Microarray Data Analysis

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7 Νοε 2013 (πριν από 3 χρόνια και 8 μήνες)

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Literature Survey:

Microarray Data Analysis

Ei
-
Ei Gaw

Arizona State University

CSE 591

April 24, 2003

cDNA Microarray Procedure

http://www.anst.uu.se/frgra677/projekt_eng.html

Microarray Data


Expression patterns of thousands of genes
simultaneously.


Usually the n
umber of experiments is small compare to the number
of genes.



Random and systematic variations.


Systematic variations due to complexity of the method.


Remove low
-
quality measurements.

Preprocessing


Transformation


Aim: Change data to reflect assumptions (
Homologous
variance and normal distribution) of statistical
techniques.


Log and
variance
-
stabilizing transformation.


Normalization


Aim: Account for random and systematic variations.


Global, lowness, location, and scale normalization
methods.



Missing data


K Nearest Neighbors (KNN) algorithm, a Singular
Value Decomposition based method (SVD), and simple
row (gene) average.


Reduce dimensionality

Classification


Hierarchical clustering


Classify tumor and find previously unrecognized tumor subtypes


Identify differentially expressed genes


Cluster co
-
expressed genes, but not suited to find multiple ways
expression patterns are similar


Self
-
organizing map


Suited to find a small number of prominent classes


Class discovery


Support vector machine


Operate in extremely high
-
dimensional feature space


Supervised learning


take advantage of prior knowledge


Genetic Algorithm/KNN



Regulatory Networks


Two
-
stage approach


Find co
-
regulated gene using clustering algorithm and then look
for conserved motifs upstream


Unified approach


Joint likelihoods for sequence and
expression


Find co
-
regulated gene and then look for conserved motifs
upstream


Kolmogorov
-
Smirnov method


Does not require clustering


Sort red
-
green ratios


Minreg


Require prior biological knowledge


candidate regulators


One advantage is speed


Identify and characterize both regulators and regulatees


Assign biological function to regulators

Genetic Networks


Association rules


Global gene expression profiling


Can revel relationship between different genes and relationship
between environment and expression


Bayesian Networks


Boolean Networks


REVEAL (REVerse Engineering Algorithm)


NetWork




Bibliography


Durbin, B. P., Hardin, J. S., Hawkins, D. M., and Rocke, D. M. (2002) A variance
-
stabilizing
transformation for gene
-
expression microarray data.
Bioinformatics
, 18:S105
-
S110.



Kerr, M. Kathleen, Martin, Mitchell, and Churchill, Gary A. (2000) Analysis of Variance for Gene
Expression Microarray Data.
Journal of Computational Biology
, 7:819
-
837



Yang, Yee Hwa, Dudoit, Sandrine, Luu, Percy et.al (2002) Normalization for cDNA microarry data:
a robust composite method addressing single and multiple slide systematic variation.
Nucleic Acids
Research
, 30:e15.



Quackenbush, John (2002) Microarray data normalization and transformation.
Nature Genetics
Supplement

32:496
-
501.



Troyanskaya, Olga et. al. (2001) Missing value estimation methods for DNA l;.
Bioinformatics
,
17:520
-
525.



Antoniadis, A., Lambert
-
', S. and Leblanc, F. (2003) Effective dimension reduction methods for
tumor classification using gene expression data.
Bioinformatics
, 19, 563
-
570.



Golub, T. R. et. al. (1999) Molecular classification of Cancer: class Discovery and Class Prediction
by Gene Expression Monitoring.
Science

286:531
-
537.



Rickman, David S. et. al. (2001) Distinctive Molecular profile of High
-
Grade and Low
-
Grade
Gliomas Based on Oligonucleotide Microarray Analysis.
Cancer Research

61:6885
-
6891.



Eisen, Michael B. et. al. (1998) Cluster analysis and display of genome
-
wide expression patterns.
Proc. Natl. Acad. Sci
. USA 95:14863
-
14868.



Bibliography


Brown, Michael P. S. et. al. (2000) Knowledge
-
based analysis of microarray gene expression data by
using support vector machines.
Proc. Natl. Acad. Sci. USA

97:262
-
267.



Li, Leping et. al. Gene Assessment and Sample Classification for Gene Expression Data Using a
Genetic Algorithm/k
-
nearest Neighbor Method.



Holmes, Ian, Bruno, (2000) William J. Finding Regulatory Elements Using Joint Likelihoods for
Sequence and Expression Profile Data.
American Association for Artificial Intelligence

(www.aaai.org).



Van Helden, J., Andre, B., and Collado
-
Vides, J. (1998) Extracting Regulatory Sites from the
Upstream Region of Yeast Genes by Computational analysis of Oligonucleotide Frequencies.
J.
Mol. Biol.

281:827
-
842.



Pe’er, Dana, Regev, Aviv, and Tanay, Amos (2002) Minreg: Inferring an active regulator set.
Bioinformatics

18:S258
-
S267.



Jensen, Lars and Knudsen, Steen (2002) Automatic discovery of regulatory patterns in promoter
regions based on whole cell expression data and functional annotation.
Bioinformatics

16:326
-
333.



Creighton, Chad and Hanash, Samir (2003) Mining gene expression databases for association rules.
Bioinformatics

19:79
-
86.



Friedman, Nir et. al. (2000) Using Bayesian Networks to Analyze Expression Data
. J. Comp. Bio.

7:601
-
620.


Bibliography


Liang S., Fuhrman, S. and Somogyi, R. (1998) REVEAL, A General Reverse Engineering Algorithm
for Inference of Genetic Network Architectures.
Pacific Symposium on Biocomputing

3:18
-
29
(1998).



Akutsu, T., Miyano, S. and S. Kuhara S. (1999) Identification of Genetic Networks from a Small
Number of Gene Expression Patterns Under the Boolean Network Model.
Pacific Symposium on
Biocomputing

4:17
-
28.



Samsonova, M.G. and Serov, V.N. (1999) NetWork: An Interactive Interface to the Tools for
Analysis of Genetic Network Structure and Dynamics.
Pacific Symposium on Biocomputing

4:102
-
111.