Package: SPICEY 1.3.0

Georgina Fuentes-Páez

SPICEY: Calculates cell type specificity from single cell data

SPICEY (SPecificity Index for Coding and Epigenetic activitY) is an R package designed to quantify cell-type specificity in single-cell transcriptomic and epigenomic data, particularly scRNA-seq and scATAC-seq. It introduces two complementary indices: the Gene Expression Tissue Specificity Index (GETSI) and the Regulatory Element Tissue Specificity Index (RETSI), both based on entropy to provide continuous, interpretable measures of specificity. By integrating gene expression and chromatin accessibility, SPICEY enables standardized analysis of cell-type-specific regulatory programs across diverse tissues and conditions.

Authors:Georgina Fuentes-Páez [aut, cre], Nacho Molina [aut], Mireia Ramos-Rodriguez [aut], Lorenzo Pasquali [aut], Ministerio de Ciencia e Innovación Spain [fnd]

SPICEY_1.3.0.tar.gz
SPICEY_1.3.0.zip(r-4.7)SPICEY_1.3.0.zip(r-4.6)SPICEY_1.3.0.zip(r-4.5)
SPICEY_1.3.0.tgz(r-4.6-any)SPICEY_1.3.0.tgz(r-4.5-any)
SPICEY_1.3.0.tar.gz(r-4.7-any)SPICEY_1.3.0.tar.gz(r-4.6-any)
SPICEY_1.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
SPICEY/json (API)
NEWS

# Install 'SPICEY' in R:
install.packages('SPICEY', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/georginafp/spicey/issues

Datasets:
  • atac - Example single-cell ATAC-seq differential accessibility data
  • cicero_links - Example Cicero co-accessibility links
  • rna - Example single-cell RNA-seq differential expression data

On BioConductor:SPICEY-1.3.0(bioc 3.24)SPICEY-1.2.0(bioc 3.23)

transcriptomicsepigeneticssinglecelldifferentialexpressiondifferentialpeakcallinggeneregulationgenetargetgeneexpressiontranscription

4.88 score 1 stars 3 scripts 209 downloads 4 exports 90 dependencies

Last updated from:8c65e8a22d. Checks:1 NOTE, 9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksNOTE222
linux-devel-x86_64OK334
source / vignettesOK291
linux-release-x86_64OK361
macos-release-arm64OK182
macos-oldrel-arm64OK201
windows-develOK247
windows-releaseOK227
windows-oldrelOK220
wasm-releaseOK206

Exports:annotate_with_coaccessibilityannotate_with_nearestSPICEYspicey_heatmap

Dependencies:abindAnnotationDbiaskpassBHBiobaseBiocBaseUtilsBiocGenericsBiocIOBiocParallelBiostringsbitbit64bitopsblobcachemcigarilloclicodetoolscowplotcpp11crayoncurlDBIDelayedArraydplyrfarverfastmapformatRfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomicAlignmentsGenomicFeaturesGenomicRangesggplot2gluegtablehttrIRangesisobandjsonliteKEGGRESTlabelinglambda.rlatticelifecyclemagrittrMatrixMatrixGenericsmatrixStatsmemoisemimeopensslpillarpkgconfigpngpurrrR6RColorBrewerRCurlrestfulrRhtslibrjsonrlangRsamtoolsRSQLitertracklayerS4ArraysS4VectorsS7scalesSeqinfosnowSparseArraystringistringrSummarizedExperimentsystibbletidyrtidyselectUCSC.utilsutf8vctrsviridisLitewithrXMLXVectoryaml

Measuring tissue specificity from single cell data with SPICEY

Georgina Fuentes-Páez

Rendered fromSPICEY.Rmdusingknitr::rmarkdownon May 30 2026.

Last update: 2025-10-08
Started: 2025-06-03

Readme and manuals

Help Manual

Help pageTopics
Add TSS annotation to peaks.add_tss_annotation
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..parse_input_diff
Standardizes peak input, ensuring that input peaks is a 'GRanges' object, removes alternate scaffolds ('_alt', 'random', 'fix', 'Un'), and assigns region IDs as names..standardize_peaks
Annotate peaks with co-accessible genes using Cicero linksannotate_with_coaccessibility
Annotates regulatory elements (e.g., ATAC-seq peaks) to the nearest geneannotate_with_nearest
Example single-cell ATAC-seq differential accessibility dataatac
Example Cicero co-accessibility linkscicero_links
Compute cell type specificity scores from single-cell RNA and/or ATAC datacompute_spicey_index
Calculate normalized Shannon-entropy of specificity scoresentropy_index
Overlap peaks with gene promoters to obtain gene annotationsextract_gene_peak_annotations
Extract promoter regions annotated gene symbols from a TxDb and AnnotationDbi objectget_promoters
Link RETSI regions to GETSI scores using gene-based association methodslink_spicey
Plot a SPICEY score gene-by-cell-type heatmapplot_heatmap
Prepare data for SPICEY heatmapprepare_heatmap_data
Example single-cell RNA-seq differential expression datarna
Calculate specificity scores for grouped featuresspecificity_index
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
SPICEY heatmap for gene specificity across cell typesspicey_heatmap