Importing data into TreeSummarizedExperiment
Last updated on 2026-10-20 | Edit this page
Overview
Questions
- How do we load microbiome data into R?
- Why do we store the data in a single object?
- What is in our study data?
Objectives
- Create a
TreeSummarizedExperimentobject from an abundance table, a taxonomy table, sample information and a tree. - Access the abundance table, the taxonomy and the sample information of the object.
- Select a subset of the samples.
- Plot how many samples are in each study group.
Where are we?
The sequencing reads of our study have already been turned into tables. In this episode, we bring these tables into R and combine them into a single data object. All later steps start from this object.
Load packages
R
library(mia)
library(miaViz)
Read the data files
We start by reading the four files from episode 3 into R. The first column of each CSV
file contains the row names, so we use row.names = 1.
R
counts <- read.csv(file.path("data", "taxonomy_abundance.csv"), row.names = 1)
taxa <- read.csv(file.path("data", "taxonomy_table.csv"), row.names = 1)
samples <- read.csv(file.path("data", "sample_metadata.csv"), row.names = 1)
tree <- ape::read.tree(file.path("data", "phylogeny.tree"))
Let’s take a quick look at each of them. The abundance table has one
row per taxon and one column per sample. The row names start with the
taxonomic rank of the taxon, such as species-, and the
column names are sample IDs:
R
dim(counts)
OUTPUT
[1] 308 60R
counts[1:3, 1:3]
OUTPUT
GupDM_A_11 GupDM_A_15 GupDM_A1
species-Escherichia_coli 1632336 5666898 6814491
species-Alistipes_putredinis 964884 0 225222
species-Porphyromonas_asaccharolytica 463162 0 92810The taxonomy table has one row per taxon:
R
head(taxa, n = 3)
OUTPUT
superkingdom phylum
species-Escherichia_coli Bacteria Proteobacteria
species-Alistipes_putredinis Bacteria Bacteroidota
species-Porphyromonas_asaccharolytica Bacteria Bacteroidota
class order
species-Escherichia_coli Gammaproteobacteria Enterobacterales
species-Alistipes_putredinis Bacteroidia Bacteroidales
species-Porphyromonas_asaccharolytica Bacteroidia Bacteroidales
family genus
species-Escherichia_coli Enterobacteriaceae Escherichia
species-Alistipes_putredinis Rikenellaceae Alistipes
species-Porphyromonas_asaccharolytica Porphyromonadaceae Porphyromonas
species
species-Escherichia_coli Escherichia coli
species-Alistipes_putredinis Alistipes putredinis
species-Porphyromonas_asaccharolytica Porphyromonas asaccharolyticaThe sample information has one row per sample:
R
samples[1:3, c("disease", "age", "gender", "location")]
OUTPUT
disease age gender location
GupDM_A_11 CRC 41 female Kerala
GupDM_A_15 CRC 59 male Kerala
GupDM_A1 CRC 62 male KeralaAnd the tree connects the taxa:
R
tree
OUTPUT
Phylogenetic tree with 10430 tips and 10429 internal nodes.
Tip labels:
k__Archaea|p__Candidatus_Micrarchaeota|c__Candidatus_Micrarchaeota_unclassified|o__Candidatus_Micrarchaeota_unclassified|f__Candidatus_Micrarchaeota_unclassified|g__Candidatus_Micrarchaeota_unclassified|s__Candidatus_Micrarchaeota_archaeon_CG1_02_55_22, k__Archaea|p__Archaea_unclassified|c__Archaea_unclassified|o__Archaea_unclassified|f__Archaea_unclassified|g__Archaea_unclassified|s__archaeon_GW2011_AR15, k__Archaea|p__Candidatus_Diapherotrites|c__Candidatus_Diapherotrites_unclassified|o__Candidatus_Diapherotrites_unclassified|f__Candidatus_Diapherotrites_unclassified|g__Candidatus_Diapherotrites_unclassified|s__Candidatus_Diapherotrites_archaeon_CG08_land_8_20_14_0_20_34_12, k__Archaea|p__Archaea_unclassified|c__Archaea_unclassified|o__Archaea_unclassified|f__Archaea_unclassified|g__Archaea_unclassified|s__archaeon_GW2011_AR10, k__Archaea|p__Candidatus_Diapherotrites|c__Candidatus_Diapherotrites_unclassified|o__Candidatus_Diapherotrites_unclassified|f__Candidatus_Diapherotrites_unclassified|g__Candidatus_Diapherotrites_unclassified|s__Candidatus_Diapherotrites_archaeon_CG11_big_fil_rev_8_21_14_0_20_37_9, k__Archaea|p__Euryarchaeota|c__Euryarchaeota_unclassified|o__Euryarchaeota_unclassified|f__Euryarchaeota_unclassified|g__Euryarchaeota_unclassified|s__Euryarchaeota_archaeon_TMED173, ...
