diff --git a/.gitignore b/.gitignore
index cdf82c5..53d3b2c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,3 +6,6 @@
 .Rbuildignore
 SPAROscore.Rproj
 inst/doc
+/vignettes/*.html
+/vignettes/*.R
+
diff --git a/DESCRIPTION b/DESCRIPTION
index 8682a84..323961c 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,24 +1,26 @@
-Package: SPAROscore
 Type: Package
-Title: A package to compute gene signature scores from transcriptomics data
+Package: SPAROscore
+Title: A package to compute gene signature scores from transcriptomics
+    data
 Version: 0.99.0
-Authors@R: c(
-    person(
-      "Venkatesh", "Kamaraj",
-      email = "venkatesh.kamaraj@adelaide.edu.au",
-      role = c("aut", "cre"),
-      comment = c(ORCID = "0009-0008-7309-9810")
-    )
-  )
-Description: SPAROscore is a gene signature scoring method designed to be 
-    robust across diverse gene expression datasets. SPAROscore adapts to 
-    varying levels of sparsity, allowing signature scores to be efficiently 
-    computed across bulk, single-cell, and spatial transcriptomic datasets.
-biocViews: GeneExpression, SingleCell, Spatial, Transcriptomics, 
-    GeneSetEnrichment, Pathways, Software
+Authors@R: 
+    person("Venkatesh", "Kamaraj", , "venkatesh.kamaraj@adelaide.edu.au", role = c("aut", "cre"),
+           comment = c(ORCID = "0009-0008-7309-9810"))
+Description: SPAROscore is a gene signature scoring method designed to be
+    robust across diverse gene expression datasets. SPAROscore adapts to
+    varying levels of sparsity, allowing signature scores to be
+    efficiently computed across bulk, single-cell, and spatial
+    transcriptomic datasets.  The resulting scores quantify the relative
+    expression of a gene signature compared with the background expression
+    of each sample/cell/domain, making the scores straightforward to
+    interpret biologically.  Internally, SPAROscore ranks genes within
+    each column and computes the Spearman footrule distance between the
+    observed ranks of the signature genes and a background gene expression
+    rank estimated from the geometric mean expression of the sample, cell,
+    or spatial domain.
 License: GPL-3
-Encoding: UTF-8
-RoxygenNote: 7.3.3
+URL: https://github.com/MangiolaLaboratory/SPAROscore
+BugReports: https://github.com/MangiolaLaboratory/SPAROscore/issues
 Imports: 
     DelayedArray,
     DelayedMatrixStats,
@@ -37,18 +39,20 @@ Suggests:
     GSEABase,
     knitr,
     msigdbr,
+    muscData,
     rmarkdown,
     scater,
     Seurat,
-    SeuratData,
     SingleCellExperiment,
     SpatialExperiment,
     spatialLIBD,
     SummarizedExperiment,
     testthat (>= 3.0.0),
     tidyr
-VignetteBuilder: knitr
+VignetteBuilder: 
+    knitr
+biocViews: GeneExpression, SingleCell, Spatial, Transcriptomics,
+    GeneSetEnrichment, Pathways, Software
 Config/testthat/edition: 3
-Remotes: satijalab/seurat-data
-URL: https://github.com/MangiolaLaboratory/SPAROscore
-BugReports: https://github.com/MangiolaLaboratory/SPAROscore/issues
+Encoding: UTF-8
+RoxygenNote: 7.3.3
diff --git a/R/get_scores_generic.R b/R/get_scores_generic.R
index f4abe61..a9ff6b2 100644
--- a/R/get_scores_generic.R
+++ b/R/get_scores_generic.R
@@ -59,7 +59,7 @@
 #'  Defaults to NULL.
 #'
 #' When down_signatures is not NULL,
-#' Final Score = Score(signatures) - Score(down_signatures)
+#' Final Score <- Score(signatures) - Score(down_signatures)
 #'
 #'
 #' @param prefix Character string to be appended before the headers of the
diff --git a/R/helper_functions.R b/R/helper_functions.R
index 328c45a..839e86f 100644
--- a/R/helper_functions.R
+++ b/R/helper_functions.R
@@ -229,7 +229,7 @@ append_to_matrix_like_object <- function(matrix_like_object, numeric_vector){
 #'
 #'
 #' @examples
-#' \dontrun{
+#' \donttest{
 #' counts <- matrix(
 #' c(0, 5, 10,
 #' 2, 0, 20,
diff --git a/R/sparoscore_generic.R b/R/sparoscore_generic.R
index f5fc845..92e23c6 100644
--- a/R/sparoscore_generic.R
+++ b/R/sparoscore_generic.R
@@ -111,7 +111,7 @@
 #'  Defaults to NULL.
 #'
 #' When down_signatures is not NULL,
-#' Final Score = Score(signatures) - Score(down_signatures)
+#' Final Score <- Score(signatures) - Score(down_signatures)
 #'
 #'
 #' @param prefix Character string to be appended before the headers of the
@@ -218,7 +218,7 @@
 #' # Seurat object
 #' # ------------------------------------------------------------------
 #'
-#' \dontrun{
+#' \donttest{
 #' seurat_object <- sparoscore(
 #' data = seurat_object,
 #' signatures = c("CCR7", "IL7R", "LTB")
@@ -257,7 +257,7 @@
 #' # SingleCellExperiment / SummarizedExperiment
 #' # ------------------------------------------------------------------
 #'
-#' \dontrun{
+#' \donttest{
 #' sce <- sparoscore(
 #' data = sce,
 #' signatures = c("CCR7", "IL7R", "LTB")
diff --git a/vignettes/.gitignore b/vignettes/.gitignore
deleted file mode 100644
index 097b241..0000000
--- a/vignettes/.gitignore
+++ /dev/null
@@ -1,2 +0,0 @@
-*.html
-*.R
diff --git a/vignettes/sparoscore_data_structures.Rmd b/vignettes/sparoscore_data_structures.Rmd
index 2ddb5df..4e656cb 100644
--- a/vignettes/sparoscore_data_structures.Rmd
+++ b/vignettes/sparoscore_data_structures.Rmd
@@ -10,7 +10,7 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-```{r, include = FALSE}
+```{r global-options, include = FALSE}
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
@@ -97,7 +97,7 @@ as well as the hallmark inflammatory response genes taken from the
 Molecular Signatures Database (MSigDB).
 
