.
- Give an overview of the
SummarizedExperiment, extensively used in omics analyses.
Next steps
Data in bioinformatics is often complex. To deal with this, developers define specialised data containers (termed classes) that match the properties of the data they need to handle.
This aspect is central to the Bioconductor1 project which uses the same core data infrastructure across packages. This certainly contributed to Bioconductor’s success. Bioconductor package developers are advised to make use of existing infrastructure to provide coherence, interoperability, and stability to the project as a whole.
To illustrate such an omics data container, we’ll present the
SummarizedExperiment class.
SummarizedExperiment
The figure below represents the anatomy of the SummarizedExperiment class.
Objects of the class SummarizedExperiment contain :
One (or more) assay(s) containing the quantitative omics data (expression data), stored as a matrix-like object. Features (genes, transcripts, proteins, …) are defined along the rows, and samples along the columns.
A sample metadata slot containing sample co-variates, stored as a data frame. Rows from this table represent samples (rows match exactly the columns of the expression data).
A feature metadata slot containing feature co-variates, stored as a data frame. The rows of this data frame match exactly the rows of the expression data.
The coordinated nature of the SummarizedExperiment
guarantees that during data manipulation, the dimensions of the
different slots will always match (i.e the columns in the expression
data and then rows in the sample metadata, as well as the rows in the
expression data and feature metadata) during data manipulation. For
example, if we had to exclude one sample from the assay, it would be
automatically removed from the sample metadata in the same
operation.
The metadata slots can grow additional co-variates (columns) without affecting the other structures.
Creating a SummarizedExperiment
In order to create a SummarizedExperiment, we will
create the individual components, i.e the count matrix, the sample and
gene metadata from csv files. These are typically how RNA-Seq data are
provided (after raw data have been processed).
-
An expression matrix: we load the count matrix,
specifying that the first columns contains row/gene names, and convert
the
data.frameto amatrix. You can download it by clicking this link.
R
count_matrix <- read.csv("data/count_matrix.csv",
row.names = 1) |>
as.matrix()
count_matrix[1:5, ]
OUTPUT
GSM2545336 GSM2545337 GSM2545338 GSM2545339 GSM2545340 GSM2545341
Asl 1170 361 400 586 626 988
Apod 36194 10347 9173 10620 13021 29594
Cyp2d22 4060 1616 1603 1901 2171 3349
Klk6 287 629 641 578 448 195
Fcrls 85 233 244 237 180 38
GSM2545342 GSM2545343 GSM2545344 GSM2545345 GSM2545346 GSM2545347
Asl 836 535 586 597 938 1035
Apod 24959 13668 13230 15868 27769 34301
Cyp2d22 3122 2008 2254 2277 2985 3452
Klk6 186 1101 537 567 327 233
Fcrls 68 375 199 177 89 67
GSM2545348 GSM2545349 GSM2545350 GSM2545351 GSM2545352 GSM2545353
Asl 494 481 666 937 803 541
Apod 11258 11812 15816 29242 20415 13682
Cyp2d22 1883 2014 2417 3678 2920 2216
Klk6 742 881 828 250 798 710
Fcrls 300 233 231 81 303 285
GSM2545354 GSM2545362 GSM2545363 GSM2545380
Asl 473 748 576 1192
Apod 11088 15916 11166 38148
Cyp2d22 1821 2842 2011 4019
Klk6 894 501 598 259
Fcrls 248 179 184 68R
dim(count_matrix)
OUTPUT
[1] 1474 22- A table describing the samples, available at this link.
