The read_qiime2 function reads various types of .qza files created by qiime2, and creates a `strollur` object.
Usage
read_qiime2(
qza,
metadata = NULL,
dataset_name = "",
dir_path = NULL,
remove_unpacked_artifacts = TRUE
)Arguments
- qza
vector of filenames, .qza files containing your data from qiime2.
- metadata
filename, a .tsv file containing metadata
- dataset_name
A string containing a name for your dataset.
- dir_path
a string containing the name of directory where the artifacts files should be unpacked. Default = current working directory.
- remove_unpacked_artifacts
boolean, When TRUE, the unpacked artifacts and temporary directories will be removed. Default = TRUE.
Examples
# Using the example files from moving-pictures, we add FASTA data, assign
# taxonomy and abundance for features, and add a newick tree and
# metadata.
qza_files <- c(
strollur_example("rep_seqs.qza"),
strollur_example("table.qza"),
strollur_example("taxonomy.qza"),
strollur_example("rooted-tree.qza")
)
data <- read_qiime2(
qza = qza_files,
metadata = strollur_example("sample_metadata.tsv"),
dataset_name = "qiime2_moving_pictures"
)
#> Added metadata.
#> Added 759 sequences.
#> Assigned 759 sequence abundances.
#> Assigned 759 asv bins.
#> Assigned 759 asv bin taxonomies.
data
#> qiime2_moving_pictures:
#>
#> starts ends nbases ambigs polymers numns numseqs
#> Minimum: 1 120 120 0 3 0 1.00
#> 2.5%-tile: 1 120 120 0 3 0 3933.45
#> 25%-tile: 1 120 120 0 4 0 39325.50
#> Median: 1 120 120 0 4 0 78650.00
#> 75%-tile: 1 120 120 0 4 0 117974.50
#> 97.5%-tile: 1 120 120 0 6 0 153366.55
#> Maximum: 1 120 120 0 8 0 157298.00
#> Mean: 1 120 120 0 4 0 0.00
#>
#> Number of unique seqs: 759
#> Total number of seqs: 157298
#>
#> Total number of samples: 34
#> Total number of asvs: 759
#> Total number of asv bin classifications: 759
#> Your dataset includes metadata
#>