Converting Common Data Formats to Phyloseq and TreeSummarizedExperiment
Source:vignettes/data_import.Rmd
data_import.RmdThe “dar” package is a versatile and user-friendly tool designed to
accept inputs in a variety of formats. It primarily utilizes the
phyloseq format but also supports the
TreeSummarizedExperiment format. This flexibility allows
users to conduct differential abundance analysis smoothly, irrespective
of their initial data format. To facilitate this, a detailed guide is
available to aid users in converting other prevalent data formats, such
as biome, mothur, metaphlan, and
more, into the necessary phyloseq or
TreeSummarizedExperiment formats.
Importing Data from biome Format
The biome format is a commonly used format in
bioinformatics to represent microbiome sequencing data. Here’s how you
can import data in biome format to both
phyloseq and TreeSummarizedExperiment.
To Phyloseq
To convert data from the biome format to the
phyloseq format, you can use the
phyloseq::import_biom() function. Here’s a step-by-step
example of how to perform this conversion:
# Example of a rich dense biom file
rich_dense_biom <-
system.file("extdata", "rich_dense_otu_table.biom", package = "phyloseq")
# Import biom as a phyloseq-class object
phy <- phyloseq::import_biom(
rich_dense_biom,
parseFunction = parse_taxonomy_greengenes
)
phy
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 5 taxa and 6 samples ]
#> sample_data() Sample Data: [ 6 samples by 4 sample variables ]
#> tax_table() Taxonomy Table: [ 5 taxa by 7 taxonomic ranks ]
# Print sample_data
phyloseq::sample_data(phy)
#> BarcodeSequence LinkerPrimerSequence BODY_SITE Description
#> Sample1 CGCTTATCGAGA CATGCTGCCTCCCGTAGGAGT gut human gut
#> Sample2 CATACCAGTAGC CATGCTGCCTCCCGTAGGAGT gut human gut
#> Sample3 CTCTCTACCTGT CATGCTGCCTCCCGTAGGAGT gut human gut
#> Sample4 CTCTCGGCCTGT CATGCTGCCTCCCGTAGGAGT skin human skin
#> Sample5 CTCTCTACCAAT CATGCTGCCTCCCGTAGGAGT skin human skin
#> Sample6 CTAACTACCAAT CATGCTGCCTCCCGTAGGAGT skin human skin
# Print tax_table
phyloseq::tax_table(phy)
#> Taxonomy Table: [5 taxa by 7 taxonomic ranks]:
#> Kingdom Phylum Class Order
#> GG_OTU_1 "Bacteria" "Proteobacteria" "Gammaproteobacteria" "Enterobacteriales"
#> GG_OTU_2 "Bacteria" "Cyanobacteria" "Nostocophycideae" "Nostocales"
#> GG_OTU_3 "Archaea" "Euryarchaeota" "Methanomicrobia" "Methanosarcinales"
#> GG_OTU_4 "Bacteria" "Firmicutes" "Clostridia" "Halanaerobiales"
#> GG_OTU_5 "Bacteria" "Proteobacteria" "Gammaproteobacteria" "Enterobacteriales"
#> Family Genus Species
#> GG_OTU_1 "Enterobacteriaceae" "Escherichia" NA
#> GG_OTU_2 "Nostocaceae" "Dolichospermum" NA
#> GG_OTU_3 "Methanosarcinaceae" "Methanosarcina" NA
#> GG_OTU_4 "Halanaerobiaceae" "Halanaerobium" "Halanaerobiumsaccharolyticum"
#> GG_OTU_5 "Enterobacteriaceae" "Escherichia" NA
# Recipe init
rec <- dar::recipe(phy) |>
dar::add_model(~ BODY_SITE, targets = "BODY_SITE", tax_level = "Genus")
rec
#> ── DAR Recipe ──────────────────────────────────────────────────────────────────
#> Inputs:
#>
#> ℹ phyloseq object with 5 taxa and 6 samples
#> ℹ variable of interes BODY_SITE (class: character, levels: gut, skin)
#> ℹ taxonomic level Genus
#>
#> Statistical model:
#>
#> ℹ ~BODY_SITEInspecting Recipe Components
Recipe accessors expose the complete underlying components and do not
depend on the targets or taxonomic level selected by
add_model(). Their ordering is stable: samples follow
phyloseq::sample_names(), taxa follow
phyloseq::taxa_names(), and counts are always returned with
taxa in rows.
