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SpatialFeatureExperiment - Integrating SpatialExperiment with Simple Features in sf

A new S4 class integrating Simple Features with the R package sf to bring geospatial data analysis methods based on vector data to spatial transcriptomics. Also implements management of spatial neighborhood graphs and geometric operations. This pakage builds upon SpatialExperiment and SingleCellExperiment, hence methods for these parent classes can still be used.

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datarepresentationtranscriptomicsspatial

10.05 score 58 stars 2 dependents 710 scripts 826 downloads

Voyager - From geospatial to spatial omics

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 Voyager package also implements plotting functions to plot SFE data and ESDA results.

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geneexpressionspatialtranscriptomicsvisualizationbioconductoredaesdaexploratory-data-analysisomicsspatial-statisticsspatial-transcriptomics

8.97 score 105 stars 442 scripts 627 downloads

SFEData - Example SpatialFeatureExperiment datasets

Example spatial transcriptomics datasets with Simple Feature annotations as SpatialFeatureExperiment objects. Technologies include Visium, slide-seq, Nanostring CoxMX, Vizgen MERFISH, and 10X Xenium. Tissues include mouse skeletal muscle, human melanoma metastasis, human lung, breast cancer, and mouse liver.

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experimenthubexpressiondatamus_musculus_datahomo_sapiens_dataspatialdatatissuesinglecelldatadatapkg

6.79 score 2 stars 1 dependents 343 scripts

BUSpaRse - kallisto | bustools R utilities

The kallisto | bustools pipeline is a fast and modular set of tools to convert single cell RNA-seq reads in fastq files into gene count or transcript compatibility counts (TCC) matrices for downstream analysis. Central to this pipeline is the barcode, UMI, and set (BUS) file format. This package serves the following purposes: First, this package allows users to manipulate BUS format files as data frames in R and then convert them into gene count or TCC matrices. Furthermore, since R and Rcpp code is easier to handle than pure C++ code, users are encouraged to tweak the source code of this package to experiment with new uses of BUS format and different ways to convert the BUS file into gene count matrix. Second, this package can conveniently generate files required to generate gene count matrices for spliced and unspliced transcripts for RNA velocity. Here biotypes can be filtered and scaffolds and haplotypes can be removed, and the filtered transcriptome can be extracted and written to disk. Third, this package implements utility functions to get transcripts and associated genes required to convert BUS files to gene count matrices, to write the transcript to gene information in the format required by bustools, and to read output of bustools into R as sparses matrices.

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singlecellrnaseqworkflowstepcpp

6.20 score 11 stars 191 scripts 708 downloads

alabaster.sfe - Language agnostic on disk serialization of SpatialFeatureExperiment

Builds upon the existing ArtifactDB project, expending alabaster.spatial for language agnostic on disk serialization of SpatialFeatureExperiment.

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datarepresentationspatialopenjdk

5.40 score 252 scripts 197 downloads

TENxBUSData - Single cell dataset from 10x in BUS format

Download Barcode, UMI, and Set (BUS) format of 10x datasets from within R. This package accompanies the package BUSpaRse, which can load BUS format into R as a sparse matrix, and which has utility functions related to using the C++ command line package bustools.

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singlecelldatasequencingdataexperimenthubexperimentdataexpressiondatadatapkg

4.20 score 16 scripts