 ### Load the gene expression dataset
-```{r}
+```{r load-data}
 # load the airway dataset
 data(airway)
 
@@ -109,7 +109,7 @@ metadata <- as.data.frame(colData(airway))
 ```
 
 ### Load the gene signatures
-```{r}
+```{r load-signature}
 # set the glucocorticoid response markers from the original study as a signature 
 response_genes <- c("ENSG00000112936", "ENSG00000170606", "ENSG00000120129", 
                      "ENSG00000152795", "ENSG00000211445", "ENSG00000163884",
@@ -131,7 +131,7 @@ inflammation_genes <- msigdbr(species = "Homo sapiens", collection = "H") %>%
 # Gene expression as matrix-like data structures
 
 ## Dense matrix
-```{r}
+```{r matrix}
 # load gene expression data as dense matrix
 counts_matrix <- as.matrix(counts_data)
 
@@ -147,7 +147,7 @@ Note that one signature gene is absent from the input expression data,
 and SPAROscore reports this as a warning during scoring.
 
 ## SparseMatrix
-```{r}
+```{r smatrix}
 # load gene expression data as sparseMatrix
 counts_sparse <- as(counts_matrix, "sparseMatrix")
 
@@ -164,7 +164,7 @@ them to dense matrices, reducing memory usage and improving computational
 efficiency.
 
 ## DelayedArrays
-```{r}
+```{r dmatrix}
 # load gene expression data as delayedArray
 counts_delayed <- DelayedArray(counts_matrix)
 
@@ -180,7 +180,7 @@ Note that the gene ranking is performed natively on the DelayedArray object,
 enabling improved computational efficiency.
 
 ## Data frame
-```{r}
+```{r dataframe}
 # load gene expression data as delayedArray
 counts_df <- as.data.frame(counts_matrix)
 
@@ -197,7 +197,7 @@ because matrix operations are more efficient for this step.
 
 ### Verify if the output scores are the same with differing input formats
 
-```{r}
+```{r check-results}
 identical(scores_matrix, scores_sparse)
 identical(scores_matrix, scores_delayed)
 identical(scores_matrix, scores_df)
@@ -206,7 +206,7 @@ identical(scores_matrix, scores_df)
 # Gene signature representations
 
 ## Single signature as a character vector
-```{r}
+```{r character}
 # compute scores from a character vector signature
 scores_for_vector <- sparoscore(data = counts_matrix, 
                                  signatures = response_genes)
@@ -217,20 +217,21 @@ scores_for_vector
 
 
 ## Single signature as a GeneSet object
-```{r}
+```{r geneset}
 # get a GeneSet object
 response_geneset <- GeneSet(response_genes)
 
 # compute scores from a GeneSet object
 scores_for_geneset <- sparoscore(data = counts_sparse, 
-                                 signatures = response_geneset, prefix = "hello")
+                                 signatures = response_geneset, 
+                                 prefix = "hello")
 
 # view scores
 scores_for_geneset
 ```
 
