diff --git a/vignettes/read_scp.Rmd b/vignettes/read_scp.Rmd
index a577a48..94a2623 100644
--- a/vignettes/read_scp.Rmd
+++ b/vignettes/read_scp.Rmd
@@ -31,7 +31,7 @@ knitr::opts_chunk$set(
 # The `scp` data framework
 
 Our data structure is relying on two curated data classes: `QFeatures`
-(@Gatto2020-ry) and `SingleCellExperiment` (@Amezquita2019-bf).
+(@Gatto2020-ry) and `SingleCellExperiment` ([@Amezquita2020-bf]).
 `QFeatures` is dedicated to the manipulation and processing of 
 MS-based quantitative data. It explicitly records the successive steps
 to allow users to navigate up and down the different MS levels.
diff --git a/vignettes/scp.Rmd b/vignettes/scp.Rmd
index 6cce912..db85122 100644
--- a/vignettes/scp.Rmd
+++ b/vignettes/scp.Rmd
@@ -360,7 +360,7 @@ SCR. We collect the `rowData` from several assays in a single table
 
 ```{r plot_SCR, warning=FALSE, message=FALSE}
 rbindRowData(scp, i = 1:3) |>
-    data.frame |>
+    data.frame() |>
     ggplot(aes(x = MeanSCR)) +
     geom_histogram() +
     geom_vline(xintercept = c(1/200, 0.1),
@@ -579,7 +579,7 @@ low-quality cells.
 
 ```{r plot_medianRI, warning=FALSE, message=FALSE}
 colData(scp) |>
-    data.frame |>
+    data.frame() |>
     ggplot() +
     aes(x = MedianRI, 
         y = SampleType,
@@ -632,8 +632,8 @@ that contain noisy quantification.
 
 ```{r plot_medianCV, message = FALSE, warning = FALSE}
 getWithColData(scp, "peptides") |>
-    colData |>
-    data.frame |>
+    colData() |>
+    data.frame() |>
     ggplot(aes(x = MedianCV,
                fill = SampleType)) +
     geom_boxplot() +
@@ -795,9 +795,9 @@ The protein data contains a lot of missing values.
 
 ```{r missingness}
 scp[["proteins_norm"]] |>
-    assay |>
-    is.na |>
-    mean
+    assay() |>
+    is.na() |>
+    mean()
 ```
 
 The average missingness in the `proteins` assay is around 25
@@ -824,9 +824,9 @@ Note that after imputation, no value are missing.
 
 ```{r missingness_imputed}
 scp[["proteins_imptd"]] |>
-    assay |>
-    is.na |>
-    mean
+    assay() |>
+    is.na() |>
+    mean()
 ```
 
 
@@ -984,7 +984,7 @@ formatted to a long format table that can easily be plugged in the
 subsetByFeature(scp, "P13796") |>
     ## Format the `QFeatures` to a long format table
     longFormat(colvars = c("Raw.file", "SampleType", "Channel")) |>
-    data.frame |>
+    data.frame() |>
     ## This is used to preserve ordering of the samples and assays in ggplot2
     mutate(assay = factor(assay, levels = names(scp)),
            Channel = sub("Reporter.intensity.", "TMT-", Channel),
diff --git a/vignettes/scp_data_modelling.Rmd b/vignettes/scp_data_modelling.Rmd
index 6b92d20..ed56898 100644
--- a/vignettes/scp_data_modelling.Rmd
+++ b/vignettes/scp_data_modelling.Rmd
@@ -44,7 +44,7 @@ The last point will allow you to generate SCP data that is suitable
 for downstream analysis, such as clustering or trajectory inference.
 The figure below provides a roadmap of the workflow: 
 
-![modelling workflow](figs/ScpModel-class.png)
+![modelling workflow](figures/ScpModel-class.png)
 
 The vignette will start with the processed data extracted as a 
 `SingleCellExperiment` object from a processed `QFeatures` object. We