R
sample_metadata <- read.csv("data/sample_metadata.csv")
sample_metadata
OUTPUT
sample organism age sex infection strain time tissue mouse
1 GSM2545336 Mus musculus 8 Female InfluenzaA C57BL/6 8 Cerebellum 14
2 GSM2545337 Mus musculus 8 Female NonInfected C57BL/6 0 Cerebellum 9
3 GSM2545338 Mus musculus 8 Female NonInfected C57BL/6 0 Cerebellum 10
4 GSM2545339 Mus musculus 8 Female InfluenzaA C57BL/6 4 Cerebellum 15
5 GSM2545340 Mus musculus 8 Male InfluenzaA C57BL/6 4 Cerebellum 18
6 GSM2545341 Mus musculus 8 Male InfluenzaA C57BL/6 8 Cerebellum 6
7 GSM2545342 Mus musculus 8 Female InfluenzaA C57BL/6 8 Cerebellum 5
8 GSM2545343 Mus musculus 8 Male NonInfected C57BL/6 0 Cerebellum 11
9 GSM2545344 Mus musculus 8 Female InfluenzaA C57BL/6 4 Cerebellum 22
10 GSM2545345 Mus musculus 8 Male InfluenzaA C57BL/6 4 Cerebellum 13
11 GSM2545346 Mus musculus 8 Male InfluenzaA C57BL/6 8 Cerebellum 23
12 GSM2545347 Mus musculus 8 Male InfluenzaA C57BL/6 8 Cerebellum 24
13 GSM2545348 Mus musculus 8 Female NonInfected C57BL/6 0 Cerebellum 8
14 GSM2545349 Mus musculus 8 Male NonInfected C57BL/6 0 Cerebellum 7
15 GSM2545350 Mus musculus 8 Male InfluenzaA C57BL/6 4 Cerebellum 1
16 GSM2545351 Mus musculus 8 Female InfluenzaA C57BL/6 8 Cerebellum 16
17 GSM2545352 Mus musculus 8 Female InfluenzaA C57BL/6 4 Cerebellum 21
18 GSM2545353 Mus musculus 8 Female NonInfected C57BL/6 0 Cerebellum 4
19 GSM2545354 Mus musculus 8 Male NonInfected C57BL/6 0 Cerebellum 2
20 GSM2545362 Mus musculus 8 Female InfluenzaA C57BL/6 4 Cerebellum 20
21 GSM2545363 Mus musculus 8 Male InfluenzaA C57BL/6 4 Cerebellum 12
22 GSM2545380 Mus musculus 8 Female InfluenzaA C57BL/6 8 Cerebellum 19R
dim(sample_metadata)
OUTPUT
[1] 22 9- A table describing the genes, available at this link.
R
gene_metadata <- read.csv("data/gene_metadata.csv")
gene_metadata[1:10, 1:4]
OUTPUT
gene ENTREZID
1 Asl 109900
2 Apod 11815
3 Cyp2d22 56448
4 Klk6 19144
5 Fcrls 80891
6 Slc2a4 20528
7 Exd2 97827
8 Gjc2 118454
9 Plp1 18823
10 Gnb4 14696
product
1 argininosuccinate lyase, transcript variant X1
2 apolipoprotein D, transcript variant 3
3 cytochrome P450, family 2, subfamily d, polypeptide 22, transcript variant 2
4 kallikrein related-peptidase 6, transcript variant 2
5 Fc receptor-like S, scavenger receptor, transcript variant X1
6 solute carrier family 2 (facilitated glucose transporter), member 4
7 exonuclease 3'-5' domain containing 2
8 gap junction protein, gamma 2, transcript variant 1
9 proteolipid protein (myelin) 1, transcript variant 1
10 guanine nucleotide binding protein (G protein), beta 4, transcript variant X2
ensembl_gene_id
1 ENSMUSG00000025533
2 ENSMUSG00000022548
3 ENSMUSG00000061740
4 ENSMUSG00000050063
5 ENSMUSG00000015852
6 ENSMUSG00000018566
7 ENSMUSG00000032705
8 ENSMUSG00000043448
9 ENSMUSG00000031425
10 ENSMUSG00000027669R
dim(gene_metadata)
OUTPUT
[1] 1474 9We will create a SummarizedExperiment from these
tables:
The count matrix that will be used as the
assayThe table describing the samples will be used as the sample metadata slot
The table describing the genes will be used as the features metadata slot
To do this we can put the different parts together using the
SummarizedExperiment constructor:
R
## BiocManager::install("SummarizedExperiment")
library("SummarizedExperiment")
ERROR
Error in `library()`:
! there is no package called 'SummarizedExperiment'First, we make sure that the samples are in the same order in the count matrix and the sample annotation, and the same for the genes in the count matrix and the gene annotation.