Rooted; includes branch length(s).The tree is a reference tree from MetaPhlAn, the tool that produced our tables. It contains 10430 species, many more than the 308 in our data. That is fine: each of our taxa has a tip with the same name in the tree, and this is how R knows which tip belongs to which taxon. The printout shows the first few tip labels, which belong to species that are not in our data.
Why put everything into one object?
We now have four separate objects that describe the same data. This is risky: if we remove a sample from the abundance table, we have to remember to remove it from the sample information too. If we forget, the tables no longer match, and our results will be wrong without any warning.
The solution is to store everything in a single object that keeps the
parts in sync. In Bioconductor, microbiome data is stored in a
TreeSummarizedExperiment (TreeSE for
short).
Figure: the TreeSummarizedExperiment class (Huang et al. 2021, F1000Research). Source: OMA.
The figure shows all the parts a TreeSE can hold. In this lesson, we only need these four:
| Part | Contains | In our data |
|---|---|---|
assays |
The abundance tables. Rows are taxa, columns are samples. | Read counts |
rowData |
Information about the rows (taxa) | Taxonomy |
colData |
Information about the columns (samples) | Disease, age, gender, location, … |
rowTree |
A tree that connects the rows | Phylogenetic tree |
TreeSE and SummarizedExperiment
If you have used the SummarizedExperiment class before,
for example in the Introduction
to data analysis with R and Bioconductor lesson: a TreeSE is a
SummarizedExperiment with extra room for trees. Everything
you know about SummarizedExperiment also works for TreeSE.
The only difference in our data is what the rows are: genes in that
lesson, taxa here. The functions assay(),
rowData() and colData() and subsetting with
[ , ] work exactly as you learned there.
Create the TreeSE
We combine the four parts with the function
TreeSummarizedExperiment(). The abundance table must be a
matrix, so we convert it with as.matrix(). We also give the
data a name with mainExpName. This becomes useful in the
bonus section of the next episode, where we store the data at several
taxonomic ranks in the same object.
R
tse <- TreeSummarizedExperiment(
assays = list(counts = as.matrix(counts)),
rowData = taxa,
colData = samples,
rowTree = tree,
mainExpName = "main"
)
tse
OUTPUT
class: TreeSummarizedExperiment
dim: 308 60
metadata(0):
assays(1): counts
rownames(308): species-Escherichia_coli species-Alistipes_putredinis
... species-Campylobacter_ureolyticus
species-Prevotella_sp._oral_taxon_376
rowData names(7): superkingdom phylum ... genus species
colnames(60): GupDM_A_11 GupDM_A_15 ... GupDM_JO GupDM_JP
colData names(27): study_name subject_id ... disease_stage
disease_location
reducedDimNames(0):
mainExpName: main
altExpNames(0):
rowLinks: a LinkDataFrame (308 rows)
rowTree: 1 phylo tree(s) (10430 leaves)
colLinks: NULL
colTree: NULLThe printout summarizes the object:
-
dim: 308 taxa (rows) and 60 samples (columns). -
assays(1): counts: one abundance table, namedcounts. -
rowData names(7): the taxonomy columns. -
colData names(27): the columns of sample information. -
rowTree: the phylogenetic tree.
Look inside the TreeSE
Each part has its own accessor function.
