Data science and bioinformatics tutorials
Tag: omics
Embracing Python in this tutorial series has long been a matter of time. For the last five years I have been championing R mostly because of its wide applicability and quite frankly, my own convenience. However, there is little any programming language can do to singlehandedly solve a variety of statistical and computational challenges and … Continue reading UMAP clustering in Python
This time I elaborate on a much more specific subject that will mostly concern biologists and geneticists. I will try my best to outline the approach as to ensure non-experts will still have a basic understanding. This tutorial illustrates the power of genome-wide association (GWA) studies by mapping the genetic determinants of cholesterol levels using … Continue reading Genome-wide association studies in R
My last entry introduces principal component analysis (PCA), one of many unsupervised learning tools. I concluded the post with a demonstration of principal component regression (PCR), which essentially is a ordinary least squares (OLS) fit using the first $latex k &s=1$ principal components (PCs) from the predictors. This brings about many advantages: There is virtually no … Continue reading Partial least squares in R
poissonisfish 