From c8295d44b0a88c0f97ead3de0a8111b36022dcc5 Mon Sep 17 00:00:00 2001 From: raivo-otus Date: Tue, 14 Apr 2026 13:31:38 +0300 Subject: [PATCH] Clearer defination --- inst/pages/quality_control.qmd | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/inst/pages/quality_control.qmd b/inst/pages/quality_control.qmd index 2981b273..cdc09e9b 100644 --- a/inst/pages/quality_control.qmd +++ b/inst/pages/quality_control.qmd @@ -73,10 +73,11 @@ sizes. Moreover, we can observe that there are no singletons in the dataset (library size and singletons are discussed in more detail in [@sec-qc-outliers]). Another type of summary can be generated using the `summarizeDominance()` -function. This function returns a table displaying both the absolute and -relative abundance of each taxon — that is, how many times a taxon was detected -in the dataset and the proportion of samples in which it was identified. Below, -we create a summary table for genera. +function. This function identifies the most abundant taxon in each sample and +summarizes these results across the entire dataset. It returns a table +displaying both the absolute count and the relative frequency (proportion of +samples) of each dominant taxa. Below, we create a summary table +for genera. ```{r} #| label: explore3 @@ -85,8 +86,8 @@ df <- summarizeDominance(tse, rank = "Genus") df ``` -Based on the summary table, `r df[["dominant_taxa"]][[1L]]` seems to be highly -presented in the baboon gut. +Based on the summary table, `r df[["dominant_taxa"]][[1L]]` appears dominant +in a large portion of samples. `r BiocStyle::Biocpkg("mia")` also provides other functions to summarize the dataset. For example, you can retrieve unique, top, prevalent, or rare