Single cell Higher Order Testing (scHOT) is an R package that facilitates testing changes in higher order structure of gene expression along either a developmental trajectory or across space. scHOT is general and modular in nature, can be run in multiple data contexts such as along a continuous trajectory, between discrete groups, and over spatial orientations; as well as accommodate any higher order measurement such as variability or correlation. scHOT meaningfully adds to first order effect testing, such as differential expression, and provides a framework for interrogating higher order interactions from single cell data.

R client and utilities for Seven Bridges platform API, from Cancer Genomics Cloud to other Seven Bridges supported platforms.

This data package contains the code used to analyse the single-cell RNA-seq and the bulk ATAC-seq data from the manuscript titled: Single-cell transcriptome analysis reveals coordinated ectopic-gene expression patterns in medullary thymic epithelial cells. This paper was published in Nature Immunology 16,933-941(2015). The data objects provided in this package has been pre-processed: the raw data files can be downloaded from ArrayExpress under the accession identifiers E-MTAB-3346 and E-MTAB-3624. The vignette of this data package provides a documented and reproducible workflow that includes the code that was used to generate each statistic and figure from the manuscript.

This package aims to quantify and remove putative double strand DNA from a strand-specific RNA sample. There are also options and methods to plot the positive/negative proportions of all sliding windows, which allow users to have an idea of how much the sample was contaminated and the appropriate threshold to be used for filtering.

This package defines interfaces from R to scvi-tools. A vignette works through the totalVI tutorial for analyzing CITE-seq data. Another vignette compares outputs of Chapter 12 of the OSCA book with analogous outputs based on totalVI quantifications. Future work will address other components of scvi-tools, with a focus on building understanding of probabilistic methods based on variational autoencoders.

SCAN is a microarray normalization method to facilitate personalized-medicine workflows. Rather than processing microarray samples as groups, which can introduce biases and present logistical challenges, SCAN normalizes each sample individually by modeling and removing probe- and array-specific background noise using only data from within each array. SCAN can be applied to one-channel (e.g., Affymetrix) or two-channel (e.g., Agilent) microarrays. The Universal exPression Codes (UPC) method is an extension of SCAN that estimates whether a given gene/transcript is active above background levels in a given sample. The UPC method can be applied to one-channel or two-channel microarrays as well as to RNA-Seq read counts. Because UPC values are represented on the same scale and have an identical interpretation for each platform, they can be used for cross-platform data integration.

SNAGEE data - gene list and correlation matrix.

Extends the Seurat classes and functions to support Genomic Data Structure (GDS) files as a DelayedArray backend for data representation. It relies on the implementation of GDS-based DelayedMatrix in the SCArray package to represent single cell RNA-seq data. The common optimized algorithms leveraging GDS-based and single cell-specific DelayedMatrix (SC_GDSMatrix) are implemented in the SCArray package. SCArray.sat introduces a new SCArrayAssay class (derived from the Seurat Assay), which wraps raw counts, normalized expressions and scaled data matrix based on GDS-specific DelayedMatrix. It is designed to integrate seamlessly with the Seurat package to provide common data analysis in the SeuratObject-based workflow. Compared with Seurat, SCArray.sat significantly reduces the memory usage without downsampling and can be applied to very large datasets.

This Package utilizes a Semi-parametric Differential Abundance/expression analysis (SDA) method for metabolomics and proteomics data from mass spectrometry as well as single-cell RNA sequencing data. SDA is able to robustly handle non-normally distributed data and provides a clear quantification of the effect size.

High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression.

This is an ExperimentHub package that provides access to the data generated and analyzed in the [smoking-nicotine-mouse](https://github.com/LieberInstitute/smoking-nicotine-mouse/) LIBD project. The datasets contain the expression data of mouse genes, transcripts, exons, and exon-exon junctions across 208 samples from pup and adult mouse brain, and adult blood, that were exposed to nicotine, cigarette smoke, or controls. They also contain relevant metadata of these samples and gene expression features, such QC metrics, if they were used after filtering steps and also if the features were differently expressed in the different experiments.

