> ## Documentation Index > Fetch the complete documentation index at: https://docs.cosmosid.com/llms.txt > Use this file to discover all available pages before exploring further. # Extending MaAsLin3: Exporting Input Data and Building Prevalence Models > Learn how to export MaAsLin3 input data from Cosmos‑Hub and build custom presence/absence logistic regression models aligned with the MaAsLin3 prevalence framework. # Building Custom Prevalence Models MaAsLin3 in Cosmos‑Hub simultaneously models both **abundance** and **prevalence** associations from the same microbiome feature table, making it one of the most comprehensive tools available for differential microbiome analysis. This guide explains how MaAsLin3 defines prevalence internally, how to export the correct data from Cosmos‑Hub, and how to use that data to build custom logistic regression models for secondary prevalence analyses. This guide is intended for researchers who have already run MaAsLin3 in Cosmos‑Hub and want to extend their prevalence analysis using external tools such as R or Python. If you have not yet run MaAsLin3, start with the [MaAsLin3 overview](https://docs.cosmosid.com/docs/maaslin3). *** ## How MaAsLin3 Models Prevalence MaAsLin3 is a generalized multivariable modeling framework designed to identify microbial associations in complex, high-dimensional datasets. Unlike approaches that model only abundance, MaAsLin3 captures two complementary biological signals from the same input feature table: * **Prevalence** – *how often* a feature is detected across samples, modeled via logistic regression on a binary presence/absence profile. * **Abundance** – *how much* of a feature is present when detected, modeled via log‑linear regression on non‑zero abundances. As described in the MaAsLin3 paper ([Nature Methods, 2025](https://www.nature.com/articles/s41592-025-02923-9)): > "MaAsLin 3 takes as input a table of microbial community feature abundances and metadata. These feature data are normalized, filtered, split into prevalence (present versus absent) and log‑transformed nonzero abundances, and fit with a modified logistic model and a linear model, respectively." When MaAsLin3 runs in Cosmos‑Hub, the workflow proceeds as follows: 1. The input feature abundance table (taxa, pathways, or functional features) is normalized — by default using total‑sum scaling to relative abundances. 2. Optional filtering is applied to remove extremely rare or low-variance features based on **minimum prevalence**, **minimum abundance**, and **minimum variance** thresholds. 3. The filtered table is split into: * A **binary prevalence profile** (present = 1, absent = 0) for logistic regression. * A **non‑zero abundance subset** (log‑transformed) for linear regression. This means that the same abundance values in the `Input Data` export are the source of both the prevalence and abundance models. *** ## Defining Presence for Custom Logistic Regression A common question when extending MaAsLin3 results is: **what threshold should I use to define "presence"?** The answer is straightforward. MaAsLin3 defines presence as **any non‑zero value** in the normalized feature table. There is no alternative recommended cutoff (such as >1e‑4 or >1% relative abundance) in the MaAsLin3 documentation or paper. Sparsity is managed through **feature filtering parameters** applied before the model, not by changing the definition of presence itself. ### Recommended binary coding | Value in Input Data | Prevalence Code | Interpretation | | ------------------- | --------------- | -------------- | | > 0 | **1** | Present | | = 0 | **0** | Absent | Using this coding ensures your external logistic regression models are conceptually consistent with the prevalence associations reported by MaAsLin3 in Cosmos‑Hub. If your study has a validated **limit of detection (LOD)** — for example, from spike‑in calibration or qPCR — you may choose to define presence as "above LOD" rather than strictly "> 0." This is a valid study‑specific decision but is not required by MaAsLin3 itself. *** ## Exporting MaAsLin3 Input Data from Cosmos‑Hub To build a presence/absence matrix that mirrors MaAsLin3, start from the exact feature table used in the Hub run. Navigate to your MaAsLin3 run from the [Comparative Analysis dashboard](https://docs.cosmosid.com/docs/new-comparative-analysis) in Cosmos‑Hub. Click the **Export** button in the top‑right of the analysis view. Cosmos‑Hub will generate and download a ZIP archive containing all output files for your MaAsLin3 run. Inside the ZIP, you will find: * **`Input Data`** — A `.tsv` abundance matrix used as MaAsLin3's feature input (samples as rows, features as columns). This is the primary file for building presence/absence variables. * **`Input Metadata`** — A `.tsv` file with all metadata variables used as covariates and outcomes in the model. * **`Association Results`** — The full MaAsLin3 output table with model type (abundance vs prevalence), beta coefficients, p‑values, and q‑values (FDR). The official documentation for MaAsLin3 results and exports is available here:\ [https://docs.cosmosid.com/docs/maaslin3-view-results](https://docs.cosmosid.com/docs/maaslin3-view-results) *** ## Building