Measuring Gene Expression
with Microarrays
Atul Narkhede
Central Dogma
⇨ genes --> proteins --> function
● genes do not act in isolation
● interaction of multiple genes results in...
Gene Expression
⇨ Process by which information from a gene is
used to synthesize functional product (protein,
mRNA, etc) ~...
Microarray
⇨ Microarrays measure expression level of
thousands of genes simultaneously
⇨ Applications of Microarray techno...
Microarray Design
⇨ Thousands of 'spots' on a single substrate
● each spot contains a complementary strand of DNA,
which u...
Fluorescence Image
Information flow
Image
Analysis Data Analysis
Biological
inference
public
databases
Data Analysis Flow
Filtering
Normalisation
Differential Expression Analysis
Clustering Classification
Text
Mining
Gene Ann...
Differential Expression
• Find genes whose expression level is genuinely
different.
– Single experiment:
• R/G ratio (fold...
K-Means Clusters
⇨ Genes are grouped into ‘k’ distinct clusters. Each
cluster has similarly behaving genes
Dendrogram
⇨ Similar genes are grouped together, based on expression
patterns
Classification:
Supervised Machine Learning
⇨ Decision trees
Classification: Unsupervised
⇨ Support Vector Machine: finds ‘optimal’ hyper-
plane which separates classes
Beyond Numerical Analysis
⇨ Use numerical results along with known
biological information to make intelligent
conjectures
...
Thank you
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Measuring Gene Expression

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A brief overview of how microarrays are used to measure gene expression.

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Measuring Gene Expression

  1. 1. Measuring Gene Expression with Microarrays Atul Narkhede
  2. 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. 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. 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. 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. 6. Fluorescence Image
  7. 7. Information flow Image Analysis Data Analysis Biological inference public databases
  8. 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. 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. 10. K-Means Clusters ⇨ Genes are grouped into ‘k’ distinct clusters. Each cluster has similarly behaving genes
  11. 11. Dendrogram ⇨ Similar genes are grouped together, based on expression patterns
  12. 12. Classification: Supervised Machine Learning ⇨ Decision trees
  13. 13. Classification: Unsupervised ⇨ Support Vector Machine: finds ‘optimal’ hyper- plane which separates classes
  14. 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. 15. Thank you Watch the recording at

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