> For the complete documentation index, see [llms.txt](https://help.bioinsight.illumina.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.bioinsight.illumina.com/icm/analyses/analysis-functionality/task-menu/combine-multiomics-data/mofa-multiomics-factor-analysis.md).

# MOFA (multiomics Factor Analysis)

Integrate bulk multiomics data to identify shared and assay-specific sources of variation.

Multi-Omics Factor Analysis (MOFA) integrates two or more omics assays from the same study [1](#reference). It identifies latent factors that explain shared and assay-specific variation.

MOFA supports bulk sample-level data. Assays may have missing samples.

Use normalized, quality-controlled data from each assay. Each input must be a numeric sample-by-feature matrix. Log-transformed values are recommended.

### Run MOFA

1. Select an input data node.
2. Choose **Combine multiomics data → MOFA**.

   <div align="left"><figure><img src="https://580316046-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FWMxqQAMFOJtu98OBk9KN%2Fuploads%2Fgit-blob-535bbca3ce57561eb3b867a773e275d62edf047f%2Fmofa.png?alt=media" alt=""><figcaption></figcaption></figure></div>
3. Click **Select data nodes**.
4. Select the input nodes for the other assays.
5. Click **Select** in the lower-right corner.
6. Set the parameters, then select **Finish**.

#### Parameters

**Number of factors**: Sets the initial number of latent factors. Use 5–25 factors. Too few factors can miss biological variation. Too many can capture noise.

**Grouping variable**: Selects a categorical sample attribute with two or more groups. MOFA decomposes variance by group and factor. Leave this unset for unbiased factor discovery.

**Variance quantile**: Retains highly variable features. Enter a value from 0 to 1. Use `0` to retain all features. Use `0.9` to retain the top 90% by variance.

**Max missing fraction**: Sets the maximum missing-value fraction allowed per feature. Enter a value from 0 to 1. For example, `0.5` removes features missing in over 50% of samples.

Click **Configure** under *Advanced options* to set additional parameters and the random seed.

<div align="left"><figure><img src="https://580316046-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FWMxqQAMFOJtu98OBk9KN%2Fuploads%2Fgit-blob-ba158ce9274e5eca1a9bdbfa6d6569dd0be7ee68%2Fmofa_adv.png?alt=media" alt="" width="398"><figcaption></figcaption></figure></div>

#### Advanced options

**Convergence mode**: Choose **Fast** for exploration, **Medium** for standard analysis, or **Slow** for the final model.

**Random seed**: Use the same value to reproduce results.

**Spike-and-slab weights**: When enabled, only a subset of features loads on each factor. This improves identification of driving features. When disabled, all features have nonzero loadings. The resulting weights are less interpretable.

**ARD weights**: Enables automatic relevance determination for factor weights in each assay. ARD shrinks weights toward zero when a factor is not relevant. Disable it to force every factor to remain active across assays.

**Scale views**: Scales each assay so its total feature variance equals 1. Enable this option to give assays equal influence. Disable it when higher-variance assays should contribute more. Assays with more features usually have higher total variance.

**Center groups**: Available when a grouping variable is set. Centers each feature within each group. This removes group-level mean differences and captures within-group variation. Disable this option only when group-level mean differences are relevant.

**Scale groups**: Available when a grouping variable is set. After centering, scales each group to unit variance. Enable this option to give groups equal influence. Disable it to favor groups with greater variance.

**Drop factor threshold**: Removes factors that explain less than the threshold in every assay. Set `0` to retain all factors. Use `0.001`–`0.05` to enable factor removal.

**Max iterations**: Sets the training-iteration limit. Use 100–5,000 iterations. Too few iterations can produce unreliable factors. More iterations increase runtime.

**Disable likelihood auto-detection**: Disables automatic likelihood selection based on data type. Specify the likelihood for each assay manually. Use this option when automatic detection selects an unsuitable distribution.

**Minimum variance**: Removes features with variance below the specified threshold.

### Outputs

MOFA creates factor, variance, and feature-weight nodes for each input assay.

<div align="left"><figure><img src="https://580316046-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FWMxqQAMFOJtu98OBk9KN%2Fuploads%2Fgit-blob-dc7844b9036902d186cfd0ea171e223345080b35%2Fmofa_output.png?alt=media" alt="" width="162"><figcaption></figcaption></figure></div>

#### MOFA factors

This node contains a sample-by-factor matrix of latent factor values. Double-click the node to open a two- or three-dimensional sample scatter plot. The first two or three factors define the plot axes.

<div align="left"><figure><img src="https://580316046-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FWMxqQAMFOJtu98OBk9KN%2Fuploads%2Fgit-blob-62efb08076da7945ed832bd643cc6e6f536b436c%2Fmofa_scatterplot.png?alt=media" alt="" width="563"><figcaption></figcaption></figure></div>

Use sample attributes to configure the plot.

#### Variance

This node contains the proportion of variance explained by each factor in each assay. Values range from 0 to 1. Double-click the node to view the results as a heatmap.

<div align="left"><figure><img src="https://580316046-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FWMxqQAMFOJtu98OBk9KN%2Fuploads%2Fgit-blob-779d7c07f8afa92d3a471988fa6a7647ac5c733e%2Fmofa_heatmap.png?alt=media" alt="" width="434"><figcaption></figcaption></figure></div>

If grouping variable is selected, the proprotion of the variance explained by each assay are reported on each subgroup respectively.

#### Feature weights

Each assay has a feature-weights node. It contains feature loadings for each factor. Use these values to identify features that drive factor variation.

When Spike-and-slab weights is enabled, most of the values are 0. If a value is greater than 0, it means the feature is positively associated with the factor; if a value is less than 0, it means the feature is negatively associated with the factor.

Double click on a data node to view the information in heatmap.

<div align="left"><figure><img src="https://580316046-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FWMxqQAMFOJtu98OBk9KN%2Fuploads%2Fgit-blob-c1859cb8732819ff9532000e2e3c88a0127cf141%2Fmofa_weight.png?alt=media" alt=""><figcaption></figcaption></figure></div>

## Reference

1. Argelaguet R, Velten B, Arnol D, Dietrich S, Zenz T, Marioni JC, Buettner F, Huber W, Stegle O. Multi-Omics Factor Analysis — a framework for unsupervised integration of multi-omics data sets. *Molecular Systems Biology*. 2018;14(6):e8124. <https://doi.org/10.15252/msb.20178124>
2. [MOFA2 documentation](https://github.com/bioFAM/MOFA2/tree/master)


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