Evaluation of Two Methods to Cluster Gene Expression Data Odisse Azizgolshani Adam Wadsworth Protein Pathways SoCalBSI
Dec 21, 2015
Evaluation of Two Methods to Cluster Gene Expression Data
Odisse AzizgolshaniAdam Wadsworth
Protein PathwaysSoCalBSI
Overview:
Background information Statement of the project Materials and methods Results Discussion and conclusion Acknowledgements
Microarray Data
Transcriptional response of genes to variations in cellular states
Cellular States: Mutations, Compound-Treated
State 1 State 2 State 3 … State Y
Gene 1 0.054 … … … …
Gene 2 … … … … …
Gene 3 … … … … …
… … … … … …
Gene X … … … … …
The data values are the log ratios of the level of gene expression in the mutant or compound-treated state over the level of expression in the wild-type state
Clustering
Clustering: Organizing into groups genes with similar expression profiles
Correlation Coefficient: The metric used to determine the similarity between two expression profiles
Hierarchical Clustering: A way of forming a multi-level hierarchy of gene expression profiles, which can be cut off at certain places to form gene clusters
Project: Evaluating two different methods of hierarchically clustering expression data
Hierarchical ClusteringMethod 1
State 1 State 2 … State Y
Gene 1 0.054 … … …
Gene 2 … … … …
… … … … …
Gene X … … … …
Correlation Calculations
Gene 1 Gene 2 … Gene X
Gene 1 0 1.242 … …
Gene 2 1.242 0 … …
… … … 0 …
Gene X … … … 0
EXPRESSION PROFILES
GENE CORRELATIONS
gene 1 gene 2 gene 3 gene 4 gene 5
Linking genes by expression similarity
DENDROGRAM
Method 1 Example
Hughes, T.R., et al. (2000). Functional Discovery via a Compendium of Expression Profiles. Cell 102, 109-126.
Hierarchical ClusteringMethod 2
State 1 State 2 … State Y
Gene 1 0.054 … … …
Gene 2 … … … …
… … … … …
Gene X … … … …
Correlation Calculations
Gene 1 Gene 2 … Gene X
Gene 1 0 1.242 … …
Gene 2 1.242 0 … …
… … … 0 …
Gene X … … … 0
EXPRESSION PROFILES
GENE CORRELATIONS 1
gene 1 gene 2 gene 3 gene 4 gene 5
Linking genes by correlation similarity
DENDROGRAM
GENE CORRELATIONS 2
Correlation of Correlations
Gene 1 Gene 2 … Gene X
Gene 1 0 0.766 … …
Gene 2 0.766 0 … …
… … … 0 …
Gene X … … … 0
Applications of Clustering
Functional Genomics: Gaining information about the possible function of genes with unknown function
Looking at the function of genes that cluster together with genes of unknown function
Diagnostics: Tissues from clinical samples can be clustered together to determine disease subtypes (e.g. tumor classification)
Project Details
Project Question: In the process of hierarchically clustering gene expression data, which metric generates better clusters:
1. The correlation of gene expression ratios (Method 1)
2. The correlation of the correlations (Method 2)
Dataset: Yeast microarray gene expression data (6317 genes, 300 strains)*
Programming Environment: MATLAB v 6.5*Hughes, T.R., et al. (2000). Functional Discovery via a Compendium of Expression Profiles. Cell 102, 109-126.
Two Approaches
Problem: Determining the quality of clusters formed so as to evaluate the two clustering methods
Approach I: Determine the quality of the clusters by seeing if genes with the same function have clustered together more often in one method over the other method
Approach II: Determine the quality of the clusters by analyzing the variances of the clusters and seeing if there is a difference between clustering methods
Approach I Gene Function Analysis
If clusters contain lots of genes with the same function (i.e. transcription, then the clustering method is good.
