Introduction | Example datasets | Bulk Transcriptomics Analysis | Load the dataset and the gene signature | Compute SPAROscore | Combine scores with the sample metadata | Compare treated and untreated samples | Statistical comparison | Visualising pathway activity on a PCA | Relationship between SPAROscore and individual marker genes | Summary | Single-cell RNA-seq analysis | Load the dataset from muscData | Load the gene signature from MSigDB | Comparing stimulated and control cells | Interferon activity across cell types | Visualising pathway activity on the UMAP | Efficiently scoring additional pathways | Pseudo-bulk analyses | Aggregate cells into pseudo-bulk profiles | Compute scores at pseudo-bulk level | Visualise pseudo-bulk level scores | Spatial transcriptomics analysis | Load the DLPFC dataset from SpatialLIBD | Visualise the tissue morphology | Defining a oligodendrocyte gene signature | Visualising pathway activity across the tissue | Pathway activity across anatomical regions | Conclusion

Supported input data structures | Example datasets | Load the gene expression dataset | Load the gene signatures | Gene expression as matrix-like data structures | Dense matrix | SparseMatrix | DelayedArrays | Data frame | Verify if the output scores are the same with differing input formats | Gene signature representations | Single signature as a character vector | Single signature as a GeneSet object | Multiple signatures as a named list | Multiple signatures as a GeneSetCollection object | SummarizedExperiment and derived objects | Seurat objects

What is SPAROscore? | How to use SPAROscore? | Load the dataset | Compute signature scores using SPAROscore | Visualise the results | What transcriptomics technologies can SPAROscore handle? | What data structures can SPAROscore handle? | What are some advanced capabilities of SPAROscore?