AnansiWeb S4 container class

AnansiWeb is an S4 class containing two feature tables as well as a dictionary to link them. AnansiWeb is the main container that will hold your input data throughout the anansi pipeline.

Typical use of the anansi package will involve generating an AnansiWeb object using the weaveWeb() function.

The function AnansiWeb() constructs an AnansiWeb object from two feature tables and an adjacency matrix.

Usage

## Constructor for `AnansiWeb` objects
AnansiWeb(tableX, tableY, dictionary, metadata = list(), ...)

## Accessors
# S4 method for class 'AnansiWeb'
dimnames(x)
# S4 method for class 'AnansiWeb'
dim(x)
# S4 method for class 'AnansiWeb'
names(x)

# S4 method for class 'AnansiWeb'
tableY(x, ...)
# S4 method for class 'AnansiWeb'
tableY(x, ...) <- value
# S4 method for class 'AnansiWeb'
tableX(x, ...)
# S4 method for class 'AnansiWeb'
tableX(x, ...) <- value
# S4 method for class 'AnansiWeb'
dictionary(x, ...)
# S4 method for class 'AnansiWeb'
dictionary(x, ...) <- value
# S4 method for class 'AnansiWeb'
metadata(x, simplify = TRUE, ...)
# S4 method for class 'AnansiWeb'
metadata(x, simplify = TRUE, ...) <- value

## Coercion
asMAE(x)
# S4 method for class 'AnansiWeb'
as.list(x, ...)
# S4 method for class 'AnansiWeb'
as.data.frame(
    x, row.names = NULL, optional = FALSE, ...
    )

## Utilities on feature pairs
# S4 method for class 'AnansiWeb'
which(x, arr.ind = TRUE, useNames = FALSE)
# S4 method for class 'AnansiWeb'
getFeaturePairs(
    x, which = NULL, with.metadata = FALSE, ...
)
# S4 method for class 'AnansiWeb'
mapply(
    FUN, ..., MoreArgs = NULL, SIMPLIFY = TRUE, USE.NAMES = TRUE
    )

# S4 method for class 'AnansiWeb'
show(object)

Arguments

tableY, tableX: A table containing features of interest. Rows should be samples and columns should be features. Y and X refer to the position of the features in a formula: Y ~ X.
dictionary: A binary adjacency matrix of class Matrix, or coercible to Matrix
metadata: list of metadata. Optional.
...: additional arguments (currently not used).
x, object: an AnansiWeb object on which a method will be applied.
simplify: boolean. If TRUE (Default), handles single data.frame arguments while ensuring compatibility with S4Vectors method.
value: replacement matrix with same number of rows target.
arr.ind, useNames: See ?base::which. AnansiWeb default returns a two-column array index.
FUN: a function with at least two arguments. The variables x and y, in order, refer to the corresponding values of feature pairs in tableX and tableY.
MoreArgs, SIMPLIFY, USE.NAMES: see ?base::mapply
which: integer matrix, indicating pair positions in tableY(x) and tableX(x), respectively. If NULL (default): Matrix::which(dictionary(x), TRUE).
with.metadata: Logical scalar whether to append metadata to output
row.names, optional: Ignored, for S4 generic. See ?base::as.data.frame.

Value

an AnansiWeb object, with sparse binary biadjacency matrix with features from y as rows and features from x as columns in dictionary slot.

A list of data.frames with the paired data

Slots

tableY,tableX: Two matrix objects of measurements, data. Rows are samples and columns are features. Access with tableY() and tableX().
dictionary: Matrix, binary adjacency matrix. Optionally sparse. Typically generated using the weaveWeb() function. Access with dictionary().
metadata: Optional data.frame of sample metadata. Access with metadata().

Examples


# Use AnansiWeb() to consrtuct an AnansiWeb object from components:
tX <- `dimnames<-`(replicate(5, (rnorm(36))),
    value = list(
        as.character(seq_len(36)),
        letters[1:5]
    )
)
tY <- `dimnames<-`(replicate(3, (rnorm(36))),
    value = list(
        as.character(seq_len(36)),
        LETTERS[1:3]
    )
)

d <- matrix(TRUE,
    nrow = NCOL(tY), ncol = NCOL(tX),

    # Note: Dictionary should have named dimensions
    dimnames = list(
        y_names = colnames(tY),
        x_names = colnames(tX)
    )
)
web <- AnansiWeb(tableX = tX, tableY = tY, dictionary = d)

