Source Code & Apps

R Library Code

Clustering and cluster optimization algorithms

Purpose: Identifying the ideal number of groups (e.g., cancer patients, cells, social networking groups).
Benefits: Computationally efficient. Conserves on the number of samples needed. Compatible with multiple clustering approaches.
Current applications: Designing clinical trials. Identifying the effectiveness of human cell patterning.
Availability: As of Nov 2015, Progeny Clustering code package is available open source in the R repository: “progenyClust: Finding the Optimal Cluster Number Using Progeny Clustering”, as described in (Hu & Qutub, R Journal 2016). A user-friendly outline of the progenyClust R package is available online here thanks to Ian Howson. Matlab package to be available in 2016

Matlab Code

Whole tissue metabolic models

CORDA2 library (Proc Pacific Symposium of Biocomputing 2017)
Purpose: Provides a method to produce cell and tissue-specific metabolic models
Benefits: Predicts metabolism in healthy and diseased cells based on experimentally-obtained molecular expression data. Faster than CORDA and noise-independent. Optimized for comparing subtypes of human tissues and cells.
Current applications: Identifying differences in metabolism within subtypes of cancer and healthy mammalian cells. Generating patient-specific models.
Library availability: SBML and MATLAB formats.
MATLAB: CORDA2 function file as provided in the paper’s supplemental information.

Matrix-Form Artificially Centered Hit and Run, mfACHR (Proc Pacific Symposium of Biocomputing 2017)
Purpose: Provides a method to improve upon Monte Carlo Sampling of high dimensional network models
Benefits: Speeds up sampling of a model’s solution space compared to prior algorithms.
Current applications: Identifying steady-state metabolic flux distributions within cell and patient-specific whole genome wide metabolic models.
Library availability: MATLAB formats.
MATLAB: mfACHR function file as provided in the paper’s supplemental information.

CORDA library (PLOS Comp Bio 2016)
Purpose: Provides a library of tissue specific metabolic models
Benefits: Predicts metabolism in healthy and diseased tissues based on experimentally-obtained molecular expression data
Current applications: Identifying differences in metabolism across cancerous and healthy mammalian tissue
Library availability: SBML and MATLAB formats.
MATLAB: CORDA function file as provided in the paper’s supplemental information.
Python: Python version of CORDA developed by Christian Diener.

corsoFBA (BMC Sys Bio 2015)
Purpose: Models the flux of metabolites through tissue.
Benefits: Overcomes biomass production assumptions of other FBA methods.
Current applications: Modeling the metabolism of mammalian cells, and changes in disease.

Protein signaling pathway models

HIF1 hydroxylation chemical-kinetic models (2006-2008)
Purpose: Models post-translational regulation of hypoxia-inducible factor 1 (HIF1), a protein activating hundreds of genes as a function of oxygen availability
Benefits: Predicts levels of HIF1 as a function of its cofactors
Current applications: Helping optimize the design of experimental modulation of hypoxia-inducible factor proteins in cancer and neural progenitor cells
Availability: papers & m-files

Apps with User-Interfaces

GAIN Neuron Counting App (2016)
Purpose: Counting neural progenitor cells and neurons, and measuring dendrite outgrowth from individual cell bodies in images
Benefits: Overcomes limitations of applying prior algorithms to single cell analysis by a rule-based tracking method that maps soma to their dendrites. Interactive friendly graphical user-interface is integrated with the code. For use by all researchers.
Current applications: Automatically processing neuronal cell microscope images to help determine differentiation state of cells in different mechanical environments and in response to drugs.

Interactive Data Visualization Software

Biowheel2016Biowheel

Purpose: Visually interpret high-dimensional data through interactive graphs
Benefits: Ease of use & speed. No programming needed. Drag & drop files into the cloud-based tool. Collaborate on biomedical data science projects.
Current applications: Collaboration on data challenges. Expert-informed learning. Teaching tool for clustering and pattern recognition. Clinical decision-making.

Biomedical Data Challenge Links

ProteomicsDREAM2DREAM 9 Acute Myeloid Leukemia Outcome Prediction Challenge (2014-2015)

DREAM 8 HPN Breast Cancer Challenge (2013-2014)