Bioc style plotting wrapper for anansi output

plotAnansi generates an association plot from the output of anansi() in the table format. It provides a convenient way to visually assess relevant results from the anansi analysis, either in the form of a dotplot or a graph.

Usage

plotAnansi(x, ...)

# S4 method for class 'data.frame'
plotAnansi(
  x,
  layout = "dotplot",
  association.type = NULL,
  model.var = NULL,
  group = "All",
  signif.threshold = NULL,
  colour_by = NULL,
  color_by = colour_by,
  fill_by = "group",
  size_by = NULL,
  shape_by = NULL,
  y_position = "right",
  x_lab = "cor",
  y_lab = "",
  show.cor = FALSE
)

Arguments

x: a data.frame object output of anansi() in the table format.
...: additional parameters
layout: Character scalar. Specifies the plot layout to generate. It must be one of c("dotplot, graph). (Default: dotplot)
association.type: Character scalar. Specifies the type of association to show in the plot. One of "disjointed", "emergent" and "full". (Default: NULL)
model.var: Character scalar. Specifies the name of a variable in the anansi model. It is relevant only when association.type is "disjointed" or "emergent". (Default: NULL)
group: Character scalar. Selects one of the groups included in the anansi model. It is relevant only when layout is graph. (Default: All)
signif.threshold: Numeric scalar. Specifies the threshold to mark the significance of association.type. (Default: NULL)
colour_by: Character scalar. Specifies one of the groups terms used in the original anansi call, x by which points should be coloured. (Default: NULL)
color_by: Character scalar. Alias to colour_by.
fill_by: Character scalar. Specifies one of the groups terms used in the original anansi call, x by which points should be filled (Default: "group")
size_by: Character scalar. Specifies one of the groups terms used in the original anansi call, x by which points should be sized. (Default: NULL)
shape_by: Character scalar. Specifies one of the groups terms used in the original anansi call, x by which points should be shaped. (Default: NULL)
y_position: Character scalar. Specifies the position of the y labels. It should be either "left" or "right". (Default: "right")
x_lab: Character scalar. Specifies the label of the x axis. (Default: "cor")
y_lab: Character scalar. Specifies the label of the y axis. (Default: "")
show.cor: Logical scalar. Whether correlation edges should be labelled with correlation coefficients when layout is graph. (Default: FALSE)

Value

A ggplot2 object.

Details

plotAnansi provides a standardised method to visualise the results of anansi by means of a differential association plot. The input for this function should be generated from anansi() or anansi(), with return.format = "table"

Examples

# Import libraries
library(mia)
#> Loading required package: MultiAssayExperiment
#> Loading required package: SummarizedExperiment
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#> 
#> Attaching package: ‘MatrixGenerics’
#> The following objects are masked from ‘package:matrixStats’:
#> 
#>     colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#>     colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#>     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#>     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#>     colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#>     colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#>     colWeightedMeans, colWeightedMedians, colWeightedSds,
#>     colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#>     rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#>     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#>     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#>     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#>     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#>     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#>     rowWeightedSds, rowWeightedVars
#> Loading required package: GenomicRanges
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#> Loading required package: generics
#> 
#> Attaching package: ‘generics’
#> The following objects are masked from ‘package:base’:
#> 
#>     as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
#>     setequal, union
#> 
#> Attaching package: ‘BiocGenerics’
#> The following objects are masked from ‘package:stats’:
#> 
#>     IQR, mad, sd, var, xtabs
#> The following objects are masked from ‘package:base’:
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#>     as.data.frame, basename, cbind, colnames, dirname, do.call,
#>     duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
#>     mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
#>     rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
#>     unsplit, which.max, which.min
#> Loading required package: S4Vectors
#> 
#> Attaching package: ‘S4Vectors’
#> The following object is masked from ‘package:tidyr’:
#> 
#>     expand
#> The following object is masked from ‘package:utils’:
#> 
#>     findMatches
#> The following objects are masked from ‘package:base’:
#> 
#>     I, expand.grid, unname
#> Loading required package: IRanges
#> Loading required package: Seqinfo
#> Loading required package: Biobase
#> Welcome to Bioconductor
#> 
#>     Vignettes contain introductory material; view with
#>     'browseVignettes()'. To cite Bioconductor, see
#>     'citation("Biobase")', and for packages 'citation("pkgname")'.
#> 
#> Attaching package: ‘Biobase’
#> The following object is masked from ‘package:MatrixGenerics’:
#> 
#>     rowMedians
#> The following objects are masked from ‘package:matrixStats’:
#> 
#>     anyMissing, rowMedians
#> Loading required package: SingleCellExperiment
#> Loading required package: TreeSummarizedExperiment
#> Loading required package: Biostrings
#> Loading required package: XVector
#> 
#> Attaching package: ‘Biostrings’
#> The following object is masked from ‘package:base’:
#> 
#>     strsplit
#> This is mia version 1.17.0
#> - Online documentation and vignettes: https://microbiome.github.io/mia/
#> - Online book 'Orchestrating Microbiome Analysis (OMA)': https://microbiome.github.io/OMA/docs/devel/
library(TreeSummarizedExperiment)
library(MultiAssayExperiment)
library(ggraph)

web <- randomWeb(n_samples = 100)
mae <- as(web, "MultiAssayExperiment")

# Perform anansi analysis
out <- weaveWeb(mae,
    tableY = "y", tableX = "x"
) |> anansi(formula = ~group_ab)
#> Fitting least-squares for following model:
#> ~ x + group_ab + x:group_ab 
#> Running correlations for the following groups:
#>  b, a

# Select significant interactions
out <- out[out$full_p.values < 0.05, ]

# Visualise disjointed associations filled by group
plotAnansi(out,
    association.type = "disjointed",
    model.var = "group_ab",
    signif.threshold = 0.05,
    fill_by = "group"
)


# Visualise full associations filled by group
plotAnansi(out,
    association.type = "full",
    signif.threshold = 0.05,
    fill_by = "group"
)


# Visualise full associations as graph
plotAnansi(out,
    layout = "graph",
    association.type = "full",
    signif.threshold = 0.05,
    show.cor = TRUE
)


# Visualise disjointed associations as graph
plotAnansi(out,
    layout = "graph",
    association.type = "disjointed",
    model.var = "group_ab",
    signif.threshold = 0.05
)