 ## Multiple signatures as a named list
-```{r}
+```{r list}
 # create a named list of vectors to score for both signatures simultaneously
 gene_signatures <- list("glucocorticoid_response" = response_genes,
                         "inflammation_response" = inflammation_genes)
@@ -244,7 +245,7 @@ scores_for_list
 ```
 
 ## Multiple signatures as a GeneSetCollection object
-```{r}
+```{r genesetcollection}
 # create a GeneSetCollection object to score for both signatures simultaneously
 gene_set_collection <- GeneSetCollection(
   mapply(function(g, n) GeneSet(unique(g), setName = n), 
@@ -268,7 +269,7 @@ named `ranks`.
 
 In this example, let us work with a SingleCellExperiment object
 
-```{r}
+```{r sce}
 # convert airways to a SingleCellExperiment object
 airway_sce <- as(airway, "SingleCellExperiment")
 
@@ -309,7 +310,7 @@ For Seurat objects, after scoring, SPAROscore returns a Seurat object
 with the scores and the rank caps appended to the metadata. The ranks are stored
 in a layer named `ranks` in the same assay as the counts.
 
-```{r}
+```{r seurat}
 # convert airway to a seurat object 
 airway_seurat <- CreateSeuratObject(
   counts = assay(airway),
@@ -364,6 +365,6 @@ For examples of in-depth transcriptomics analyses using SPAROscore, refer here:
 For examples of additional options and advanced usage, see:
 [Advanced options](sparoscore_advanced_options.html)
 
-```{r}
+```{r session-info}
 sessionInfo()
 ```
diff --git a/vignettes/sparoscore_getting_started.Rmd b/vignettes/sparoscore_getting_started.Rmd
index ffb3c3b..496b502 100644
--- a/vignettes/sparoscore_getting_started.Rmd
+++ b/vignettes/sparoscore_getting_started.Rmd
@@ -10,7 +10,7 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-```{r, include = FALSE}
+```{r global-options, include = FALSE}
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
@@ -61,7 +61,7 @@ reported in the [original study](https://doi.org/10.1371/journal.pone.0099625).
 The expression matrix contains genes in rows and samples in columns. 
 The accompanying metadata indicates whether each sample received dexamethasone 
 treatment (trt) or served as an untreated control (untrt).
-```{r}
+```{r load-dataset}
 # load the airway dataset
 data(airway)
 
