A brief overview of how microarrays are used to measure gene expression. Watch the complete recording of the talk at http://bit.ly/1qQ1awQ

1. Measuring Gene Expression
with Microarrays
Atul Narkhede

2. Central Dogma
⇨ genes --> proteins --> function
● genes do not act in isolation
● interaction of multiple genes results in a particular
behaviour in the organism
● require high-throughput and scalable technology to
observe genes simultaneously

3. Gene Expression
⇨ Process by which information from a gene is
used to synthesize functional product (protein,
mRNA, etc) ~ ‘manifestation’ of the gene
⇨ The pattern of gene expression characterises
current state of the cells

4. Microarray
⇨ Microarrays measure expression level of
thousands of genes simultaneously
⇨ Applications of Microarray technology
● Identification of complex genetic diseases
● Drug discovery
● Comparative studies between diseased/normal tissue

5. Microarray Design
⇨ Thousands of 'spots' on a single substrate
● each spot contains a complementary strand of DNA,
which uniquely identifies the gene (probe)
● substrate washed with fluorescently labelled sample
(target)
● Normal tissue  green, Diseased Tissue  red
● complementary sequences bind to the spots
● the resulting fluorescence at each spot is a measure of
the expression level of the corresponding gene

6. Fluorescence Image

7. Information flow
Image
Analysis Data Analysis
Biological
inference
public
databases

8. Data Analysis Flow
Filtering
Normalisation
Differential Expression Analysis
Clustering Classification
Text
Mining
Gene Annotation
Pathways,
Genetic Networks
~40,000
~30,000
~30,000
~2000
~200

9. Differential Expression
• Find genes whose expression level is genuinely
different.
– Single experiment:
• R/G ratio (fold change) >= threshold (2)
– Multiple experiments:
• T-Test
• ANOVA

10. K-Means Clusters
⇨ Genes are grouped into ‘k’ distinct clusters. Each
cluster has similarly behaving genes

11. Dendrogram
⇨ Similar genes are grouped together, based on expression
patterns

12. Classification:
Supervised Machine Learning
⇨ Decision trees

13. Classification: Unsupervised
⇨ Support Vector Machine: finds ‘optimal’ hyper-
plane which separates classes

14. Beyond Numerical Analysis
⇨ Use numerical results along with known
biological information to make intelligent
conjectures
⇨ Annotate genes to obtain functional info
⇨ Text mining on function, literature (ex. PubMed)
● which genes have similar function ?
● which genes are part of the same pathway ?
● which genes are referred together in literature ?

15. Thank you
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