PCA Analysis

PCA Analysis

What is it? Principle Component Analysis (PCA) transforms high-dimensional data into a lower-dimensional structure to improve data presentation, pattern recognition, and analysis. PCA determines which dimensions will result in the largest variability of measurements (e.g., expression of specific proteins) across all samples. It does not separate the different groups from…

Hierarchical Clustering

Hierarchical Clustering

What is it? Hierarchical clustering characterizes how similar (or dissimilar) the samples are based on overall patterns of measurements. For example, the groups may be patients and the overall patterns may be derived from the protein expression across numerous proteins. Hierarchical clustering analyzes the similarity in a binary fashion starting…

SAM (Significance Analysis of Microarray)

SAM (Significance Analysis of Microarray)

What is it? SAM is a method used for large-scale gene or protein expression data like those collected with microarrays. It addresses the issue of analyzing large-scale data in which a microarray experiment of 10,000 proteins would identify 100 proteins by chance using a p-value cut-off of 0.01. Therefore, SAM…

t-test & ANOVA (Analysis of Variance)

t-test & ANOVA (Analysis of Variance)

What are they? The t-test is a method that determines whether two populations are statistically different from each other, whereas ANOVA determines whether three or more populations are statistically different from each other. Both of them look at the difference in means and the spread of the distributions (i.e., variance)…

Common Biostatistical Methods Explained

Common Biostatistical Methods Explained

In the biology field, we often ask: how does this sample group compare to another sample group? Biostatisticians and bioinformaticians can help answer this question by applying complex algorithms or concepts. They love the math, but … most of us simply want to understand the basic principles behind these analyses.…