Plausible reasoning requires constructing rational arguments by use of syllogisms, and their analysis by deductive and inductive logic. Using this method of reasoning and expressing our beliefs, in a scientific hypothesis, in a numerical manner using probability theory is one of my interest. I try to condense the material from the first 4 chapters of... Continue Reading →

# Methods of handling and working with missing data (part 1)

Description In biology, the presence of missing values is a common occurrence for example in proteomics and metabolomics study. This represents a real challenge if one intends to perform an objective statistical analysis avoiding misleading conclusions. The leading causes of incompletely observed data are truncation and censoring which are often wrongly used interchangeably. You can... 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 →

# Hierarchical Linear Regression – 2 Level Random Effects Model

Regression is a popular approach to modelling where a response variable is modelled as a function of certain predictors - to understand the relations between variables. I used a linear model in a previous post, using the bread and peace model - and various ways to solve the equation. In this post, I want to fit... Continue Reading →

# Normalising Nanostring data

This is a quick R guide to learn about Nanostring technology (nCounter) and how to pre-process the data profiled on this platform. Description The nCounter system from Nanostring Technologies is a direct, reliable and highly sensitive multiplexed measurement of nucleic acids (DNA and RNA) based on a novel digital barcode technology. It involves Custom Codeset... Continue Reading →

# Using R to export results into Excel

Applying conditional formatting on a sheet based on the values from a different sheet This is the first post in the series "Tips and Tricks for Data Science". In this post I will show how to create Excel files with conditional formatting in R. As an example I will focus on colouring cells in a... Continue Reading →

# Compare Transformations & Batch Effects in Omics Data

While analysing high dimensional data, e.g. from Omics (Genomics, Transcriptomics, Proteomics etc.) - we are essentially measuring multiple response variables (i.e. genes, proteins, metabolites etc.) in multiple samples, resulting in a $latex rXn$ matrix X with r variables and n samples. The data capture can lead to multiple batches or groups in the data -... 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 →