dar::sample_data(rec)
#> # A tibble: 6 × 5
#> sample_id BarcodeSequence LinkerPrimerSequence BODY_SITE Description
#> <chr> <chr> <chr> <chr> <chr>
#> 1 Sample1 CGCTTATCGAGA CATGCTGCCTCCCGTAGGAGT gut human gut
#> 2 Sample2 CATACCAGTAGC CATGCTGCCTCCCGTAGGAGT gut human gut
#> 3 Sample3 CTCTCTACCTGT CATGCTGCCTCCCGTAGGAGT gut human gut
#> 4 Sample4 CTCTCGGCCTGT CATGCTGCCTCCCGTAGGAGT skin human skin
#> 5 Sample5 CTCTCTACCAAT CATGCTGCCTCCCGTAGGAGT skin human skin
#> 6 Sample6 CTAACTACCAAT CATGCTGCCTCCCGTAGGAGT skin human skin
dar::tax_table(rec)
#> # A tibble: 5 × 8
#> taxa_id Kingdom Phylum Class Order Family Genus Species
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 GG_OTU_1 Bacteria Proteobacteria Gammaproteobacter… Ente… Enter… Esch… NA
#> 2 GG_OTU_2 Bacteria Cyanobacteria Nostocophycideae Nost… Nosto… Doli… NA
#> 3 GG_OTU_3 Archaea Euryarchaeota Methanomicrobia Meth… Metha… Meth… NA
#> 4 GG_OTU_4 Bacteria Firmicutes Clostridia Hala… Halan… Hala… Halana…
#> 5 GG_OTU_5 Bacteria Proteobacteria Gammaproteobacter… Ente… Enter… Esch… NA
dar::otu_table(rec)
#> # A tibble: 5 × 7
#> taxa_id Sample1 Sample2 Sample3 Sample4 Sample5 Sample6
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 GG_OTU_1 0 0 1 0 0 0
#> 2 GG_OTU_2 5 1 0 2 3 1
#> 3 GG_OTU_3 0 0 1 4 2 0
#> 4 GG_OTU_4 2 1 1 0 0 1
#> 5 GG_OTU_5 0 1 1 0 0 0When sample metadata or taxonomy is absent, the corresponding accessor returns an ID-only tibble. Analysis engines use separate internal target and rank views, so additional metadata and taxonomy columns are not passed implicitly to a statistical method.
To TreeSummarizedExperiment
To convert data from the biome format to the
TreeSummarizedExperiment format, you can use the
mia::importBIOM() function. Here’s a step-by-step example
of how to perform this conversion:
# Example of a rich dense biom file
rich_dense_biom <-
system.file("extdata", "rich_dense_otu_table.biom", package = "phyloseq")
# Import biom as a phyloseq-class object
tse <- mia::importBIOM(rich_dense_biom)
tse
#> class: TreeSummarizedExperiment
#> dim: 5 6
#> metadata(0):
#> assays(1): counts
#> rownames(5): GG_OTU_1 GG_OTU_2 GG_OTU_3 GG_OTU_4 GG_OTU_5
#> rowData names(7): taxonomy1 taxonomy2 ... taxonomy6 taxonomy7
#> colnames(6): Sample1 Sample2 ... Sample5 Sample6
#> colData names(4): BarcodeSequence LinkerPrimerSequence BODY_SITE
#> Description
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowLinks: NULL
#> rowTree: NULL
#> colLinks: NULL
#> colTree: NULL
# Print sample_data
colData(tse)
#> DataFrame with 6 rows and 4 columns
#> BarcodeSequence LinkerPrimerSequence BODY_SITE Description
#> <character> <character> <character> <character>
#> Sample1 CGCTTATCGAGA CATGCTGCCTCCCGTAGGAGT gut human gut
#> Sample2 CATACCAGTAGC CATGCTGCCTCCCGTAGGAGT gut human gut
#> Sample3 CTCTCTACCTGT CATGCTGCCTCCCGTAGGAGT gut human gut