R
stopifnot(rownames(count_matrix) == gene_metadata$gene)
stopifnot(colnames(count_matrix) == sample_metadata$sample)
R
se <- SummarizedExperiment(assays = list(counts = count_matrix),
colData = sample_metadata,
rowData = gene_metadata)
ERROR
Error in `SummarizedExperiment()`:
! could not find function "SummarizedExperiment"R
se
ERROR
Error:
! object 'se' not foundSaving data
Exporting data to a spreadsheet, as we did in a previous episode, has
several limitations, such as those described in the first chapter
(possible inconsistencies with , and . for
decimal separators and lack of variable type definitions). Furthermore,
exporting data to a spreadsheet is only relevant for rectangular data
such as dataframes and matrices.
A more general way to save data, that is specific to R and is
guaranteed to work on any operating system, is to use the
saveRDS function. Saving objects like this will generate a
binary representation on disk (using the rds file extension
here), which can be loaded back into R using the readRDS
function.
R
saveRDS(se, file = "data_output/se.rds")
rm(se)
se <- readRDS("data_output/se.rds")
head(se)
To conclude, when it comes to saving data from R that will be loaded
again in R, saving and loading with saveRDS and
readRDS is the preferred approach. If tabular data need to
be shared with somebody that is not using R, then exporting to a
text-based spreadsheet is a good alternative.
Using this data structure, we can access the expression matrix with
the assay function:
R
head(assay(se))
ERROR
Error in `assay()`:
! could not find function "assay"R
dim(assay(se))
ERROR
Error in `assay()`:
! could not find function "assay"We can access the sample metadata using the colData
function:
R
colData(se)
ERROR
Error in `colData()`:
! could not find function "colData"R
dim(colData(se))
ERROR
Error in `colData()`:
! could not find function "colData"We can also access the feature metadata using the
rowData function:
R
head(rowData(se))
ERROR
Error in `rowData()`:
! could not find function "rowData"R
dim(rowData(se))
ERROR
Error in `rowData()`:
! could not find function "rowData"Subsetting a SummarizedExperiment
SummarizedExperiment can be subset just like with data frames, with numerics or with characters of logicals.
Below, we create a new instance of class SummarizedExperiment that contains only the 5 first features for the 3 first samples.
R
se1 <- se[1:5, 1:3]
ERROR
Error:
! object 'se' not foundR
se1
ERROR
Error:
! object 'se1' not foundR
colData(se1)
ERROR
Error in `colData()`:
! could not find function "colData"R
rowData(se1)
ERROR
Error in `rowData()`:
! could not find function "rowData"We can also use the colData() function to subset on
something from the sample metadata or the rowData() to
subset on something from the feature metadata. For example, here we keep
only miRNAs and the non infected samples:
R
se1 <- se[rowData(se)$gene_biotype == "miRNA",
colData(se)$infection == "NonInfected"]
ERROR
Error:
! object 'se' not foundR
se1
ERROR
Error:
! object 'se1' not foundR
assay(se1)
ERROR
Error in `assay()`:
! could not find function "assay"R
colData(se1)
ERROR
Error in `colData()`:
! could not find function "colData"R
rowData(se1)
ERROR
Error in `rowData()`:
! could not find function "rowData"Challenge
Extract the gene expression levels of the 3 first genes in samples at time 0 and at time 8.
R
assay(se)[1:3, colData(se)$time != 4]
ERROR
Error in `assay()`:
! could not find function "assay"R
# Equivalent to
assay(se)[1:3, colData(se)$time == 0 | colData(se)$time == 8]
ERROR
Error in `assay()`:
! could not find function "assay"Challenge
Verify that you get the same values using the long rna
table.