# Methods for AnansiWeb
dimnames(web)
#> $y_names
#> [1] "A" "B" "C"
#> 
#> $x_names
#> [1] "a" "b" "c" "d" "e"
#> 
dim(web)
#> [1] 3 5
names(web)
#> [1] "y_names" "x_names"

tableX(web)
#>               a           b           c           d           e
#> 1  -1.400043517 -1.91008747  1.33732041  0.22712713  0.97995670
#> 2   0.255317055 -0.27923724  0.23669628  0.97845492  1.32178099
#> 3  -2.437263611 -0.31344598  1.31829338 -0.20888265 -1.11971083
#> 4  -0.005571287  1.06730788  0.52390979 -1.39941046  0.51459982
#> 5   0.621552721  0.07003485  0.60674805  0.25853729 -1.50909984
#> 6   1.148411606 -0.63912332 -0.10993567 -0.44179945  1.53274148
#> 7  -1.821817661 -0.04996490  0.17218172  0.56859986  0.42914737
#> 8  -0.247325302 -0.25148344 -0.09032729  2.12685046  0.12210341
#> 9  -0.244199607  0.44479712  1.92434334  0.42485844 -1.13801240
#> 10 -0.282705449  2.75541758  1.29839276 -1.68428153 -0.55801513
#> 11 -0.553699384  0.04653138  0.74879127  0.24940178  1.05253854
#> 12  0.628982042  0.57770907  0.55622433  1.07283825  0.67768364
#> 13  2.065024895  0.11819487 -0.54825726  2.03936926  0.03849955
#> 14 -1.630989402 -1.91172049  1.11053489  0.44945378 -0.35638119
#> 15  0.512426950  0.86208648 -2.61233433  1.39181405  0.78284410
#> 16 -1.863011492 -0.24323674 -0.15569378  0.42656655  0.80441162
#> 17 -0.522012515 -0.20608719  0.43388979  0.10758399 -1.90006082
#> 18 -0.052601910  0.01917759 -0.38195111  0.02229473  0.93578429
#> 19  0.542996343  0.02956075  0.42418757  0.60361101 -0.30905150
#> 20 -0.914074827  0.54982754  1.06310200 -0.26265057  0.26306668
#> 21  0.468154420 -2.27411486  1.04871262 -0.52826408 -1.79059186
#> 22  0.362951256  2.68255718 -0.03810289  0.19214942 -0.78825884
#> 23 -1.304543545 -0.36122126  0.48614892 -1.14619967 -1.13302167
#> 24  0.737776321  0.21335575  1.67288261  0.84618466  0.36365257
#> 25  1.888504929  1.07434588 -0.35436116  0.08171963 -0.28588791
#> 26 -0.097445104 -0.66508825  0.94634789 -1.30511701  0.51766913
#> 27 -0.935847354  1.11395242  1.31682636 -0.94491206 -0.10290867
#> 28 -0.015950311 -0.24589641 -0.29664002  0.45434159 -0.97406959
#> 29 -0.826788954 -1.17756331 -0.38721358 -0.85520250  1.27067230
#> 30 -1.512399651 -0.97585062 -0.78543266 -0.28689522  0.96086479
#> 31  0.935363190  1.06505732 -1.05673687  0.89496163  0.76872137
#> 32  0.176488611  0.13167063 -0.79554143  0.06730444  1.03593077
#> 33  0.243685465  0.48862881 -1.75627543 -0.16267634 -0.47388707
#> 34  1.623548883 -1.69945057 -0.69053790 -0.82731017 -1.27533487
#> 35  0.112038083 -1.47073631 -0.55854199  1.87650562 -0.30562067
#> 36 -0.133997013  0.28415034 -0.53666333  0.76644020  2.21176949
tableY(web)
#>              A           B            C
#> 1  -1.04166838  0.55851442 -1.193641182
#> 2  -1.14652385  0.41540640 -0.751723323
#> 3  -1.67532730 -1.45229977  1.455841403
#> 4   1.52593866  0.94120612 -0.828603533
#> 5   0.55418551 -0.33893587  0.289774460
#> 6   1.99311026 -0.07557425 -0.480053484
#> 7  -0.15412074  0.04020439 -0.604829354
#> 8   2.56440834  0.12430107  1.460110180
#> 9   1.06199914 -0.99843255  0.149679354
#> 10  1.14269488  1.23339006 -1.433321100
#> 11  1.12383884  0.34042449 -0.010303319
#> 12 -0.39700149 -0.47270248 -0.212236035
#> 13 -0.82326115  0.70875306 -0.906340179
#> 14 -0.57888462 -1.52895871 -2.102152479
#> 15  1.76378938  0.23742535  1.893360464
#> 16  0.13299215 -1.31281425 -0.968125837
#> 17  0.37649933  0.74702859 -0.102603036
#> 18  1.13870765 -1.56251843  0.239959572
#> 19  1.24126308  0.07105336  0.060898893
#> 20  0.61209094 -0.63953477 -2.177576028
#> 21 -0.42938009 -0.84519574 -0.117860143
#> 22  1.36046133  0.67524470  0.112294787
#> 23 -0.07085743  1.15337579  0.007886198
#> 24 -0.27215368 -1.68650474  1.877743872
#> 25 -2.44668003 -0.90281495  2.158756554
#> 26  0.06548664  1.31763370  0.709714522
#> 27 -1.09850890  1.10018974  0.766983379
#> 28 -0.63317818  1.20376784 -0.308211421
#> 29 -2.06365445 -1.43127078  1.012001849
#> 30  2.64893203  1.38291086 -0.919051597
#> 31 -1.15339839  0.00312594  0.563380077
#> 32 -0.34063788 -0.07788682  0.322482749
#> 33  0.78636258  0.44142823  0.366674363
#> 34 -1.27051311  0.12892290  1.129835153
#> 35  0.54214155 -0.83021426 -0.941498076
#> 36  0.07510590 -0.50359291  0.217837643
dictionary(web)
#> 3 x 5 sparse Matrix of class "lgCMatrix"
#>        x_names
#> y_names a b c d e
#>       A | | | | |
#>       B | | | | |
#>       C | | | | |