#> Sample4 CTCTCGGCCTGT CATGCTGCCTCCCGTAGGAGT skin human skin
#> Sample5 CTCTCTACCAAT CATGCTGCCTCCCGTAGGAGT skin human skin
#> Sample6 CTAACTACCAAT CATGCTGCCTCCCGTAGGAGT skin human skin
# Print tax_table
rowData(tse)
#> DataFrame with 5 rows and 7 columns
#> taxonomy1 taxonomy2 taxonomy3
#> <character> <character> <character>
#> GG_OTU_1 k__Bacteria p__Proteobacteria c__Gammaproteobacteria
#> GG_OTU_2 k__Bacteria p__Cyanobacteria c__Nostocophycideae
#> GG_OTU_3 k__Archaea p__Euryarchaeota c__Methanomicrobia
#> GG_OTU_4 k__Bacteria p__Firmicutes c__Clostridia
#> GG_OTU_5 k__Bacteria p__Proteobacteria c__Gammaproteobacteria
#> taxonomy4 taxonomy5 taxonomy6
#> <character> <character> <character>
#> GG_OTU_1 o__Enterobacteriales f__Enterobacteriaceae g__Escherichia
#> GG_OTU_2 o__Nostocales f__Nostocaceae g__Dolichospermum
#> GG_OTU_3 o__Methanosarcinales f__Methanosarcinaceae g__Methanosarcina
#> GG_OTU_4 o__Halanaerobiales f__Halanaerobiaceae g__Halanaerobium
#> GG_OTU_5 o__Enterobacteriales f__Enterobacteriaceae g__Escherichia
#> taxonomy7
#> <character>
#> GG_OTU_1 s__
#> GG_OTU_2 s__
#> GG_OTU_3 s__
#> GG_OTU_4 s__Halanaerobiumsacc..
#> GG_OTU_5 s__
# Change the column names of the tax_table
colnames(rowData(tse)) <-
c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species")
rowData(tse)
#> DataFrame with 5 rows and 7 columns
#> Kingdom Phylum Class
#> <character> <character> <character>
#> GG_OTU_1 k__Bacteria p__Proteobacteria c__Gammaproteobacteria
#> GG_OTU_2 k__Bacteria p__Cyanobacteria c__Nostocophycideae
#> GG_OTU_3 k__Archaea p__Euryarchaeota c__Methanomicrobia
#> GG_OTU_4 k__Bacteria p__Firmicutes c__Clostridia
#> GG_OTU_5 k__Bacteria p__Proteobacteria c__Gammaproteobacteria
#> Order Family Genus
#> <character> <character> <character>
#> GG_OTU_1 o__Enterobacteriales f__Enterobacteriaceae g__Escherichia
#> GG_OTU_2 o__Nostocales f__Nostocaceae g__Dolichospermum
#> GG_OTU_3 o__Methanosarcinales f__Methanosarcinaceae g__Methanosarcina
#> GG_OTU_4 o__Halanaerobiales f__Halanaerobiaceae g__Halanaerobium
#> GG_OTU_5 o__Enterobacteriales f__Enterobacteriaceae g__Escherichia
#> Species
#> <character>
#> GG_OTU_1 s__
#> GG_OTU_2 s__
#> GG_OTU_3 s__
#> GG_OTU_4 s__Halanaerobiumsacc..
#> GG_OTU_5 s__
# Recipe init
rec <- dar::recipe(tse) |>
dar::add_model(~ BODY_SITE, targets = "BODY_SITE", tax_level = "Genus")
rec
#> ── DAR Recipe ──────────────────────────────────────────────────────────────────
#> Inputs:
#>
#> ℹ phyloseq object with 5 taxa and 6 samples
#> ℹ variable of interes BODY_SITE (class: character, levels: gut, skin)
#> ℹ taxonomic level Genus
#>
#> Statistical model:
#>
#> ℹ ~BODY_SITEImporting Data from qiime Format
The qiime format is another commonly used format in
bioinformatics for microbiome sequencing data. Here’s how you can import
data in qiime format to both Phyloseq and
TreeSummarizedExperiment.
