Calculates cell type specificity from single cell data

Documentation for package ‘SPICEY’ version 0.99.3

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.add_tss_annotation	Add TSS annotation to peaks
.parse_input_diff	Parses input data of various types (e.g., named lists of 'GRanges' or 'data.frame', or a 'GRangesList') into a single tidy 'data.frame', with a 'cell_type' column.
.standardize_peaks	Standardizes peak input, ensuring that input peaks is a 'GRanges' object, removes alternate scaffolds ('_alt', 'random', 'fix', 'Un'), and assigns region IDs as names.
annotate_with_coaccessibility	Annotate peaks with co-accessible genes using Cicero links
annotate_with_nearest	Annotates regulatory elements (e.g., ATAC-seq peaks) to the nearest gene
atac	Example single-cell ATAC-seq differential accessibility data
cicero_links	Example Cicero co-accessibility links
compute_spicey_index	Compute cell type specificity scores from single-cell RNA and/or ATAC data
entropy_index	Calculate normalized Shannon-entropy of specificity scores
extract_gene_peak_annotations	Overlap peaks with gene promoters to obtain gene annotations
get_promoters	Extract promoter regions annotated gene symbols from a TxDb and AnnotationDbi object
link_spicey	Link RETSI regions to GETSI scores using gene-based association methods
plot_heatmap	Plot a z-scored gene-by-cell-type heatmap
prepare_heatmap_data	Prepare data for SPICEY heatmap
rna	Example single-cell RNA-seq differential expression data
specificity_index	Calculate specificity scores for grouped features
SPICEY	SPICEY: Tissue specificity analysis for single-cell data The SPICEY package provides a user-friendly pipeline for quantifying and visualizing tissue specificity specificity from single-cell ATAC-seq and/or single cell RNA-seq datasets, typically processed with tools such as Seurat or Signac. The core outputs of SPICEY are two tissue specific metrics, combined with entropy-based measures.
spicey_heatmap	SPICEY heatmap for gene specificity across cell types