26 Storing experiments with SummarizedExperiment

Author

Affiliation

University of Colorado
Anschutz School of Medicine

Published

June 1, 2024

Modified

June 2, 2026

An RNA-seq or microarray experiment hands you several things at once: a matrix of numbers (counts or intensities), a description of every feature down the side (which gene is in each row), and a description of every sample across the top (which condition, which patient). Keep these in three separate variables and they will drift out of sync — you drop a bad sample from the count matrix, forget to drop it from the sample table, and now your “treated vs. control” labels point at the wrong columns. Mismatches exactly like this have produced retracted papers.

SummarizedExperiment is Bioconductor’s answer: one object that holds the assay matrix, the feature data, and the sample data together, and keeps them aligned for you whenever you subset.

It is, in many ways, a modern replacement for the historical ExpressionSet. The main difference is that SummarizedExperiment is more flexible about its feature (row) information: features can be described either by genomic ranges (GRanges) or by an arbitrary table (DataFrame). That flexibility makes it a natural fit for sequencing experiments such as RNA-seq and ChIP-seq.

26.1 What you’ll learn

By the end of this chapter you will be able to:

Describe the three coordinated parts of a SummarizedExperiment: assays(), rowData()/rowRanges(), and colData().
Subset an experiment by sample or feature and trust that the metadata follows.
Reach into assays, sample data, and experiment-wide metadata with the accessor functions.
Build a SummarizedExperiment from raw matrices and data frames.

This chapter assumes you’ve met GRanges and DataFrame. If “genomic ranges” isn’t familiar yet, the GenomicRanges chapters come earlier in the Bioconductor part of the book — skim those first.

Installing the packages

The examples below use the SummarizedExperiment class and the airway example dataset. Install both once with BiocManager:

BiocManager::install(c("SummarizedExperiment", "airway"))

26.2 Anatomy of a `SummarizedExperiment`

The SummarizedExperiment package contains two classes: SummarizedExperiment and RangedSummarizedExperiment.

SummarizedExperiment is a matrix-like container where rows represent features of interest (e.g. genes, transcripts, exons, etc.) and columns represent samples. The objects contain one or more assays, each represented by a matrix-like object of numeric or other mode. The rows of a SummarizedExperiment object represent features of interest. Information about these features is stored in a DataFrame object, accessible using the function rowData(). Each row of the DataFrame provides information on the feature in the corresponding row of the SummarizedExperiment object. Columns of the DataFrame represent different attributes of the features of interest, e.g., gene or transcript IDs, etc.

RangedSummarizedExperiment is the “child” of the SummarizedExperiment class, which means that all the methods on SummarizedExperiment also work on a RangedSummarizedExperiment.

The fundamental difference between the two classes is that the rows of a RangedSummarizedExperiment object represent genomic ranges of interest instead of a DataFrame of features. The RangedSummarizedExperiment ranges are described by a GRanges or a GRangesList object, accessible using the rowRanges() function.

Figure 26.1 displays the class geometry and highlights the vertical (column) and horizontal (row) relationships.

Figure 26.1: Summarized Experiment. There are three main components, the `colData()`, the `rowData()` and the `assays()`. The accessors for the various parts of a complete SummarizedExperiment object match the names.

A mental model: three linked sheets

Picture a spreadsheet workbook with three sheets that are locked together:

the assay sheet — the matrix of numbers, features (genes) × samples;
the row sheet (rowData/rowRanges) — one row of annotation per feature, lined up with the assay’s rows;
the column sheet (colData) — one row of annotation per sample, lined up with the assay’s columns.

The magic is the lock: when you delete a sample, all three sheets update together, so the numbers and their labels can never disagree. Every accessor below is just a way of looking at one of these sheets.

26.2.1 Assays

The airway package contains an example dataset from an RNA-seq experiment of read counts per gene for airway smooth muscle cells. These data are stored in a RangedSummarizedExperiment object that holds read counts for tens of thousands of gene transcripts across 8 samples (we’ll confirm the exact dimensions in a moment).

Loading required package: airway

library(SummarizedExperiment)
data(airway, package="airway")
se <- airway
se

class: RangedSummarizedExperiment 
dim: 63677 8 
metadata(1): ''
assays(1): counts
rownames(63677): ENSG00000000003 ENSG00000000005 ... ENSG00000273492
  ENSG00000273493
rowData names(10): gene_id gene_name ... seq_coord_system symbol
colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
colData names(9): SampleName cell ... Sample BioSample

Read that printout like a label on the box: dim tells you it’s 63,677 features across 8 samples, and the assays, rowRanges, and colData lines preview the three linked sheets from the model above. The sections that follow open each one in turn.

