romic represents high-dimensional measurements by persistently tracking features, samples, and measurements as a dataset is modified and reformatted. It provides a suite of functions that build on this framework to support data analysis and visualization. These functions are combined into shiny apps tailored to exploratory data analysis.

software to detect doublets

RNA-seq prediction with deep convolutional neural networks.

Analyses related to the Borzoi paper.

Machine learning methods for DNA sequence analysis.

Python implementation of extracting body weight dynamics in diversity outbred mice using ARHMM.

Fit phenomenological sigmoid and impulse curves with priors to improve interpretability

Sequential regulatory activity predictions with deep convolutional neural networks.

Calico Lipidomics and Metabolomics (CLaM) Analysis provides a suite of functions which support data manipulation, statistical analysis and visualizations of mzrollDB files created by MAVEN.