README.md

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

# Knowledge-based multi-modal integration using anansi ## Introduction The `anansi` package computes and compares the association between the features of two ’omics datasets that are known to interact based on a database such as KEGG. Studies including both microbiome and metabolomics data are becoming more common. Often, it would be helpful to integrate both datasets in order to see if they corroborate each others patterns. All vs all association is imprecise and likely to yield spurious associations. This package takes a knowledge-based approach to constrain association search space, only considering metabolite-function interactions that have been recorded in a pathway database. This package also provides a framework to assess differential association. While `anansi` is geared towards metabolite-function interactions in the context of host-microbe interactions, it is perfectly capable of handling any other pair of datasets where some features interact canonically. Please note this package is in active development. If you use this software, please cite our work. wzxhzdk:0 ## To cite anansi in publications use: ## ## Bastiaanssen TFS, Quinn TP, Cryan JF (2023) Knowledge-based ## Integration of Multi-Omic Datasets with Anansi: Annotation-based ## Analysis of Specific Interactions arXiv. doi: ## 10.48550/arXiv.2305.10832 ## ## A BibTeX entry for LaTeX users is ## ## @Article{, ## title = {Knowledge-based Integration of Multi-Omic Datasets with Anansi: Annotation-based Analysis of Specific Interactions}, ## author = {Thomaz F S Bastiaanssen and Thomas P Quinn and John F Cryan}, ## journal = {arXiv}, ## year = {2023}, ## doi = {10.48550/arXiv.2305.10832}, ## } ## Setup OK, now let’s get started. We’ll load a complementary training dataset using `data(FMT_data)`. This loads a curated snippet from the dataset described in more detail here: A very early version of `anansi` was used to generate “Extended Data Fig. 7” in that paper. wzxhzdk:1 ## Data preparation The main `anansi` function expects data in the `anansiWeb` format; Basically a list with exactly three tables: The first table, `tableY`, should be a count table of metabolites. The second table, `tableX`, should be a count table of functions. Both tables should have columns as features and rows as samples. The third table should be a binary adjacency matrix with the column names of `tableY` as rows and the column names of `tableX` as columns. Such an adjacency matrix is provided in the `anansi` library and is referred to as a dictionary (because you use it to look up which metabolites interact with which functions). Though this example uses metabolites and functions, `anansi` is able to handle any type of ’omics data, as long as there is a dictionary available. Because of this, anansi uses the type-naive nomenclature `tableY` and `tableX`. The Y and X refer to the position these measurements will have in the linear modeling framework: $$Y \sim X \times {\text{covariates}}$$ ### A note on functional microbiome data Two common questions in the host-microbiome field are “Who’s there?” and “What are they doing?”. Techniques like 16S sequencing and shotgun metagenomics sequencing are most commonly used to answer the first question. The second question can be a bit more tricky - often we’ll need functional inference software to address them. For 16S sequencing, algorithms like PICRUSt2 and Piphillin can be used to infer function. For shotgun metagenomics, HUMANn3 in the bioBakery suite can be used. All of these algorithms can produce functional count data in terms of KEGG Orthologues (KOs). These tables can be directly plugged in to `anansi`. wzxhzdk:2 ## Weave a web🕸️ The `weaveWebFromTables()` function can be used to parse the tables that we prepared above into an `anansiWeb` object. The `anansiWeb` format is a necessary input file for the main `anansi` workflow. wzxhzdk:3 ## Operating in interaction mode ## 3 were matched between table 1 and the columns of the adjacency matrix ## 50 were matched between table 2 and the rows of the adjacency matrix ## Run anansi🕷️ The main workspider in this package is called `anansi`. Generally, you want to give it three arguments. First, there’s `web`, which is an `anansiWeb` object, such as the one we generated in the above step. Second, there’s `formula`, which should be a formula. For instance, to assess differential associations between treatments, we use the formula `~Legend`, provided we have a column with that name in our `metadata` object, the Third argument. wzxhzdk:4 ## Fitting least-squares for following model: ## ~ x + Legend + x:Legend ## Running correlations for the following groups: Aged yFMT, Aged oFMT, Young yFMT ## Adjusting p-values using Benjamini & Hochberg's procedure. ## Spin to a table📝 `anansi` gives a complex nested `anansiYarn` object as an output. Two functions exist that will wrangle your data to more friendly formats for you. You can either use `spinToLong()` or `spinToWide()`. They will give you long or wide format data.frames, respectively. For general reporting, we recommend sticking to the wide format as it’s the most legible. You can also use the `plot()` method on an `anansiYarn` object to gain some insights in the state of your p, q, R and R2 parameters. wzxhzdk:5 ## Plot the results The long format can be helpful to plug the data into `ggplot2`. Here, we recreate part of the results from the FMT Aging study. wzxhzdk:6 

thomazbastiaanssen/anansi documentation built on Feb. 9, 2025, 2:07 p.m.