To retrieve the experiment data from a SummarizedExperiment object one can use the assays() accessor. An object can have multiple assay datasets each of which can be accessed using the $ operator. The airway dataset contains only one assay (counts). Here each row represents a gene transcript and each column one of the samples.

assays(se)$counts

	SRR1039508	SRR1039509	SRR1039512	SRR1039513	SRR1039516	SRR1039517	SRR1039520	SRR1039521
ENSG00000000003	679	448	873	408	1138	1047	770	572
ENSG00000000005	0	0	0	0	0	0	0	0
ENSG00000000419	467	515	621	365	587	799	417	508
ENSG00000000457	260	211	263	164	245	331	233	229
ENSG00000000460	60	55	40	35	78	63	76	60
ENSG00000000938	0	0	2	0	1	0	0	0
ENSG00000000971	3251	3679	6177	4252	6721	11027	5176	7995
ENSG00000001036	1433	1062	1733	881	1424	1439	1359	1109
ENSG00000001084	519	380	595	493	820	714	696	704
ENSG00000001167	394	236	464	175	658	584	360	269

26.2.2 ‘Row’ (regions-of-interest) data

The rowRanges() accessor is used to view the range information for a RangedSummarizedExperiment. (Note if this were the parent SummarizedExperiment class we’d use rowData()). The data are stored in a GRangesList object, where each list element corresponds to one gene transcript and the ranges in each GRanges correspond to the exons in the transcript.

rowRanges(se)

GRangesList object of length 63677:
$ENSG00000000003
GRanges object with 17 ranges and 2 metadata columns:
       seqnames            ranges strand |   exon_id       exon_name
          <Rle>         <IRanges>  <Rle> | <integer>     <character>
   [1]        X 99883667-99884983      - |    667145 ENSE00001459322
   [2]        X 99885756-99885863      - |    667146 ENSE00000868868
   [3]        X 99887482-99887565      - |    667147 ENSE00000401072
   [4]        X 99887538-99887565      - |    667148 ENSE00001849132
   [5]        X 99888402-99888536      - |    667149 ENSE00003554016
   ...      ...               ...    ... .       ...             ...
  [13]        X 99890555-99890743      - |    667156 ENSE00003512331
  [14]        X 99891188-99891686      - |    667158 ENSE00001886883
  [15]        X 99891605-99891803      - |    667159 ENSE00001855382
  [16]        X 99891790-99892101      - |    667160 ENSE00001863395
  [17]        X 99894942-99894988      - |    667161 ENSE00001828996
  -------
  seqinfo: 722 sequences (1 circular) from an unspecified genome

...
<63676 more elements>

26.2.3 ‘Column’ (sample) data

Sample meta-data describing the samples can be accessed using colData(), and is a DataFrame that can store any number of descriptive columns for each sample row.

colData(se)

DataFrame with 8 rows and 9 columns
           SampleName     cell      dex    albut        Run avgLength
             <factor> <factor> <factor> <factor>   <factor> <integer>
SRR1039508 GSM1275862  N61311     untrt    untrt SRR1039508       126
SRR1039509 GSM1275863  N61311     trt      untrt SRR1039509       126
SRR1039512 GSM1275866  N052611    untrt    untrt SRR1039512       126
SRR1039513 GSM1275867  N052611    trt      untrt SRR1039513        87
SRR1039516 GSM1275870  N080611    untrt    untrt SRR1039516       120
SRR1039517 GSM1275871  N080611    trt      untrt SRR1039517       126
SRR1039520 GSM1275874  N061011    untrt    untrt SRR1039520       101
SRR1039521 GSM1275875  N061011    trt      untrt SRR1039521        98
           Experiment    Sample    BioSample
             <factor>  <factor>     <factor>
SRR1039508  SRX384345 SRS508568 SAMN02422669
SRR1039509  SRX384346 SRS508567 SAMN02422675
SRR1039512  SRX384349 SRS508571 SAMN02422678
SRR1039513  SRX384350 SRS508572 SAMN02422670
SRR1039516  SRX384353 SRS508575 SAMN02422682
SRR1039517  SRX384354 SRS508576 SAMN02422673
SRR1039520  SRX384357 SRS508579 SAMN02422683
SRR1039521  SRX384358 SRS508580 SAMN02422677

Each row here is one sample, and each column is something we know about that sample — including dex, which records whether the cells were treated with dexamethasone. That dex column is what makes the next operation possible.