SpatialCPie is an R package designed to facilitate cluster evaluation for spatial transcriptomics data by providing intuitive visualizations that display the relationships between clusters in order to guide the user during cluster identification and other downstream applications. The package is built around a shiny "gadget" to allow the exploration of the data with multiple plots in parallel and an interactive UI. The user can easily toggle between different cluster resolutions in order to choose the most appropriate visual cues.

Signal-to-Noise applied to Gene Expression Experiments. Signal-to-noise ratios can be used as a proxy for quality of gene expression studies and samples. The SNRs can be calculated on any gene expression data set as long as gene IDs are available, no access to the raw data files is necessary. This allows to flag problematic studies and samples in any public data set.

saseR is a highly performant and fast framework for aberrant expression and splicing analyses. The main functions are: \itemize\item \code\linkBamtoAspliCounts - Process BAM files to ASpli counts \item \code\linkconvertASpli - Get gene, bin or junction counts from ASpli SummarizedExperiment \item \code\linkcalculateOffsets - Create an offsets assays for aberrant expression or splicing analysis \item \code\linksaseRfindEncodingDim - Estimate the optimal number of latent factors to include when estimating the mean expression \item \code\linksaseRfit - Parameter estimation of the negative binomial distribution and compute p-values for aberrant expression and splicing For information upon how to use these functions, check out our vignette at \urlhttps://github.com/statOmics/saseR/blob/main/vignettes/Vignette.Rmd and the saseR paper: Segers, A. et al. (2023). Juggling offsets unlocks RNA-seq tools for fast scalable differential usage, aberrant splicing and expression analyses. bioRxiv. \urlhttps://doi.org/10.1101/2023.06.29.547014.

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.

scMultiSim simulates paired single cell RNA-seq, single cell ATAC-seq and RNA velocity data, while incorporating mechanisms of gene regulatory networks, chromatin accessibility and cell-cell interactions. It allows users to tune various parameters controlling the amount of each biological factor, variation of gene-expression levels, the influence of chromatin accessibility on RNA sequence data, and so on. It can be used to benchmark various computational methods for single cell multi-omics data, and to assist in experimental design of wet-lab experiments.

This includes a set of convenience functions for working with the SummarizedExperiment class. Note that plotting functions historically in this package have been moved to the sechm package (see vignette for details).

Finds associations between two human genomic datasets.

Builds hexbin plots for variables and dimension reduction stored in single cell omics data such as SingleCellExperiment. The ideas used in this package are based on the excellent work of Dan Carr, Nicholas Lewin-Koh, Martin Maechler and Thomas Lumley.

Subtypes are defined as groups of samples that have distinct molecular and clinical features. Genomic data can be analyzed for discovering patient subtypes, associated with clinical data, especially for survival information. This package is aimed to identify subtypes that are both clinically relevant and biologically meaningful.

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).

Read in imaging-based spatial transcriptomics technology data. Current available modules are for Xenium by 10X Genomics, CosMx by Nanostring, MERSCOPE by Vizgen, or STARmapPLUS from Broad Institute. You can choose to read the data in as a SpatialExperiment or a SingleCellExperiment object.

The scRNAseqApp is a Shiny app package designed for interactive visualization of single-cell data. It is an enhanced version derived from the ShinyCell, repackaged to accommodate multiple datasets. The app enables users to visualize data containing various types of information simultaneously, facilitating comprehensive analysis. Additionally, it includes a user management system to regulate database accessibility for different users.

snapcount is a client interface to the Snaptron webservices which support querying by gene name or genomic region. Results include raw expression counts derived from alignment of RNA-seq samples and/or various summarized measures of expression across one or more regions/genes per-sample (e.g. percent spliced in).