We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs, and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories, and spatial locations; and generates in silico negative and positive controls for benchmarking computational tools.

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.

The soGGi package provides a toolset to create genomic interval aggregate/summary plots of signal or motif occurence from BAM and bigWig files as well as PWM, rlelist, GRanges and GAlignments Bioconductor objects. soGGi allows for normalisation, transformation and arithmetic operation on and between summary plot objects as well as grouping and subsetting of plots by GRanges objects and user supplied metadata. Plots are created using the GGplot2 libary to allow user defined manipulation of the returned plot object. Coupled together, soGGi features a broad set of methods to visualise genomics data in the context of groups of genomic intervals such as genes, superenhancers and transcription factor binding events.

This package provides useful functions for visualizing virus reassortment events.

spiky implements methods and model generation for cfMeDIP (cell-free methylated DNA immunoprecipitation) with spike-in controls. CfMeDIP is an enrichment protocol which avoids destructive conversion of scarce template, making it ideal as a "liquid biopsy," but creating certain challenges in comparing results across specimens, subjects, and experiments. The use of synthetic spike-in standard oligos allows diagnostics performed with cfMeDIP to quantitatively compare samples across subjects, experiments, and time points in both relative and absolute terms.

This package contains a systems biology markup language (SBML) interface to R.

Scale4C is an R/Bioconductor package for scale-space transformation and visualization of 4C-seq data. The scale-space transformation is a multi-scale visualization technique to transform a 2D signal (e.g. 4C-seq reads on a genomic interval of choice) into a tesselation in the scale space (2D, genomic position x scale factor) by applying different smoothing kernels (Gauss, with increasing sigma). This transformation allows for explorative analysis and comparisons of the data's structure with other samples.

An example dataset for use with the SVM2CRM package.

The Sequence Read Archive (SRA) is the largest public repository of sequencing data from the next generation of sequencing platforms including Roche 454 GS System, Illumina Genome Analyzer, Applied Biosystems SOLiD System, Helicos Heliscope, and others. However, finding data of interest can be challenging using current tools. SRAdb is an attempt to make access to the metadata associated with submission, study, sample, experiment and run much more feasible. This is accomplished by parsing all the NCBI SRA metadata into a SQLite database that can be stored and queried locally. Fulltext search in the package make querying metadata very flexible and powerful. fastq and sra files can be downloaded for doing alignment locally. Beside ftp protocol, the SRAdb has funcitons supporting fastp protocol (ascp from Aspera Connect) for faster downloading large data files over long distance. The SQLite database is updated regularly as new data is added to SRA and can be downloaded at will for the most up-to-date metadata.

This package contains: 1. A microarray gene expression dataset from a human breast cancer study. 2. A RNA-Seq gene expression dataset from a mouse study on IFNG knockout. 3. ID mapping tables between gene IDs and SBGN-ML file glyph IDs. 4. Percent of orthologs detected in other species of the genes in a pathway. Cutoffs of this percentage for defining if a pathway exists in another species. 5. XML text of SBGN-ML files for all pre-collected pathways.

This package provides a pipeline for analysis of GC-MS data acquired in selected ion monitoring (SIM) mode. The tool also provides a guidance in choosing appropriate fragments for the targets of interest by using an optimization algorithm. This is done by considering overlapping peaks from a provided library by the user.

Shared order between genomic sequences provide a great deal of information. Synteny objects produced by the R package DECIPHER provides quantitative information about that shared order. SynExtend provides tools for extracting information from Synteny objects.

This package implements a parametric semi-supervised mixture model. The permutation test detects markers with main or interactive effects, without distinguishing them. Possible applications include genome-wide association analysis and differential expression analysis.

Seahtrue organizes oxygen consumption and extracellular acidification analysis data from experiments performed on an XF analyzer into structured nested tibbles.This allows for detailed processing of raw data and advanced data visualization and statistics. Seahtrue introduces an open and reproducible way to analyze these XF experiments. It uses file paths to .xlsx files. These .xlsx files are supplied by the userand are generated by the user in the Wave software from Agilent from the assay result files (.asyr). The .xlsx file contains different sheets of important data for the experiment; 1. Assay Information - Details about how the experiment was set up. 2. Rate Data - Information about the OCR and ECAR rates. 3. Raw Data - The original raw data collected during the experiment. 4. Calibration Data - Data related to calibrating the instrument. Seahtrue focuses on getting the specific data needed for analysis. Once this data is extracted, it is prepared for calculations through preprocessing. To make sure everything is accurate, both the initial data and the preprocessed data go through thorough checks.

The package provides access to the copy of the Synaptic proteome database. It was designed as an accompaniment for Synaptome.DB package. Database provides information for specific synaptic genes and allows building the protein-protein interaction graph for gene sets, synaptic compartments, and brain regions. In the current update we added 6 more synaptic proteome studies, which resulted in total of 64 studies. We introduced Synaptic Vesicle as a separate compartment. We also added coding mutations for Autistic Spectral disorder and Epilepsy collected from publicly available databases.

An unsupervised cross-validation method to select the optimal number of mutational signatures. A data set of mutational counts is split into training and validation data.Signatures are estimated in the training data and then used to predict the mutations in the validation data.

This package provides a package containing an environment representing the Sugar_Cane.cdf file.

The spqn package implements spatial quantile normalization (SpQN). This method was developed to remove a mean-correlation relationship in correlation matrices built from gene expression data. It can serve as pre-processing step prior to a co-expression analysis.

This package implements methods to calculate information accretion for a given version of the gene ontology and uses this data to calculate remaining uncertainty, misinformation, and semantic similarity for given sets of predicted annotations and true annotations from a protein function predictor.

Dot plots of single-cell RNA-seq data allow for an examination of the relationships between cell groupings (e.g. clusters) and marker gene expression. The scDotPlot package offers a unified approach to perform a hierarchical clustering analysis and add annotations to the columns and/or rows of a scRNA-seq dot plot. It works with SingleCellExperiment and Seurat objects as well as data frames.

In the single cell World, which includes flow cytometry, mass cytometry, single-cell RNA-seq (scRNA-seq), and others, there is a need to improve data visualisation and to bring analysis capabilities to researchers even from non-technical backgrounds. scDataviz attempts to fit into this space, while also catering for advanced users. Additonally, due to the way that scDataviz is designed, which is based on SingleCellExperiment, it has a plug and play feel, and immediately lends itself as flexibile and compatibile with studies that go beyond scDataviz. Finally, the graphics in scDataviz are generated via the ggplot engine, which means that users can add on features to these with ease.

An interface to the fast-access storage format for VCF data provided in SeqArray, with tools for common operations and analysis.

This package is a Shiny app for interactively analyzing and visualizing Nanostring GeoMX Whole Transcriptome Atlas data. Users have the option of exploring a sample data to explore this app's functionality. Regions of interest (ROIs) can be filtered based on any user-provided metadata. Upon taking two or more groups of interest, all pairwise and ANOVA-like testing are automatically performed. Available ouputs include PCA, Volcano plots, tables and heatmaps. Aesthetics of each output are highly customizable.

By leveraging statistical properties (log-rank test for survival) of patient cohorts defined by binary thresholds, poor-prognosis patients are identified by the sigsquared package via optimization over a cost function reducing type I and II error.