Package index • tinydenseR

Core Pipeline

Entry points for running tinydenseR and managing TDRObj objects

tinydenseR tinydenseR-package: tinydenseR: Linking Cell-To-Cell Variation to Sample-to-Sample Variation
RunTDR(): Run the full tinydenseR pipeline
GetTDR(): Extract a TDRObj from a container object
SetTDR(): Store a TDRObj inside a container object
`$`(<TDRObj>) `$<-`(<TDRObj>) names(<TDRObj>) show(<TDRObj>): TDRObj: S4 class for tinydenseR analysis objects
TDRObj(): Construct a TDRObj
setup.tdr.obj() setup.lm.obj(): Initialize tinydenseR object for landmark-based analysis
is.TDRObj(): Check if an object is a TDRObj

Differential Density Analysis

Linear modeling of landmark-level density across conditions

get.lm(): Differential Density Testing
get.dea() deprecated: Pseudobulk Differential Expression Analysis (Deprecated)
get.density(): Access fuzzy density matrices from a TDRObj

Pseudobulk Differential Expression

Manifold-informed pseudobulk DE in design and marker modes

get.pbDE(): Pseudobulk Differential Expression Analysis
get.markerDE() deprecated: Marker Gene/Protein Identification (Deprecated)
get.marker(): Deprecated: Use get.pbDE(.mode = "marker") instead

plsD Decomposition

Graph-diffused partial least squares decomposition

get.plsD(): Graph-Diffused, Density Contrast-Aligned PLS Decomposition (plsD)

Sample Embedding

PCA, diffusion-map, and partial-effect PC embeddings

get.embedding(): Compute Sample Embedding from Partial Fitted Values

Cell Typing and Clustering

Cell type annotation, clustering, and resolution management

celltyping(): Manually assign cell type labels to clusters
set_active_celltyping(): Set active celltyping solution
list_celltyping_solutions(): List stored celltyping solutions
import_cell_annotations(): Import multiple cell-level annotation columns as landmark-level celltyping solutions
recluster(): Recluster landmarks
set_active_clustering(): Set active clustering solution
leiden.cluster(): Leiden clustering with straggler absorption
lm.cluster(): Leiden clustering of landmarks

Accessors and Utilities

Extract data from containers and helper functions

get.cells(): Create .cells Object with Automatic Format Detection
get.cells.SCE(): Create .cells Object from SingleCellExperiment Object
get.cells.Seurat(): Create .cells Object from Seurat v4 Object
get.cells.Seurat5(): Create .cells Object from Seurat v5 Object
get.cells.list.mat(): Create .cells Object from Count Matrices
get.cellmap(): Access per-cell map data for a single sample
get.features(): Graph-based feature discovery for landmarks
get.graph(): Graph embedding of landmarks
get.landmarks(): Identify landmarks via leverage score sampling
get.map(): Mapping cells to landmarks
get.meta(): Extract Sample-Level Metadata with Automatic Format Detection
get.meta.HDF5AnnData(): Extract Sample-Level Metadata from HDF5AnnData Object
get.meta.SCE(): Extract Sample-Level Metadata from SingleCellExperiment Object
get.meta.Seurat(): Extract Sample-Level Metadata from Seurat v4 Object
get.meta.Seurat5(): Extract Sample-Level Metadata from Seurat v5 Object
get.adj.matrix(): Sparse matrix representation of nearest neighbors
get.subset(): Create a hierarchical subset TDRObj
goi.summary(): Summarize gene expression patterns for genes of interest
is.hpc(): Detect if running on High-Performance Computing cluster (internal)
fast.jaccard.r(): Shared nearest neighbors via fast Jaccard index calculation
elbow.sec.deriv(): Find elbow point using second derivative method (internal)

Visualization

Plotting functions for landmarks, samples, and results

plotUMAP(): Plot UMAP Plot UMAP
plotPCA(): Plot PCA
plotDensity(): Plot Density
plotDEA() deprecated: Plot Differential Expression Analysis Results (Deprecated)
plotBeeswarm(): Bee Swarm Plot of Density Estimate Change
plotHeatmap(): Plot Mean Expression Heatmap
plotMarkerDE(): Plot Marker DE Results
plotPbDE(): Plot Pseudobulk Differential Expression Results
plotPlsD(): Plot plsD Scores (Diagnostic and Component Views)
plotPlsDHeatmap(): Plot plsD Expression Heatmap
plotSampleEmbedding(): Plot Sample Embedding from get.embedding
plotSamplePCA() deprecated: Plot Sample PCA (Deprecated)
plotTradPerc(): Plot Traditional Percentages
plotTradStats(): Plot Traditional Statistics
plot2Markers(): Bidimensional Hexbin Plot for Marker Expression
scatterPlot(): Scatter Plot with Feature Coloring

Data and Simulation

Bundled datasets and simulation functions

sim_trajectory_tdr: Simulated scRNA-seq trajectory with condition-dependent differential abundance
simulate_DA_data(): Simulate differential abundance (DA) flow cytometry data
simulate_DE_data(): Simulate differential expression (DE) flow cytometry data
fetch_trajectory_data(): Fetch trajectory simulation dataset from miloR
Color.Palette: Color Palette