@@ -87,7 +87,7 @@ head(signature_genes %in% rownames(counts_data))
 ```
 
 ## Compute signature scores using SPAROscore
-```{r}
+```{r compute-scores}
 scores <- sparoscore(data = counts_data, signatures = signature_genes)
 
 # view the scores
@@ -99,7 +99,7 @@ scores
 Since dexamethasone activates glucocorticoid signalling, treated samples are 
 expected to exhibit higher glucocorticoid response scores than untreated 
 controls.
-```{r}
+```{r visualise-results}
 # add the signature scores to the metadata
 metadata$SPAROscore <- scores[, 1]
 
@@ -159,6 +159,6 @@ For examples of additional options and more advanced usage, see:
 [Advanced options](sparoscore_advanced_options.html)
 
 
-```{r}
+```{r session-info}
 sessionInfo()
 ```
diff --git a/vignettes/sparoscore_transcriptomics_technologies.Rmd b/vignettes/sparoscore_transcriptomics_technologies.Rmd
index d20017c..88ed0a0 100644
--- a/vignettes/sparoscore_transcriptomics_technologies.Rmd
+++ b/vignettes/sparoscore_transcriptomics_technologies.Rmd
@@ -10,7 +10,7 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-```{r, include = FALSE}
+```{r global-options, include = FALSE}
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
@@ -29,7 +29,7 @@ library(ggplot2)
 library(dplyr)
 library(edgeR)
 library(Seurat)
-library(SeuratData)
+library(muscData)
 library(msigdbr)
 library(scater)
 library(spatialLIBD)
@@ -103,7 +103,7 @@ The expression matrix contains genes in rows and samples in columns.
 The accompanying metadata indicates whether each sample received dexamethasone 
 treatment (trt) or served as an untreated control (untrt).
 
-```{r}
+```{r bulk-1}
 # load the airway dataset
 data(airway)
 
@@ -132,7 +132,7 @@ The number of signature missing from the dataset will also be reported by
 SPAROscore as a warning.
 
 ## Compute SPAROscore
-```{r}
+```{r bulk-2}
 bulk_scores <- sparoscore( data = counts_data, signatures = gc_response_genes) 
 
 bulk_scores
@@ -143,7 +143,7 @@ signature genes have higher expression relative to the background expression
 of the sample, consistent with stronger pathway activation.
 
 ## Combine scores with the sample metadata
-```{r}
+```{r bulk-3}
 metadata$SPAROscore <- bulk_scores[,1]
 
 metadata[, c("Run", "dex", "SPAROscore")]
@@ -157,7 +157,7 @@ Since dexamethasone activates glucocorticoid signalling, treated samples are
 expected to exhibit higher glucocorticoid response scores than untreated 
 controls.
 
-```{r}
+```{r bulk-4}
 metadata %>%
     ggplot(aes(x = dex, y = SPAROscore, fill = dex)) +
     geom_boxplot(width = 0.6, outlier.shape = NA) +
@@ -177,7 +177,7 @@ successfully captures the biological response induced by the treatment.
 The observed difference can be formally evaluated using the non-parametric
 Wilcoxon ranksum test
 
