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 Large Effect Sizes: Missing information produce misleading results.

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High Dimensional Data & Hierarchical Regression

In a high-throughput experiment one performs measurements on thousands of variables (e.g. genes or proteins) across two or more experimental conditions. In bioinformatics, we come across such data generated using technologies like Microarrays, Next generation sequencing, Mass spec etc. Data from these technologies have their own pre-processing, normalising and quality checks (see here and here … Continue reading High Dimensional Data & Hierarchical Regression

Logistic “Aggression”: binary classification problems

Binary problems, where the outcome can be either True or False are very common in data analysis, from an inference or classification point of view. A previous post on binomial modelling deals with a similar problem, but this time we frame the problem from a regression or generalized linear model (GLM) view point. Previously we … Continue reading Logistic “Aggression”: binary classification problems

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 -┬áto 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 Hierarchical Linear Regression – 2 Level Random Effects Model