docs/clarity improvement to mia::summarizeDominance

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