Package index • PhiSpace

All functions
`CLRnorm()`	Center log ratio normalisation
`CVTune_ncomp()`	Tune ncomp using cross-validation.
`CVTune_nfeat()`	Tune nfeat using cross-validation.
`KeepCommonGenes()`	Keep common genes of two SingleCellExperiment objects.
`PhiSpace()`	PhiSpace continuous phenotyping
`PhiSpaceR_1ref()`	PhiSpace using a single reference
`RTassay()`	Apply rank transform to a gene by cell matrix.
`RankTransf()`	Rank transform of a `SingleCellExperiment` object.
`SuperPC()`	Compute regression model using selected features.
`VizSpatial()`	Visualising features (eg gene expression), metadata (eg cell type) or reducedDim component (eg PhiSpace cell type score) in 2D tissue space.
`align_clusters()`	Align clustering results.
`as.sparse.matrix()`	Convert a matrix to sparse metrix.
`basicPlotFormat()`	Plot formatting for presentation and publication
`cellTypeCorMat()`	Create cell type correlation matrices
`classErr()`	Calculate classification errors.
`codeY()`	Convert discrete annotations to dummy variable matrices.
`codeY_vec()`	Convert discrete annotations to dummy variable matrices.
`computeUMAP()`	Compute UMAP.
`doubleCent()`	Double center a matrix by column and row means, resulting in a new matrix with zero row and column means.
`getClass()`	Dicretise soft annotation to labels.
`getErr()`	Compute different errors for cross-validation.
`getErr_nfeat()`	Calculate errors for the cross-validation for selecting nfeat.
`getPC()`	Principal component analysis (PCA) based on partial singular value decomposition (SVD).
`loadBarplot()`	Plot loadings of dimension reduction as bar plot.
`logTransf()`	Relative counts normalisation.
`matrixPlot()`	Score matrix plot
`mvr()`	Multivariate regression via principal component analysis (PCA) or partial least squares (PLS).
`normPhiScores()`	Normalise PhiSpace phenotypic scores by row or by column.
`phenotype()`	Soft phenotyping of query assay.
`plotPhiSpaceHeatMap()`	Plot PhiSpace scores as a heatmap
`plotSankey()`	Plot Sankey diagram for two classification results.
`plotSankey3()`	Plot Sankey diagram for three hard classification results.
`pseudoBulk()`	Converting single cells to pseudo-bulks.
`reNameCols()`	Turn a matrix to a data.frame withe newly defined colnames.
`scranTransf()`	Run scran normalisation
`selectFeat()`	Select nfeat top features from each column (label) of impScores
`split2()`	Create partitions of data for cross-validation.
`subsample()`	Subsample from a sce object
`translateLabel()`	Translate cell type labels from finer to coarser according to a dictionary.
`tunePhiSpace()`	Title
`zeroFeatQC()`	Minimal quality control (QC): remove features with all zero values.

Reference

All functions