To Phyloseq
To convert data from the qiime format to the
Phyloseq format, you can use the
phyloseq::import_qiime() function. Here’s a step-by-step
example of how to perform this conversion:
# Import QIIME data
phy_qiime <- phyloseq::import_qiime(
otufilename = system.file("extdata", "GP_otu_table_rand_short.txt.gz", package = "phyloseq"),
mapfilename = system.file("extdata", "master_map.txt", package = "phyloseq"),
treefilename = system.file("extdata", "GP_tree_rand_short.newick.gz", package = "phyloseq")
)
#> Processing map file...
#> Processing otu/tax file...
#> Reading file into memory prior to parsing...
#> Detecting first header line...
#> Header is on line 2
#> Converting input file to a table...
#> Defining OTU table...
#> Parsing taxonomy table...
#> Processing phylogenetic tree...
#> /__w/_temp/Library/phyloseq/extdata/GP_tree_rand_short.newick.gz ...
phy_qiime
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 500 taxa and 26 samples ]
#> sample_data() Sample Data: [ 26 samples by 7 sample variables ]
#> tax_table() Taxonomy Table: [ 500 taxa by 7 taxonomic ranks ]
#> phy_tree() Phylogenetic Tree: [ 500 tips and 499 internal nodes ]
# Recipe init
rec <- dar::recipe(phy_qiime) |>
dar::add_model(~ SampleType, targets = "SampleType", tax_level = "Genus")
rec
#> ── DAR Recipe ──────────────────────────────────────────────────────────────────
#> Inputs:
#>
#> ℹ phyloseq object with 500 taxa and 26 samples
#> ℹ variable of interes SampleType (class: character, levels: Feces, Freshwater, Freshwater (creek), Mock, Ocean, Sediment (estuary), Skin, Soil, Tongue)
#> ℹ taxonomic level Genus
#>
#> Statistical model:
#>
#> ℹ ~SampleTypeTo TreeSummarizedExperiment
To convert data from the qiime format to the
TreeSummarizedExperiment format, you can use the
mia::importQIIME2() function. Here’s a step-by-step example
of how to perform this conversion:
# Import QIIME data to tse
tse_qiime <- mia::importQIIME2(
featureTableFile = system.file("extdata", "table.qza", package = "mia"),
taxonomyTableFile = system.file("extdata", "taxonomy.qza", package = "mia"),
sampleMetaFile = system.file("extdata", "sample-metadata.tsv", package = "mia"),
refSeqFile = system.file("extdata", "refseq.qza", package = "mia"),
phyTreeFile = system.file("extdata", "tree.qza", package = "mia")
)
tse_qiime
# Recipe init
rec <- dar::recipe(tse_qiime) |>
dar::add_model(~ body.site, targets = "body.site", tax_level = "Genus")
recImporting Data from mothur Format
The mothur format is another commonly used format in
bioinformatics for microbiome sequencing data. Here’s how you can import
data in mothur format to both Phyloseq and
TreeSummarizedExperiment.
To Phyloseq
To convert data from the mothur format to the
Phyloseq format, you can use the
phyloseq::import_mothur() function. Here’s a step-by-step
example of how to perform this conversion:
# Import Mothur data
phy_mothur <- phyloseq::import_mothur(
mothur_list_file = system.file("extdata", "esophagus.fn.list.gz", package = "phyloseq"),
mothur_group_file = system.file("extdata", "esophagus.good.groups.gz", package = "phyloseq"),
mothur_tree_file = system.file("extdata", "esophagus.tree.gz", package = "phyloseq")
)
phy_mothur
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 591 taxa and 3 samples ]
#> phy_tree() Phylogenetic Tree: [ 591 tips and 590 internal nodes ]
# Recipe init
rec <- dar::recipe(phy_mothur)
rec
#> ── DAR Recipe ──────────────────────────────────────────────────────────────────
#> Inputs:
#>
#> ℹ phyloseq object with 591 taxa and 3 samples
#> ✖ undefined analysis target. Use add_model() to define the analysis!
