Author: PLN team
Date: 2026-08-29

# Zero-inflated PLN models for multivariate count data with excess zeros

#### PLN team

#### 2026-08-29

## Preliminaries

This vignette illustrates the `ZIPLN` and `ZIPLNnetwork` functions and the methods accompanying the R6 classes `ZIPLNfit` and `ZIPLNnetworkfamily`. These models extend `PLN` and `PLNnetwork` (see the corresponding vignettes) to count data with an excess of zeros that a (sparse) Poisson lognormal model alone cannot capture.

### Requirements

The packages required for the analysis are PLNmodels plus some others for data manipulation and representation:

    library(PLNmodels)
    library(ggplot2)

### Data set

We illustrate zero-inflation with the `microcosm` data set (Mariadassou et al. 2023): the evolution of the microbiota of lactating cows, sampled in 4 body sites (Oral, Nasal, Vaginal, Milk) at several time points, with p = 259 taxa, n = 880 samples and an average of 90% zeroes. To keep this vignette fast to compile while remaining representative, we restrict the analysis to the 30 most abundant taxa, which still display substantial zero-inflation:

    data(microcosm)
    most_abundant <- order(colSums(microcosm$Abundance), decreasing = TRUE)[1:30]
    microcosm$Abundance <- microcosm$Abundance[, most_abundant]
    mean(microcosm$Abundance == 0)

    ## [1] 0.8018182

### Mathematical background

The zero-inflated PLN model (ZIPLN) (Batardière et al. 2025) combines the Poisson lognormal model (Aitchison and Ho 1989) – see the PLN vignette – with a zero-inflation mechanism: each count \(Y_{ij}\) is either a structural zero (with probability \(\pi_{ij}\)) or drawn from the usual PLN generative process: \[\begin{equation} \begin{array}{rcl} \text{latent space } & \mathbf{Z}_i \sim \mathcal{N}\left({\boldsymbol\mu},\boldsymbol\Sigma\right) & \\ \text{zero-inflation } & W_{ij} \sim \mathcal{B}(\pi_{ij}) & \text{independent of } \mathbf{Z}_i\\ \text{observation space } & Y_{ij} \,|\, Z_{ij}, W_{ij} = 0 \sim \mathcal{P}\left(\exp\{Z_{ij}\}\right), & Y_{ij} \,|\, W_{ij} = 1 \;=\; 0 \end{array} \end{equation}\]

Just like PLN, \({\boldsymbol\mu}\) generalizes to \(\mathbf{o}_i + \mathbf{x}_i^\top\mathbf{B}\) to account for offsets and covariates. The zero-inflation probabilities \(\pi_{ij}\) can be parameterized in several ways, controlled by the `zi` argument of `ZIPLN()`:

- `"single"`: a single \(\pi\) shared by all entries (default).
- `"row"`: one \(\pi_i\) per sample.
- `"col"`: one \(\pi_j\) per species.
- covariates: \(\text{logit}(\pi_{ij}) = \mathbf{x}_{0,i}^\top\mathbf{B}_{0,j}\), specified with the formula syntax `Y ~ PLN effect | ZI effect` (see below).

`ZIPLNnetwork` further adds a sparsity penalty on \(\boldsymbol\Omega = \boldsymbol\Sigma^{-1}\), exactly as `PLNnetwork` does for PLN (see the PLNnetwork vignette and Chiquet et al. (2019)), so that both the excess of zeros and the residual dependency structure between taxa are accounted for. See Tous et al. (2025) for an application to species association networks from count data with structural zeros.

## Analysis of microcosm with ZIPLN

### Comparing zero-inflation parameterizations

We fit a plain `PLN` model and the four `ZIPLN` parameterizations, all with the sampling site as a covariate for the count part, to check how much accounting for zero-inflation improves the fit:

    myPLN     <- PLN(Abundance ~ 0 + site + offset(log(Offset)), data = microcosm)
    zi_single <- ZIPLN(Abundance ~ 0 + site + offset(log(Offset)), data = microcosm)
    zi_row    <- ZIPLN(Abundance ~ 0 + site + offset(log(Offset)), data = microcosm, zi = "row")
    zi_col    <- ZIPLN(Abundance ~ 0 + site + offset(log(Offset)), data = microcosm, zi = "col")
    zi_site   <- ZIPLN(Abundance ~ 0 + site + offset(log(Offset)) | 0 + site, data = microcosm)

    data.frame(
      model  = c("PLN", "ZIPLN (single)", "ZIPLN (row)", "ZIPLN (col)", "ZIPLN (site-dependent)"),
      loglik = c(myPLN$loglik, zi_single$loglik, zi_row$loglik, zi_col$loglik, zi_site$loglik),
      BIC    = c(myPLN$BIC, zi_single$BIC, zi_row$BIC, zi_col$BIC, zi_site$BIC),
      ICL    = c(myPLN$ICL, zi_single$ICL, zi_row$ICL, zi_col$ICL, zi_site$ICL)
    ) %>% knitr::kable(digits = 1)

model | loglik | BIC | ICL
--- | --- | --- | ---
PLN | -51697.8 | -53680.9 | -101328.6
ZIPLN (single) | -48847.5 | -50834.0 | -89988.5
ZIPLN (row) | -46875.9 | -51842.2 | -89899.3
ZIPLN (col) | -48147.4 | -50232.3 | -87715.3
ZIPLN (site-dependent) | -47647.5 | -50037.4 | -85962.4

Accounting for zero-inflation brings a large improvement over plain `PLN`, and letting the zero-inflation probability depend on the body site (`zi_site` / “ZIPLN (site-dependent)) gives the best BIC and ICL among the parameterizations considered – not surprising given how different the four body sites are.

