Access main data observation matrix data from biom-class.
Retrieve and organize main data from biom-class,
represented as a matrix with index names.
biom_data(x, rows, columns, parallel = FALSE) ## S4 method for signature 'biom,missing,missing' biom_data(x, rows, columns, parallel = FALSE) ## S4 method for signature 'biom,character,ANY' biom_data(x, rows, columns, parallel = FALSE) ## S4 method for signature 'biom,ANY,character' biom_data(x, rows, columns, parallel = FALSE) ## S4 method for signature 'biom,numeric,missing' biom_data(x, rows, columns, parallel = FALSE) ## S4 method for signature 'biom,missing,numeric' biom_data(x, rows, columns, parallel = FALSE) ## S4 method for signature 'biom,numeric,numeric' biom_data(x, rows, columns, parallel = FALSE)
x |
(Required). An instance of the |
rows |
(Optional). The subset of row indices described in the
returned object. For large datasets, specifying the row subset here,
rather than after creating the whole matrix first,
can improve speed/efficiency.
Can be vector of index numbers ( |
columns |
(Optional). The subset of column indices described in the
returned object. For large datasets, specifying the column subset here,
rather than after creating the whole matrix first,
can improve speed/efficiency.
Can be vector of index numbers ( |
parallel |
(Optional). Logical. Whether to perform the accession parsing
using a parallel-computing backend supported by the |
A matrix containing the main observation data, with index names.
The type of data (numeric or character)
will depend on the results of matrix_element_type(x).
The class of the matrix returned will depend on the sparsity of the data,
and whether it has numeric or character data.
For now, only numeric data can be stored in a Matrix-class,
which will be stored sparsely, if possible.
Character data will be returned as a vanilla matrix-class.
min_dense_file = system.file("extdata", "min_dense_otu_table.biom", package = "biomformat")
min_sparse_file = system.file("extdata", "min_sparse_otu_table.biom", package = "biomformat")
rich_dense_file = system.file("extdata", "rich_dense_otu_table.biom", package = "biomformat")
rich_sparse_file = system.file("extdata", "rich_sparse_otu_table.biom", package = "biomformat")
min_dense_file = system.file("extdata", "min_dense_otu_table.biom", package = "biomformat")
rich_dense_char = system.file("extdata", "rich_dense_char.biom", package = "biomformat")
rich_sparse_char = system.file("extdata", "rich_sparse_char.biom", package = "biomformat")
# Read the biom-format files
x1 = read_biom(min_dense_file)
x2 = read_biom(min_sparse_file)
x3 = read_biom(rich_dense_file)
x4 = read_biom(rich_sparse_file)
x5 = read_biom(rich_dense_char)
x6 = read_biom(rich_sparse_char)
# Extract the data matrices
biom_data(x1)
biom_data(x2)
biom_data(x3)
biom_data(x4)
biom_data(x5)
biom_data(x6)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.