Analysis of High-throughput Gene Expression Profiling

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Analysis of High-throughput Gene Expression
Profiling
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Why to Measure Gene Expression

• 1. Determines which genes are induced/repressed
  in
• response to a developmental phase or to an
• environmental change.
• 2. Sets of genes whose expression rises and falls
• under the same condition are likely to have a
• related function.
• 3. Features such as a common regulatory motif can
  be
• detected within co-expressed genes.
• 4. A pattern of gene expression may be used as an
• indicator of abnormal cellular regulation.
• A useful tool for cancer diagnosis

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Transitional vs. High-throughput Approaches
Why to Measure Gene Expression in Large Scale?
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Techniques Used to Detect Gene Expression Level

• Microarray (single or dual channel)
• SAGE
• EST/cDNA library
• Northern Blots
• Subtractive hybridisation
• Differential hybridisation
• Representational difference analysis (RDA)
• DNA/RNA Fingerprinting (RAP-PCR)
• Differential Display (DD-PCR)
• aCGH array CGH (DNA level)

High-throughput
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Basic Information of Microarray, SAGE and cDNA
Library
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(DNA) Microarray

• 1. Developed around 1987.
• 2. Employ methods previously exploited in
  immunoassay context specific binding and
  marking techniques.
• 3. Two types of probes
• Format I probe cDNA (5005,000 bases long) is
  immobilized to a solid surface such as glass
  widely considered as developed at Stanford
  University Traditionally called DNA microarrays.
  
• Format II an array of oligonucleotide
  (2080-mer oligos) probes is synthesized either
  in situ(on-chip) or by conventional synthesis
  followed by on-chip immobilization developed at
  Affymetrix, Inc. Many companies are anufacturing
  oligonucleotide based chips using alternative
  in-situ synthesis or depositioning technologies.
  Historically called DNA chips.

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Microarray

• Single Channel sub-type classification
• Dual Channel differential expression gene
  screening
• Tissue microarray
• Protein microarray

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Array CGH

• Detecting DNA copy variation via microarray
  approach
• A hotspot in recent research works, especially in
  Cancer research

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Microarray Analysis
Which genes are up-regulated, down-regulated,
co-regulated, not-regulated?

• gene discovery
• pattern discovery
• inferences about biological processes
• classification of biological processes

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SAGE

• Experimental technique assigned to gain a
  quantitive measure of gene expression.
• 10-20 base tags are produced (immediately
  adjacent to the 3 end of the 3 most NlaIII
  restriction site).
• The SAGE technique measures not the expression
  level of a gene, but quantifies a "tag" which
  represents the transcription product of a gene.

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SAGE
Tags are isolated and concatermized. Relative
expression levels can be compared between cells
in different states.
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SAGEmap (http//cgap.nci.nih.gov)
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SAGE comparing two relational libraries
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EST library (UniGene)
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Gene expression info from Unigene Library
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An Example of In-house EST Library Analysis
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The Algorithms and Challenges of High-throughput
Gene Expression Analysis
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Seeing is believing?
No, need to correct errors.
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SAGE

• A typical experiment requires 30,000 gene
  expression comparisons where normal and a
  diseased cell is compared.
• The results were subject to the size and
  reliabilities of the SAGE libraries.
• Statistical measures are used to filter out
  candidate genes to reduce the dimensionality of
  the data but it is tedious and time consuming to
  play with these measures until a good set is
  found.

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SAGE

• TPM a simple normalization method
• TPMCount1000,000/TotalCount
• Bayesian approach http//cancerres.aacrjournals.or
  g/cgi/content/full/59/21/5403

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Microarray Sources of errors

• systematic
• random

log signal intensity
log RNA abundance
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Sources of Errors (Cont.)

• Printing and/or tip problems
• Labeling and dye effects (differing amounts of
  RNA labeled between the 2 channels)
• Differences in the power of the two lasers (or
  other scanner problems)
• Difference in DNA concentration on arrays (plate
  effects)
• Spatial biases in ratios across the surface of
  the microarray due to uneven hybridization
• cDNA array cannot distinguish alternatively
  spliced forms

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Errors that cannot be corrected by statistics

• Competitive hybridization of different targets on
  the chip
• Failure to distinguish different splicing forms
• Misinterpretation of time course data when there
  are not sufficient points
• Misinterpretation of relative intensity

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Does clustered time course really mean
co-expression?
Picture taken from http//genomics.stanford.edu/ye
ast/additional_figures_link.html
Yes, you can study known system (such as cell
cycle) this way but, how about the unknown
systems?
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Normalization by iterative linear regression

• fit a line (ymxb) to the data set
• set aside outliers (residuals gt 2 x s.e.)

• D Finkelstein et al.
• http//www.camda.duke.edu/CAMDA00/abstracts.asp

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Normalization (Curvilinear)
G Tseng et al., NAR 2001
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After Normalization

• Differentially Expressed (DE) Gene screeing
• T-test
• T-statistics
• SVM
• Clustering
• Hierarchical
• SOM
• K-means
• Network (Pathway) analysis
• BioCarta, KEGG, GO databases
• Bayesian network learning
• Topology

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Bioinformatics challenges

• 1. data management
• 2. utilizing data from multiple experiments
• 3. utilizing data from multiple groups
• with different technologies
• with only processed data available

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Bioinformatics Analysis of Integrated Analysis of
Gene Expression Profiling
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• Large-scale meta-analysis of cancer microarray
  data identifies common transcriptional profiles
  of neoplastic transformation and progression
• Daniel R. et al. PNAS, 2004(101), 9309-9314
• T-test
• Q values (estimated false discovery rates) were
  calculated as
• where P is P value, n is the total number of
  genes, and i is the sorted rank of P value.

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Cont. Meta-Profiling.

• The purpose of meta-profiling is to address the
  hypothesis that a selected set of differential
  expression signatures shares a significant
  intersection of genes (a meta-signature), thus
  inferring a biological relatedness.

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67 genes were screened by mata-analysis
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Integrated Cancer Gene Expression Map
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7 genes were discovered by the system
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THANX!!