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nCal
rBHP/cHMM
chngpt

nCal

nCal (dose response curve) and prc (paired response curve)

These two R packages are hosted on nCal: Nonlinear Calibration and prc: Paired Response Curve.
In addition, here is a FAQ focused on installation and graphical user interface (GUI) of nCal.

References

• Fong, Y, Yu, X. (2016), "Transformation Model Choice in Nonlinear Regression Analysis of Serial Dilution Assays." Statistics in Biopharmaceutical Research, 8(1):1-11.
  
• Cumberland, W.N., Fong, Y†, Yu, X., Defawe, O., Frahm, N., DeRosa, S. (2015), "Nonlinear calibration model choice between the four and five parameter logistic models." Journal of Biopharmaceutical Statistics, 25(5):972-983.
• Eckels, J., Nathe, C., Nelson, E., Shoemaker, S., Van Nostrand, E., Ashley, V., Yates, N., Harris, L., Bollenbeck, M., Fong, Y., Tomaras, G., Piehler,B. (2013), "Analysis, quality control and secure sharing of Luminex immunoassay data using the open source LabKey Server system." BMC Bioinformatics, 14(1):145-162.
• Fong, Y., Wakefield, J., DeRosa, S., Frahm, N. (2012), "A robust Bayesian random effects model for nonlinear calibration problems." Biometrics, 68(4):1103--1112.
• Fong, Y., Sebestyen, K., Yu, X, Gilbert, P. and Self, S. (2013), "nCal: a R package for nonlinear calibration." Bioinformatics, 29(20):2653-2654.
• Defawe OD, Fong Y, Pickett M, Vasilyeva E, Carter DK, Gabriel E, Frahm N, McElrath M.J., De Rosa SC. (2012), "Optimization and qualification of a multiplex bead array to assess cytokine and chemokine production by vaccine-specific cells." Journal of Immunological Method, 382(1):117-128.

rBHP/cHMM

Statistical methods for protein sequences

Regression with protein sequence covariates

The R package krm provides a way to fit regression model with protein sequence covariates through a kernel-based random effect model.

References

• Fong, Y.‡, Datta, S.‡, Georgiev, I., Kwong, P., Tomaras, G. (2014), "Mutual information kernel logistic models with application in HIV vaccine studies." Biostatistics, in press. (‡ equal contribution)

Clustering protein sequences into subfamilies

rBHP is a general clustering/mixture modeling algorithm that is based on randomized Bottom-up Hierarchical clustering Pruned (rBHP) splits.

cHMM is a mixture model based clustering method for identifying protein subfamily, and it uses rBHP as part of the inference machinery. 

• Source code
• Windows binary
• Linux binary (SUSE)

Brief Guide

• Type cbclust.exe to see help message
• To run cHMM, do "cbclust.exe -m ProteinSequence ..." 

References

• Fong, Y., Wakefield, J. and Rice, K. (2012), "An Efficient Markov Chain Monte Carlo Method for Mixture Models by Neighborhood Pruning." Journal of Computational and Graphical Statistics, 21:1.
• Fong, Y., Wakefield, J. and Rice, K. (2010), "Bayesian mixture modeling using a hybrid sampler with application to protein subfamily identification." Biostatistics, 11:1.

chngpt

Threshold regression modeling

The R chngpt package is hosted on chngpt: Estimation and Hypothesis Testing for Threshold Regression, and more up-to-date versions may be found here.

References

• Fong, Y., Huang, Y., Gilbert, P., Permar S. (2017), "chngpt: threshold regression model estimation and inference." BMC Bioinformatics, 18(1):454.
• Fong, Y., Di, C., Huang, Y., Gilbert, P. (2017), "Model-robust inference for continuous threshold regression models." Biometrics, 73(2):452-462.
• Fong, Y., Di, C., Perma, S. (2014), "Change point testing in logistic regression models with interaction term." Statistics in Medicine, 34(9):1483--1494.