This sample metadata can be accessed using the $ accessor, which makes it easy to subset the entire object by a given phenotype.

# subset for only those samples treated with dexamethasone
se[, se$dex == "trt"]

class: RangedSummarizedExperiment 
dim: 63677 4 
metadata(1): ''
assays(1): counts
rownames(63677): ENSG00000000003 ENSG00000000005 ... ENSG00000273492
  ENSG00000273493
rowData names(10): gene_id gene_name ... seq_coord_system symbol
colnames(4): SRR1039509 SRR1039513 SRR1039517 SRR1039521
colData names(9): SampleName cell ... Sample BioSample

26.2.4 Experiment-wide metadata

Meta-data describing the experimental methods and publication references can be accessed using metadata().

metadata(se)

[[1]]
Experiment data
  Experimenter name: Himes BE 
  Laboratory: NA 
  Contact information:  
  Title: RNA-Seq transcriptome profiling identifies CRISPLD2 as a glucocorticoid responsive gene that modulates cytokine function in airway smooth muscle cells. 
  URL: http://www.ncbi.nlm.nih.gov/pubmed/24926665 
  PMIDs: 24926665 

  Abstract: A 226 word abstract is available. Use 'abstract' method.

Note that metadata() is just a simple list, so it is appropriate for any experiment wide metadata the user wishes to save, such as storing model formulas.

metadata(se)$formula <- counts ~ dex + albut

metadata(se)

[[1]]
Experiment data
  Experimenter name: Himes BE 
  Laboratory: NA 
  Contact information:  
  Title: RNA-Seq transcriptome profiling identifies CRISPLD2 as a glucocorticoid responsive gene that modulates cytokine function in airway smooth muscle cells. 
  URL: http://www.ncbi.nlm.nih.gov/pubmed/24926665 
  PMIDs: 24926665 

  Abstract: A 226 word abstract is available. Use 'abstract' method.

$formula
counts ~ dex + albut

26.3 Common operations on `SummarizedExperiment`

26.3.1 Subsetting

[ Performs two dimensional subsetting, just like subsetting a matrix or data frame.

# subset the first five transcripts and first three samples
se[1:5, 1:3]

class: RangedSummarizedExperiment 
dim: 5 3 
metadata(2): '' formula
assays(1): counts
rownames(5): ENSG00000000003 ENSG00000000005 ENSG00000000419
  ENSG00000000457 ENSG00000000460
rowData names(10): gene_id gene_name ... seq_coord_system symbol
colnames(3): SRR1039508 SRR1039509 SRR1039512
colData names(9): SampleName cell ... Sample BioSample

$ operates on colData() columns, for easy sample extraction.

se[, se$cell == "N61311"]

class: RangedSummarizedExperiment 
dim: 63677 2 
metadata(2): '' formula
assays(1): counts
rownames(63677): ENSG00000000003 ENSG00000000005 ... ENSG00000273492
  ENSG00000273493
rowData names(10): gene_id gene_name ... seq_coord_system symbol
colnames(2): SRR1039508 SRR1039509
colData names(9): SampleName cell ... Sample BioSample

26.3.2 Getters and setters

rowRanges() / (rowData()), colData(), metadata()

counts <- matrix(1:15, 5, 3, dimnames=list(LETTERS[1:5], LETTERS[1:3]))

dates <- SummarizedExperiment(assays=list(counts=counts),
                              rowData=DataFrame(month=month.name[1:5], day=1:5))

# Subset all January assays
dates[rowData(dates)$month == "January", ]

class: SummarizedExperiment 
dim: 1 3 
metadata(0):
assays(1): counts
rownames(1): A
rowData names(2): month day
colnames(3): A B C
colData names(0):

assay() versus assays() There are two accessor functions for extracting the assay data from a SummarizedExperiment object. assays() operates on the entire list of assay data as a whole, while assay() operates on only one assay at a time. assay(x, i) is simply a convenience function which is equivalent to assays(x)[[i]].

assays(se)

List of length 1
names(1): counts

assays(se)[[1]][1:5, 1:5]

                SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516
ENSG00000000003        679        448        873        408       1138
ENSG00000000005          0          0          0          0          0
ENSG00000000419        467        515        621        365        587
ENSG00000000457        260        211        263        164        245
ENSG00000000460         60         55         40         35         78

