diff --git a/01_quality_assessment/scRNA_QC.qmd b/01_quality_assessment/scRNA_QC.qmd
index 0e9b43a..81c2acc 100644
--- a/01_quality_assessment/scRNA_QC.qmd
+++ b/01_quality_assessment/scRNA_QC.qmd
@@ -178,10 +178,12 @@ metadata1 <- metadata1 %>% dplyr::rename(
 )
 ```
 
-# QC metrics: raw data {.tabset}
+# QC metrics: raw data
 
 In this section, we review quality control (QC) metrics for the **raw feature matrices** generated by `Cellranger`. Only a low level filter excluding cells with <100 nUMIs (= number of unique molecular identifiers, or sequenced reads per cell) was applied when uploading the data into `R`. 
 
+:::{.panel-tabset}
+
 ## Cells per sample
 
 ```{r cells raw}
@@ -351,13 +353,16 @@ metadata0 %>%
   theme(plot.title = element_text(hjust = 0.5, face = "bold"))
 ```
 
+:::
 
-# QC metrics: Filtered data {.tabset}
+# QC metrics: Filtered data
 
 Based on the above QC metrics, we filtered the dataset to isolate cells passing the following thresholds: >`r nCount_RNA_cutoff` UMIs, >`r nFeature_RNA_cutoff` genes, <`r mitoRatio_cutoff` mitochondrial gene ratio, and >`r Log10GenesPerUMI_cutoff` complexity.
 
 In this section, we review QC metrics for our filtered dataset.
 
+:::{.panel-tabset}
+
 ## Cells per sample
 
 ```{r cells filtered}
@@ -521,6 +526,7 @@ metadata1 %>%
   theme(plot.title = element_text(hjust = 0.5, face = "bold"))
 ```
 
+:::
 
 # R session