A Comparison Between PCA and Hierarchical Clustering
White Paper Feb 23, 2016
Graphical representations of high-dimensional data sets are at the backbone of straightforward exploratory analysis and hypothesis generation. Within the life sciences, two of the most commonly used methods for this purpose are heatmaps combined with hierarchical clustering and principal component analysis (PCA).
We will use the terminology ‘data set’ to describe the measured data. The data set consists of a number of samples for which a set of variables has been measured. All variables are measured for all samples.
Related White Papers
Using Micro Flow Imaging (MFI) to Measure Protein AggregationWhite Paper
Download this free white paper from Protein Simple to learn about the differences between MFI and how MFI provides crucial information about your protein therapeutic.READ MORE
Meeting Modern Data Integrity and Compliance RequirementsWhite Paper
The accuracy and completeness of data is crucial for safe product development and to prevent serious implications regarding human health.READ MORE
How to Identify Low-Abundance Modified Peptides with Proteomics Mass SpectrometryWhite Paper
We present a new simplicity-focused analytics methodology, called Sorcerer Score™ that allows low-abundance, modified peptides (LAMPs) to be rigorously identified within a hypothesis-driven framework based on high-accuracy precursor and fragment mass data. Accurate peptide ID is fundamental to accurate protein quantitation and post-translational modification (PTM) analysis.READ MORE