# assay defaults to the first assay if no i is given
assay(se)[1:5, 1:5]

                SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516
ENSG00000000003        679        448        873        408       1138
ENSG00000000005          0          0          0          0          0
ENSG00000000419        467        515        621        365        587
ENSG00000000457        260        211        263        164        245
ENSG00000000460         60         55         40         35         78

assay(se, 1)[1:5, 1:5]

                SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516
ENSG00000000003        679        448        873        408       1138
ENSG00000000005          0          0          0          0          0
ENSG00000000419        467        515        621        365        587
ENSG00000000457        260        211        263        164        245
ENSG00000000460         60         55         40         35         78

26.3.3 Range-based operations

subsetByOverlaps() SummarizedExperiment objects support all of the findOverlaps() methods and associated functions. This includes subsetByOverlaps(), which makes it easy to subset a SummarizedExperiment object by an interval.

In the next code block, we define a region of interest (a single genomic range) and then subset our SummarizedExperiment to the genes that overlap it.

# Subset for only the genes that fall in the interval 100,000 to 1,100,000
# of chromosome 1
roi <- GRanges(seqnames="1", ranges=IRanges(start=100000, end=1100000))

Warning in S4Vectors:::anyMissing(runValue(x_seqnames)): 'S4Vectors:::anyMissing()' is deprecated.
Use 'anyNA()' instead.
See help("Deprecated")

Warning in S4Vectors:::anyMissing(runValue(strand(x))): 'S4Vectors:::anyMissing()' is deprecated.
Use 'anyNA()' instead.
See help("Deprecated")

sub_se <- subsetByOverlaps(se, roi)

Warning in S4Vectors:::anyMissing(gapwidth): 'S4Vectors:::anyMissing()' is deprecated.
Use 'anyNA()' instead.
See help("Deprecated")

sub_se

class: RangedSummarizedExperiment 
dim: 74 8 
metadata(2): '' formula
assays(1): counts
rownames(74): ENSG00000131591 ENSG00000177757 ... ENSG00000272512
  ENSG00000273443
rowData names(10): gene_id gene_name ... seq_coord_system symbol
colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
colData names(9): SampleName cell ... Sample BioSample

dim(sub_se)

[1] 74  8

Notice that subsetByOverlaps() worked on the ranges (rowRanges) but returned a full SummarizedExperiment — the assay matrix and colData came along automatically, kept in sync with the smaller set of genes.

26.4 Constructing a `SummarizedExperiment`

To construct a SummarizedExperiment object, you need to provide the following components: - assays: A list of matrices or matrix-like objects containing the data. - rowData: A DataFrame containing information about the features (rows). - colData: A DataFrame containing information about the samples (columns). - +/- metadata: A list containing additional metadata about the experiment.

For a nearly real example, we will use the DeRisi dataset. We’ll start with the original data, which is a data.frame with the first couple of columns containing the gene information and the rest of the columns containing the data.

# Load the DeRisi dataset
deRisi <- read.csv("https://raw.githubusercontent.com/seandavi/RBiocBook/refs/heads/main/data/derisi.csv")
head(deRisi)

      ORF  Name    R1    R2    R3    R4   R5   R6    R7 R1.Bkg R2.Bkg R3.Bkg
1 YHR007C ERG11  7896  7484 14679 14617 9853 7599  6490   1155   1984   1323
2 YBR218C  PYC2 12144 11177 10241  4820 4950 7047 17035   1074   1694   1243
3 YAL051W FUN43  4478  6435  6230  6848 5111 7180  4497   1140   1950   1649
4 YAL053W        6343  8243  6743  3304 3556 4694  3849   1020   1897   1196
5 YAL054C  ACS1  1542  3044  2076  1695 1753 4806 10802   1082   1940   1504
6 YAL055W        1769  3243  2094  1367 1853 3580  1956    975   1821   1185
  R4.Bkg R5.Bkg R6.Bkg R7.Bkg    G1    G2    G3    G4    G5    G6    G7 G1.Bkg
1   1171    914   2445    981  8432  7173 11736 16798 12315 16111 13931   2404
2    876   1211   2444    742 11509 10226 13372  6500  6255  9024  6904   2148
3   1183    898   2637    927  5865  5895  5345  6302  5400  7933  5026   2422
4    881   1045   2518    697  6762  7454  6323  3595  4689  5660  4145   2107
5   1108    902   2610    980  3138  3785  2419  2114  2763  3561  1897   2405
6    851   1047   2536    698  2844  4069  2583  1651  2530  3484  1550   1674
  G2.Bkg G3.Bkg G4.Bkg G5.Bkg G6.Bkg G7.Bkg
1   2561   1598   1506   1696   2667   1244
2   2527   1641   1196   1553   2569    848
3   2496   1902   1501   1644   2808   1154
4   2663   1607   1162   1577   2544    857
5   2528   1847   1445   1713   2767   1142
6   2648   1591   1114   1528   2668    870