The abundance table is accessed with assay():
R
assay(tse, "counts")[1:3, 1:3]
OUTPUT
GupDM_A_11 GupDM_A_15 GupDM_A1
species-Escherichia_coli 1632336 5666898 6814491
species-Alistipes_putredinis 964884 0 225222
species-Porphyromonas_asaccharolytica 463162 0 92810The taxonomy is accessed with rowData():
R
rowData(tse)
OUTPUT
DataFrame with 308 rows and 7 columns
superkingdom phylum
<character> <character>
species-Escherichia_coli Bacteria Proteobacteria
species-Alistipes_putredinis Bacteria Bacteroidota
species-Porphyromonas_asaccharolytica Bacteria Bacteroidota
species-Bacteroides_uniformis Bacteria Bacteroidota
species-Prevotella_stercorea Bacteria Bacteroidota
... ... ...
species-Ruminococcus_obeum_CAG-39 Bacteria Firmicutes
species-Streptococcus_lutetiensis Bacteria Firmicutes
species-Prevotella_timonensis Bacteria Bacteroidota
species-Campylobacter_ureolyticus Bacteria Proteobacteria
species-Prevotella_sp._oral_taxon_376 Bacteria Bacteroidota
class order
<character> <character>
species-Escherichia_coli Gammaproteobacteria Enterobacterales
species-Alistipes_putredinis Bacteroidia Bacteroidales
species-Porphyromonas_asaccharolytica Bacteroidia Bacteroidales
species-Bacteroides_uniformis Bacteroidia Bacteroidales
species-Prevotella_stercorea Bacteroidia Bacteroidales
... ... ...
species-Ruminococcus_obeum_CAG-39 Clostridia Eubacteriales
species-Streptococcus_lutetiensis Bacilli Lactobacillales
species-Prevotella_timonensis Bacteroidia Bacteroidales
species-Campylobacter_ureolyticus Epsilonproteobacteria Campylobacterales
species-Prevotella_sp._oral_taxon_376 Bacteroidia Bacteroidales
family genus
<character> <character>
species-Escherichia_coli Enterobacteriaceae Escherichia
species-Alistipes_putredinis Rikenellaceae Alistipes
species-Porphyromonas_asaccharolytica Porphyromonadaceae Porphyromonas
species-Bacteroides_uniformis Bacteroidaceae Bacteroides
species-Prevotella_stercorea Prevotellaceae Prevotella
... ... ...
species-Ruminococcus_obeum_CAG-39 Oscillospiraceae Ruminococcus
species-Streptococcus_lutetiensis Streptococcaceae Streptococcus
species-Prevotella_timonensis Prevotellaceae Prevotella
species-Campylobacter_ureolyticus Campylobacteraceae Campylobacter
species-Prevotella_sp._oral_taxon_376 Prevotellaceae Prevotella
species
<character>
species-Escherichia_coli Escherichia coli
species-Alistipes_putredinis Alistipes putredinis
species-Porphyromonas_asaccharolytica Porphyromonas asacch..
species-Bacteroides_uniformis Bacteroides uniformis
species-Prevotella_stercorea Prevotella stercorea
... ...
species-Ruminococcus_obeum_CAG-39 Ruminococcus obeum C..
species-Streptococcus_lutetiensis Streptococcus luteti..
species-Prevotella_timonensis Prevotella timonensis
species-Campylobacter_ureolyticus Campylobacter ureoly..
species-Prevotella_sp._oral_taxon_376 Prevotella sp. oral ..The sample information is accessed with colData():
R
colData(tse)
OUTPUT
DataFrame with 60 rows and 27 columns
study_name subject_id body_site antibiotics_current_use
<character> <character> <character> <character>
GupDM_A_11 GuptaA_2019 GupDM_A11 stool no
GupDM_A_15 GuptaA_2019 GupDM_A15 stool no
GupDM_A1 GuptaA_2019 GupDM_A1 stool no
GupDM_A10 GuptaA_2019 GupDM_A10 stool no
GupDM_A12 GuptaA_2019 GupDM_A12 stool no
... ... ... ... ...
GupDM_JL GuptaA_2019 GupDM_JL stool no
GupDM_JM GuptaA_2019 GupDM_JM stool no
GupDM_JN GuptaA_2019 GupDM_JN stool no
GupDM_JO GuptaA_2019 GupDM_JO stool no
GupDM_JP GuptaA_2019 GupDM_JP stool no
study_condition disease age age_category gender
<character> <character> <integer> <character> <character>
GupDM_A_11 CRC CRC 41 adult female
GupDM_A_15 CRC CRC 59 adult male
GupDM_A1 CRC CRC 62 adult male
GupDM_A10 CRC CRC 65 adult female
GupDM_A12 CRC CRC 65 adult male
... ... ... ... ... ...
