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Copy file name to clipboardExpand all lines: DESCRIPTION
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which serves as a guide to connect all the pieces together by aligning them into a single object that is defined as an 'omics' class.
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Once this unified structure is established, users can perform manual subsetting, visualisation, and statistical analysis, or leverage the automated 'autoFlow' method to generate a comprehensive report.
[](https://anaconda.org/agusinac/r-omicflow)
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[](https://hub.docker.com/r/agusinac/autoflow)
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OmicFlow
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================
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## Overview
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OmicFlow is a generalised data structure for fast and efficient loading of various sparse omics data. It can handle metataxonomics/metagenomics data in text or [BIOM](https://biom-format.org/documentation/format_versions/biom-2.0.html) and extends to `proteomics` and other `omics` types. It also supports non-sparse data, but it's performance peaks in sparsity.
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## Installation
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pak::pkg_install('agusinac/OmicFlow@dev')
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```
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## 📋 Metadata File Specification
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OmicFlow expects your sample metadata to follow a **simple, but strict** structure so that all datasets are compatible and validated up‑front. Sample metadata can be supplied as a **CSV/TSV** file or as a `data.table` in R. In both cases the sample metadata should contain a header (this is your first line if you supply a file) where **each row = one sample** Additional column names not mentioned here are allowed and will be ignored during the metadata validation step.
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---
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### **Minimum requirement**
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-**`SAMPLE_ID`** ➡ every row **must** have a unique, non‑empty sample identifier.
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- No spaces are allowed in IDs — use underscores `_` or dashes `-` instead.
|`SAMPLEPAIR_ID`| string | Optional — no spaces. Use when samples are paired and belong to an individual source/subject |
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#### 🔹 Pattern‑based columns
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You can define extra variables using special prefixes:
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-**`CONTRAST_...`** → grouping/category labels used in differential comparisons
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Example: `CONTRAST_Treatment` with values `Drug` / `Placebo`
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-**`VARIABLE_...`** → numeric or string variables for statistical analysis
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Example: `VARIABLE_Age` with values `42`, `51`, etc.
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The pattern-based columns are only used during the `autoFlow` function. At the moment only columns with prefix `CONTRAST_` are supported.
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Example: **Outputs a `report.html` file in the current working directory**
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```R
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taxa$autoFlow(
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normalize=FALSE,
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weighted=TRUE,
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pvalue.threshold=0.05
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)
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```
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---
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## Usage
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> [!NOTE]
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> Make sure your metadata meets the requirements!
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The abstract class `omics` can be used for any type of omics data where a `treeData` is not required. Let's say you have a `metaData` and `countData` (file or a matrix with rownames), these can be supplied directly to `omics` and a `featureData` field is then automatically generated. You can change all fields via `<-` and these will be automatically synced in the background.
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The `metagenomics` class has extra support for biom files in both HDF5 ([version 2](https://biom-format.org/documentation/format_versions/biom-2.0.html)) as JSON data structure to be passed via `biomData` on top of the default `omics` fields. The `proteomics` class is more an extension of the `omics` class that also allows the input of a `treeData` and performs alignment by the `treeData` tip labels.
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Initialize the `metagenomics` or any `omics` object from a filepath or pre-loaded object.
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```R
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library("OmicFlow")
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### Visualisations
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> [!NOTE]
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> All visualizations use by default color-blind palettes!
> Since v1.5 OmicFlow computes dissimilarity metrics from both sparse and dense matrices!
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By default PERMANOVA is applied pairwise against each group within the specified contrast, via `group_by` that is used in `pairwise_adonis`. The permutation design in `vegan::adonis2` is by default set to `free`. But this may not always be the right test when you have paired samples and you also want to restrict permutations between different sites or genders. Therefore, `pairwise_adonis` supports a custom permutation design, which can be constructed via [permute](https://cran.r-project.org/web/packages/permute/vignettes/permutations.html) and fed into `vegan::adonis2` as a function via `pairwise_adonis` with the flag `perm_design`. See the examples below.
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By default PERMANOVA is applied pairwise against each group within the specified contrast, via `group_by` that is used in `pairwise_adonis`. The permutation design in `vegan::adonis2` is by default set to `free`. But this may not always be the right test when you have paired samples and you also want to restrict permutations between correlated values. Therefore, `pairwise_adonis` supports a custom permutation design, which can be constructed via [permute](https://cran.r-project.org/web/packages/permute/vignettes/permutations.html) and fed into `vegan::adonis2` as a function via `pairwise_adonis` with the flag `perm_design`.
The `volcano_plot` will contain the average percentage abundance for each Genus between the two contrasts. Additional parameters can be used to only filter for relevant bacteria based on the `pvalue.threshold`, `foldchange.threshold` and `abundance.threshold`. The returned p-values can be adjusted and used for a new volcano plot via `OmicFlow::volcano_plot`.
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```R
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res<-taxa$DFE(
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feature_rank="Genus",
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feature_filter= c("uncultured"),
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paired=FALSE,
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condition.group="CONTRAST_sex",
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condition_A="male",
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condition_B="female"
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)
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res$volcano_plot
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```
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## Run OmicFlow and autoFlow standalone script with docker!
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> [!NOTE]
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> Symbolic links do not work with mounting, please only copy the original file!
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## Docker
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Example: **Outputs a `report.html` file in current work directory**
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