This package provides functions for kriging and point pattern analysis.

This package contains the HGU133 and HGU95 spikein experiment data.

This package is used for cell type identification in spatial transcriptomics. It also handles cell type-specific differential expression.

This package provides a computational toolkit in R for the integration, exploration, and analysis of high-dimensional single-cell cytometry and imaging data.

This package provides some basic linear algebra functionality for sparse matrices. It includes Cholesky decomposition and backsolving as well as standard R subsetting and Kronecker products.

This package performs score test using saddlepoint approximation to estimate the null distribution. It also prepares summary statistics for meta-analysis and performs meta-analysis to combine multiple association results.

Currently there are many functions in S-PLUS that are missing in R. To facilitate the conversion of S-PLUS packages to R packages, this package provides some missing S-PLUS functionality in R.

This package implements the spatially aware library size normalisation algorithm, SpaNorm. SpaNorm normalises out library size effects while retaining biology through the modelling of smooth functions for each effect. Normalisation is performed in a gene- and cell-/spot- specific manner, yielding library size adjusted data.

This package provides a unified interface to a variety of GSEA techniques from different bioconductor packages. Results are harmonized into a single object and can be interrogated uniformly for quick exploration and interpretation of results. Interactive exploration of GSEA results is enabled through a shiny app provided by a sparrow.shiny sibling package.

The Spectra package defines an efficient infrastructure for storing and handling mass spectrometry spectra and functionality to subset, process, visualize and compare spectra data. It provides different implementations (backends) to store mass spectrometry data. These comprise backends tuned for fast data access and processing and backends for very large data sets ensuring a small memory footprint.

This package performs a gene expression data analysis to detect condition-specific genes. Such genes are significantly up- or down-regulated in a small number of conditions. It does so by fitting a mixture of normal distributions to the expression values. Conditions can be environmental conditions, different tissues, organs or any other sources that you wish to compare in terms of gene expression.

Identifies motifs that are significantly co-enriched from enhancer-promoter interaction data. While enhancer-promoter annotation is commonly used to define groups of interaction anchors, spatzie also supports co-enrichment analysis between preprocessed interaction anchors. Supports BEDPE interaction data derived from genome-wide assays such as HiC, ChIA-PET, and HiChIP. Can also be used to look for differentially enriched motif pairs between two interaction experiments.

Spaniel includes a series of tools to aid the quality control and analysis of Spatial Transcriptomics data. Spaniel can import data from either the original Spatial Transcriptomics system or 10X Visium technology. The package contains functions to create a SingleCellExperiment Seurat object and provides a method of loading a histologial image into R. The spanielPlot function allows visualisation of metrics contained within the S4 object overlaid onto the image of the tissue.

SpikeLI is a package that performs the analysis of the Affymetrix spike-in data using the Langmuir Isotherm. The aim of this package is to show the advantages of a physical-chemistry based analysis of the Affymetrix microarray data compared to the traditional methods. The spike-in (or Latin square) data for the HGU95 and HGU133 chipsets have been downloaded from the Affymetrix web site. The model used in the spikeLI package is described in details in E. Carlon and T. Heim, Physica A 362, 433 (2006).

The speckle package contains functions for the analysis of single cell RNA-seq data. The speckle package currently contains functions to analyse differences in cell type proportions. There are also functions to estimate the parameters of the Beta distribution based on a given counts matrix, and a function to normalise a counts matrix to the median library size. There are plotting functions to visualise cell type proportions and the mean-variance relationship in cell type proportions and counts. As our research into specialised analyses of single cell data continues we anticipate that the package will be updated with new functions.

The package contains functions that can be used to compare expression measures on different array platforms.

This package provides tools for fitting linear models and generalized linear models to large data sets by updating algorithms.

Bulk RNA-seq from GTEx on 4,000 randomly selected, expressed genes. Data has been processed for co-expression analysis.

This package provides tools to analyze alternative splicing sites, interpret outcomes based on sequence information, select and design primers for site validiation and give visual representation of the event to guide downstream experiments.

This package constructs basis functions of B-splines, M-splines, I-splines, convex splines (C-splines), periodic splines, natural cubic splines, generalized Bernstein polynomials, their derivatives, and integrals (except C-splines) by closed-form recursive formulas. It also contains a C++ head-only library integrated with Rcpp.

Splatter is a package for the simulation of single-cell RNA sequencing count data. It provides a simple interface for creating complex simulations that are reproducible and well-documented. Parameters can be estimated from real data and functions are provided for comparing real and simulated datasets.

SPsimSeq uses a specially designed exponential family for density estimation to constructs the distribution of gene expression levels from a given real RNA sequencing data (single-cell or bulk), and subsequently simulates a new dataset from the estimated marginal distributions using Gaussian-copulas to retain the dependence between genes. It allows simulation of multiple groups and batches with any required sample size and library size.

This is an R package for spell checking common document formats including LaTeX, markdown, manual pages, and DESCRIPTION files. It includes utilities to automate checking of documentation and vignettes as a unit test during R CMD check. Both British and American English are supported out of the box and other languages can be added. In addition, packages may define a wordlist to allow custom terminology without having to abuse punctuation.

This package provides a comprehensive toolbox for analysing Spatial Point Patterns. It is focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. It also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. It supports spatial covariate data such as pixel images and contains over 2000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference.