Transcript Hierarchical Clustering of Gene Expression Data

Hierarchical Clustering of Gene
Expression Data
Author : Feng Luo, Kun Tang
Latifur Khan
Graduate : Chien-Ming Hsiao
Outline
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Motivation
Objective
Introduction
Hierarchical Clustering
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Self-Organizing Tree algorithm
hierarchical growing self-organizing tree algorithm
Preliminary result
Conclusions
Personal Opinion
Motivation
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Rapid development of biological
technologies generates a hug amount of data.
Analyzation and interpretation of these
massive data is a challenging task.
we are interested in data analysis tools that
can help researchers to detect patterns
hidden behind these complex initial data.
Objective
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to extract useful and rational fundamental
patterns of gene expression inherent in these
huge data.
introduction
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Current approaches for measuring gene
expression profiles
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SAGE, RT/PCR, cDNA, oligonucleotide microarray
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Sample of Microarray
introduction
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Two classes of algorithms have been successfully
used to analyze gene expression data.
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hierarchical clustering
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a self-organizing tree
Hierarchical Clustering
Self-Organizing Tree algorithm
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Self-Organizing Tree algorithm (SOTA)
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based on the Kohonen’s self-organizing map
(SOM) and Fritzke’s growing cell structures
output of SOTA is a binary tree topological
neural network
Self-Organizing Tree algorithm
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Step 1: Initially the system as a binary tree with
three nodes
Node
Node
Node
Cell
Cell
w
m
Cell
Cell
A
w
s
Cell
B
(A) Initial Architecture of SOTA.
(B) Two Difference Reference Vector Updating Schemas.
Self-Organizing Tree algorithm
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Step 2: Present all data and compute distances
from each data to all external Cells (tree leaves)
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Euclidean distances
cosine distances
Step 3: Select output winning cell c with
minimum distance dij for each data.
Self-Organizing Tree algorithm
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Step 4: Update reference vector of winning cell and its
neighbors
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wi   (t )  ( x  wi )
Where (t) is the learning function:
  (t )     (t )
The (t) is the learning rate function, (t) = 1/t
 is a learning constant.
 will have a different value for the winning cell and
Its neighbors.
Self-Organizing Tree algorithm
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Step 2,3,4 form a Cycle. While relative error of the entire tree is
greater than a threshold repeat the cycle.
Step 5: If a cycle finished, increase the network size: two new
cells are attached to the cell with highest resources. This cell
becomes a node.
 Resources: an average of the distances of the input data
associated this cell to itself.
D
d ( xi , wi )
D
j 1
Re sourcei  
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Step 6: Repeat Step 2 until convergence (resources are below a
threshold).
Self-Organizing Tree algorithm
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Time Complexity of SOTA is O( n log N)
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Space Complexity of SOTA is O (n)
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The incorrect capture of the hierarchical relationship
SOTA
hierarchical growing selforganizing tree algorithm
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hierarchical growing self-organizing tree
algorithm (HGSOT)
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The HGSOT grows
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vertical grows
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adds descendents
the same strategy used in SOTA
horizontal grows
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adds more siblings
a level threshold : controlling growth in the sibling
generation
hierarchical growing selforganizing tree algorithm
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To determine horizontal growth
hierarchical growing selforganizing tree algorithm
Initialization
Vertical Growing
Horizontal
Growing
Distribution
Distribution
the error of the
entire tree
the error of the
entire tree
to grow
The pseudo code of HGSOT
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Initialization
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2.
Vertical Growing
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change the leaf cell to a node and add two children to each. The
reference vector of a new cell is initialized as the node’s reference
vector.
Distribution
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initially the tree only has one root node. Initialize its reference
vector with the centroid of entire data and all data will be
associated with the root.
distribute each input datum between two newly created cells; find
the winning cell (using KLD, see 2.2.1), and then update the
reference vector of the winning cell and its neighbor.
Error
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when the error of the entire tree is larger than a threshold, called
error threshold (TE), repeat Step 3.
The pseudo code of HGSOT
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Horizontal Growing
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6.
Distribution
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distribute the input data associated with x into its descendents
along siblings; find the winning cell (using KLD, see 2.2.), then
update the reference vector of the winning cell and its neighbor.
Error
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8.
when the difference between the minimum and maximum distance
of all children cells of a node (x) is less than a threshold, called
level threshold (TL), a child is added to this node; on the other
hand if the difference is greater than the TL, a child is deleted
from this node, and the horizontal growth terminated.
if the error of the entire tree is greater than (TE), then repeat Step
6.
if there are more levels to grow in the hierarchy, and then
return to Step 2, otherwise, stop.
Hierarchical Cluster Algorithms
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How we can distribute input data of selected node
among these new created cells.
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Similar to the SOTA approach.
Input data of selected node will be distributed not only
its new created cells but also its neighbor cells.
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We determine K level apart ancestor node of selected node.
We determine sub-tree of rooted by the ancestor node and
input data of selected cell will be distributed among all cells
(leaf) of this sub-tree. The latter approach is known as K level
distribution (KLD).
Hierarchical Cluster Algorithms
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KLD: We need to distribute data associated with node M to new
created cells. For K=1, Data of node M will be distributed to cells, B,
C, D & E. If K=0, data of M will be distributed between B and C.
Preliminary result
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Experiment setup
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Experiment Data
 112 genes expression data of rat central nervous system (CNS)
 Four Gene Families: Neuro-Glial Markers Family (NGMs),
Neurotransmitter receptors Family (NTRs), Peptide Signaling
Family (PepS) and Diverse
 These gene expression data were measured by using RT-PCR
in mRNA expression in rat’s cervical spinal cord tissue over
nine different developmental time points from embryonic days
11 through 21, postnatal days 0 through 14 and adult.
 For each gene, data are normalized to the maximal expression
level among the nine time points
Preliminary result
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Experiment setup
 The Parameters of HGSOT
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The winner learning rate w and sibling learning rate s
of HGSOT is 0.15 and 0.015.
The error threshold is 0.001.
The level threshold is 0.8, which means the minimum
distance will not be less than 80% of the maximum
distance.
The distribution level K is equal to 4.
Euclidean distance is used to calculate the similarity.
Preliminary result
Preliminary result
Conclusions
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can successfully gain five clusters similar to
Wen et al’s original HAC result and gives a
better hierarchical structure.
this algorithm can detect more subtle
patterns at the lower hierarchical levels, and
it shows a more suitable clustering than
HAC on some genes.
Personal Opinion
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we would like to do more experiments on
different data sets