Update tidytranscriptomics.Rmd

Author: mblue9

vignettes/tidytranscriptomics.Rmd

@@ -184,7 +184,7 @@ From this tidybulk tibble, we can perform differential expression analysis with
 ## Filtering lowly expressed genes  
 Genes with very low counts across all libraries provide little evidence for differential expression and they interfere with some of the statistical approximations that are used later in the pipeline. They also add to the multiple testing burden when estimating false discovery rates, reducing power to detect differentially expressed genes. These genes should be filtered out prior to further analysis.
 
-With tidybulk, it is not really necessary to explicitly filter lowly transcribed genes, as all calculations (e.g., scaling, removal of unwanted variation, differential expression testing) are performed on abundantly transcribed genes; although in case of scaling, the scaling is applied back to all genes in the dataset. Tidybulk can use the *edgeR* `filterByExpr` function described in [@law2016rna]. By default, this will keep genes with ~10 counts in a minimum number of samples, the number of the samples in the smallest group. In this dataset the smallest group size is 2 samples. tidybulk performs this filtering in the functions we will use `scale_abundance` and `test_differential_abundance`.
+With tidybulk, it is not really necessary to explicitly filter lowly transcribed genes, as all calculations (e.g., scaling, removal of unwanted variation, differential expression testing) are performed on abundantly transcribed genes; although in case of scaling, the scaling is applied back to all genes in the dataset. Tidybulk can use the *edgeR* `filterByExpr` function described in [@law2016rna]. By default, this will keep genes with ~10 counts in a minimum number of samples, the number of the samples in the smallest group. In this dataset the smallest group size is four (as we have four dex-treated samples vs four untreated). tidybulk performs this filtering in the functions we will use `scale_abundance` and `test_differential_abundance`.
 
 ## Scaling counts to normalise