Hello, I have an experiment and a control group to test differentially abundant taxa from amplicon sequencing data.
The problem is that after filtering by adjusted p-value < 0.01 , some of the differentially abundant OTUs are not present in both datasets, which is a problem for downstream analysis. I need to have the same taxa for both groups. ¿WHy do I have significant differentially abundant OTUs that are not present in both datasets? I thought the program internally only computes differences from taxa that are present in both groups.
design <- ~ age + asthma_rhinitis dds_oral_data <- phyloseq_to_deseq2(merged_oral, design = design) wald_test_merged_oral <- DESeq(dds_oral_data, parallel = TRUE) results_oral_NO_AR <- results(wald_test_merged_oral, contrast = c("asthma_rhinitis", "AR","NO")) # Extract the significant differences df_diff_expressed_ARNO <- results_oral_NO_AR %>% as.data.frame() %>% rownames_to_column(var = "ASV") %>% as_tibble() %>% dplyr::filter(padj < 0.01) plseq_oral_AR <- prune_taxa(df_diff_expressed_ARNO$ASV, plseq_oral_AR) plseq_oral_NO <- prune_taxa(df_diff_expressed_ARNO$ASV, plseq_oral_NO)
the above two last variables are the respective phyloseq objects of the contrasted groups and both have different number of ASVs and they should have the same number of taxa given the fact that a differential abundance test was performed. How to deal with this?