Two Function Annotation Options 2221 annotated genes with 318 different functions obtained from
http://mips.gsf.de/genre/proj/yeast/index.jsp 1155 annotated genes with 99 different functions obtained from http://genome.ad.jp/kegg/kegg2.html
Approach I StepsFor both annotation options…
Out of the 6317 yeast genes, select only those genes that have known functions
Cluster the genes according to the two methods
For each cluster, compare each gene to every other gene in that cluster and see how many pairs have the same function
If a cluster contains n genes, then there are (n)(n-1) / 2 gene pairs to compare
6317 genes
2000 genes
ANNOTATE
1000 genes 600 genes 400 genes
CLUSTER
Cluster 1 Cluster 2 Cluster 3
499500 pairs 179700 pairs 79800 pairs
NUMBER OF PAIRS TO COMPARE
NUMBER OF PAIRS THAT HAVE SAME FUNCTION
759000 pairs total
150000 pairs 40200 pairs 3204 pairs
193404 pairs same
193404 / 759000 = 0.25
When the genes are partitioned into three clusters, 25% of the gene pairs have the same function
Avg % Same vs. No. Clusters
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400
Number of Clusters
Ave
rag
e %
Pai
rs w
/ Sam
e F
un
ctio
n
Annotations I, Method I
Annotations I, Method II
Annotations II, Method I
Annotations II, Method II
Approach I Results
Approach II (1):
Determining the quality of the clusters based on their volume.
Comparing the average volume of the clusters generated by method 1 and method 2.
Approach II (2):
If there are M genes in each cluster and for each gene, N experiments are chosen:
We’ll have: M vectors in a N-dimensional space that can be
visualized as M points. The M points generate an ellipsoid if M >
dimensionality of the space. The closer the points to each other, the more
correlated they are together, and the smaller the volume of the cluster.
The smaller the volume of the ellipsoid (cluster), the better the quality of the cluster.
Approach II (3): To compute the volume of the cluster, we first
compute its covariance matrix.
We then use Principal Components Analysis (PCA) to estimate the dimensions of the cluster.
PCA will construct a new space using N orthogonal linear combinations of old vectors of the space. (Each linear combination is a principal component.)
Approach II (4):
In the new space, the ellipsoid is transformed into a centered ellipsoid, and the covariance matrix is diagonalized.
The axes of the centered ellipsoid are the elements on the diagonal of the diagonalized matrix, which are the variance of the data points in the new space along the principal components.
The volume of the ellipsoid = 4/3 x x D1xD2 x…x DN
i
j
k
D1
D2
D3
PCA and diagonalizing the covariance matrix
M points in the new 3-D space
M original points in the 3-D space
V1 = c1i + c2j + c3k
V2 = c4i + c5j + c6k
V3 = c7i + c8j + c9k
??
?
Covariance matrix
0.0058 0.0015 0.0057
0.0015 0.0068 0.0023
0.0057 0.0023 0.0232
Diagonalized Covariance Matrix
0.0039 0 0
0 0.0066 0
0 0 0.0253
D1
v1
v2
v3
Volume Calculation Results:
Average log cluster volume vs. number of clusters
-65
-60
-55
-50
-450 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110
Number of clusters
Ave
rage
log
clus
ter
volu
me
avelogvolumemethod 1
avelogvolumemethod 2
Similarity of the Clusters from Two Methods:
Make a KxK matrix (K: the number of clusters in each method) whose elements are:
aij = Difference (cluster i in method I , cluster j in method II)
N((A,B)) Difference (A,B) =
N(AB) + N(AB) where:
A and B are two sets (Here: cluster i from method I and cluster j from method II) (A,B): symmetrical difference of A and B N((A,B)) = N(A-B) + N(B-A) AB: Union of the two sets AB: Intersect of the two sets 0 < Difference(A,B) < 1
1 2 6 32 21
7 89 43
A B26 94 10 11
A-B = {1,2,32,7,89} N(A-B) = 5
B-A = {26,94,10,11} N(B-A) = 4
N(AB) = N(A-B) + N(B-A) = 5 + 4 = 9
AB = {1,2,6,32,21,7,89,43,26,94,10,11}
N(AB) = 12
AB = {6,21,43} N(AB) = 3
Dissimilarity score (A,B) = 9/(12 + 3 ) = 0.6
1 43 89 22 16 73
B
A
A = B
A – B = B – A = Ø
N(AB) = 0
Dissimilarity score(A,B) = 0
BA1 3 54 76 98 6
45
11 88 23 13
N(A-B) = A
N(B-A) = B
N(AB) = N(A) + N(B)
N(AB) = N(A) + N(B)
N(AB) = 0
Dissimilarity score = 1
An Example:
A: cluster i from method I
B: cluster j from method II
Results: Dissimilarity Matrix
Dissimilarity score for cluster 1 from method 1 and cluster 2 from method 2
K: the number of clusters generated for each method
Discussion and Conclusion (1):
Conclusions: Neither approach can favor one method over the
other with certainty; however, Approach I favors method I when the number of
clusters is small. In the range of 1-100 clusters, while approach I
favors method I, approach II fluctuates in choosing the better method or the other.
