Alizadeh et. al. 2000

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Alizadeh et. al. (2000)

• Stephen Ayers
• 12/2/01

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Clustering

• Clustering is finding a natural grouping in a
  set of data, so that samples within a cluster
  will be more similar to each other than they are
  to samples in other clusters.
• Finding groups of correlated genes signature
  groups
• Genes without well established relationships
• Extract features of groups

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Hierarchical Clustering

• Tiers of points from a bottom layer of 1 point in
  each of n clusters to top level of n points, all
  in one cluster
• Usually represented in dendrogram

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Divisive

• Top-down
• Start with all samples and successively split
  into separate clusters

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Agglomerative

• Bottom-up approach
• Less computationally intensive
• Start with n singletons and successively merge
  clusters
• Place all values in separate clusters
• Merge most similar clusters into higher clusters
• Repeat until all clusters have been merged

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Average-Linkage Method

• Available ltlthttp//rana.stanford.edu/clustering
  gtgt
• Compute similarity matrix
• Scan matrix to find most highest similarity
• Uses form of the correlation coefficient
• A node is created between these values
• Values are replaced by node

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Diffuse Large B-cell Lymphoma

• Most common subtype of non-Hodgkins Lymphoma
• 25,000 cases/year
• 40 of patients respond well
• Possible undetected heterogeneity
• Found 2 classes using clustering (Eisen 1998)
  Germinal Center B-like and Activated B-like

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Lymphochip

• 17,856 cDNA clones total
• 12,069 germinal center B-cell library
• 2,338 lymphomic cancer genes
• 3,186 genes important to lymphocyte or cancer
  biology
• ¼ of genes duplicates

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Expression Analysis

• DLBCL, Follicular Lymphoma, Chronic Lympohcytic
  Leukemia
• Lymphocyte subpopulations with a range of
  conditions
• -normal human tonsils, lymph nodes
• -lymphoma, leukemia cell lines

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Figure 1
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Figure 2
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Figure 3
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Figure 4
GC
Activated
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Figure 5
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Conclusions

• More categories likely
• Changes in treatment
• Possible drug targets