| Name : R-singleCellHaystack
| |
| Version : 1.0.2
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp153.1.4
| Date : 2024-06-14 11:58:01
|
| Group : Development/Libraries/Other
| Source RPM : R-singleCellHaystack-1.0.2-lp153.1.4.src.rpm
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| Size : 0.59 MB
| |
| Packager : (none)
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| Summary : A Universal Differential Expression Prediction Tool for Single-Cell and Spatial Genomics Data
|
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>.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
