ImmuneSpace is a data management and analysis engine where standardized datasets can be easily explored and analyzed using state-of-the-art computational tools. All the data housed here is generated by The Human Immunology Project Consortium (HIPC) program.
DataSpace is a data sharing and discovery tool developed to empower HIV vaccine researchers. This LabKey-based software application is designed to facilitate self-guided data exploration across studies and increase awareness of the scientific questions being evaluated in the field of HIV vaccines. Currently, binding antibody, neutralization antibody, and cellular immunoassay results from over 192 vaccine products tested in 64 studies conducted in the CAVD have been harmonized and are available for exploration and download. Data are included from both clinical trials and studies of non-human primates and other animals.
CAVD DataSpace on Twitter
The Cytoverse is a collection of R packages facilitating all core aspects of cytometry data analysis, including compensation, transformation, manual and automated gating analysis, and visualization. All of these operations are designed for optimal high-throughput efficiency and to scale well for very large datasets stored locally or in the cloud. The Cytoverse also interfaces with workspace files from other cytometry software packages, including FlowJo, BDFACSDiva, and Cytobank.
It takes advantage of the standardization of the database to hide all the Rlabkey specific code away from the user. Study-specific datasets can be accessed via an object-oriented paradigm.
A thin wrapper around Rlabkey to access the ImmuneSpace database from R.
This package simplifies access to the HIPC ImmuneSpace database for R programmers. It takes advantage of the standardization of the database to hide all the Rlabkey specific code away from the user. The study-specific datasets can be accessed via an object-oriented paradigm.
An R package that providing an automated data analysis pipeline for flow cytometry.
BayesSpace provides tools for clustering and enhancing the resolution of spatial gene expression experiments.
BayesSpace clusters a low-dimensional representation of the gene expression matrix, incorporating a spatial prior to encourage neighboring spots to cluster together. The method can enhance the resolution of the low-dimensional representation into “sub-spots”, for which features such as gene expression or cell type composition can be imputed.
An R package for the augmentation of flow cytometry data using machine learning enabling the quantification of 100s of proteins across millions of single cells.
MAST is an R/Bioconductor package for managing and analyzing qPCR and sequencing-based single-cell gene expression data, as well as data from other types of single-cell assays. Our goal is to support assays that have multiple features (genes, markers, etc) per well (cell, etc) in a flexible manner. Assays are assumed to be mostly complete in the sense that most wells contain measurements for all features.
COMPASS is a statistical framework that enables unbiased analysis of antigen-specific T-cell subsets. COMPASS uses a Bayesian hierarchical framework to model all observed cell-subsets and select the most likely to be antigen-specific while regularizing the small cell counts that often arise in multi-parameter space. The model provides a posterior probability of specificity for each cell subset and each sample, which can be used to profile a subject’s immune response to external stimuli such as infection or vaccination.
MIMOSA is a package for fitting mixtures of beta-binomial or dirichlet-multinomial models to paired count data from single-cell assays, as typically appear in immunological studies (i.e. ICS, intracellular cytokine staining assay, or Fluidigm Biomark single-cell gene expression assays).
The method is, generally, more sensitive and specific to detect differences between conditions (i.e. stimulated vs. unstimulated samples) than alternative approaches such as Fisher's exact test, or empirical ad-hoc methods like ranking by log-fold change.
DataPackageR aims to simplify data package construction.
It provides mechanisms for reproducibility, data processing and tidying raw data into into documented, versioned, and packaged analysis-ready data sets.