a Presence/Absence Matrix Once you have exported the `Input Data` file, follow these steps to create a binary presence/absence matrix for logistic regression. Import the `Input Data` `.tsv` file into R, Python, or your preferred statistical environment. The table has samples as rows and microbial features (taxa, pathways, etc.) as columns. For each feature (column), apply the following rule: * Assign **1** if the value is **> 0** (present). * Assign **0** if the value is **= 0** (absent). Before fitting logistic models, exclude features that are extremely rare. See the section below on choosing a minimum prevalence threshold. Use the binary presence/absence matrix as the response variable and the `Input Metadata` variables as predictors. Because you are using the same feature table that MaAsLin3 used, your custom prevalence models will be directly comparable to the "Prevalence" associations shown in the MaAsLin3 Association Results tab and export files. *** ## Choosing a Minimum Prevalence Threshold Running logistic regression on extremely rare features — those present in only a few samples — leads to unstable model estimates, separation issues, and uninterpretable results. It is therefore recommended to apply a **minimum prevalence filter** to your feature set before modeling. ### How to calculate prevalence For each feature, compute its prevalence as: *Prevalence (%) = \[Number of samples where feature is present (> 0)]/\[Total number of samples]]* ### Recommended thresholds | Study size | Suggested minimum prevalence | | -------------------------- | ---------------------------- | | Large cohort (n > 100) | 5% of samples | | Medium cohort (n = 50–100) | 10% of samples | | Small cohort (n \< 50) | 10–20% of samples | These thresholds are informed by common MaAsLin‑style workflow guidance, which recommends removing low-prevalence features prior to association testing to improve model stability and interpretability. Reference: [Running MaAsLin2 Workflow – Cosmos‑Hub](https://docs.cosmosid.com/docs/maaslin2-running-workflow) (prevalence filtering guidance is equally applicable to MaAsLin3 downstream analyses). Setting your minimum prevalence threshold too low (e.g., 1% of samples in a small cohort) may introduce unstable logistic models with inflated or non-convergent estimates. Always check that your included features have sufficient "events" (presence observations) to support the number of predictors in your model. *** ## Example Methods Text Use the following template for your manuscript or internal SOP methods section, adapted to your study: > "For secondary prevalence analyses, the MaAsLin3 input abundance matrix was exported from Cosmos‑Hub ([www.cosmos-hub.com](http://www.cosmos-hub.com), Cmbio, Germantown, MD). Each microbial feature was coded as present (1) when its input abundance was non‑zero and absent (0) otherwise, consistent with the binary presence/absence framework used by MaAsLin3 for logistic prevalence modeling \[cite MaAsLin3 paper]. Features with prevalence below \[X]% of samples were excluded prior to logistic regression to reduce model instability driven by extremely rare taxa." Replace **\[X]%** with your chosen minimum prevalence threshold (e.g., 5%, 10%). *** ## Frequently Asked Questions Use the **Input Data** file. This contains the normalized abundance matrix that MaAsLin3 used as input, from which you can derive binary presence/absence values. The Association Results file contains model outputs (coefficients, p‑values), not the raw feature table. No. The MaAsLin3 paper and Bioconductor documentation do not recommend any threshold other than non-zero for defining presence. Sparsity is controlled via pre-model filtering parameters (minimum prevalence, minimum abundance), not by adjusting the presence definition. You can, but only if justified by a study-specific rationale such as a known LOD. It is not consistent with how MaAsLin3 internally defines prevalence, and it may reduce comparability with your MaAsLin3 results. By default, MaAsLin3 applies minimum prevalence, minimum abundance, and minimum variance filters to remove uninformative features. The exact parameters used in your Cosmos-Hub run are reflected in the filtered `Input Data` export, so any features that were excluded by MaAsLin3 prior to modeling will not appear in the export. *** ## Reference Links Overview and setup guide for running MaAsLin3 in the Cosmos‑Hub statistics toolbox. Guide to interpreting MaAsLin3 outputs and exporting Input Data, Input Metadata, and Association Results. Nearing et al. (2025). MaAsLin 3: refining and extending generalized multivariable linear models for meta‑omic association discovery. Open-access version of the MaAsLin3 methods paper via PubMed Central. Full Bioconductor vignette covering MaAsLin3 parameters, normalization, filtering, and model types. Step-by-step MaAsLin3 tutorial from the Huttenhower Lab covering input formats, model options, and outputs. How to create and configure a comparative analysis in Cosmos‑Hub for running MaAsLin3 and other statistics. Filtering guidance for MaAsLin-style workflows, including minimum prevalence thresholds applicable to downstream MaAsLin3 analyses.