R
rna |>
filter(gene %in% c("Asl", "Apod", "Cyd2d22")) |>
filter(time != 4) |>
select(expression)
OUTPUT
# A tibble: 28 × 1
expression
<dbl>
1 1170
2 36194
3 361
4 10347
5 400
6 9173
7 988
8 29594
9 836
10 24959
# ℹ 18 more rowsThe long table and the SummarizedExperiment contain the
same information, but are simply structured differently. Each approach
has its own advantages: the former is a good fit for the
tidyverse packages, while the latter is the preferred
structure for many bioinformatics and statistical processing steps. For
example, a typical RNA-Seq analyses using the DESeq2
package.
Adding variables to metadata
We can also add information to the metadata. Suppose that you want to add the center where the samples were collected…
R
colData(se)$center <- rep("University of Illinois", nrow(colData(se)))
ERROR
Error in `colData()`:
! could not find function "colData"R
colData(se)
ERROR
Error in `colData()`:
! could not find function "colData"This illustrates that the metadata slots can grow indefinitely without affecting the other structures!
tidySummarizedExperiment
You may be wondering, can we use tidyverse commands to interact with
SummarizedExperiment objects? The answer is yes, we can
with the tidySummarizedExperiment package.
Remember what our SummarizedExperiment object looks like:
R
se
ERROR
Error:
! object 'se' not foundLoad tidySummarizedExperiment and then take a look at
the se object again.
R
## BiocManager::install("tidySummarizedExperiment")
library("tidySummarizedExperiment")
ERROR
Error in `library()`:
! there is no package called 'tidySummarizedExperiment'R
se
ERROR
Error:
! object 'se' not foundIt’s still a SummarizedExperiment object, so maintains
the efficient structure, but now we can view it as a tibble. Note the
first line of the output says this, it’s a
SummarizedExperiment-tibble abstraction. We
can also see in the second line of the output the number of transcripts
and samples.
We can now use tidyverse commands to interact with the
SummarizedExperiment object.
We can use filter to filter for rows using a condition
e.g. to view all rows for one sample.
R
se |> filter(sample == "GSM2545336")
ERROR
Error:
! object 'se' not foundR
se |> filter(sex == "Female")
ERROR
Error:
! object 'se' not foundWe can use select to specify columns we want to
view.
R
se |> select(sample, sex)
ERROR
Error:
! object 'se' not foundWe can use mutate to add metadata info.
R
se |> mutate(center = "Heidelberg University")
ERROR
Error:
! object 'se' not foundWe can also combine commands with the tidyverse pipe
|>. For example, we could combine group_by
and summarise to get the total counts for each sample.
R
se |>
group_by(.sample) |>
summarise(total_counts=sum(counts))
ERROR
Error:
! object 'se' not foundWe can treat the tidy SummarizedExperiment object as a normal tibble for plotting.
Here we plot the distribution of counts per sample.
R
se |>
ggplot(aes(counts + 1, group=.sample, color=infection)) +
geom_density() +
scale_x_log10() +
theme_bw()
ERROR
Error:
! object 'se' not foundFor more information on tidySummarizedExperiment, see
the package
website.
Take-home message
SummarizedExperimentrepresents an efficient way to store and handle omics data.They are used in many Bioconductor packages.
If you follow the next training focused on RNA sequencing analysis,
you will learn to use the Bioconductor DESeq2 package to do
some differential expression analyses. The whole analysis of the
DESeq2 package is handled in a
SummarizedExperiment.
- Bioconductor is a project provide support and packages for the comprehension of high high-throughput biology data.
- A
SummarizedExperimentis a type of object useful to store and manage high-throughput omics data.
The Bioconductor was initiated by Robert Gentleman, one of the two creators of the R language. Bioconductor provides tools dedicated to omics data analysis. Bioconductor uses the R statistical programming language and is open source and open development.↩︎