This is the released version of DESpace; for the devel version, see DESpace.
DESpace: a framework to discover spatially variable genes and differential spatial patterns across conditionsBioconductor version: Release (3.21)
Intuitive framework for identifying spatially variable genes (SVGs) and differential spatial variable pattern (DSP) between conditions via edgeR, a popular method for performing differential expression analyses. Based on pre-annotated spatial clusters as summarized spatial information, DESpace models gene expression using a negative binomial (NB), via edgeR, with spatial clusters as covariates. SVGs are then identified by testing the significance of spatial clusters. For multi-sample, multi-condition datasets, we again fit a NB model via edgeR, incorporating spatial clusters, conditions and their interactions as covariates. DSP genes-representing differences in spatial gene expression patterns across experimental conditions-are identified by testing the interaction between spatial clusters and conditions.
Author: Peiying Cai [aut, cre] ORCID: 0009-0001-9229-2244 , Simone Tiberi [aut] ORCID: 0000-0002-3054-9964
Maintainer: Peiying Cai <peiying.cai at uzh.ch>
Citation (from within R, entercitation("DESpace")): Installation
To install this package, start R (version "4.5") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("DESpace")
For older versions of R, please refer to the appropriate Bioconductor release.
DocumentationTo view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("DESpace")Follow Installation instructions to use this package in your R session.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4