This document summarizes Md. Bipul Hossen's presentation on comparing clustering techniques for gene expression data in bioinformatics. It introduces the topic and objectives, which are to find significant clusters in gene expression data and compare hierarchical and k-means clustering methods. It then provides details on different clustering algorithms and evaluation metrics. Results show complete linkage with Euclidean distance performed best for Affymetrix data, while k-means performed best for cDNA data. The conclusions state the comparative study helps document performance of these methods. Future work could involve comparing to other clustering techniques.

1. Welcome to my presentation
on
A Comparative Study of Clustering for Gene
Expression Data in Bioinformatics
Roll: 08054746
Reg: 1484
Department of Statistics
Rajshahi University
Rajshahi-6205
Md. Bipul Hossen, Dept. of Statistics, University of Rajshahi
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2. Outline
1. Why choosing clustering technique ?
2. Some Objectives
3. Methods and materials
4. Results and Discussions
5. Conclusion
Md. Bipul Hossen, Dept. of Statistics, University of Rajshahi
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3. 1. Why choosing Clustering Technique
Cluster analysis programs are routinely run as a first
step of data summary and grouping genes in a
microarray data analysis.
Mainly the gene expression data is so much
noisy, mixture with expression pattern, down
regulated and up regulated.
That’s why we show here a comparative study of four
clustering algorithms and two proximity measures
applied on most commonly used iris data, simulated
data and six real cancer gene expression data sets.
Md. Bipul Hossen, Dept. of Statistics, University of Rajshahi
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4. 2. Some Objectives
 Find significant cluster according to
similarities, intensities and regulations among it’s
objects.
 Compare several method of HC with K-means
based on two proximity measures.
 To asses the quality and reliability of clustering by
Calinaski Harabasz (CH) and Daviece Bouldin (DB)
index.
Bioinformatics Lab, Dept. of Statistics, University of Rajshahi
Md. Bipul Hossen, Dept. of Statistics, University of Rajshahi
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5. Methods
1. Single Linkage or Nearest
Neighbor Method
2. Complete Linkage or
Furthest Neighbor Method
3. Average Linkage Method
K-means clustering
Md. Bipul Hossen, Dept. of Statistics, University of Rajshahi
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6. Davies–Bouldin (DB) Index
The Davies–Bouldin index is a metric for evaluating
clustering algorithms (Davies and Bouldin, 1969). This
is an internal evaluation scheme and it is a cluster
separation measure.
Md. Bipul Hossen, Dept. of Statistics, University of Rajshahi
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7. Calinski-Harabasz (CH) Index
Where, SSB is the overall between-cluster variance, SSW is the overall within-cluster
variance, k is the number of clusters, and N is the number of observations.
Md. Bipul Hossen, Dept. of Statistics, University of Rajshahi
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8. Data sets
Chip Tissue
n
Armstrong-V2 [2]
Affy
Blood
72
3
Bhattacharjee
[3]
Nutt-V1 [6]
Affy
Lung
203
Affy
Brain
Alizadeh-V2 [1]
cDNA
Garber [4]
m
d
24,20,28
12582
2194
5
139,17,6,21,20
12600
1543
50
4
14,7,14,15
12625
1377
Blood
62
3
42,9,11
4022
2093
cDNA
Liang [5]
Lung
66
4
17,40,4,5
24192
4553
cDNA
Dataset
#C Dist. Classes
Brain
37
3
28,6,3
24192
1411
Md. Bipul Hossen, Dept. of Statistics, University of Rajshahi
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9. 2 Clusters
3 Clusters
In this example, the objects g1, g2, g3, g4, g5, g6, g7, g8, g9 and g10 have been clustered. The
place at the bottom of the tree, where the object names are written, are called leaves. The
junctions are called nodes. It is possible to use a hierarchical clustering algorithm to find groups
in the data, by cutting the tree at a certain height. For instance, it might be considered than on
the example there are two groups, (g2, g3, g1, g8) and (g6, g10, g5, g7, g4, g9) or three groups
(g2, g3, g1, g8), (g6, g10) and (g5, g7, g4, g9) or ten groups, each containing only one leaf.
Md. Bipul Hossen, Dept. of Statistics, University of Rajshahi
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10. Hierarchical Clustering of Simulated Data
Fig: Heat map
Green color dendrogram shows the best result and we make a Heat map by
this method. i.e Complete HC with respect to Euclidean distance give the
best result then other methods.
Md. Bipul Hossen, Dept. of Statistics, University of Rajshahi
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11. K-means of Simulated Data
Table: Davies-Bouldin index
No. of Cluster
Cluster Size
DB index
K=2
20,40
0.897
K=3
20,20,20
0.321
K=4
12,20,8,20
0.797
K=5
4,4,12,20,20
0.825
From the above table we see, when the number of cluster k=3
the DB index give the lower value. Therefore we may conclude
that three clusters are present in this data set.
Md. Bipul Hossen, Dept. of Statistics, University of Rajshahi
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12. HC of Armstrong-V2 Data(d)
Md. Bipul Hossen, Dept. of Statistics, University of Rajshahi
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13. Several HC Nutt-V1 Data (c)
Md. Bipul Hossen, Dept. of Statistics, University of Rajshahi
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14. K-means of Alizadeh-V2 Data
Table: Davies-Bouldin index
No. of Cluster
Cluster Size
DB index
K=2
K=3
, ,
K=4
K=5
22 9 3 10 18
2.