#> ✖ undefined taxonomic level. Use add_model() to define the analysis!To TreeSummarizedExperiment
To convert data from the mothur format to the
TreeSummarizedExperiment format, you can use the
mia::importMothur() function. Here’s a step-by-step example
of how to perform this conversion:
# Import Mothur data to TreeSummarizedExperiment
tse_mothur <- mia::importMothur(
sharedFile = system.file("extdata", "mothur_example.shared", package = "mia"),
taxonomyFile = system.file("extdata", "mothur_example.cons.taxonomy", package = "mia"),
designFile = system.file("extdata", "mothur_example.design", package = "mia")
) |> methods::as("TreeSummarizedExperiment")
tse_mothur
#> class: TreeSummarizedExperiment
#> dim: 100 100
#> metadata(0):
#> assays(1): counts
#> rownames(100): Otu001 Otu002 ... Otu099 Otu100
#> rowData names(8): OTU Size ... Family Genus
#> colnames(100): Sample1 Sample2 ... Sample99 Sample100
#> colData names(7): group sex ... numOtus Group
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> rowLinks: NULL
#> rowTree: NULL
#> colLinks: NULL
#> colTree: NULL
# Recipe init
rec <- dar::recipe(tse_mothur) |>
dar::add_model(~ drug, targets = "drug", tax_level = "Genus")
rec
#> ── DAR Recipe ──────────────────────────────────────────────────────────────────
#> Inputs:
#>
#> ℹ phyloseq object with 100 taxa and 100 samples
#> ℹ variable of interes drug (class: character, levels: A, B)
#> ℹ taxonomic level Genus
#>
#> Statistical model:
#>
#> ℹ ~drugImporting Data from metaphlan Format
The metaphlan format is another commonly used format in
bioinformatics for microbiome sequencing data. Here’s how you can import
data in metaphlan format to
TreeSummarizedExperiment.
To TreeSummarizedExperiment
To convert data from the metaphlan format to the
TreeSummarizedExperiment format, you can use the
mia::importMetaPhlAn() function. Here’s a step-by-step
example of how to perform this conversion:
# Importing data from Metaphlan
tse_metaphlan <- mia::importMetaPhlAn(
file = system.file("extdata", "merged_abundance_table.txt", package = "mia")
)
# Recipe init
tse_metaphlan <- TreeSummarizedExperiment::TreeSummarizedExperiment(
assays = list(counts = SummarizedExperiment::assay(tse_metaphlan)),
rowData = SummarizedExperiment::rowData(tse_metaphlan),
colData = SummarizedExperiment::colData(tse_metaphlan) |>
as.data.frame() |>
dplyr::mutate(condition = rep(c("A", "B"), times = 3))
)
rec <- dar::recipe(tse_metaphlan) |>
dar::add_model(~ condition, targets = "condition", tax_level = "Genus")
rec
#> ── DAR Recipe ──────────────────────────────────────────────────────────────────
#> Inputs:
#>
#> ℹ phyloseq object with 16 taxa and 6 samples
#> ℹ variable of interes condition (class: character, levels: A, B)
#> ℹ taxonomic level Genus
#>
#> Statistical model:
#>
#> ℹ ~conditionConclusion
In this guide, we have explored various methods for importing
microbiome sequencing data from different formats into
Phyloseq and TreeSummarizedExperiment. We’ve
covered the biome, qiime, mothur,
metaphlan, and humann formats, providing
step-by-step examples for each.
The flexibility of these tools allows for a smooth transition between different data formats, making it easier to conduct your analysis irrespective of the initial data format. By following the steps outlined in this guide, you should be able to successfully convert your data and carry out further differential abundance analysis.
Remember, the specific details of your data may require you to adjust the parameters in the import functions. Always inspect your data after conversion to ensure it has been imported correctly.
Session info
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