In our previous work, while working in tuberculosis diagnostics research, we developed some workflows to detect possible biomarkers using Omics data from large cohort studies. Discovered in 19th century, Tuberculosis (TB) is still a serious public health problem and it is estimated that one third of the World’s population is infected with Mycobacterium Tuberculosis (mTB). A... Continue Reading →

# Large Effect Sizes: Missing information produce misleading results.

Recently I came across the problem with suspiciously large difference in the averages of two groups while analysing some Omics data. An article dealing with similar issues can be seen here. The data distribution is shown below in Figure 1 (FYI: the fold change was around 6 - which is very large for this kind... Continue Reading →

# Regression & Finite Mixture Models

I wrote a post a while back about Mixture Distributions and Model Comparisons. This post continues on that theme and tries to model multiple data generating processes into a single model. The code for this post is available at the github repository. There were many useful resources that helped me understand this model, and some... Continue Reading →

# Model Checking: Scoring and Comparing Models

This is another post in the series of model checking posts. Previously we looked at which aspects of the data and model are compatible, using posterior predictive checks. Once we have selected a model or a set of models for the data, we would like to score and compare them. One aspect of comparison using... Continue Reading →

# Model Checking: Posterior Predictive Checks

Once a model is fit and parameters estimated, we would look at how well the model explains the data and what aspects of the data generation process in nature are not captured by the model. Most of the material covered in this post follows the examples from: [1] Gelman, A., Carlin, J. B., Stern, H. S.,... Continue Reading →

# Mixture Distributions and Model Comparison

The following text and code snippets show examples from two books on Bayesian Data Analysis: [1] Kruschke, J. K. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan, second edition. Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition. http://doi.org/10.1016/B978-0-12-405888-0.09999-2 [2] Albert, J., Gentleman, R., Parmigiani, G., & Hornik, K.... Continue Reading →