Table represent, when the number of cluster k=3 the DB index give the lower
value. The sizes of the cluster is ,
and
and the actual cluster size is , 9
and 11. When the number of cluster is 3 than the DB index gives the lower value.
Therefore we may conclude that three clusters are present in Alizadeh-V2 data.
Md. Bipul Hossen, Dept. of Statistics, University of Rajshahi
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15. K-means of Liang Data
Table: Davies-Bouldin index
No. of Cluster
Cluster Size
DB index
K=2
29 8
1.23
K=3
, ,
K=4
6 9 3 19
2.09
K=5
1 2 19 14 1
1.215
Table 4.3 represent, when the number of cluster k=3 the DB index give the lower
value. The sizes of the cluster is ,
and 3 and the actual cluster size is ,
and
. When the number of cluster is 3 than the DB index gives the lower value.
Therefore we may conclude that three clusters are present in Armstrong-V2 data.
Md. Bipul Hossen, Dept. of Statistics, University of Rajshahi
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16. Several HC Liang Data(c,d,e,f)
28,6,3
Md. Bipul Hossen, Dept. of Statistics, University of Rajshahi
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17. Heat map of Liang Data
Md. Bipul Hossen, Dept. of Statistics, University of Rajshahi
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18. Compare HC with K-means for Affymetrix data sets
Dataset
Distance Method
Cluster Method
Calinski-Harabasz (CH)
Armstrong-V2
Euclidean
Single
1.889
Euclidean
Complete
11.803
Euclidean
Average
6.674
Pearson
Single
0.914
Pearson
Complete
12.559
Pearson
Average
10.393
K-means
11.943
Euclidean
Single
1.786
Euclidean
Complete
34.702
Euclidean
Average
26.850
Pearson
Single
1.700
Pearson
Complete
26.512
Pearson
Average
12.902
K-means
22.924
Euclidean
Single
3.167
Euclidean
Complete
7.938
Euclidean
Average
5.269
Pearson
Single
0.941
Pearson
Complete
4.273
Pearson
Average
2.987
K-means
6.051
Bhattacharjee
Nutt-V1
Bioinformatics Lab, Dept. of Statistics, University of Rajshahi
Md. Bipul Hossen, Dept. of Statistics, University of Rajshahi
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19. Compare HC with K-means for Affymetrix data sets by visualization technique
Mean of the CH index for Affy Chip
20
18
16
CH index
14
12
Pearson
Euclidean
10
8
6
4
2
0
Single
Average
Complete
K-Means
From the above graph we see that Complete linkage with Euclidean achieves CH index
of 18.14 which is larger CH than Single, Average and K-means with respect to their
proximity measure. Therefore we may conclude that the complete linkage method
gives the better result for the Affymetrix data sets.
Bioinformatics Lab, Dept. of Statistics, University of Rajshahi
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20. Compare HC with K-means for cDNA data sets
Dataset
Distance Method
Cluster Method
Calinski-Harabasz (CH)
Alizadeh-V2
Euclidean
Euclidean
Euclidean
Pearson
Pearson
Pearson
Single
Complete
Average
Single
Complete
Average
2.047
11.161
11.068
0.980
11.229
10.319
K-means
13.003
Single
2.772
Garber
Euclidean
Euclidean
Euclidean
Pearson
Pearson
Pearson
Liang
Complete
19.097
5.166
0.855
7.693
18.912
9.269
9.057
19.665
10.279
19.665
19.665
19.665
K-means
Euclidean
Euclidean
Euclidean
Pearson
Pearson
Pearson
Average
Single
Complete
Average
K-means
Single
Complete
Average
Single
Complete
Average
23.781
Md. Bipul Hossen, Dept. of Statistics, University of Rajshahi
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21. Compare HC with K-means for cDNA data sets by visualization technique
Mean of the CH index for cDNA Chip
18
16
14
CH index
12
10
Euclidean
Pearson
8
6
4
2
0
Single
Complete
Average
K-Means
From the above graph we see that K-means achieves a CH index of 17.01 which is
larger CH than Single, Complete and Average with respect to their proximity
measure. Therefore we may conclude that the K-means method gives the better
result for the cDNA data sets.
Md. Bipul Hossen, Dept. of Statistics, University of Rajshahi
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22. Conclusions
Our results reveal that the complete linkage with euclidean
distance exhibited the best performance for Affymetrix data
sets. For cDNA data sets the K-means clustering exhibited the
best performance in terms of recovering the true structure of
the data sets. To the best of our knowledge, the comparative
study of several HC and K-means with the validity index as CH
and DB are poorly documented in literature.
Md. Bipul Hossen, Dept. of Statistics, University of Rajshahi
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23. Future Research Interest
1. Comparison on Hierarchical clustering method with the
Self-Organizing Maps method and other existing update
clustering methods.
2. Investigate the performance of the different hierarchical
clustering method in a comparison of the other existing
methods by false discovery rate (FDR), misclassification
error rate (MER), receiver operating characteristic (ROC)
and area under ROC curve using resampling technique.
3. Comparing both supervised and unsupervised methods
for gene expression data.
Md. Bipul Hossen, Dept. of Statistics, University of Rajshahi
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24. Thank
you

25. Reference
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