To convert this to a SummarizedExperiment, we need to extract the assay data, row data, and column data. The assay data will be the numeric values in the data frame, the row data will be the gene information, and the column data will be the sample information.

26.4.1 rowData, or feature information

Let’s start with the rowData, which will be a DataFrame containing the gene information. We can use the first two columns of the data frame for this purpose.

rdata <- deRisi[, 1:2]
head(rdata)

      ORF  Name
1 YHR007C ERG11
2 YBR218C  PYC2
3 YAL051W FUN43
4 YAL053W      
5 YAL054C  ACS1
6 YAL055W

26.4.2 colData, or sample information

Next, we will create the colData, which will be a DataFrame containing the sample information. Since the sample information really isn’t in the dataset, we will create a simple DataFrame with sample names.

cdata <- DataFrame(sample=paste("Sample", 0:6), timepoint = 0:6,
    hours = c(0, 9.5,11.5,13.5,15.5,18.5,20.5))
head(cdata)

DataFrame with 6 rows and 3 columns
       sample timepoint     hours
  <character> <integer> <numeric>
1    Sample 0         0       0.0
2    Sample 1         1       9.5
3    Sample 2         2      11.5
4    Sample 3         3      13.5
5    Sample 4         4      15.5
6    Sample 5         5      18.5

26.4.3 assays, or the data

Remember that the DeRisi dataset has four different assays,

assay	description
R	Red fluorescence
G	Green fluorescence
Rb	Red background fluorescence
Gb	Green background fluorescence

We will create a list of matrices, one for each assay. The matrices will be the numeric values in the data frame, excluding the first two columns.

R <- as.matrix(deRisi[, 3:9])
G <- as.matrix(deRisi[, 10:16])
Rb <- as.matrix(deRisi[, 17:23])
Gb <- as.matrix(deRisi[, 24:30])

When we create a SummarizedExperiment object, the “constructor” will check to see that the colnames of the matrices in the list are the same as the rownames of the colData DataFrame, and that the rownames of the matrices in the list are the same as the rownames of the rowData DataFrame.

So, we need to fix that all up. Let’s start with the rownames of the rowData DataFrame:

rownames(rdata) <- rdata$ORF

Now, let’s set the rownames of the coldata DataFrame to the sample names:

rownames(cdata) <- cdata$sample

Now, we can fix the rownames and colnames of the matrices for our R, G, Rb, and Gb assays:

rownames(R) <- rdata$ORF
rownames(G) <- rdata$ORF
rownames(Rb) <- rdata$ORF
rownames(Gb) <- rdata$ORF
colnames(R) <- cdata$sample
colnames(G) <- cdata$sample
colnames(Rb) <- cdata$sample
colnames(Gb) <- cdata$sample

Take a look at the matrices to make sure they look right.

26.4.4 Putting it all together

se <- SummarizedExperiment(assays=list(R=R, G=G, Rb=Rb, Gb=Gb), rowData=rdata, colData=cdata)

26.4.5 Getting logRatios

Now that we have a SummarizedExperiment object, we can easily compute the log ratios of the Red and Green foreground fluorescence. This is a common operation in microarray data analysis.

logRatios <- log2(
    (assay(se, "R") - assay(se, "Rb")) / (assay(se, "G") - assay(se, "Gb"))
)

Warning: NaNs produced

assays(se)$logRatios <- logRatios

Now, we’ve added a new assay to the SummarizedExperiment object called logRatios. This assay contains the log ratios of the Red and Green foreground fluorescence.

assays(se)

List of length 5
names(5): R G Rb Gb logRatios

And if we want to access the log ratios, we can do so using the assay() method:

head(assay(se, "logRatios"))

