Function reference • rliger

Read Data

createH5LigerDataset(): Create on-disk ligerDataset Object

createLiger(): Create liger object

createLigerDataset(): Create in-memory ligerDataset object

readLiger(): Read liger object from RDS file

read10X() read10XRNA() read10XATAC(): Load in data from 10X

read10XFiles(): Read 10X cellranger files (matrix, barcodes and features) into R session

as.ligerDataset(): Converting other classes of data to a ligerDataset object

seuratToLiger() as.liger(): Converting other classes of data to a liger object

updateLigerObject(): Update old liger object to up-to-date structure

Example Data

importPBMC() importBMMC() importCGE(): Import prepared dataset publically available

pbmc: liger object of PBMC subsample data with Control and Stimulated datasets

pbmcPlot: liger object of PBMC subsample data with plotting information available

bmmc: liger object of bone marrow subsample data with RNA and ATAC modality

deg.marker: Data frame for example marker DEG test result

deg.pw: Data frame for example pairwise DEG test result

Preprocessing

runGeneralQC(): General QC for liger object

runDoubletFinder(): Doublet detection with DoubletFinder

removeMissing() removeMissingObs(): Remove missing cells or features from liger object

normalize() normalizePeak(): Normalize raw counts data

selectGenes(): Select a subset of informative genes

scaleNotCenter(): Scale genes by root-mean-square across cells

reverseMethData(): Create "scaled data" for DNA methylation datasets

downsample(): Downsample datasets

selectGenesVST(): Select variable genes from one dataset with Seurat VST method

Integration

Perform iNMF and Variants

runIntegration(): Integrate scaled datasets with iNMF or variant methods

runINMF(): Perform iNMF on scaled datasets

runOnlineINMF(): Perform online iNMF on scaled datasets

runUINMF(): Perform Mosaic iNMF (UINMF) on scaled datasets with unshared features

runCINMF(): Perform consensus iNMF on scaled datasets

Post-Factorization Alignment

alignFactors(): Align factor loadings to get final integration

quantileNorm(): Quantile Align (Normalize) Factor Loadings

centroidAlign(): Align factor loading by centroid alignment (beta)

Evaluation

calcAlignment(): Calculate alignment metric after integration

calcAgreement(): Calculate agreement metric after integration

calcDatasetSpecificity(): Calculate a dataset-specificity score for each factor

Optimization with New Parameter

optimizeNewData(): Perform factorization for new data

optimizeNewK(): Perform factorization for new value of k

optimizeNewLambda(): Perform factorization for new lambda value

optimizeSubset(): Perform factorization for subset of data

Downstream Analysis

Dimensionality Reduction

runUMAP(): Perform UMAP Dimensionality Reduction

runTSNE(): Perform t-SNE dimensionality reduction

Clustering

runCluster(): SNN Graph Based Community Detection

mapCellMeta(): Create new variable from categories in cellMeta

calcARI(): Calculate adjusted Rand index (ARI) by comparing two cluster labeling variables

calcPurity(): Calculate purity by comparing two cluster labeling variables

calcNMI(): Calculate Normalized Mutual Information (NMI) by comparing two cluster labeling variables

Differential Expression

runPairwiseDEG() runMarkerDEG() runWilcoxon(): Find DEG between groups

getFactorMarkers(): Find shared and dataset-specific markers

GO and GSEA

runGOEnrich(): Run Gene Ontology enrichment analysis on differentially expressed genes.

runGSEA(): Analyze biological interpretations of metagene

ATAC Specific Analysis

imputeKNN(): Impute the peak counts from gene expression data referring to an ATAC dataset after integration

linkGenesAndPeaks(): Linking genes to putative regulatory elements

exportInteractTrack(): Export predicted gene-pair interaction

Visualization

plotBarcodeRank(): Create barcode-rank plot for each dataset

plotCellViolin(): Generate violin/box plot(s) using liger object

plotClusterFactorDot(): Make dot plot of factor loading in cell groups

plotClusterGeneDot(): Make dot plot of gene expression in cell groups

plotClusterGeneViolin(): Create violin plot for multiple genes grouped by clusters

plotDensityDimRed(): Create density plot basing on specified coordinates

plotDimRed() plotClusterDimRed() plotDatasetDimRed() plotByDatasetAndCluster() plotGeneDimRed() plotPeakDimRed() plotFactorDimRed(): Generate scatter plot(s) using liger object

plotEnhancedVolcano(): Create volcano plot with EnhancedVolcano

plotGODot(): Visualize GO enrichment test result in dot plot

plotGeneLoadings() plotGeneLoadingRank(): Visualize factor expression and gene loading

plotGroupClusterDimRed(): Comprehensive group splited cluster plot on dimension reduction with proportion

plotGeneHeatmap() plotFactorHeatmap(): Plot Heatmap of Gene Expression or Factor Loading

plotMarkerHeatmap(): Create heatmap for showing top marker expression in conditions

plotPairwiseDEGHeatmap(): Create heatmap for pairwise DEG analysis result

plotProportion() plotProportionDot() plotProportionBar() plotClusterProportions() plotProportionPie(): Visualize proportion across two categorical variables

plotProportionBox(): Box plot of cluster proportion in each dataset, grouped by condition

plotSankey(): Make Riverplot/Sankey diagram that shows label mapping across datasets

plotSpatial2D(): Visualize a spatial dataset

plotVarFeatures(): Plot the variance vs mean of feature expression

plotGeneViolin() plotTotalCountViolin() plotGeneDetectedViolin(): Visualize gene expression or cell metadata with violin plot

plotVolcano(): Create volcano plot for Wilcoxon test result

makeInteractTrack() louvainCluster() optimizeALS() online_iNMF() quantile_norm() makeRiverplot(): Deprecated functions in package rliger.

