Colorectal Cancer Incidence Prediction Model

16 Jun 2008 | Tools | Contributor(s): Eric Sherer, Mohd Haziq Rahmad

Stochastic simulation of polyp and colorectal cancer (CRC) incidence with patient age.

GCxGC-MS Data Classification and Alignment

02 Dec 2008 | Tools | Contributor(s): Dabao Zhang, Min Zhang, Jason Catlin

Align and classify GCxGC-MS data

Monte Carlo Simulation for four CRC Incidence Models

12 Dec 2008 | Tools | Contributor(s): Eric Sherer

A Monte Carlo Simulator determines the risk of colorectal cancer (CRC) development in a population by calculating multiple, individual patient trajectories.

Workspace

27 Apr 2009 | Tools | Contributor(s): Nicholas Kisseberth

Workspace

Spectrum Deconvolution of LC-MS Data

03 Dec 2008 | Tools | Contributor(s): Ann Christine Catlin, George Howlett

Spectral deconvolution differentiates analyte signals from contaminants or instrumental noise, and reduces data dimensionality to benefit downstream statistical analysis.

Peak Alignment of LC-MS Data

24 Jun 2009 | Tools | Contributor(s): Ann Christine Catlin, George Howlett

Peak alignment addresses retention time shift by recongnizing and aligning significant peaks; it then uses discrete deconvolutio to align overlapped peaks.

Normalization of Aligned LC-MS Data

18 Jun 2009 | Tools | Contributor(s): Ann Christine Catlin, George Howlett

Normalization attempts to quantitatively filter overall peak intensity variations due to experiment errors such as systematic variable injection volumes loaded onto LC-MS.

Significance Testing of Normalized LC-MS Data

23 Jun 2009 | Tools | Contributor(s): Ann Christine Catlin

Several statistical significance tests are employed to identify peptide or metabolite peaks that either make significant contributions to the molecular profile of a sample or distinguish a group of samples from others.

Pattern Recognition for Normalized LC-MS Data

01 Jul 2009 | Tools | Contributor(s): Ann Christine Catlin, George Howlett

This tool provides principal component analysis (PCA), linear discriminate analysis (LDA), and canonical discriminate analysis (CDA) for data clustering on aligned, normalized LC-MS datasets.

The MACH 1.0 Markov Chain based Haplotyper

15 Aug 2010 | Tools | Contributor(s): Yanzhu Lin, Guoheng Chen, Ann Christine Catlin

The MaCH Tool is used to infer missing genotypes in a two step process, where Step 1 estimates the model parameters, and Step 2 uses the parameters estimated in Step 1 -- calibrated to your specific dataset and genotyping platform -- to impute all SNPs.