The efficiency of both approaches in clustering genes is dependant on the number of clusters.
The similarity of the clusters from method I and method II decreases as the number of clusters increases; in fact, the two methods generate very different clusters.
Discussion and Conclusion (2): Problems faced and future questions:
What is the best cutoff value for clustering? In approach I, not all genes were annotated,
so around 2/3 of the dataset was ignored. Gene annotations are somewhat arbitrary. What are other ways to quantify the quality
of clusters? Memory problem: We couldn’t include all the
genes and all the experiments at the same time to analyze the quality of clusters.
Acknowledgments:
Special thanks to: Our mentor: Dr. Matteo Pellegrini Protein Pathways team:
Dr. Darin Taverna Dr. Peter BowersDr. Mike Thompson Leon Kopelevich
SoCalBSI faculty: Dr. Jamil Momand Dr. Silvia Heubach Dr. Sandra Sharp Dr. Elizabeth Torres
Dr. Wendie Johnston Dr. Jennifer Faust Dr. Nancy Warter-Perez Dr. Beverly Krilowicz
NIH and NSF : whose funding made this internship possible.
Appendix I: Covariance (1)
The covariance of two features is the measure of how the two features vary together. If they both have an increasing or decreasing trend,
c ij> 0.
If one decreases while the other one increases, c ij < 0.
If the changes of one is independent of the changes of the other, c ij = 0.
*
*: http://www.engr.sjsu.edu/~knapp/HCIRODPR/PR_Mahal/cov.htm
Appendix I: Covariance (2)
If we have M variables and each variable has N measurements, the covariance matrix can be obtained as below: (xi - x) (yi - y )
cij = M
Where: cij ( i j ) is the covariance of (measurement i and measurement j) for all M variables.
The diagonals are the variances of each measurement. Variance: A measure of how much the points vary around the
mean.
Appendix I: Diagonalizing The Covariance Matrix
data =
-0.0300 0.2500 0.0340
0.1430 0.2230 0.1900
-0.0230 0.0410 -0.0110
-0.0060 0.1780 0.3100
-0.0400 0.2120 -0.0560
covariance_data = cov(data);
covariance_data =
0.0058 0.0015 0.0057
0.0015 0.0068 0.0023
0.0057 0.0023 0.0232Covariance matrix
[V, D] = eig (covariance_data); Diagonalize the Covariance matrix
D =
0.0039 0 0
0 0.0066 0
0 0 0.0253
V =
-0.9285 -0.2370 0.2859
0.2856 -0.9478 0.1417
0.2374 0.2133 0.9477
(xi - x) (yi - y )
cij =
M
Appendix II: Eigenvalues and Eigenvectors
An eigenvector of an nxn matrix is a nonzero vector x such that Ax = x for some scalar . A scalar is called an eigenvalue of A if there is a nontrivial solution x of Ax = x; such an x is called an eigenvector corresponding to .*
*: Lay, C. David. Linear Algebra and Its Applications. 3rd ed. P. 303
Appendix III: Principal Components Analysis (PCA)
data =
-0.0300 0.2500 0.0340
0.1430 0.2230 0.1900
-0.0230 0.0410 -0.0110
-0.0060 0.1780 0.3100
-0.0400 0.2120 -0.0560
[ pcs , newdata , variances , t2 ] = princomp (data) ;
PCA
pcs =
0.2859 -0.2370 0.9285
0.1417 -0.9478 -0.2856
0.9477 0.2133 -0.2374
newdata =
-0.0576 -0.0691 -0.0417
0.1359 -0.0512 0.0896
-0.1278 0.1178 0.0352
0.2006 0.0524 -0.0644
-0.1511 -0.0499 -0.0188
variances =
0.0253
0.0066
0.0039
t2 =
1.2996
3.1981
3.0600
3.0737
1.3686
Volume Calculation Results: (300 experiments chosen first, then the dimensionality of the space was reduced to 20):
Average log of cluster volumes vs. number of clusters
-25
-24
-23
-22
-21
-20
-19
-18
-17
-16
-15
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110
number of clusters
av
e lo
g o
f c
lus
ters
avelogvolumemethod 2
avelogvolumemethod1
Method 1 vs. Method 2 in a More Conceptual View:
Method 1 links together the two genes that have the most similar expression patterns.
Method 2 links together the two genes whose correlation with all other genes is most similar; i.e. it looks at a genes in a more global view (in a context of all other genes).