### Inspecting the fit

As for `PLN`, fitted values stay close to the observed counts:

    data.frame(
      fitted   = as.vector(fitted(zi_site)),
      observed = as.vector(microcosm$Abundance)
    ) %>%
      ggplot(aes(x = observed, y = fitted)) +
        geom_point(size = .5, alpha = .25) +
        scale_x_log10(limits = c(1, NA)) +
        scale_y_log10(limits = c(1, NA)) +
        theme_bw() + ggplot2::annotation_logticks()

Fitted vs. observed values

The site-dependent model also lets us recover, for each species, an estimated zero-inflation probability per body site (`model_par$Pi`), revealing substantial heterogeneity:

    one_obs_per_site <- !duplicated(microcosm$site)
    pi_hat <- zi_site$model_par$Pi[one_obs_per_site, ]
    rownames(pi_hat) <- as.character(microcosm$site[one_obs_per_site])

    data.frame(site = rep(rownames(pi_hat), ncol(pi_hat)), zi_prob = as.vector(pi_hat)) %>%
      ggplot(aes(x = site, y = zi_prob)) +
        geom_boxplot() + theme_bw() +
        labs(x = "Body site", y = "Zero-inflation probability (per species)")

Estimated zero-inflation probability by body site, across the 30 species

Coefficient matrices for the count and zero-inflation parts can be inspected with `coefficients()` – here both components share the same `site` design, so rows of \(\mathbf{B}\) (count) and \(\mathbf{B}_0\) (zero-inflation) line up one-to-one with body sites:

    pheatmap::pheatmap(coefficients(zi_site, "zero"), cluster_rows = FALSE)

Estimated regression coefficients of the zero-inflation component (\(B_0\))

    pheatmap::pheatmap(coefficients(zi_site, "count"), cluster_rows = FALSE)

Estimated regression coefficients of the count component (\(B\))

## Sparse network inference with ZIPLNnetwork

`ZIPLNnetwork` adjusts the model for a series of penalties controlling the number of edges in the network, just like `PLNnetwork`, but on top of a zero-inflation component. We keep the default `zi = "single"` here for simplicity and focus on the network. The default `backend = "builtin"` is used (it now finds a consistently better ELBO than `"nlopt"` here, at the cost of being slower); we lower `min_ratio` to explore a wider, sparser range of the penalty path:

    zi_models <- ZIPLNnetwork(Abundance ~ site + offset(log(Offset)), data = microcosm, control = ZIPLNnetwork_param(min_ratio = 0.01))

As for `PLNnetwork`, a diagnostic plot and the evolution of the criteria along the path are available:

    plot(zi_models, "diagnostic")

Diagnostic of the ZIPLNnetwork fits

    plot(zi_models)

Evolution of model selection criteria

We select a network with `getBestModel()` and represent it:

    zi_net <- getBestModel(zi_models, "EBIC")
    plot(zi_net)

Sparse residual network between the 30 most abundant taxa

As for `PLNnetwork`, a more robust (but more computationally intensive) `stability_selection()`-based choice of penalty is available. We do not use it here to keep the runtime of this vignette fast, see the PLNnetwork vignette for an example.

## References

Aitchison, J., and C. H. Ho. 1989. “The Multivariate Poisson-Log Normal Distribution.” Biometrika 76 (4): 643–53.
Batardière, Bastien, Julien Chiquet, François Gindraud, and Mahendra Mariadassou. 2025. “Zero-Inflation in the Multivariate Poisson Lognormal Family.” Statistics and Computing 35. https://doi.org/10.1007/s11222-025-10729-0.
Chiquet, Julien, Stephane Robin, and Mahendra Mariadassou. 2019. “Variational Inference for Sparse Network Reconstruction from Count Data.” In Proceedings of the 36th International Conference on Machine Learning, edited by Kamalika Chaudhuri and Ruslan Salakhutdinov, vol. 97. Proceedings of Machine Learning Research. PMLR. http://proceedings.mlr.press/v97/chiquet19a.html.
Mariadassou, Mahendra, Lucie X Nouvel, Fabienne Constant, et al. 2023. “Microbiota Members from Body Sites of Dairy Cows Are Largely Shared Within Individual Hosts Throughout Lactation but Sharing Is Limited in the Herd.” Animal Microbiome 5 (32). https://doi.org/10.1186/s42523-023-00252-w.
Tous, Jeanne, Julien Chiquet, Amy E. Deacon, Ada Fontrodona-Eslava, Douglas F. Fraser, and Anne E. Magurran. 2025. “A JSDM with Zero-Inflation to Improve Inference of Association Networks from Count Community Data with Structural Zeros.” https://doi.org/10.1101/2025.07.24.666553.