          Sample 0     Sample 1  Sample 2   Sample 3   Sample 4 Sample 5
YHR007C -1.2204686          NaN       NaN 2.70275677  1.6545902 5.260867
YBR218C        NaN          NaN 2.9757832 2.39231742  1.9319816 3.983313
YAL051W  0.1135715          NaN       NaN        NaN -1.3681061 2.138654
YAL053W -1.3753298          NaN       NaN 0.05044902  1.0906497 5.215440
YAL054C  0.2706476  0.333657387 0.0000000 0.31420165  0.3165815      NaN
YAL055W  0.6209723 -0.001745548 0.2683547 0.11082813  0.4931189      NaN
         Sample 6
YHR007C 4.8223618
YBR218C       NaN
YAL051W 1.2205754
YAL053W 0.8875253
YAL054C       NaN
YAL055W       NaN

## OR
head(assays(se)$logRatios)

          Sample 0     Sample 1  Sample 2   Sample 3   Sample 4 Sample 5
YHR007C -1.2204686          NaN       NaN 2.70275677  1.6545902 5.260867
YBR218C        NaN          NaN 2.9757832 2.39231742  1.9319816 3.983313
YAL051W  0.1135715          NaN       NaN        NaN -1.3681061 2.138654
YAL053W -1.3753298          NaN       NaN 0.05044902  1.0906497 5.215440
YAL054C  0.2706476  0.333657387 0.0000000 0.31420165  0.3165815      NaN
YAL055W  0.6209723 -0.001745548 0.2683547 0.11082813  0.4931189      NaN
         Sample 6
YHR007C 4.8223618
YBR218C       NaN
YAL051W 1.2205754
YAL053W 0.8875253
YAL054C       NaN
YAL055W       NaN

hist(assays(se)$logRatios, breaks=50, main="Log Ratios of Red and Green Foreground Fluorescence", xlab="Log Ratio")

26.5 Summary

A SummarizedExperiment bundles an assay matrix together with aligned feature data (rowData/rowRanges) and sample data (colData) into a single object that stays internally consistent when you subset. In this chapter you toured the accessors (assays(), rowData(), colData(), metadata()), subset an experiment by phenotype and by genomic range, and built a SummarizedExperiment from scratch out of the DeRisi microarray data. From here on, most Bioconductor analysis packages will hand you — or expect — an object shaped exactly like this.

26.6 Exercises

The airway dataset is a good sandbox. Load a fresh copy so you’re not relying on any object created earlier in the chapter:

data(airway, package = "airway")
air <- airway

How many genes (rows) and samples (columns) does air have? Which accessor tells you each sample’s treatment status?
Subset air to just the untreated samples (dex == "untrt") and confirm that both the assay matrix and the colData shrank to match.
Pull the counts assay for the first 5 genes and first 3 samples into a plain matrix.

Solutions

# 1. shape and the treatment column
dim(air)              # genes x samples

[1] 63677     8

colData(air)$dex      # treatment per sample

[1] untrt trt   untrt trt   untrt trt   untrt trt  
Levels: trt untrt

# 2. subsetting carries the metadata along
untrt <- air[, air$dex == "untrt"]
dim(untrt)            # fewer columns; same number of rows

[1] 63677     4

ncol(colData(untrt))  # colData has the same columns, fewer rows

[1] 9

# 3. reach into one assay and slice it
assay(air, "counts")[1:5, 1:3]

                SRR1039508 SRR1039509 SRR1039512
ENSG00000000003        679        448        873
ENSG00000000005          0          0          0
ENSG00000000419        467        515        621
ENSG00000000457        260        211        263
ENSG00000000460         60         55         40

The key idea in exercise 2: you subset the object, and colData follows in lockstep — you never have to remember to subset the sample table separately.

26.1 What you’ll learn

26.2 Anatomy of a SummarizedExperiment

26.2.1 Assays

26.2.2 ‘Row’ (regions-of-interest) data

26.2.3 ‘Column’ (sample) data

26.2.4 Experiment-wide metadata

26.3 Common operations on SummarizedExperiment

26.3.1 Subsetting

26.3.2 Getters and setters

26.3.3 Range-based operations

26.4 Constructing a SummarizedExperiment

26.4.1 rowData, or feature information

26.4.2 colData, or sample information

26.4.3 assays, or the data

26.4.4 Putting it all together

26.4.5 Getting logRatios

26.5 Summary

26.6 Exercises

26.2 Anatomy of a `SummarizedExperiment`

26.3 Common operations on `SummarizedExperiment`

26.4 Constructing a `SummarizedExperiment`