Description Usage Arguments Value Examples
Retrieve and organize main data from
biom-class
, represented as a matrix with
index names.
1 |
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
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | min_dense_file = system.file("extdata", "min_dense_otu_table.biom", package = "biom")
min_sparse_file = system.file("extdata", "min_sparse_otu_table.biom", package = "biom")
rich_dense_file = system.file("extdata", "rich_dense_otu_table.biom", package = "biom")
rich_sparse_file = system.file("extdata", "rich_sparse_otu_table.biom", package = "biom")
min_dense_file = system.file("extdata", "min_dense_otu_table.biom", package = "biom")
rich_dense_char = system.file("extdata", "rich_dense_char.biom", package = "biom")
rich_sparse_char = system.file("extdata", "rich_sparse_char.biom", package = "biom")
# 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)
|
5 x 6 Matrix of class "dgeMatrix"
Sample1 Sample2 Sample3 Sample4 Sample5 Sample6
GG_OTU_1 0 0 1 0 0 0
GG_OTU_2 5 1 0 2 3 1
GG_OTU_3 0 0 1 4 2 0
GG_OTU_4 2 1 1 0 0 1
GG_OTU_5 0 1 1 0 0 0
5 x 6 sparse Matrix of class "dgCMatrix"
Sample1 Sample2 Sample3 Sample4 Sample5 Sample6
GG_OTU_1 . . 1 . . .
GG_OTU_2 5 1 . 2 3 1
GG_OTU_3 . . 1 4 . 2
GG_OTU_4 2 1 1 . . 1
GG_OTU_5 . 1 1 . . .
5 x 6 Matrix of class "dgeMatrix"
Sample1 Sample2 Sample3 Sample4 Sample5 Sample6
GG_OTU_1 0 0 1 0 0 0
GG_OTU_2 5 1 0 2 3 1
GG_OTU_3 0 0 1 4 2 0
GG_OTU_4 2 1 1 0 0 1
GG_OTU_5 0 1 1 0 0 0
5 x 6 sparse Matrix of class "dgCMatrix"
Sample1 Sample2 Sample3 Sample4 Sample5 Sample6
GG_OTU_1 . . 1 . . .
GG_OTU_2 5 1 . 2 3 1
GG_OTU_3 . . 1 4 . 2
GG_OTU_4 2 1 1 . . 1
GG_OTU_5 . 1 1 . . .
Sample1 Sample2 Sample3 Sample4 Sample5 Sample6
GG_OTU_1 "0" "sky" "1" "0" "clouds" "0"
GG_OTU_2 "5" "1" "0" "2" "3" "1"
GG_OTU_3 "0" "lightning" "1" "4" "2" "0"
GG_OTU_4 "2" "1" "1" "0" "gray" "1"
GG_OTU_5 "0" "1" "1" "0" "0" "bottle"
Sample1 Sample2 Sample3 Sample4 Sample5 Sample6
GG_OTU_1 NA NA "sky" NA NA NA
GG_OTU_2 "clouds" "lightning" NA "gray" "bottle" "1"
GG_OTU_3 NA NA "1" "4" NA "2"
GG_OTU_4 "2" "1" "1" NA NA "1"
GG_OTU_5 NA "1" "1" NA NA NA
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