-```{r}
+```{r bulk-5}
 wilcox.test(SPAROscore ~ dex, data = metadata)
 ```
 
@@ -191,7 +191,7 @@ sample-to-sample variation in transcriptomic datasets. By colouring samples
 according to their SPAROscore rather than their treatment group, we can 
 determine whether pathway activity explains the dominant source of variation.
 
-```{r, warning=FALSE}
+```{r bulk-6, warning=FALSE}
 # Create the DGEList object
 bulk_dge <- DGEList(counts = counts_data)
 
@@ -243,7 +243,7 @@ To illustrate this, we compare the SPAROscore with the expression of most
 differentially expressed glucocorticoid response gene (C7) from the 
 [original study](https://doi.org/10.1371/journal.pone.0099625).
 
-```{r}
+```{r bulk-7}
 # assign the top gene from the original study
 top_gene <- "ENSG00000112936"
 
@@ -295,7 +295,8 @@ cell types and experimental conditions to be explored.
 
 In this example, the IFN-β stimulation dataset from 
 [Kang et al.](https://doi.org/10.1038/nbt.4042), which is available through the 
-SeuratData package will be used. The dataset consists of ~14,000 
+muscData package will be used. We will work with a seurat object of the same to 
+showcase the versatality of SPAROscore. The dataset consists of ~29,000 
 peripheral blood mononuclear cells (PBMCs) collected from 
 lupus patients before and after stimulation with interferon-β (IFN-β). 
 
@@ -305,17 +306,22 @@ Although the cells were stimulated with IFN-β, the Hallmark collection contains
 a single canonical type I interferon response signature, which captures the 
 conserved transcriptional response induced by both IFN-α and IFN-β signalling.
 
-## Load the dataset from SeuratData 
+## Load the dataset from muscData 
 
-```{r, warning=FALSE}
-# install the dataset for first-time use
-InstallData("ifnb")
+```{r sc-1, warning=FALSE}
+# load the ifnb data from the muscData library
+ifnb_sce_data <- Kang18_8vs8()
 
-# load the ifnb dataset
-ifnb <- LoadData("ifnb")
+# working with a seurat object
+ifnb <- CreateSeuratObject(
+  counts = counts(ifnb_sce_data), 
+  meta.data = as.data.frame(colData(ifnb_sce_data)),
+  assay = "RNA"
+)
 
 # check the layer in the RNA assay
 Layers(ifnb[["RNA"]])
+#> [1] "counts" "data"
 
 # view the metadata of the ifnb seurat object
 head(ifnb[[]])
@@ -328,7 +334,7 @@ are also given under the `seurat_annotations` metadata column.
 
 ## Load the gene signature from MSigDB
 
-```{r}
+```{r sc-2}
 ifn_signature <- msigdbr( species = "Homo sapiens", collection = "H" ) %>% 
     filter(gs_name == "HALLMARK_INTERFERON_ALPHA_RESPONSE") %>% 
     pull(gene_symbol) %>% 
@@ -342,7 +348,7 @@ head(ifn_signature %in% rownames(ifnb))
 ## Compute SPAROscore
 The interferon-beta response for every cell can be quantified using SPAROscore
 
-```{r}
+```{r sc-3}
 ifnb <- sparoscore(data = ifnb, 
                    assay = "RNA", 
                    layer = "counts", 
@@ -370,7 +376,7 @@ Since interferon signalling is expected to increase following stimulation,
 the distribution of SPAROscore between the two experimental conditions should 
 reflect that.
 
-```{r}
+```{r sc-4}
 VlnPlot(ifnb, 
         features = "IFN_SPAROscore", 
         group.by = "stim", 
@@ -388,7 +394,7 @@ scores than unstimulated controls.
 The difference between the two experimental groups can also be assessed 
 statistically using the non-parametric Wilcoxon ranksum test.
 
-```{r}
+```{r sc-5}
 wilcox.test(IFN_SPAROscore ~ stim, ifnb[[]])
 ```
 
@@ -401,10 +407,10 @@ to quantify that biological phenomenon.
 Because pathway scores are calculated for every individual cell, 
 they can be compared across annotated immune cell populations.
 
-```{r, warning=FALSE}
+```{r sc-6, warning=FALSE}
 VlnPlot(ifnb,
         features = "IFN_SPAROscore", 
-        group.by = "seurat_annotations", 
+        group.by = "cell", 
         pt.size = 0, 
         flip = TRUE )
 
@@ -420,7 +426,7 @@ transcriptional response following interferon stimulation.
 One of the most useful ways to explore pathway activity is to visualise the 
 scores on the UMAP embeddings.
 
-```{r, warning=FALSE, message=FALSE}
+```{r sc-7, warning=FALSE, message=FALSE}
 # get umap embedding from the gene expression data
 
 # normalise the gene expression
@@ -461,7 +467,7 @@ computationally intensive ranking step.
 
 For example, an inflammatory response signature could be scored as follows.
 
-```{r}
+```{r sc-8}
 # load the inflammatory response hallmark genes from msigdb
 inflammation_signature <- msigdbr(species = "Homo sapiens",collection = "H") %>% 
     filter(gs_name == "HALLMARK_INFLAMMATORY_RESPONSE") %>% 
@@ -500,9 +506,9 @@ quantify interferon pathway activity across each cell population.
 
 
 ### Aggregate cells into pseudo-bulk profiles
-```{r}
+```{r pb-1}
 pseudobulk_ifn <- AggregateExpression(ifnb, 
-                                     group.by = c("seurat_annotations", "stim"), 
+                                     group.by = c("cell", "stim"), 
                                      assays = "RNA", 
                                      return.seurat = TRUE,
                                      slot = "counts")
@@ -514,7 +520,7 @@ cell type and treatment (for example, "B_CTRL" or "B_STIM"), while rows
 represent genes.
 
