Transcript Lecture slides

Analysis of GO annotation
at cluster level
by Agnieszka S. Juncker
The DNA Array Analysis Pipeline
Question
Experimental Design
Array design
Probe design
Sample Preparation
Hybridization
Buy Chip/Array
Image analysis
Normalization
Expression Index
Calculation
GO annotations
Comparable
Gene Expression Data
Statistical Analysis
Fit to Model (time series)
Advanced Data Analysis
Clustering
Meta analysis
PCA
Classification
Promoter Analysis
Survival analysis
Regulatory Network
Gene Ontology
Gene Ontology (GO) is a collection of controlled vocabularies
describing the biology of a gene product in any organism
There are 3 independent sets of vocabularies, or ontologies:
• Molecular Function (MF)
– e.g. ”DNA binding” and ”catalytic activity”
• Cellular Component (CC)
– e.g. ”organelle membrane” and ”cytoskeleton”
• Biological Process (BP)
– e.g. ”DNA replication” and ”response to stimulus”
Gene Ontology structure
GO structure, example 2
KEGG pathways
• KEGG PATHWAYS:
– collection of manually drawn pathway maps representing our
knowledge on the molecular interaction and reaction networks,
for a large selection of organisms
• 1. Metabolism
– Carbohydrate, Energy, Lipid, Nucleotide, Amino acid, Other
amino acid, Glycan, PK/NRP, Cofactor/vitamin, Secondary
metabolite, Xenobiotics
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2. Genetic Information Processing
3. Environmental Information Processing
4. Cellular Processes
5. Human Diseases
6. Drug Development
KEGG pathway example 1
KEGG pathway example 2
Cluster analysis and GO
Analysis example:
• Partitioning clustering of genes into e.g. 15 clusters based
on expression profiles
• Assignment of GO terms to genes in clusters
• Looking for GO terms overrepresented in clusters
Hypergeometric test
• The hypergeometric distribution arises from
sampling from a fixed population.
20 white balls
out of
100 balls
10 balls
• We want to calculate the probability for drawing 7 or
more white balls out of 10 balls given the
distribution of balls in the urn
Yeast cell cycle
Sampling
Time series
experiment:
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Time
Gene expression
profiles:
Gene1
Gene2
Time
R stuff
Indexing of a matrix (used when you wish to select a subset of your
data, e.g. specific rows or columns):
• Example 1
rowindex <- 1:10
colindex <- 1:5
datamatrix[rowindex, colindex] # first 10 rows, first 5 columns
datamatrix[1:10, 1:5] # gives the same as above
“Missing” rowindex (or columnindex) means that all rows (or
columns) are selected
• Example 2
datamatrix[1:5,] # 5 first rows, all columns
datamatrix[,5:10] # all rows, columns 5 to 10
datamatrix[,] # is the same as datamatrix