Feb 9

Clustering: Grouping in a particular way based on some sort of algorithm with given parameters

Why cluster? Exploration of huge data, extract patterns and make predictions on these patterns (hypothesis generation and testing)

Gene expression data:

Induction looks much more dramatic than the repression (be sure and remember this), equivalent on the fold change, but look very dissimilar

A log transformation "normalizing" the way this data looks for fold changes

Negative correlations are as informative as the positive correlations

Scatter/line plots are a different way to represent a heat map

Comparing Gene Expression Profiles or Guilt by expression:

Co-regulation or directly regulating each other

Proximity Measures:

Want to understand relationships genes and expression level over time or samples

Correlation, Euclidean distance (distance formula), Inner product x y, Hamming distance, L1 distance, Dissimilarities may or may not be metrics

Correlation is very sensitive to outliners (percent change) so the other measures could be good

Linkage Methods:

Find some center point in a cluster, treat it as a "gene" and measure it from the gene of interest

Could average all the distances between the gene of interest and all in cluster

Could do the minimum or the maximum distance of a gene in the cluster to the gene of interest

Single linkage, Average Linkage, etc. Each will produce different clusters

Hierarchical Clustering

Join two most similar genes

Join next two most similar "objects", repeat until all genes have been joined (can never be pulled apart in your cluster once they are joined)

Iterative and stringent