Methods for AnansiWeb S7 container class
See also
weaveWeb()
: for general use.AnansiWeb-pairwise: for methods for pairwise operations
Examples
# Setup
web <- randomWeb(n_samp = 36)
# Accessors
dimnames(web)
#> $y
#> [1] "y_1" "y_2" "y_3" "y_4" "y_5" "y_6" "y_7" "y_8" "y_9" "y_10"
#> [11] "y_11" "y_12"
#>
#> $x
#> [1] "x_1" "x_2" "x_3" "x_4" "x_5" "x_6" "x_7" "x_8"
#>
dim(web)
#> [1] 12 8
names(web)
#> [1] "y" "x"
# Getters and setters:
tableX(web)[1:5, 1:5]
#> x
#> sample_id x_1 x_2 x_3 x_4 x_5
#> anansi_ID_sample_1_1 1.36977586 -0.7175387 0.3624240 0.7432281 -0.91592054
#> anansi_ID_sample_2_1 -0.02011003 0.3616948 2.1693445 0.5571536 0.07326746
#> anansi_ID_sample_3_1 -0.10921759 1.3990043 0.1391305 -1.2485832 1.23817132
#> anansi_ID_sample_4_1 0.26466174 0.3726990 1.3763265 -0.2078743 0.71837134
#> anansi_ID_sample_5_1 0.30384826 -1.5656443 -0.4914500 0.4239695 0.53946650
tableY(web)[1:5, 1:5]
#> y
#> sample_id y_1 y_2 y_3 y_4 y_5
#> anansi_ID_sample_1_1 -0.2217445 1.3939778 1.8034834 -0.3780286 -0.4892583
#> anansi_ID_sample_2_1 -1.0095287 0.3602783 -0.1050687 1.7361110 -1.1660523
#> anansi_ID_sample_3_1 0.4807253 0.6545503 0.9824534 -0.8452478 -0.4796690
#> anansi_ID_sample_4_1 1.6044073 1.0521554 -1.7133026 -0.9615715 0.1153482
#> anansi_ID_sample_5_1 -1.5150245 -1.9795551 -0.8320195 1.0174911 -1.7680484
dictionary(web)
#> 12 x 8 sparse Matrix of class "ngCMatrix"
#> x
#> y x_1 x_2 x_3 x_4 x_5 x_6 x_7 x_8
#> y_1 | . | | | | . |
#> y_2 | . . | . | | .
#> y_3 | . . . . . | |
#> y_4 | . . . . | . .
#> y_5 | | . . | | . .
#> y_6 . | . . . . | .
#> y_7 | | . . . | | .
#> y_8 . | . . | | . .
#> y_9 | | | | | . . |
#> y_10 | | | . | . | |
#> y_11 . | . . . | | |
#> y_12 | . | . | . | .
head(metadata(web))
#> sample_id repeated group_ab subtype score_a score_b
#> anansi_ID_sample_1_1 sample_1 rep_1 a x -0.7189809 2.7151442
#> anansi_ID_sample_2_1 sample_2 rep_1 a z -1.1165613 -1.3393870
#> anansi_ID_sample_3_1 sample_3 rep_1 a z -0.7802699 -0.6460152
#> anansi_ID_sample_4_1 sample_4 rep_1 a z -1.7769585 -0.9324546
#> anansi_ID_sample_5_1 sample_5 rep_1 b y -0.4278349 -0.7690869
#> anansi_ID_sample_6_1 sample_6 rep_1 b z -2.0310270 0.3715798
#> score_c
#> anansi_ID_sample_1_1 0.3376581
#> anansi_ID_sample_2_1 -0.6075021
#> anansi_ID_sample_3_1 -0.2955603
#> anansi_ID_sample_4_1 -0.1345371
#> anansi_ID_sample_5_1 0.8147844
#> anansi_ID_sample_6_1 -0.2729217
# Assign some random metadata
metadata(web) <- data.frame(
id = row.names(tableY(web)),
a = rnorm(36),
b = sample(c("a", "b"), 36, TRUE),
row.names = "id"
)
# Coerce to list
weblist <- as.list(web)
# Coerce to Data.frame
webdf <- as.data.frame(web)
# Coerce to MultiAssayExperiment
mae <- asMAE(web)
# Coerce to TreeSummarizedExperiment
tse <- asTSE(web)