# Zero imputation prior adjust\_batch application

**URL:** <https://forum.biobakery.org/t/zero-imputation-prior-adjust-batch-application/8833>\
**Category:** Downstream analysis and statistics\
**Created:** [March 19, 2026, 5:02pm UTC](https://forum.biobakery.org/t/zero-imputation-prior-adjust-batch-application/8833 "2026-03-19T17:02:49Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![MCC](https://yyz1.discourse-cdn.com/flex027/user_avatar/forum.biobakery.org/mcc/32/3600_2.png) [@MCC](https://forum.biobakery.org/u/MCC)\
**Post date:** [March 19, 2026, 5:02pm UTC](https://forum.biobakery.org/t/zero-imputation-prior-adjust-batch-application/8833/1 "2026-03-19T17:02:49Z")

</div>

Hello, I’m studying 16S data from 20 CRC mice in R and I plan to use `adjust_batch()` from MMUPHin to correct for technical bias. To handle the sparsity of my matrix (factually, few genera and samples available), I first applied `cmultRepl()` (GBM) to impute zeros, which returns proportions. Then, I applied `adjust_batch()` with `zero_inflation = FALSE`, as follows:

```r
tmp ← cmultRepl(abd_gene_level,z.warning = 1, z.delete = FALSE)

fit_adjust_batch ← adjust_batch(feature_abd = t(tmp),
                                batch = “expr_run”,
                                covariates = “condition”,
                                data = metad,
                                control = list(zero_inflation = F,
                                diagnostic_plot = “mmuphin_diag.pdf”, verbose = TRUE))

```

Then I plan to apply a CLR transformation for downstream analyses.

My questions are:

1. Is it a valid approach to impute zeros first with cmultRepl and then apply `adjust_batch()` with `zero_inflation = FALSE`, or is it necessary to let the function handle zero-inflation internally to obtain proper batch correction estimates?

2. Can this same workflow (cmultRepl → `adjust_batch()` → CLR) be applied to functional abundance matrices, such as KO predictions from PICRUSt2, to correct for batch effects between sequencing runs?

Thank you in advance for your guidance.