Function reference • IBRAP

Pre-processing Functions Functions to be used to pre-process the data prior to normalisation
`perform.scrublet()`	Python module: scrublet
`perform.decontX()`	R package: celda, decontX function
`createIBRAPobject()`	Create an IBRAP class object
`add.cell.cycle()`	Scores cell cycle phases
`add.cell.cycle()`	Provides scores for a given vector of features
`find_percentage_genes()`	Calculates the fraction of counts from genes matching a pattern string
`filter_IBRAP()`	Filters object according to cell metadata
`remove.hvgs()`	Remove highly variable genes
Normalisation Functions IBRAP functions for normalisation
`perform.scanpy()`	Performs Scanpy normalisation, hvg selection, scaling and variance stabilisation and regression.
`perform.scran()`	Performs Scran normalisation, hvg selection, scaling and variance stabilisation and regression.
`perform.tpm()`	Performs TPM normalisation
`perform.sct()`	Performs SCTransform
Reduction Functions Methods to reduce the dimensionality of the data
`perform.pca()`	Performs PCA reduction
`perform.diffusion.map()`	Diffusion maps
`perform.umap()`	Performs UMAP reduction
`perform.lvish()`	Performs LargeVis reduction
`perform.tsne()`	Performs t-SNE reduction
Clustering Functions Methods to cluster reduced dimensions
`perform.nn()`	Finds the shared nearest neighbourhood for the cells. This supplies a graph.
`perform.graph.cluster()`	Performs graph-based clustering
`perform.graph.subclustering()`	Seurat subclustering
`perform.sc3.reduction.cluster()`	Performs SC3 clustering on reduced embeddings
`perform.sc3.slot.cluster()`	Performs SC3 clustering on matrix slot
`perform.reduction.kmeans()`	Performs Kmeans/PAM clustering on a reduction
Integration functions Methods to remove batch effects from multi-sample projects
`perform.harmony()`	Performs Harmony integration
`perform.scanorama()`	Performs Scanorama integration
`perform.seurat.integration()`	Performs Seurat Integration
`perform.bbknn()`	Performs BBKNN integration
Gene Analsysis Functions
`perform.diffexp()`	Perform differential expression
`perform.diffexp.all()`	Perform differential expression one cluster vs all
`perform.GO.enrichment()`	Gene Ontology enrichment
Plotting Functions These plots can be used to plot results produced in IBRAP
`plot.QC.scatter()`	Plots two QC metrices in scatter format
`plot.QC.vln()`	Plots a given QC metric
`plot(<reduced.dim>)`	Plot of reduced dimensions and labels
`plot(<reduced.dim.interactive>)`	Interactive plot of reduced dimensions and labels
`plot(<dot.plot>)`	Plots a dot plot of gene expression
`plot(<features>)`	Plot of reduced dimensions and features
`plot(<vln>)`	Plot of violin plot of defined features
`plot(<GO.output>)`	Plot GO enrichment output
`plot(<slingshot>)`	Plots slingshot results
`plot(<variance>)`	Plot reduction explained variance
Benchmarking Functions
`benchmark.clustering()`	Benchmarks the cluster assignments
`benchmark.intergation()`	Benchmarks the cluster assignmentws
Miscellaneous Functions
`Read10X_output()`	Produce counts matrix from CellRanger output
`perform.singleR.annotation()`	Performs automated cell annotation on query datasets using reference data
`perform.slingshot.trajectory()`	Performs Slingshot trajectory inference
`showObjectContents()`	Shows the contents in your IBRAP object
`splitIBRAP()`	Split the IBRAP object up
Backend Functions
`prepare.reticulate()`	Installs or identifies if python modules are installed
`run.IBRAP.rshiny()`	Rshiny application initiator
S4 Class Object Structures
`IBRAP-object`	S4 class object that contains method-assays
`methods-object`	An S4 class object of method-assays

Reference

Pre-processing Functions

Normalisation Functions

Reduction Functions

Clustering Functions

Integration functions

Gene Analsysis Functions

Plotting Functions

Benchmarking Functions

Miscellaneous Functions

Backend Functions

S4 Class Object Structures