Devel

.gitignore

@@ -6,3 +6,6 @@
 .Rbuildignore
 SPAROscore.Rproj
 inst/doc
+/vignettes/*.html
+/vignettes/*.R
+

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

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

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,

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")

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()
 ```

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()
 ```