diff --git a/.github/workflows/R-CMD-check.yml b/.github/workflows/R-CMD-check.yml
index e63f6c6..0c1530a 100644
--- a/.github/workflows/R-CMD-check.yml
+++ b/.github/workflows/R-CMD-check.yml
@@ -60,13 +60,6 @@ jobs:
 
       - uses: r-lib/actions/setup-pandoc@v2
 
-      - name: Cache R packages
-        uses: actions/cache@v4
-        with:
-          path: /home/runner/work/_temp/Library
-          key: ${{ env.cache-version }}-${{ matrix.config.os }}-biocversion-${{ steps.BIOC.outputs.version}}-r-${{ steps.R.outputs.version}}-${{ hashFiles('.github/depends.Rds') }}
-          restore-keys: ${{ env.cache-version }}-${{ matrix.config.os }}-biocversion-${{ steps.BIOC.outputs.version}}-r-${{ steps.R.outputs.version}}-
-
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:
           extra-packages: any::rcmdcheck
diff --git a/.github/workflows/vignette.yml b/.github/workflows/vignette.yml
index 0702e2b..7cab28e 100644
--- a/.github/workflows/vignette.yml
+++ b/.github/workflows/vignette.yml
@@ -79,13 +79,6 @@ jobs:
 
       - uses: r-lib/actions/setup-pandoc@v2
 
-      - name: Cache R packages
-        uses: actions/cache@v4
-        with:
-          path: /home/runner/work/_temp/Library
-          key: ${{ env.cache-version }}-${{ matrix.config.os }}-biocversion-${{ steps.BIOC.outputs.version}}-r-${{ steps.R.outputs.version}}-${{ hashFiles('.github/depends.Rds') }}
-          restore-keys: ${{ env.cache-version }}-${{ matrix.config.os }}-biocversion-${{ steps.BIOC.outputs.version}}-r-${{ steps.R.outputs.version}}-
-
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:
           extra-packages: pkgdown
diff --git a/DESCRIPTION b/DESCRIPTION
index 07f6dfc..7adc631 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: dandelionR
 Title: Single-cell Immune Repertoire Trajectory Analysis in R
-Version: 0.99.3
+Version: 0.99.4
 Authors@R:
     c(person(given="Jiawei", family="Yu", email = "jiawei.yu@uq.edu.au", role = c("aut"), comment = c(ORCID = "0009-0005-9170-7881")),
       person(given="Nicholas", family="Borcherding", email = "borcherding@wustl.edu", role = c("aut"), comment = c(ORCID = "0000-0003-1427-6342")),
diff --git a/vignettes/vignette_from_scRepertoire.Rmd b/vignettes/vignette_from_scRepertoire.Rmd
index 8cf8f51..45d4229 100644
--- a/vignettes/vignette_from_scRepertoire.Rmd
+++ b/vignettes/vignette_from_scRepertoire.Rmd
@@ -78,6 +78,10 @@ data(demo_sce)
 data(demo_airr)
 ```
 
+We will set the seed so that the plots and results are consistent.
+```{r}
+set.seed(123)
+```
 ### Use `scRepertoire` to load the VDJ data
 For the trajectory analysis work here, we are focusing on the main productive TCR chains. Therefore we will flag `filterMulti = TRUE`, which will keep the selection of the 2 corresponding chains with the highest expression for a single barcode. For more details, refer to `scRepertoire`'s [documentation](https://www.borch.dev/uploads/screpertoire/reference/combinetcr).
 
@@ -121,6 +125,11 @@ sce <- setupVdjPseudobulk(sce,
 )
 ```
 
+The main output of this function is a `SingleCellExperiment` object with the relevant VDJ information appended to the `colData`, particularly the columns with the `_main` suffix e.g. `v_call_abT_VJ_main`, `j_call_abT_VJ_main` etc.
+```{r}
+head(colData(sce))
+```
+
 Visualise the UMAP of the filtered data.
 ```{r}
 plotUMAP(sce, color_by = "anno_lvl_2_final_clean")
@@ -152,12 +161,16 @@ plotUMAP(milo_object, color_by = "anno_lvl_2_final_clean", dimred = "UMAP_knngra
 Next, we will construct the pseudobulked VDJ feature space using the neighbourhood graph constructed above. We will also run PCA on the pseudobulked VDJ feature space.
 ```{r}
 pb.milo <- vdjPseudobulk(milo_object, mode_option = "abT", col_to_take = "anno_lvl_2_final_clean")
+```
 
-pb.milo <- runPCA(pb.milo, assay.type = "Feature_space", ncomponents = 20)
+Inspect the newly created `pb.milo` object.
+```{r}
+pb.milo 
 ```
 
-We can visualise the PCA of the pseudobulked VDJ feature space.
+We can compute and visualise the PCA of the pseudobulked VDJ feature space.
 ```{r}
+pb.milo <- runPCA(pb.milo, assay.type = "Feature_space", ncomponents = 20)
 plotPCA(pb.milo, color_by = "anno_lvl_2_final_clean")
 ```
 
@@ -183,7 +196,7 @@ library(destiny)
 dm <- DiffusionMap(t(pca), n_pcs = 10, n_eigs = 10)
 ```
 
-### Compute diffussion pseudotime on diffusion map
+### Compute diffusion pseudotime on diffusion map
 ```{r}
 dif.pse <- DPT(dm, tips = c(root, branch.tips), w_width = 0.1)
 ```
@@ -199,7 +212,7 @@ plotPCA(pb.milo, color_by = "pseudotime") + scale_colour_gradientn(colours = pal
 ```
 
 ### Markov chain construction on the pseudobulk VDJ feature space
-
+This step will compute the Markov chain probabilities on the pseudobulk VDJ feature space. It will return the branch probabilities in the `colData` and the column name corresponds to the branch tips defined earlier.
 ```{r}
 pb.milo <- markovProbability(
     milo = pb.milo,
@@ -211,6 +224,11 @@ pb.milo <- markovProbability(
 )
 ```
 
+Inspect the `pb.milo` object to see the newly added columns.
+```{r}
+head(colData(pb.milo))
+```
+
 ### Visualising branch probabilities
 
 With the Markov chain probabilities computed, we can visualise the branch probabilities towards CD4+ or CD8+ T-cell fate on the PCA plot.