Decomposition of Bulk Expression with Single-Cell Sequencing
Calculate cell proportions based on single-cell data
Correlate columns of data frame
Convert counts data in Expression Set to counts per million (CPM)
Estimate cell type proportions using first PC of expression matrix
Remove genes in Expression Set with zero expression in all samples
Remove genes in Expression Set with zero variance across samples
Generate reference profile for cell types identified in single-cell da...
Return cell type proportions from bulk
Get number of genes to use with no weighted information
Get number of genes to use with weighted PCA
Find overlapping genes in single-cell data, bulk data, and marker gene...
Find overlapping samples in single-cell and bulk data
Get unique markers present in only 1 cell type
Performs marker-based decomposition of bulk expression using marker ge...
Performs reference-based decomposition of bulk expression using single...
Transforms bulk expression of a gene using only single-cell data
Converts Seurat object to Expression Set
Simulate barcode for decomposition illustration
Simulate data for decomposition illustration
Transforms bulk expression of a gene given overlapping data
Provides tools to accurately estimate cell type abundances from heterogeneous bulk expression. A reference-based method utilizes single-cell information to generate a signature matrix and transformation of bulk expression for accurate regression based estimates. A marker-based method utilizes known cell-specific marker genes to measure relative abundances across samples. For more details, see Jew and Alvarez et al (2019) <doi:10.1101/669911>.
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