prevalence method in Decontam not Identifying contaminants with a single control sample

[JayalalKJ]

Hi, I have one control sample, and the prevalence method in Decontam is not effectively identifying contaminants. The p-values seem distributed evenly, and the isContaminant() function isn’t marking many sequences as contaminants even with an aggressive threshold. I need advice on how to proceed or any alternative approaches or changes to the following code.

attempted -> (e.g., using threshold=0.3).

identification of control samples
sample_data(physeq)$is.neg <- sample_data(physeq)$Sample_or_Control == "Control Sample"

Identify contaminants using the prevalence method with an aggressive threshold
contamdf.prev <- isContaminant(physeq, method = "prevalence", neg = "is.neg", threshold = 0.3)
table(contamdf.prev$contaminant)

visualize prevalence in positive vs negative controls
ps.pa <- transform_sample_counts(physeq, function(abund) 1 * (abund > 0))
ps.pa.neg <- prune_samples(sample_data(ps.pa)$Sample_or_Control == "Control Sample", ps.pa)
ps.pa.pos <- prune_samples(sample_data(ps.pa)$Sample_or_Control == "True Sample", ps.pa)

Create a data frame for visualization
df.pa <- data.frame(pa.pos = taxa_sums(ps.pa.pos), pa.neg = taxa_sums(ps.pa.neg),
contaminant = contamdf.prev$contaminant)

Plot the prevalence of taxa in positive vs negative controls
ggplot(data = df.pa, aes(x = pa.neg, y = pa.pos, color = contaminant)) +
geom_point() +
xlab("Prevalence (Negative Controls)") +
ylab("Prevalence (True Samples)")

Prune contaminants
physeq_clean <- prune_taxa(!contamdf.prev$contaminant, physeq)

[benjjneb]

I have one control sample

decontam-prevalence is not an appropriate method for use when you have only one negative control sample. It relies on repeated observation of contaminants across multiple negative controls. We recommend a minimum of 5, see our original paper for more on that. https://doi.org/10.1186/s40168-018-0605-2