NHE Feb 9 Notes

Nick Elder

Group 2 intestines

Clustering:

• Grouping genes, samples, etc.
  • may present the order in a way that does not reflect how would would expect/want it to
• Can be used to pull out patterns and make predictions
• Induction can look much more dramatic than repression
• Changing the y-axis to a logarithmic scale reveals much more drastic changes in repression
  • Similar relationships could be caused by direct interaction or co-regulation
  • Strong negative correlation could be as interesting as positive correlation
    • Computers are more likely to group positive correlations
• Gene Expression Profiles (Guilt by Association)
  • comparing genes or expression levels in samples over time
  • Correlation, Euclidean distance, Hamming Distance, etc....
  • Correlation is very sensitive to outliers; also, very small changes, possibly due to noise, can be interpreted as correlation with more drastic changes
• Linkage Methods
  • Can take the nucleus/average of a cluster and compare that to a desired single gene
  • Can average all the distances between all points
  • Can compare the gene of interest to all the rest of the genes individually

• Hierarchical clustering
  • Joins two most similar genes
  • Join next two most similar "objects" (genes or clusters of genes)
  • Repeat until all genes have been joined
  • Once clustered, two genes can, under no circumstances, under penalty of death, in no way possible on heaven or earth, be split apart
  • No gene left behind. Everyone has to be in the cluster; starts at +1 correlation and end at -1 correlation
    • Genes that are most distant in the cluster (opposite correlation) could be co-regulated together
  • Cutting the tree
    • Draw a line to separate the clusters
      • Decision to cut the line is completely arbitrary
• K-means Clustering
  • Specify how many clusters to form - just break into groups, not breaking the tree
  • Randomly assign each gene to one of K different clusters
  • Average expression of all genes in each cluster to create K pseudo genes
  • Rearrange genes by assigning each other to the cluster represented by the pseudo gene to which it is most similar
  • Repeat until convergence
• Supervised Clustering
  • Find genes in expression file whose patterns are highly similar to a desired gene or pattern
  • Add closest gene first
  • Then add gene that is closest to all genes already in cluster
  • Repeat, as long as added gene is within specified distance to genes already in the cluster
  • Distance from one gene to a set of genes defined to be maximum (or minimum or average) of all distances to individual members of the set (complete, single, and average linkage, respectively)
  • Dr. C says that it might be really nice to cluster genes that move with a transcription factor, since a little induction in the transcription factor can have major effects on other gene expression
• Quality Clustering: QT Clust
  • Each gene builds a supervised cluster
  • Gene with "best" list, and genes in its list, becomes the next cluster
  • Remove these genes from consideration, and repeat
  • Stop when all genes are clustered, or largest cluster is smaller than user specified threshold
  • Make 'cliques', find the largest, separate that group, remove all members from that group from all other groups, and make the next largest group from the existing members.