Package index • Matisse

Creating a Matisse object

Start here. This function combines your Seurat object with splicing data into a single Matisse object ready for analysis. Pass junction_counts for short-read (junction) mode, or transcript_counts + ioe_files for long-read (event) mode.

show(<MatisseObject>) dim(<MatisseObject>) `[`(<MatisseObject>,<ANY>,<ANY>,<ANY>) `[[`(<MatisseObject>,<ANY>,<ANY>) `$`(<MatisseObject>): The MatisseObject S4 class
CreateMatisseObject(): Create a MatisseObject

Retrieve or update your data

Functions for pulling specific data tables out of your Matisse object, or putting updated tables back in.

GetSeurat(): Get the embedded Seurat object
GetPSI(): Get the PSI matrix
SetPSI(): Set the PSI matrix
GetJunctionCounts(): Get raw junction count matrix
GetInclusionCounts(): Get inclusion read count matrix
GetExclusionCounts(): Get exclusion read count matrix
GetTranscriptCounts(): Get transcript count matrix
GetEventData(): Get splice event annotation table
GetJunctionData(): Get junction annotation table
MatisseMeta() `MatisseMeta<-`(): Get or set cell-level metadata
AddIsoformMetadata(): Add columns to the cell metadata

Normalisation

Normalise count data before clustering. SCTransform applies variance stabilisation and scales for sequencing depth. In event mode it normalises the transcript assay; in junction mode it normalises the gene-expression assay by default.

SCTransform(<MatisseObject>): SCTransform normalisation for MatisseObjects

Calculate splicing ratios (PSI)

Compute PSI (Percent Spliced In) — the fraction of transcripts in each cell that include a given exon. Values range from 0 (exon always skipped) to 1 (exon always included). In event mode this is done at construction; in junction mode call CalculatePSI() explicitly.

CalculatePSI(): Calculate PSI matrix from junction counts
SummarizePSI(): Summarize PSI distribution across cells for each event

Quality control and filtering

Identify and remove cells or splicing events that don’t have enough data for reliable analysis.

ComputeIsoformQC(): Compute per-cell isoform QC metrics
FilterCells(): Filter cells by isoform QC thresholds
FilterEvents(): Filter splice events by coverage or variance

Visualisation

Plot splicing patterns across your cells. Overlay any feature — PSI values, junction counts, or gene expression — on a UMAP, compare splicing between cell types, or inspect junction usage for a gene of interest. Pass the feature name via the feature argument.

PlotUMAP(): UMAP plot coloured by any feature
PlotViolin(): Violin plot of feature values split by cell group
PlotHeatmap(): Heatmap of feature values across cells and events
PlotCoverage(): Junction coverage bar plot for a gene
PlotQCMetrics(): Violin/ridge plot of isoform QC metrics

Utilities

Helper functions for building event tables and combining datasets.

BuildSimpleEvents(): Build a minimal junction event annotation table
MergeMatisse(): Merge two MatisseObjects by cells