# 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"
)
metadata(web)
#>              a b
#> 1   1.41541229 b
#> 2  -0.38373305 a
#> 3  -0.17408637 b
#> 4  -0.22174452 b
#> 5  -1.00952872 b
#> 6   0.48072527 b
#> 7   1.60440733 a
#> 8  -1.51502453 b
#> 9  -1.41602391 b
#> 10  0.87677733 a
#> 11  0.62413241 b
#> 12  2.11227729 a
#> 13 -0.35612442 b
#> 14 -1.06446421 a
#> 15  1.07711654 a
#> 16  1.18157557 b
#> 17  0.19839209 a
#> 18 -0.40040525 a
#> 19  0.61615428 b
#> 20  1.97415675 b
#> 21  1.88466232 b
#> 22 -1.58862055 b
#> 23 -0.53992316 b
#> 24 -1.16946146 a
#> 25  0.55910599 a
#> 26 -1.81934725 b
#> 27  0.39334397 b
#> 28  0.04213411 a
#> 29  1.17966418 b
#> 30 -0.25692118 b
#> 31 -1.05633610 a
#> 32  0.19877721 a
#> 33  0.65053355 a
#> 34  0.34391334 a
#> 35  1.47753231 a
#> 36  0.07202570 b

# coerce To list
weblist <- as.list(web)

# Coerce to MultiAssayExperiment
asMAE(web)
#> A MultiAssayExperiment object of 2 listed
#>  experiments with user-defined names and respective classes.
#>  Containing an ExperimentList class object of length 2:
#>  [1] y_names: SummarizedExperiment with 3 rows and 36 columns
#>  [2] x_names: SummarizedExperiment with 5 rows and 36 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

# Extract data.frames in pairs (only show first)
getFeaturePairs(web)[1L]
#> [[1]]
#>              A            a
#> 1  -1.04166838 -1.400043517
#> 2  -1.14652385  0.255317055
#> 3  -1.67532730 -2.437263611
#> 4   1.52593866 -0.005571287
#> 5   0.55418551  0.621552721
#> 6   1.99311026  1.148411606
#> 7  -0.15412074 -1.821817661
#> 8   2.56440834 -0.247325302
#> 9   1.06199914 -0.244199607
#> 10  1.14269488 -0.282705449
#> 11  1.12383884 -0.553699384
#> 12 -0.39700149  0.628982042
#> 13 -0.82326115  2.065024895
#> 14 -0.57888462 -1.630989402
#> 15  1.76378938  0.512426950
#> 16  0.13299215 -1.863011492
#> 17  0.37649933 -0.522012515
#> 18  1.13870765 -0.052601910
#> 19  1.24126308  0.542996343
#> 20  0.61209094 -0.914074827
#> 21 -0.42938009  0.468154420
#> 22  1.36046133  0.362951256
#> 23 -0.07085743 -1.304543545
#> 24 -0.27215368  0.737776321
#> 25 -2.44668003  1.888504929
#> 26  0.06548664 -0.097445104
#> 27 -1.09850890 -0.935847354
#> 28 -0.63317818 -0.015950311
#> 29 -2.06365445 -0.826788954
#> 30  2.64893203 -1.512399651
#> 31 -1.15339839  0.935363190
#> 32 -0.34063788  0.176488611
#> 33  0.78636258  0.243685465
#> 34 -1.27051311  1.623548883
#> 35  0.54214155  0.112038083
#> 36  0.07510590 -0.133997013
#> 

mapply(
    FUN = function(x, y) cor(x, y),
    web
)
#>          aA          aB          aC          bA          bB          bC 
#> -0.04798083  0.06035449  0.32607170  0.21244171  0.26101305  0.13758072 
#>          cA          cB          cC          dA          dB          dC 
#> -0.13706694 -0.13116275 -0.24363013  0.07428211 -0.26138248  0.07189662 
#>          eA          eB          eC 
#>  0.09469575 -0.06728680 -0.07338832

Usage

Arguments

Value

Slots

See also

Examples