Download Wavelet Analysis of Gene Expression (WAGE)

Wavelet Analysis of Gene Expression (WAGE)
Federico.E. Turkheimer, Dawn Duke, Linda Moran, and Manuel B. Graeber
Department of Neuropathology
Division of Neuroscience and Psychological Medicine
Imperial College London, London, UK
Overview
http://www.ornl.gov/TechResources/Human_Genome/publicat/primer2001/1.html
Protein synthesis
Amino acids
tRNA
4. Amino acids and
Transfer RNA
present in cytoplasm
Nucleus
3. Messenger RNA travels
to the cytoplasm
5. Messenger RNA joins at the
ribosome
tRNA brings its amino acid to
the ribosome
ribosome
1. The DNA helix
unwinds
2. Messenger RNA molecule
makes a copy of the DNA
mRNA
Biology (1998) Jones, M & Jones, G. Cambridge University Press
Protein synthesis II
6. The next tRNA brings its amino acid
The 2 amino acids are chemically linked
7. The mRNA moves along. The first tRNA is released.
The third tRNA arrives with its amino acid
tRNA
tRNA
Small
protein
8. The process continues - A small protein is made
Why RNA?: Complexity and Regulation
• A coded protein is not necessarily expressed
• DNA differences cannot explain all phenotypic variation
• These differences may be better detected at the DNA expression levels (RNA)
Proteins ~106
DNA~30,000 genes
(including post-translational modifications)
Ribosome
Why RNA? –Omics measurements with microarrays
Human genome
~ 33,000 human
genes
Editorial (1996) To affinity...
and beyond. Nature Genetics 14:367-370
Page no: 12
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Affymetrix
Microarray
05c
m
Genes
Affymetrix Microarray Preparation
Brain
Tissue
block
Isolation of
total RNA
RNA
Reverse
transcription
Double-stranded cDNA
Transcription
& fragmentation
Biotinylated
cRNA
Array
Hybridisation
& array staining
Affymetrix Microarray Analysis
Detection Analysis: Is the gene present?
Statistical
testing on the
probes assaying
the individual
gene
Scanner Image
Present/
Absent
Call
Noise
calculation
Expression
Value
Background
subtraction
Array
normalization
Signal Analysis: what is the expression value?
Affymetrix Microarray Output
Code
Gene Name & Description
Signal
Call
Data Analysis and Interpretation
Analysis and interpretation of these high dimensional data-sets is not a trivial
task. Common approaches adopt supervised methods of analysis (multiple
hypothesis testing), or unsupervised (clustering)
Multiple Hypothesis testing
Clustering
Data Analysis and Interpretation II
Alternatively one can adopt a model-based approach by re-organizing gene
expression values according to one or more of their established functions and
then searching the associated mathematical space to unveil hidden
relationships and groupings Æ e.g. Pathway Analysis
http://www.genmapp.org
WAVELET ANALYSIS OF GENE
EXPRESSION (WAGE)
Human Chromosomes and Genes
WAGE model-based approach re-organizes gene expression values according
to their chromosomal position and then searches for spatial clusters of activity
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http://www.ncbi.nlm.nih.gov/books/bookres.fcgi/
Gene expression control at chromosome level
•The physical structure of the
DNA, as it exists compacted into
chromatin, can affect the ability of
transcriptional regulatory proteins
(transcription factors) and RNA
polymerases to find access to
specific genes and to activate
transcription from them.
•Neighbour genes on a given
chromosome can be involved in
the same pathway.
•Co-localized genes share similar
vulnerability to environmental or,
with particular relevance to
tumours, serve as targets for
carcinogens.
WAGE: the method
Mapping Array Æ Chromosome
Arrays
Raw data
Wavelet
Transform
WAVELET SPACE
Filtering
Filtered data
Inverse
Wavelet
Transform
WAGE: technical detail
Wavelet Base:
Haar wavelets
Wavelet Transform:
“Cycle spinning” (Coifman, R. R. and Donoho, D. 1995)
Statistic:
z = mean(wgroup1)-mean(wgroup2);
Noise std estimate:
σ = MAD{zf}/ 0.6745 (zf=coeff. finest level)
Wavelet estimator:
Hard thresholder
Wavelet threshold:
Universal: τ= σ 2log(N)
APPLICATION I:
Brain Tumors (gliomas)
Deprez M, Turkheimer FE, Munaut C, Moran L, Scheithauer BW, Graeber MB (2004)” Microarray analysis of gliomas: biological insights from the
mathematical modeling of histopathologycal types,” Submitted
Glioblastomas:
Loss of Chromosome 10
Microarray Raw
FISH
Microarray WAGE
Glioblastomas
Gain of Chromosome 7
Microarrays Raw
FISH
Microarrays WAGE
Oligodendrogliomas
Allelic loss on chromosome 1p
Microarrays Raw
Microarrays WAGE
Astrocytomas
Gene clusters on chromosome 21
Interferon receptors alpha and gamma
[Genbank# L42243, J03171, U05875]
Acute Myeloid Leukemia (AML) proteins
[(AML 1b (Genbank # D43968), AML 1c
(Genbank # D43969) and AML
(Genbank # X90976)].
Microarrays WAGE
APPLICATION II:
Neurodegeneration
Alpha-synuclein immunohistochemistry for a subject diagnosed with alphasynucleinopathy. Note the granular, brown immunoreactivity as well as the dark,
spherical, alpha synuclein positive Lewy body (arrow).
MHC Class I/Class II Region
MHC II immunohistochemistry (CR3/43) for
one of the subjects. Note the dark brown
stained microglia (arrow).
Histones Region
It has been recently demonstrated in-vitro
and in animal models that the aggregation
of alpha-synuclein is dramatically
accelarated by the presence of histones
(Goers J, et al. Biochemistry, 42(28):846571, 2003)
Conclusions
• WAGE detects co-localized clusters of
RNA espression.
• Validation work so far confirms their
biological significance
• Further methodological work needed to
incorporate known mechanisms of
regulation (Bayesian models) and more
realistic spatial models (un-equally spaced
samples)