Package index • scSLIDE

All functions
`BuildLandmarkObject()`	Build a landmark object with correlation analysis and UMAP embedding
`CorrectSampleComBat()`	Sample-level ComBat Batch Correction
`FindmmNN()`	Find Multi-Modal Nearest Neighbors
`GenerateSampleObject()`	Generate a sample-level count matrix based on landmark assay and its weighted.nn object
`NormalizeChiSquared()`	Apply Chi-squared normalization to a Seurat object
`PlotLandmarkObject()`	Visualize landmark-trajectory correlations with UMAP plots
`PredictPLS()`	Predict Responses from a PLS Model
`PrepareSampleObject()`	Perform preprocessing procedures for a Seurat object to prepare for the sample-level analysis
`QuickCorTest()`	Perform cor.test between each gene and a response variable given a Seurat object
`RunAndProjectUMAP()`	Generate a UMAP for a sketched assay and then project it to the full data.
`RunDiffusionMap()`	Run Diffusion Map
`RunPLS()`	Run Partial Least Squares (PLS) on Seurat Objects
`SampleLevelDimPlot()`	Visualize the landmark-trajectory relevance and the sample-level cell density
`SketchDataByGroup()`	Sketch Data by Group
`TrajDETest()`	Trajectory Differential Expression Test
`cellanova_calc_BE()`	Perform CellAnova Batch correction
`cppls_ondisk(<IterableMatrix>)`	CPLS (Canonical PLS) for IterableMatrix
`cppls_ondisk()`	CPLS (Canonical PLS) for On-Disk Matrices
`kernelpls(<IterableMatrix>)`	Kernel Partial Least Squares for IterableMatrix
`kernelpls()`	Kernel Partial Least Squares

Reference