anansi: anansi: Annotation-based Analysis of Specific Interactions.

In thomazbastiaanssen/anansi: Annotation-Based Analysis of Specific Interactions

anansi: Annotation-based Analysis of Specific Interactions.

Description

The anansi package package provides tools to prepare and facilitate integrative association analysis between the features of two data sets that are known to interact.

1. Input for anansi() with AnansiWeb() and MultiFactor()

• randomAnansi, kegg_link(): Generate example input

• AnansiWeb, MultiFactor: Handle and manipulate input

2. Output and cross-compatibility

• getAnansi(): Compatibility with mia::mia and MultiAssayExperiment::MultiAssayExperiment

• getGraph(): Compatibility with igraph::igraph

• plotAnansi(): Plot output in the style of miaViz::miaViz

3. Vignettes

• 1. Getting started with anansi

• 2. Adjacency matrices

• 3. Association testing

Description: The anansi() function

The main workspider function in the anansi package is called anansi. It manages the individual functionalities of anansi, including correlation analysis, correlation by group and differential associations.

Usage

anansi(
  web,
  formula,
  groups = NULL,
  metadata = NULL,
  adjust.method = "BH",
  verbose = TRUE,
  return.format = "table",
  ...
)

Arguments

web	An AnansiWeb object, containing two tables with 'omics data and a dictionary that links them. See weaveWebFromTables() for how to weave a web.
formula	A formula object. Used to assess differential associations.
groups	A vector of the column names of categorical values in the metadata object to control which groups should be assessed for simple correlations. If no argument provided, anansi will let you know and still to regular correlations according to your dictionary.
metadata	A vector or data.frame of categorical or continuous value necessary for differential correlations. Typically a state or treatment. If no argument provided, anansi will let you know and still to regular correlations according to your dictionary.
adjust.method	Method to adjust p-values for multiple comparisons. adjust.method = "BH" is the default value. See p.adjust() in the base R stats package.
verbose	⁠Logical scalar⁠. Whether to print diagnostic information (Default: TRUE). while running. Useful for debugging errors on large datasets.
return.format	⁠Character scalar⁠. Should be one of "table", "list", or "raw". Should the output of anansi() respectively be a wide data.frame of results, a list containing the results and input, or a list of raw output (used for testing purposes). convenient use. (Default: "table")
...	additional arguments (currently not used).

Value

A list of lists containing correlation coefficients, p-values and q-values for all operations.

Author(s)

Maintainer: Thomaz Bastiaanssen [e-mail] (ORCID)

Authors:

• Thomas Quinn [e-mail] (ORCID)

• Giulio Benedetti [e-mail] (ORCID)

• Leo Lahti [e-mail] (ORCID)

See Also

Useful links:

• https://github.com/thomazbastiaanssen/anansi

• https://thomazbastiaanssen.github.io/anansi

• Report bugs at https://github.com/thomazbastiaanssen/anansi/issues

Examples

# Load example data:

data(FMT_data)

# Make sure that columns are features and rows are samples.

t1 <- t(FMT_metab)
t2 <- t(FMT_KOs)

# Run anansi pipeline.

web <- weaveWeb(
    formula = cpd ~ ko,
    tableY = t1,
    tableX = t2,
    link = kegg_link()
)

anansi_out <- anansi(
    web = web,
    formula = ~Legend,
    groups = "Legend",
    metadata = FMT_metadata,
    adjust.method = "BH",
    verbose = TRUE
)


library(tidyr)

# Use tidyr to wrangle the correlation r-values to a single column
anansiLong <- anansi_out |>
    pivot_longer(starts_with("All") | contains("FMT")) |>
    separate_wider_delim(
        name,
        delim = "_", names = c("cor_group", "param")
    ) |>
    pivot_wider(names_from = param, values_from = value)

# Only consider interactions where the entire model fits well enough.
library(ggplot2)
anansiLong <- anansiLong[anansiLong$full_p.values < 0.05, ]



ggplot(
    data = anansiLong,
    aes(
        x = r.values,
        y = feature_X,
        fill = cor_group,
        alpha = disjointed_Legend_p.values < 0.05
    )
) +

    # Make a vertical dashed red line at x = 0
    geom_vline(xintercept = 0, linetype = "dashed", colour = "red") +

    # Points show  raw correlation coefficients
    geom_point(shape = 21, size = 3) +

    # facet per compound
    ggforce::facet_col(~feature_Y, space = "free", scales = "free_y") +
    scale_fill_manual(values = c(
        "Young yFMT" = "#2166ac",
        "Aged oFMT" = "#b2182b",
        "Aged yFMT" = "#ef8a62",
        "All" = "gray"
    )) +
    theme_bw()

# Using miaViz style function:

p <- plotAnansi(anansi_out,
    association.type = "disjointed",
    model.var = "Legend",
    fill_by = "group",
    signif.threshold = 0.05,
    x_lab = "Pearson's rho"
)
p <- p +
    scale_fill_manual(values = c(
        "Young yFMT" = "#2166ac",
        "Aged oFMT" = "#b2182b",
        "Aged yFMT" = "#ef8a62",
        "All" = "gray"
    )) +
    theme_bw()

p

thomazbastiaanssen/anansi documentation built on June 9, 2025, 3:59 p.m.