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 Code

Whole tissue metabolic models

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: MATLAB formats.
MATLAB: CORDA2 function file as provided in the paper’s supplemental information.

Matrix-Form Artificially Centered Hit and Run, mfACHR (Pacific Symposium on Biocomputing, 2016)
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.
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 (2017)
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)