Conversion

Convert TDRObj to other Bioconductor containers

as.SummarizedExperiment(): Convert an object to SummarizedExperiment
as.SummarizedExperiment(<TDRObj>): Convert a TDRObj to SummarizedExperiment

Cache Management

On-disk cache for large per-sample mapping data

tdr_cache_info(): Print a human-readable summary of the on-disk cache state
tdr_cache_validate(): Validate that all on-disk cache files are intact
tdr_cache_cleanup(): Remove all cached files for a tinydenseR object

Internal

Internal helper functions (not part of the public API)

Color.Palette: Color Palette
GetTDR(): Extract a TDRObj from a container object
RunTDR(): Run the full tinydenseR pipeline
SetTDR(): Store a TDRObj inside a container object
`$`(<TDRObj>) `$<-`(<TDRObj>) names(<TDRObj>) show(<TDRObj>): TDRObj: S4 class for tinydenseR analysis objects
TDRObj(): Construct a TDRObj
as.SummarizedExperiment(): Convert an object to SummarizedExperiment
as.SummarizedExperiment(<TDRObj>): Convert a TDRObj to SummarizedExperiment
celltyping(): Manually assign cell type labels to clusters
fetch_trajectory_data(): Fetch trajectory simulation dataset from miloR
get.cellmap(): Access per-cell map data for a single sample
get.cells(): Create .cells Object with Automatic Format Detection
get.cells.SCE(): Create .cells Object from SingleCellExperiment Object
get.cells.Seurat(): Create .cells Object from Seurat v4 Object
get.cells.Seurat5(): Create .cells Object from Seurat v5 Object
get.cells.list.mat(): Create .cells Object from Count Matrices
get.density(): Access fuzzy density matrices from a TDRObj
get.embedding(): Compute Sample Embedding from Partial Fitted Values
get.features(): Graph-based feature discovery for landmarks
get.graph(): Graph embedding of landmarks
get.landmarks(): Identify landmarks via leverage score sampling
get.lm(): Differential Density Testing
get.map(): Mapping cells to landmarks
get.marker(): Deprecated: Use get.pbDE(.mode = "marker") instead
get.meta.HDF5AnnData(): Extract Sample-Level Metadata from HDF5AnnData Object
get.meta(): Extract Sample-Level Metadata with Automatic Format Detection
get.meta.SCE(): Extract Sample-Level Metadata from SingleCellExperiment Object
get.meta.Seurat(): Extract Sample-Level Metadata from Seurat v4 Object
get.meta.Seurat5(): Extract Sample-Level Metadata from Seurat v5 Object
get.pbDE(): Pseudobulk Differential Expression Analysis
get.plsD(): Graph-Diffused, Density Contrast-Aligned PLS Decomposition (plsD)
get.subset(): Create a hierarchical subset TDRObj
goi.summary(): Summarize gene expression patterns for genes of interest
import_cell_annotations(): Import multiple cell-level annotation columns as landmark-level celltyping solutions
is.TDRObj(): Check if an object is a TDRObj
leiden.cluster(): Leiden clustering with straggler absorption
list_celltyping_solutions(): List stored celltyping solutions
lm.cluster(): Leiden clustering of landmarks
plot2Markers(): Bidimensional Hexbin Plot for Marker Expression
plotBeeswarm(): Bee Swarm Plot of Density Estimate Change
plotDensity(): Plot Density
plotHeatmap(): Plot Mean Expression Heatmap
plotMarkerDE(): Plot Marker DE Results
plotPCA(): Plot PCA
plotPbDE(): Plot Pseudobulk Differential Expression Results
plotPlsD(): Plot plsD Scores (Diagnostic and Component Views)
plotPlsDHeatmap(): Plot plsD Expression Heatmap
plotSampleEmbedding(): Plot Sample Embedding from get.embedding
plotSamplePCA() deprecated: Plot Sample PCA (Deprecated)
plotTradPerc(): Plot Traditional Percentages
plotTradStats(): Plot Traditional Statistics
plotUMAP(): Plot UMAP Plot UMAP
recluster(): Recluster landmarks
scatterPlot(): Scatter Plot with Feature Coloring
set_active_celltyping(): Set active celltyping solution
set_active_clustering(): Set active clustering solution
setup.tdr.obj() setup.lm.obj(): Initialize tinydenseR object for landmark-based analysis
sim_trajectory_tdr: Simulated scRNA-seq trajectory with condition-dependent differential abundance
simulate_DA_data(): Simulate differential abundance (DA) flow cytometry data
simulate_DE_data(): Simulate differential expression (DE) flow cytometry data
tdr_cache_cleanup(): Remove all cached files for a tinydenseR object
tdr_cache_info(): Print a human-readable summary of the on-disk cache state
tdr_cache_validate(): Validate that all on-disk cache files are intact