Pattern Recognition using PCA: Variables and their Geometric Relationships

Principal component analysis is a commonly used technique in multi-variate statistics and pattern recognition literature. In this post I try to merge ideas of Geometric and Algebraic interpretation of data as vectors in a vector space and its relationship with PCA. The 3 major sources used in this blog are: [1] Thomas D. Wickens (1995). The...

Methods of handling and working with missing/censored data (part-2)

Description As discussed in my last blog here, missing data in big data analysis cannot always be ignored and requires a good understanding of the data and user decisions on how to handle this scenario. In biology, this generally occurs when the data is subjected to limits of detection or quantification (censoring or truncation mechanism). These...

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...

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...