Intuitive framework for identifying spatially variable genes (SVGs) 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. The method is flexible and robust, and is faster than the most SV methods. Furthermore, to the
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Identify spatially variable genes (SVGs) across tissue regions globally or within individual spatial clusters for single or multiple biological replicates.
library(DESpace)
library(SpatialExperiment)
# Assuming spe3 is a SpatialExperiment object with spatial clusters in 'layer_guess_reordered'
results <- svg_test(spe = spe3, cluster_col = "layer_guess_reordered", verbose = TRUE)
# Test individual clusters using pre-computed dispersions
cluster_results <- individual_svg(spe3, edgeR_y = results$estimated_y, cluster_col = "layer_guess_reordered")
# Combine results
merge_res <- top_results(results$gene_results, cluster_results)
Inputs are a SpatialExperiment object with annotated spatial clusters; outputs are gene-level and cluster-level spatial variability statistics.
Identify genes with differential spatial expression patterns (DSPs) across multiple experimental conditions or time points.
library(DESpace)
library(SpatialExperiment)
# Assuming spe.combined is a combined SpatialExperiment object with multiple samples
multi_results <- svg_test(spe = spe.combined,
cluster_col = "layer_guess_reordered",
sample_col = "sample_id",
replicates = TRUE)
# Test individual clusters across replicates
cluster_results <- individual_svg(spe.combined,
edgeR_y = multi_results$estimated_y,
replicates = TRUE,
cluster_col = "layer_guess_reordered")
# Combine results
merge_res <- top_results(multi_results$gene_results, cluster_results, select = "FDR")
Inputs are a combined multi-sample SpatialExperiment object; outputs are joint gene-level and cluster-level spatially variable gene statistics across replicates.
svg_test.individual_svg.replicates = TRUE in svg_test.top_results.BayesSpace or stLearn because DESpace requires pre-annotated spatial clusters as input.edgeR or DESeq2 because DESpace is designed for spatially resolved transcriptomics.SpatialExperiment or SingleCellExperiment object.array_row, array_col) and pre-annotated spatial clusters (e.g., layer_guess_reordered) in colData.counts assay (modeled via negative binomial in edgeR).SpatialExperiment or SingleCellExperiment object.colData(spe) containing spatial clusters.colData(spe) containing sample IDs (for multi-sample).FALSE): Logical indicating whether to fit the multi-sample model.DGEList object containing dispersion estimates to speed up individual_svg.TRUE): Logical to print progress and statistics."both"): Filter in top_results to select "high", "low", or "both" abundant genes.estimated_y) in individual_svg to significantly speed up computation.FeaturePlot with cluster outlines.estimated_y from svg_test to the edgeR_y parameter in individual_svg to avoid re-estimating dispersions.scuttle::addPerCellQC) and filter out genes with low counts before running svg_test.edgeR for non-spatial differential expression analysis.BayesSpace for subspot-resolution spatial clustering.SpatialLIBD for interactive visualization and exploration of DLPFC spatial data.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.
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This tool uses the label-tree function from HyPhy to annotate a phylogenetic tree. It allows users to select a subset of leaves using either a regular expression or a list of sequence names, and then apply a specified label to these selecte
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This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, En
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apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.
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ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).
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AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched with
日本語の概要は準備中です。原文の説明を表示しています。