Unsupervised learning on MSI data
Blog series
Mass spectrometry imaging data is commonly explored using unsupervised data analysis approaches, which aim to extract the key trends embedded within the data. A wide range of methods exist, which we will discuss in a series of blog posts.
Posts in this series
Factorization of MSI data - part 1
In this first series, we will focus on factorization methods. We will show the key concepts of some pervasive approaches and how they translate to MSI data. We will apply all methods to the same data set, to facilitate comparison. In this first post, we will discuss three linear approaches, namely Principal Component Analysis (PCA), PCA + Varimax and Independent Component Analysis (ICA).
By Nico Verbeeck on June 8, 2020
Read time: 11 minutes
Non-negative matrix factorization in MSI
In this blog post, we continue our overview of unsupervised data analysis approaches commonly used in MSI, diving deeper into factorization methods. This time we will take a closer look at the results of principal component analysis (PCA) in MSI, and compare these to the results of nonnegative matrix factorization (NMF).
By Nico Verbeeck on March 13, 2021