Microarray Data Analysis - Lecture Notes | CMSC 423, Study notes of Computer Science

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CMSC423: Bioinformatic

Algorithms, Databases and Tools

Lecture 19

Microarray data analysis

Microarray data analysis

Typical microarray experiment

• Difference in color intensity indicate differences in gene expression levels
• Red – expressed in sample
• Green – expressed in control
• Yellow – expressed in both
• Black – expressed in neither

Affymetrix arrays

• Instead of full-length RNA – set of 25-bp tags

from gene of interest

• Also array mismatch probes – differ by 1 bp from

“normal” tags.

• Gene inferred to be present if intensity of

“normal” tags is > intensity of mismatch tags

• Building an Affy array – just like a micro-chip
• Individual tags grow by 1bp only if exposed to

light

Data clustering

• (^) Agglomerative
  • Start with single observations
  • Group similar observations into the same cluster
• Divisive
  • All datapoints start in the same cluster
  • Iteratively divide cluster until you find good clustering
• Hierarchical
  • Build a tree – leaves are datapoints, internal nodes represent clusters

Measures of goodness of clustering

• (^) Homogeneity
  • All points in a cluster must be similar
• Separation
  • Points in different clusters are disimilar

Hierarchical clustering

• (^) UPGMA (remember from phylogenetic trees?)
  • compute distance between genes (e.g. euclidean distance of expression vectors)
  • join most similar genes
  • repeat
  • Key element – compute distance between a gene and a cluster, or between two clusters – average distance between all genes in the two clusters – also called “average neighbor”
• Furthest neighbor
  • distance between two clusters = largest distance between all genes
• Nearest neighbor

Hierarchical clustering...cont

• Irrespective of distance choice, algorithm is the

same

1. compute inter-gene/cluster distances
2. join together pair of genes/clusters with smallest

distance

3. recompute distances to include the newly created

cluster

4. repeat until all points in one cluster

• Output of program is a tree
• Cluster sets – defined by “cut” nodes – any

subset of internal tree nodes defines a set of

clusters – the sets of leaves in the corresponding subtrees

k-means clustering

• (^) Split data into exactly k clusters
• Basic algorithm:
  • Create k arbitrary clusters - pick k points as cluster centers and assign each other point to the closest center
  • Re-compute the center of each cluster
  • Re-assign points to clusters
  • Repeat
• Another approach: pick a point at and see if

moving it to a different cluster will improve the

quality of the overall solution. Repeat!

• K-means clustering... K=