SpatialFeatureExperiment (SFE) is a new S4 class for working with spatial single-cell genomics data. The voyager package implements basic exploratory spatial data analysis (ESDA) methods for SFE. Univariate methods include univariate global spatial ESDA methods such as Moran's I, permutation testing for Moran's I, and correlograms. Bivariate methods include Lee's L and cross variogram. Multivariate methods include MULTISPATI PCA and multivariate local Geary's C recently developed by Anselin. The
中文简介正在准备中。当前显示原文说明。
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Perform exploratory spatial data analysis (ESDA) on spatial transcriptomics data, including univariate, bivariate, and multivariate spatial statistics, and visualize the results over tissue geometries.
library(SFEData)
library(SpatialFeatureExperiment)
library(SpatialExperiment)
library(ggplot2)
library(Voyager)
library(scater)
library(scran)
library(pheatmap)
# 1. Load required libraries & 2. Read dataset
sfe <- McKellarMuscleData()
# 3. Add and mirror the tissue image
sfe <- addImg(sfe, imageSource = "tissue_lowres_5a.jpeg", sample_id = "Vis5A", image_id = "lowres", scale_fct = 1024/22208)
sfe <- mirrorImg(sfe, sample_id = "Vis5A", image_id = "lowres")
# 4. Filter spots and normalize counts
sfe <- sfe[, colData(sfe)$in_tissue]
sfe <- logNormCounts(sfe)
# 5. Construct a spatial neighborhood graph
colGraph(sfe, "visium") <- findVisiumGraph(sfe)
# 6. Run univariate spatial statistics (Moran's I and Getis-Ord Gi*)
features_use <- c("Myh1", "Myh2")
sfe <- runUnivariate(sfe, type = "moran", features = features_use, colGraphName = "visium", swap_rownames = "symbol")
sfe <- runUnivariate(sfe, type = "localG", features = features_use, colGraphName = "visium", include_self = TRUE, swap_rownames = "symbol")
# 7. Identify highly variable genes and calculate bivariate spatial correlation (Lee's L)
gs <- modelGeneVar(sfe)
hvgs <- getTopHVGs(gs, fdr.threshold = 0.01)
res <- calculateBivariate(sfe, "lee", hvgs)
# 8. Perform spatially-informed dimension reduction (MULTISPATI PCA)
hvgs2 <- getTopHVGs(gs, n = 1000)
sfe <- runMultivariate(sfe, "multispati", colGraphName = "visium", subset_row = hvgs2, nfposi = 10, nfnega = 10)
# 9. Visualize results
plotSpatialFeature(sfe, c("nCounts", "Myh1"), colGeometryName = "spotPoly", annotGeometryName = "myofiber_simplified", aes_use = "color", swap_rownames = "symbol")
plotLocalResult(sfe, "localG", features = features_use, colGeometryName = "spotPoly", divergent = TRUE, swap_rownames = "symbol")
ElbowPlot(sfe, ndims = 10, nfnega = 10, reduction = "multispati")
spatialReducedDim(sfe, "multispati", ncomponents = 2, divergent = TRUE)
Input: A SpatialFeatureExperiment object and a tissue image. Output: Spatial plots of gene expression, local spatial statistics, eigenvalues, and reduced dimensions.
SpatialFeatureExperiment (SFE) object.runUnivariate with type = "moran") and Getis-Ord Gi* (using type = "localG").calculateBivariate) to identify spatially co-expressed gene modules.runMultivariate with type = "multispati").SingleCellExperiment and scater instead.Seurat or Giotto.SpatialFeatureExperiment (SFE) object containing spatial coordinates, count matrices, and spot/annotation geometries (e.g., spotPoly).findVisiumGraph).rowData to swap rownames with (e.g., to use human-readable gene symbols instead of Ensembl IDs).findVisiumGraph before running any spatial statistics.mirrorImg if the origin of the image (top-left) does not align with the origin of the spots (bottom-left).colData(sfe)$in_tissue) and normalize counts using logNormCounts before analysis.mirrorImg to flip the image if coordinates are inverted.findVisiumGraph to colGraph(sfe, "visium") will cause downstream spatial functions to fail.spdep for classic geospatial analysis.adespatial for multivariate spatial analysis (MULTISPATI PCA).scater for non-spatial single-cell exploratory data analysis.还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问，或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.
中文简介正在准备中。当前显示原文说明。
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
中文简介正在准备中。当前显示原文说明。
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
中文简介正在准备中。当前显示原文说明。
apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.
中文简介正在准备中。当前显示原文说明。
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).
中文简介正在准备中。当前显示原文说明。
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
中文简介正在准备中。当前显示原文说明。