Digital Biomarker Discovery Pipeline. The DBDP provides complete end-to-end digital biomarker development. Modules are dedicated to data pre-processing, data analysis, algorithm development, and validation.
The Big Ideas Lab mission is to develop and test tools and infrastructure using biomedical and health data for early detection, intervention, and prevention of disease.
FlowKit is an intuitive Python toolkit for flow cytometry analysis and visualization, including GatingML 2.0 support.
An efficient and robust large-scale multiple testing method to infer high-dimensional gene co-expression networks based on sample-quantile contingency table.
A highly efficient data structure and algorithm for performing alignment of short reads from CRISPR or shRNA screens to reference barcode library. Bioconductor package.
An efficient algorithm to estimate covariate adjusted precision matrices.
A R package to fit mixed effect model based on quasi-least-square.
Bausch’s algorithm for chi-squared weighted sum. This software computes tail probability of a mixture of chi-squared random variables. Implements Bausch’s algorithm using GNU GMP library so that a tail probability (or p-value) can be evaluated to an arbitrary precision.
Efficient Local Ancestry Inference. Software performs local ancestry inference for admixed individuals.
Haplotype Quantitative Loci. This software performs association testing between local haplotypes and phenotypes at each core marker.
posterior inference via Model Averaging and Subset Selection. The software performs genome-wide joint analysis of all SNPs in association with a phenotype.
An R package to extract and enhance signals from highly sparse single-cell ATAC-seq data.
An R package to perform pseudotime analysis for single-cell genomic data.
An R package to summarize regulatory activities for single-cell ATAC-seq data.
A software tool to explore massive amounts of publicly available gene expression data.