.ggCellViolin(): Produce single violin plot with data frame passed from upstream

.ggScatter(): Produce single scatter plot with data frame passed from upstream

.ggplotLigerTheme(): Generic ggplot theme setting for rliger package

.plotHeatmap(): General heatmap plotting with prepared matrix and data.frames

.complexHeatmapDotPlot(): Generate dot plot from input matrix with ComplexHeatmap

Object Class

datasets() `datasets<-`() dataset() `dataset<-`() cellMeta() `cellMeta<-`() defaultCluster() `defaultCluster<-`() dimReds() `dimReds<-`() dimRed() `dimRed<-`() defaultDimRed() `defaultDimRed<-`() varFeatures() `varFeatures<-`() varUnsharedFeatures() `varUnsharedFeatures<-`() commands() show(<liger>) dim(<liger>) dimnames(<liger>) `dimnames<-`(<liger>,<list>) names(<liger>) `names<-`(<liger>) length(<liger>) lengths(<liger>) rawData(<liger>) `rawData<-`(<liger>,<ANY>,<ANY>,<matrixLike_OR_NULL>) `rawData<-`(<liger>,<ANY>,<ANY>,<H5D>) normData(<liger>) `normData<-`(<liger>,<ANY>,<ANY>,<matrixLike_OR_NULL>) `normData<-`(<liger>,<ANY>,<ANY>,<H5D>) scaleData(<liger>,<ANY>) `scaleData<-`(<liger>,<ANY>,<ANY>,<matrixLike_OR_NULL>) `scaleData<-`(<liger>,<ANY>,<ANY>,<H5D>) `scaleData<-`(<liger>,<ANY>,<ANY>,<H5Group>) scaleUnsharedData(<liger>,<character>) scaleUnsharedData(<liger>,<numeric>) `scaleUnsharedData<-`(<liger>,<ANY>,<ANY>,<matrixLike_OR_NULL>) `scaleUnsharedData<-`(<liger>,<ANY>,<ANY>,<H5D>) `scaleUnsharedData<-`(<liger>,<ANY>,<ANY>,<H5Group>) getMatrix(<liger>,<ANY>,<ANY>,<ANY>) getH5File(<liger>,<ANY>) `[[<-`(<liger>) `$`(<liger>) `$<-`(<liger>) fortify(<liger>) c(<liger>): liger class

ligerATACDataset-class ligerATACDataset: Subclass of ligerDataset for ATAC modality

show(<ligerCommand>): ligerCommand object: Record the input and time of a LIGER function call

rawData() `rawData<-`() normData() `normData<-`() scaleData() `scaleData<-`() scaleUnsharedData() `scaleUnsharedData<-`() getMatrix() h5fileInfo() `h5fileInfo<-`() getH5File() featureMeta() `featureMeta<-`() show(<ligerDataset>) dim(<ligerDataset>) dimnames(<ligerDataset>) `dimnames<-`(<ligerDataset>,<list>) cbind(<ligerDataset>): ligerDataset class

ligerMethDataset-class ligerMethDataset: Subclass of ligerDataset for Methylation modality

ligerRNADataset-class ligerRNADataset: Subclass of ligerDataset for RNA modality

ligerSpatialDataset-class ligerSpatialDataset: Subclass of ligerDataset for Spatial modality

`[`(<liger>): Subset liger with brackets

`[[`(<liger>): Get cell metadata variable

`[`(<ligerDataset>): Subset ligerDataset object

rawPeak() `rawPeak<-`() normPeak() `normPeak<-`(): Access ligerATACDataset peak data

coordinate() `coordinate<-`(): Access ligerSpatialDataset coordinate data

retrieveCellFeature(): Retrieve a single matrix of cells from a slot

subsetLiger(): Subset liger object

subsetLigerDataset() subsetH5LigerDataset() subsetMemLigerDataset(): Subset ligerDataset object

convertOldLiger(): Convert old liger object to latest version

ligerToSeurat(): Convert between liger and Seurat object

is.newLiger(): Check if given liger object if under new implementation

modalOf(): Return preset modality of a ligerDataset object or that of all datasets in a liger object

isH5Liger(): Check if a liger or ligerDataset object is made of HDF5 file

restoreH5Liger() restoreOnlineLiger(): Restore links (to HDF5 files) for reloaded liger/ligerDataset object

closeAllH5(): Close all links (to HDF5 files) of a liger object

Misc

H5Apply(): Apply function to chunks of H5 data in ligerDataset object

commandDiff(): Check difference of two liger command

makeFeatureMatrix(): Fast calculation of feature count matrix

mergeH5(): Merge hdf5 files

mergeSparseAll() mergeDenseAll(): Merge matrices while keeping the union of rows

writeH5(): Write in-memory data into H5 file

Deprecated

louvainCluster-deprecated: Louvain algorithm for community detection

makeInteractTrack-deprecated: Export predicted gene-pair interaction

makeRiverplot-deprecated: Generate a river (Sankey) plot

online_iNMF-deprecated: Perform online iNMF on scaled datasets

optimizeALS-deprecated: Perform iNMF on scaled datasets

quantile_norm-deprecated: Quantile align (normalize) factor loading

readSubset(): See downsample

quantileAlignSNF(): Quantile align (normalize) factor loadings

getProportionMito(): Calculate proportion mitochondrial contribution