GupDM_JL CRC CRC 59 adult female
GupDM_JM CRC CRC 60 adult male
GupDM_JN CRC CRC 58 adult male
GupDM_JO CRC CRC 71 senior male
GupDM_JP CRC CRC 67 senior male
country location non_westernized sequencing_platform
<character> <character> <character> <character>
GupDM_A_11 IND Kerala no IlluminaNextSeq
GupDM_A_15 IND Kerala no IlluminaNextSeq
GupDM_A1 IND Kerala no IlluminaNextSeq
GupDM_A10 IND Kerala no IlluminaNextSeq
GupDM_A12 IND Kerala no IlluminaNextSeq
... ... ... ... ...
GupDM_JL IND Bhopal no IlluminaNextSeq
GupDM_JM IND Bhopal no IlluminaNextSeq
GupDM_JN IND Bhopal no IlluminaNextSeq
GupDM_JO IND Bhopal no IlluminaNextSeq
GupDM_JP IND Bhopal no IlluminaNextSeq
DNA_extraction_kit PMID number_reads number_bases
<character> <integer> <integer> <numeric>
GupDM_A_11 Qiagen 31719139 6227234 883347281
GupDM_A_15 Qiagen 31719139 9884266 1384530792
GupDM_A1 Qiagen 31719139 27687226 3808647004
GupDM_A10 Qiagen 31719139 8468208 1174245166
GupDM_A12 Qiagen 31719139 6988838 984235167
... ... ... ... ...
GupDM_JL Qiagen 31719139 9878044 1387793557
GupDM_JM Qiagen 31719139 10328094 1435043694
GupDM_JN Qiagen 31719139 9789278 1347588954
GupDM_JO Qiagen 31719139 11274338 1501226163
GupDM_JP Qiagen 31719139 10400668 1428270555
minimum_read_length median_read_length NCBI_accession
<integer> <integer> <character>
GupDM_A_11 60 151 SRR8865600
GupDM_A_15 60 150 SRR8865596
GupDM_A1 60 150 SRR8865598
GupDM_A10 60 150 SRR8865599
GupDM_A12 60 150 SRR8865601
... ... ... ...
GupDM_JL 60 151 SRR8865572
GupDM_JM 60 150 SRR8865575
GupDM_JN 60 150 SRR8865574
GupDM_JO 60 150 SRR8865581
GupDM_JP 60 150 SRR8865580
curator BMI disease_subtype tnm
<character> <numeric> <character> <character>
GupDM_A_11 Arianna_Bonetti;Paol.. 19.22 adenocarcinoma t2m0n0
GupDM_A_15 Arianna_Bonetti;Paol.. 21.40 adenocarcinoma t2n0m0
GupDM_A1 Arianna_Bonetti;Paol.. 20.08 adenocarcinoma t2n0m0
GupDM_A10 Arianna_Bonetti;Paol.. 21.01 adenocarcinoma t3n2m0
GupDM_A12 Arianna_Bonetti;Paol.. 20.32 adenocarcinoma t2n2m0
... ... ... ... ...
GupDM_JL Arianna_Bonetti;Paol.. 20.34 adenocarcinoma t4n1m0
GupDM_JM Arianna_Bonetti;Paol.. 18.33 adenocarcinoma t2n0m0
GupDM_JN Arianna_Bonetti;Paol.. 19.92 adenocarcinoma t2n0m0
GupDM_JO Arianna_Bonetti;Paol.. 20.03 adenocarcinoma t4n1m0
GupDM_JP Arianna_Bonetti;Paol.. 17.69 adenocarcinoma t3n2m0
fobt disease_stage disease_location
<character> <character> <character>
GupDM_A_11 yes I rectum
GupDM_A_15 yes I rectum
GupDM_A1 yes I rectum
GupDM_A10 yes III colon
GupDM_A12 yes III rectum
... ... ... ...
