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DESCRIPTION
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DESCRIPTION
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Package: singleCellHaystack
Type: Package
Title: A Universal Differential Expression Prediction Tool for Single-Cell and Spatial Genomics Data
Version: 1.0.2
Authors@R: c(
person("Alexis", "Vandenbon", email = "[email protected]", role = c("aut", "cre"),
comment = c(ORCID = "0000-0003-2180-5732")),
person("Diego", "Diez", email = "[email protected]", role = c("aut"),
comment = c(ORCID = "0000-0002-2325-4893"))
)
Description: One key exploratory analysis step in single-cell genomics data analysis
is the prediction of features with different activity levels. For example, we want
to predict differentially expressed genes (DEGs) in single-cell RNA-seq data,
spatial DEGs in spatial transcriptomics data, or differentially accessible
regions (DARs) in single-cell ATAC-seq data. 'singleCellHaystack' predicts differentially
active features in single cell omics datasets without relying on the clustering
of cells into arbitrary clusters. 'singleCellHaystack' uses Kullback-Leibler
divergence to find features (e.g., genes, genomic regions, etc) that are active
in subsets of cells that are non-randomly positioned inside an input space (such as
1D trajectories, 2D tissue sections, multi-dimensional embeddings, etc). For
the theoretical background of 'singleCellHaystack' we refer to our original paper
Vandenbon and Diez (Nature Communications, 2020) <doi:10.1038/s41467-020-17900-3>
and our update Vandenbon and Diez (Scientific Reports, 2023) <doi:10.1038/s41598-023-38965-2>.
Imports:
methods,
Matrix,
splines,
ggplot2,
reshape2
Suggests:
knitr,
rmarkdown,
testthat,
SummarizedExperiment,
SingleCellExperiment,
SeuratObject,
cowplot,
wrswoR,
sparseMatrixStats,
ComplexHeatmap,
patchwork
License: MIT + file LICENSE
Encoding: UTF-8
URL: https://alexisvdb.github.io/singleCellHaystack/, https://github.com/alexisvdb/singleCellHaystack
BugReports: https://github.com/alexisvdb/singleCellHaystack/issues
LazyData: true
RoxygenNote: 7.2.3
VignetteBuilder: knitr