Geometric Single Cell Deconvolution
Add noise to count data
Adjust count matrix by library size
Identify cell markers
Collapse groups in cellMarkers object
Compensation heatmap
Gene signature cosine similarity matrix
Deconvolute bulk RNA-Seq using single-cell RNA-Seq signature
Diagnostics for cellMarker signatures
Fix in missing genes in bulk RNA-Seq matrix
Fix cellMarkers signature with no cell groups
Vector based best marker selection
Converts ensembl gene ids to symbols
Generate random cell number samples
Mean Objects
Merge cellMarker signatures
Calculate R-squared and metrics on deconvoluted cell subclasses
Plot compensation analysis
Residuals plot
Scatter plots to compare deconvoluted subclasses
Plot tuning curves
Quantile-quantile plot
Quantile mapping function between two scRNA-Seq datasets
Rank distance angles from a cosine similarity matrix
Reduce noise in single-cell data
Extract Deconvolution Residuals
Regression Deletion Diagnostics
Single-cell apply a function to a matrix split by a factor
Single-cell mean log gene expression across cell types
Gene signature heatmap
Simulate pseudo-bulk RNA-Seq
Apply a function to a big matrix by slicing
Specificity plot
Spillover heatmap
Stacked bar plot
Summarising deconvolution tuning
Tune deconvolution parameters
Update cellMarkers object
Cell subclass violin plot
Deconvolution of bulk RNA-Sequencing data into proportions of cells based on a reference single-cell RNA-Sequencing dataset using high-dimensional geometric methodology.