GupDM_JL yes III rectum
GupDM_JM yes I rectum
GupDM_JN yes I rectum
GupDM_JO yes III sigmoid_colon
GupDM_JP yes III rectumA single column of the sample information can be accessed with
$, just as in a data frame:
R
table(tse$disease)
OUTPUT
CRC healthy
30 30 rowData() and colData() return a
DataFrame, the Bioconductor version of a data frame. It
works like a data.frame, but prints only the first and last
rows. The tree is accessed with rowTree(tse). We do not
need to look at it now: functions that use the tree, such as
phylogenetic diversity in episode
6, find it there on their own.
Challenge 1: Where is the information?
In which part of tse would you find the age of each
person?
assay(tse, "counts")rowData(tse)colData(tse)rowTree(tse)
-
colData(tse). Age describes a sample (a person), and information about samples is stored incolData. You can get the ages withtse$age.
Challenge 2: Which phylum?
Find out which phylum the first taxon in tse belongs
to.
Hint: the taxonomy is in rowData(tse), and its
columns are named superkingdom, phylum,
class, …
R
rownames(tse)[1]
OUTPUT
[1] "species-Escherichia_coli"R
rowData(tse)$phylum[1]
OUTPUT
[1] "Proteobacteria"Select a subset of samples
A TreeSE can be subset with square brackets
[rows, columns], just like a data frame. Rows are taxa and
columns are samples. To keep only the samples from people with CRC, we
select the columns where disease is "CRC":
R
tse_crc <- tse[, tse$disease == "CRC"]
tse_crc
OUTPUT
class: TreeSummarizedExperiment
dim: 308 30
metadata(0):
assays(1): counts
rownames(308): species-Escherichia_coli species-Alistipes_putredinis
... species-Campylobacter_ureolyticus
species-Prevotella_sp._oral_taxon_376
rowData names(7): superkingdom phylum ... genus species
colnames(30): GupDM_A_11 GupDM_A_15 ... GupDM_JO GupDM_JP
colData names(27): study_name subject_id ... disease_stage
disease_location
reducedDimNames(0):
mainExpName: main
altExpNames(0):
rowLinks: a LinkDataFrame (308 rows)
rowTree: 1 phylo tree(s) (10430 leaves)
colLinks: NULL
colTree: NULLResult: the new object has 30 samples. The abundance table and the sample information were subset together, so they still match. This is the main reason we use a TreeSE.
Challenge 3: Healthy people only
- Create an object
tse_healthythat contains only the samples from healthy people. - How many samples does it have?
R
tse_healthy <- tse[, tse$disease == "healthy"]
ncol(tse_healthy)
OUTPUT
[1] 30A first look at the study
Before any analysis, it is good to know who is in the study. The
miaViz function plotBarplot() counts the samples in each
group of a colData column (col.var):
R
plotBarplot(tse, col.var = "disease")
Result: the study has 30 people with CRC and 30 healthy people.
The people come from two places: the city of Bhopal and the state of
Kerala. With fill.by, we can colour the bars by a second
variable:
R
plotBarplot(tse, col.var = "location", fill.by = "disease")
Challenge 4: What do you notice?
Look at the plot above.
- From which location do most of the healthy people come?
- Suppose we find that the microbiome of CRC patients differs from that of healthy people. Why could this be a problem?
- Most healthy people come from Bhopal, while the CRC patients come
equally from both places. You can check the numbers with
table(tse$location, tse$disease). - Diet and lifestyle differ between the places, and they affect the gut microbiome. A difference between CRC and healthy people could therefore be partly a difference between the two places. We will take this into account in the episodes on beta diversity and differential abundance.
Save the object
We save the TreeSE so that we can load it again in the next episode, without repeating the import steps.
R
saveRDS(tse, file.path("data", "tse.rds"))
Importing your own data
In this lesson, we built the TreeSE from plain tables. Your own data probably comes from a tool such as QIIME 2, MetaPhlAn or mothur, or is stored as a phyloseq object. The mia package has functions that import these formats directly into a TreeSE. See the Import chapter of OMA.
- A
TreeSummarizedExperimentkeeps the abundance table, taxonomy, sample information and tree together and in sync. - Rows are taxa and columns are samples.
-
assay(),rowData()andcolData()access the abundance table, the taxonomy and the sample information. - Subsetting with
[rows, columns]keeps all parts of the object in sync. -
plotBarplot()gives a quick overview of the study groups. In our study, the groups differ in more than disease: most healthy people come from Bhopal.