gmmDenoise

Overview

gmmDenoise is an R package for the effective filtering of erroneous amplicon sequence variants (ASVs) in eDNA metabarcoding data, based on Gaussian mixture models (GMMs), to enable accurate intraspecific diversity estimates and population genetic inferences. The package provides functions for selecting the number of mixture components (k) for an ASV abundance distribution, fitting a k-component GMM to the distribution, and determining a statistically validated abundance threshold for ASV filtering based on the fitted model. It also includes functions for visualizing these processes.

Installation

# install.packages("devtools")
devtools::install_github("YSKoseki/gmmDenoise")

Example workflow

This is an example of how gmmDenoise works for filtering erroneous ASVs.

library(gmmDenoise)

# Data: a vector of 1,217 ASV read counts, named with assigned taxonomic names
# and [ID numbers]
data(mifish)
head(mifish, n = 10)
length(mifish)
summary(mifish)

# Plot histogram for visual inspection of ASV read count distribution
asvhist(mifish)

# The density version with the number of bins arbitrary set at 30
asvhist(mifish, type = "density", nbins = 30)

# Log-transformed data
logmf <- log10(mifish)

# Cross-validation for selecting the number of components of Gaussian
# mixture model
set.seed(101)
cv <- gmmcv(logmf, epsilon = 1e-02)
autoplot(cv)  # equivalent to `autoplot.gmmcv(cv)`

# The alternative approach for the number of mixture components: Sequential
# parametric bootstrap tests 
set.seed(101)
# May take some time (8 sec on my M1 MacBook Pro)
bs <- gmmbs(logmf, B = 100, epsilon = 1e-02)
summary(bs)
p <- autoplot(bs)  # equivalent to `p <- autoplot.gmmbs(bs)`
library(cowplot)
plot_grid(plotlist = p, ncol = 2)

# Fit a 3-component Gaussian mixture model and display a graphical representation
# of the fitted model
set.seed(101)
mod <- gmmem(logmf, k = 3)
autoplot(mod) # equivalent to `autoplot.gmmem(mod)`

# Determine the cutoff threshold (default is the upper one-sided 95% confidence 
# limit of the second uppermost component) 
thresh <- quantile(mod)
autoplot(mod, vline = thresh)

# Filter ASVs with the threshold value
logmf2 <- logmf[which(logmf > thresh)]
mifish2 <- mifish[which(logmf > thresh)]
length(mifish2)
summary(mifish2)

Best practices

This is our recommended workflow for processing eDNA metabarcoding data for genetic diversity analysis, using gmmDenoise along with other tools. In this workflow, steps from primer removal of raw sequence reads (FASTQ files) through gmmDenoise filtering are performed separately for each sequencing run. The subsequent analysis is then conducted on a combined dataset, in which all runs have been merged.

Figure design inspired by that of the GATK “Best Practices for Variant Discovery in DNAseq” by Geraldine_VdAuwera.

As presented under “Denoising & Chimera Removal” in the figure above, gmmDenoise can work complementarily with existing denoising algorithms, such as DADA2 (Callahan et al. 2016) and UNOISE3 (Edgar 2016). Our analyses (Koseki et al., submitted) indicate that this complementary use enhance denoising performance, resulting in fewer false-positive ASVs.

References: - Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13(7), 581—583. - Edgar, R. C. (2016). UNOISE2: Improved error-correction for Illumina 16S and ITS amplicon sequencing. bioRxiv, 081257. - Koseki, Y., Takeshima, H., Yoneda, R., Katayanagi, K., Ito, G., & Yamanaka, H. (2025). gmmDenoise: a new method and R package for high-confidence sequence variant filtering in environmental DNA amplicon analysis. Authorea.

Cite as

To cite gmmDenoise in publications, please use:

Koseki, Y., Takeshima, H., Yoneda, R., Katayanagi, K., Ito, G., & Yamanaka, H. (2025). gmmDenoise: a new method and R package for high-confidence sequence variant filtering in environmental DNA amplicon analysis. Authorea.

A BibTeX entry for LateX users is here.