clustering jarno tuimala. clustering aim –grouping objects (genes or chips) into clusters so that...

Clustering

Jarno Tuimala

Clustering

• Aim– Grouping objects (genes or chips) into clusters so that

the objects inside one cluster are more closely related to each other than to objects in other clusters

• Exploratory data analysis– View all data simultaneously– Identify clusters and patterns in data

• Uses:– Time series analysis– Visualization of known classes

Unsupervised vs. Supervised

Clustering methods

• Hierarchical clustering– single, average (UPGMA) and complete

linkage

• Non-hierarchical clustering– K-means

Hierarchical clustering

• Two phases– Pick a distance method

• Euclidian• Pearson / Spearman correlation

– Pick the dendrogram drawing method• Single linkage• Average linkage• Complete linkage

Distances

• Euclidian– Average difference between gene or chip

expression profiles– Similar values are clustered together

• Correlation– Difference in trends– Similar trends are clustered together– Typically: Pearson or Spearman correlation

Single, average, and complete linkage

Dendrogram drawing

UPGMA example

X55123Gata3 Kcnd2

2...

Time 0 , Strain chocolate_addict



Time 0 , Strain normal



X55123Gata3 Kcnd2 Api6

3 gene...








Y18280Dyrk1b

U16297Cyb561

5 gene tree








Y18280Dyrk1b

U16297Cyb561

Y13090Casp12 Gria4

7 gene tree








Y18280Dyrk1b

U16297Cyb561

U39827Gpcr25

Y13090Casp12 Gria4

8 gene tree








Y18280Dyrk1b

U16297Cyb561

U39827Gpcr25

Y13090Casp12 Gria4

M33760Fgfr1

L06443Gdf3

10 gene tree








Y18280Dyrk1b

U16297Cyb561

U39827Gpcr25

Y13090Casp12 Gria4

M33760Fgfr1

L06443Gdf3

10 gene tree








Y18280Dyrk1b

U16297Cyb561

U39827Gpcr25

Y13090Casp12 Gria4

M33760Fgfr1

L06443Gdf3

10 gene tree








Y18280Dyrk1b

U16297Cyb561

U39827Gpcr25

Y13090Casp12 Gria4

M33760Fgfr1

L06443Gdf3

10 gene tree








Y18280Dyrk1b

U16297Cyb561

U39827Gpcr25

Y13090Casp12 Gria4

M33760Fgfr1

L06443Gdf3

10 gene tree non-binary







Hierarchical Clustering

Gata3 Kcnd2 Api6 Dyrk1b Cyb561 Casp12 Gria4 Gpcr25 Fgfr1 Gdf3

Silicon Genetics, 2003Silicon Genetics, 2003

Heatmap

K-means clustering

• Partitioning method– The dataset is divided into K clusters– User needs to deside on the K before the run

• K-means is heuristic algorithm, so different runs can give dissimilar results– Make several runs, and select the one giving

the minimum sum of within-clusters variance

K-means Clustering

Silicon Genetics, 2003Silicon Genetics, 2003

Visualization

Gene selection

• Genes are usually filtered before clustering.– This decreases calculation time.

• Typically a few hundred genes with highest variance (or standard deviation) are selected.

• If you have, e.g., two types of cancers, do not use t-test for selecting genes. You will always get a result where the cancer type is differentiates between the clusters.

clustering jarno tuimala. clustering aim –grouping objects (genes or chips) into clusters so that...

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