readyomics

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readyomics provides a pipeline for formatting, analyzing, and visualizing omics data - regardless of omics type (e.g. transcriptomics, proteomics, metabolomics, metagenomics).

It is designed for flexibility, reproducibility, and scalability across a wide range of study designs, with modular components for statistical analysis and visualization.

It includes tools to:

• Process data into analysis-ready.
• Perform multivariate analysis.
• Fit linear or mixed-effects models.
• Produce publication-quality plots.

Essential standard terms used in the package

• Platform: the technology or instrument used to generate omics data, such as next-generation sequencing (NGS), mass spectrometry (MS), or nuclear magnetic resonance spectroscopy (NMR).
• Feature(s): a general term for a biological variable that has been profiled with an omics platform, such as metabolites, lipids, genes, proteins, or microbial taxa, depending on the assay.
• Sample data (!= metadata*): biological or demographic information collected for each study sample (e.g., experimental group, age, sex, BMI).

*Note: in its strict sense, metadata ("data about data") refers to information describing the context, structure, or properties of a dataset — such as acquisition date, instrument settings, plate/well ID, or run order. It does not refer to biological or demographic variables. To avoid ambiguity, readyomics adopts the same convention as phyloseq, using the term sample data for variables describing the study samples.

Main functionalities

Data processing (normalisation, transformation, filtering)

• process_ngs(): process next-generation sequencing data.
• process_ms(): process MS or NMR data.
• build_phyloseq(): build phyloseq objects for metataxonomic data.

Multivariate analysis

• mva(): PCA, PLS and OPLS models.
• permanova(): wrapper for vegan::adonis2() function with additional options and summary results.

Differential [abundance/expression] analysis

• dana(): fit feature-wise linear fixed or mixed effects models.
• adjust_pval(): methods to adjust nominal P-values on dana() result.
• ready_plots(): visualize dana results and significant features.

Installation

r install.packages("readyomics")

You can install the development version of readyomics from GitHub:

r devtools::install_github("lmartinezgili/readyomics")

Get started

Types of input data

readyomics is as omics-agnostic and inclusive as possible.

Raw omics files (e.g., .fastq, .mzML) must first be pre-processed through external commercial or open-source pipelines into a data matrix where each row corresponds to a sample and each column corresponds to a measured omics feature.

Required files and format

• X: a .csv or .RDS table of omics data (samples in rows and features in columns).
• sample_data (or sdata): a .csv or .RDS table of study sample information (samples in rows).
• sample_id must be a column in sample_data and have unique ids for each sample.
• Row names in X and sample_data must match sample_id values, though order can differ — readyomics functions will check and align automatically.

Documentation and Examples

For tutorials, examples, and reference documentation, visit readyomics website.