Coercion functions for anansi
Usage
# S3 method for class '`anansi::AnansiWeb`'
as.list(x, ...)
# S3 method for class '`anansi::MultiFactor`'
as.list(x, ..., use.names = TRUE)
# S3 method for class '`anansi::AnansiWeb`'
as.data.frame(x, row.names, optional, ...)
asMAE(x)
asTSE(x)Examples
# AnansiWeb
x <- randomWeb(
n_samples = 5,
n_features_x = 4,
n_features_y = 6
)
as.list(x)
#> $y
#> y
#> sample_id y_1 y_2 y_3 y_4 y_5
#> anansi_ID_sample_1_1 0.26665271 2.0107346 0.9549602 0.7342024 -0.02531491
#> anansi_ID_sample_2_1 -0.21468618 1.1576628 -0.7614755 -0.3614332 -1.20992250
#> anansi_ID_sample_3_1 0.54418631 -1.1437424 -1.8017583 -0.5588636 -0.07770139
#> anansi_ID_sample_4_1 -0.73160010 -1.5832660 -1.3847598 -0.2774683 -0.60569915
#> anansi_ID_sample_5_1 -0.07715187 0.7374131 -0.2976818 0.5900611 -0.43986955
#> y
#> sample_id y_6
#> anansi_ID_sample_1_1 0.540867642
#> anansi_ID_sample_2_1 -0.899922143
#> anansi_ID_sample_3_1 -1.566842379
#> anansi_ID_sample_4_1 -0.001528403
#> anansi_ID_sample_5_1 -0.381552421
#>
#> $x
#> x
#> sample_id x_1 x_2 x_3 x_4
#> anansi_ID_sample_1_1 1.8975577 0.06014704 0.6663198 -0.7946773
#> anansi_ID_sample_2_1 0.3981683 0.26975570 0.4333921 -0.9398198
#> anansi_ID_sample_3_1 0.6794073 -0.82569929 0.9145914 0.1648717
#> anansi_ID_sample_4_1 -0.1673151 0.37878777 -1.3723373 1.2251926
#> anansi_ID_sample_5_1 -0.3377048 0.38632382 -0.9835608 -1.0493841
#>
#> $dictionary
#> 6 x 4 sparse Matrix of class "ngCMatrix"
#> x
#> y x_1 x_2 x_3 x_4
#> y_1 | . | .
#> y_2 . . . |
#> y_3 | . | |
#> y_4 . . . .
#> y_5 | | . .
#> y_6 | | | |
#>
#> $metadata
#> sample_id repeated group_ab subtype score_a
#> anansi_ID_sample_1_1 sample_1 rep_1 b x 0.4372142
#> anansi_ID_sample_2_1 sample_2 rep_1 a y 0.3158621
#> anansi_ID_sample_3_1 sample_3 rep_1 a y 0.1946105
#> anansi_ID_sample_4_1 sample_4 rep_1 a x -0.4557621
#> anansi_ID_sample_5_1 sample_5 rep_1 a y 0.8125350
#> score_b score_c
#> anansi_ID_sample_1_1 0.275042593 -0.1346410
#> anansi_ID_sample_2_1 0.006009411 1.4708322
#> anansi_ID_sample_3_1 2.010186412 -1.4751257
#> anansi_ID_sample_4_1 0.313808823 0.2044997
#> anansi_ID_sample_5_1 -0.846162712 -0.3417684
#>
as.data.frame(x)
#> y_1 y_2 y_3 y_4 y_5
#> anansi_ID_sample_1_1 0.26665271 2.0107346 0.9549602 0.7342024 -0.02531491
#> anansi_ID_sample_2_1 -0.21468618 1.1576628 -0.7614755 -0.3614332 -1.20992250
#> anansi_ID_sample_3_1 0.54418631 -1.1437424 -1.8017583 -0.5588636 -0.07770139
#> anansi_ID_sample_4_1 -0.73160010 -1.5832660 -1.3847598 -0.2774683 -0.60569915
#> anansi_ID_sample_5_1 -0.07715187 0.7374131 -0.2976818 0.5900611 -0.43986955
#> y_6 x_1 x_2 x_3 x_4
#> anansi_ID_sample_1_1 0.540867642 1.8975577 0.06014704 0.6663198 -0.7946773
#> anansi_ID_sample_2_1 -0.899922143 0.3981683 0.26975570 0.4333921 -0.9398198
#> anansi_ID_sample_3_1 -1.566842379 0.6794073 -0.82569929 0.9145914 0.1648717
#> anansi_ID_sample_4_1 -0.001528403 -0.1673151 0.37878777 -1.3723373 1.2251926
#> anansi_ID_sample_5_1 -0.381552421 -0.3377048 0.38632382 -0.9835608 -1.0493841
#> sample_id repeated group_ab subtype score_a
#> anansi_ID_sample_1_1 sample_1 rep_1 b x 0.4372142
#> anansi_ID_sample_2_1 sample_2 rep_1 a y 0.3158621
#> anansi_ID_sample_3_1 sample_3 rep_1 a y 0.1946105
#> anansi_ID_sample_4_1 sample_4 rep_1 a x -0.4557621
#> anansi_ID_sample_5_1 sample_5 rep_1 a y 0.8125350
#> score_b score_c
#> anansi_ID_sample_1_1 0.275042593 -0.1346410
#> anansi_ID_sample_2_1 0.006009411 1.4708322
#> anansi_ID_sample_3_1 2.010186412 -1.4751257
#> anansi_ID_sample_4_1 0.313808823 0.2044997
#> anansi_ID_sample_5_1 -0.846162712 -0.3417684
# AnansiWeb to MultiAssayExperiment
asMAE(x)
#> A MultiAssayExperiment object of 2 listed
#> experiments with user-defined names and respective classes.
#> Containing an ExperimentList class object of length 2:
#> [1] y: SummarizedExperiment with 6 rows and 5 columns
#> [2] x: SummarizedExperiment with 4 rows and 5 columns
#> Functionality:
#> experiments() - obtain the ExperimentList instance
#> colData() - the primary/phenotype DataFrame
#> sampleMap() - the sample coordination DataFrame
#> `$`, `[`, `[[` - extract colData columns, subset, or experiment
#> *Format() - convert into a long or wide DataFrame
#> assays() - convert ExperimentList to a SimpleList of matrices
#> exportClass() - save data to flat files
asTSE(x)
#> class: TreeSummarizedExperiment
#> dim: 6 5
#> metadata(1): dictionary
#> assays(1): y
#> rownames(6): y_1 y_2 ... y_5 y_6
#> rowData names(0):
#> colnames(5): anansi_ID_sample_1_1 anansi_ID_sample_2_1
#> anansi_ID_sample_3_1 anansi_ID_sample_4_1 anansi_ID_sample_5_1
#> colData names(7): sample_id repeated ... score_b score_c
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(1): x
#> rowLinks: NULL
#> rowTree: NULL
#> colLinks: NULL
#> colTree: NULL
# MultiFactor
x <- randomMultiFactor(n_types = 3, n_features = 3)
as.list(x, use.names = TRUE)
#> $a2b
#> a b
#> 1 a_001 b_001
#> 2 a_001 b_002
#> 3 a_001 b_003
#> 4 a_002 b_003
#> 5 a_003 b_003
#>
#> $b2c
#> b c
#> 1 b_001 c_001
#> 2 b_002 c_001
#> 3 b_002 c_002
#> 4 b_001 c_003
#> 5 b_003 c_003
#>