 ### Compute scores at pseudo-bulk level
-```{r}
+```{r pb-2}
 pseudobulk_ifn <- sparoscore(data = pseudobulk_ifn,
                              signatures = ifn_signature,
                              assay = "RNA",
@@ -522,9 +528,9 @@ pseudobulk_ifn <- sparoscore(data = pseudobulk_ifn,
 ```
 ### Visualise pseudo-bulk level scores
 
-```{r}
+```{r pb-3}
 pseudobulk_ifn[[]] %>%
-    ggplot(aes(y = seurat_annotations, x = SPAROscore, fill = stim)) +
+    ggplot(aes(y = cell, x = SPAROscore, fill = stim)) +
     geom_col(position = position_dodge(width = 0.8)) +
     theme_classic() +
     scale_fill_manual(values = c("#2166AC", "#B2182B"))+
@@ -590,7 +596,7 @@ lower in the cortex.
 
 For computational efficiency, we analyse one representative tissue section.
 
-```{r}
+```{r sp-1}
 # load the DLPFC Visium dataset
 dlpfc_spe <- fetch_data(type = "spe")
 
@@ -602,7 +608,7 @@ The object is a SpatialExperiment, containing spot-level gene expression counts,
 spatial coordinates, tissue images, and manually annotated cortical layers.
 
 ## Visualise the tissue morphology
-```{r}
+```{r sp-2}
 # for simplicity of the vignette, work with one sample
 dlpfc_spe <- dlpfc_spe[ , dlpfc_spe$sample_id == "151675"]
 
@@ -617,7 +623,7 @@ vis_clus(spe = dlpfc_spe,
 
 For demonstration purposes, we use a collection of well-established 
 oligodendrocyte marker genes.
-```{r}
+```{r sp-3}
 oligo_signature <- c(
   "ENSG00000167748", "ENSG00000166923", "ENSG00000174607", "ENSG00000135678", 
   "ENSG00000089123", "ENSG00000183018", "ENSG00000129538", "ENSG00000136937", 
@@ -636,7 +642,7 @@ head(oligo_signature %in% rownames(dlpfc_spe))
 Since SpatialExperiment objects are supported directly by SPAROscore, 
 pathway scores can be computed without extracting the expression matrix.
 
-```{r}
+```{r sp-4}
 dlpfc_spe <- sparoscore(data = dlpfc_spe,
                         assay = "counts",
                         signatures = oligo_signature,
@@ -658,7 +664,7 @@ visualise biological processes within their anatomical context.
 Using the spot coordinates stored in the SpatialExperiment object, 
 the SPAROscore can be projected directly onto the tissue section.
 
-```{r}
+```{r sp-5}
 vis_gene(spe = dlpfc_spe,
          geneid = "Oligo_SPAROscore",
          spatial = TRUE,
@@ -679,7 +685,7 @@ of oligodendrocyte gene expression.
 The manual annotations for each cortical layer enables 
 quantitative comparison of oligodendrocyte pathway activity.
 
-```{r}
+```{r sp-6}
 # differentiate white matter from cortex based on annotations
 dlpfc_metadata <- as.data.frame(colData(dlpfc_spe)) %>% 
             filter(layer_guess != "NA") %>% # remove missing annotations
@@ -710,7 +716,7 @@ matter while the cortical grey matter predominantly contains neuronal cells.
 To further illustrate the difference, oligodendrocyte signature scores between 
 cortical spots and white matter can be compared statistically.
 
-```{r}
+```{r sp-7}
 wilcox.test(Oligo_SPAROscore ~ Region, data = dlpfc_metadata)
 ```
 The significant reduction in oligodendrocyte signature scores within grey matter 
@@ -763,6 +769,6 @@ usage vignette can be found here:
 [Advanced options](sparoscore_advanced_options.html)
 
 
-```{r}
+```{r session-info}
 sessionInfo()
 ```