Microarray Data Analysis

Day 2

1. Experimental Design

2. Image Analysis – scan to intensity measures (raw data)

3. Normalization – “clean” data

4. More “low level” analysis-fold change, ANOVA, (Z-score) --data filtering

5. Data mining-how to interpret > 6000 measures– Databases– Software– Techniques-clustering, pattern recognition

etc.– Comparing to prior studies, across platforms?

6. Validation

Microarray Data Process/Outline

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Today we will be using Spotfire software to filter and search your data.

10928 records in Spotfire -5999 S. pombe specific -166 Affy controls

5763 S. cerevisiae specific

The Affy detection oligonucleotide sequences are frozen at the time of synthesis, how does this impact downstream data analysis?

660343771407819

Biology and Data Mining

Subcellular Localization, Provides a simple goal for genome-scale functional prediction

Determine how many of the ~6000 yeast proteins go into each compartment

Subcellular Localization, a standardized aspect of function

Nucleus

Membrane

Extra-cellular[secreted]

ER

Cytoplasm

Mitochondria

Golgi

"Traditionally" subcellular localization is "predicted" by sequence patterns

NLS

TM-helix

Sig. Seq.

HDEL

Nucleus

Membrane

Extra-cellular[secreted]

ER

Cytoplasm

Mitochondria

GolgiImport Sig.

Subcellular localization is associated with the level of gene expression

Nucleus

Membrane

Extra-cellular[secreted]

ER

Cytoplasm

Mitochondria

Golgi

[Expression Level in Copies/Cell]

Combine Expression Information & Sequence Patterns to Predict Localization

NLS

TM-helix

Sig. Seq.

HDEL

Nucleus

Membrane

Extra-cellular[secreted]

ER

Cytoplasm

Mitochondria

GolgiImport Sig.

[Expression Level in Copies/Cell]

Major Objective: Discover a comprehensive theory of life’s organization at the molecular level

– The major actors of molecular biology: the nucleic acids, DeoxyriboNucleic Acid (DNA) and RiboNucleic Acids (RNA)

– The central dogma of molecular biology???

Proteins are very complicated molecules with 20 different amino acids.

Epigenetics

RNA editing

Post-translational modification

Translational regulation

Data Mining

Microarray Experiment

Image Analysis

Biology Application Domain

Experiment Design and Hypothesis

Data Analysis

Artificial Intelligence (AI)

Knowledge discovery in databases (KDD)

Data Warehouse

Validation

Statistics

Higher LevelMicroarray data analysis

• Clustering and pattern detection• Data mining and visualization• Linkage between gene expression data and

gene sequence/function/metabolic pathways databases

• Discovery of common sequences in co-regulated genes

• Meta-studies using data from multiple experiments

Scatter plot of all genes in a simple comparison of two control (A) and two treatments (B: high vs. low glucose) showing changes in expression greater than 2.2 and 3 fold.

Types of Clustering

• Herarchical– Link similar genes, build up to a tree of all

• Self Organizing Maps (SOM)– Split all genes into similar sub-groups– Finds its own groups (machine learning)

Cluster by color/expression

difference

Self Organizing Maps

Public Databases

• Gene Expression data is an essential aspect of annotating the genome

• Publication and data exchange for microarray experiments

• Data mining/Meta-studies• Common data format - XML• MIAME (Minimal Information About a

Microarray Experiment)

• Molecular Function = elemental activity/task

– the tasks performed by individual gene products; examples are carbohydrate binding and ATPase activity

• Biological Process = biological goal or objective– broad biological goals, such as mitosis or purine metabolism, that are accomplished

by ordered assemblies of molecular functions

• Cellular Component = location or complex– subcellular structures, locations, and macromolecular complexes; examples include

nucleus, telomere, and RNA polymerase II holoenzyme

The 3 Gene Ontologies

One Last Note

• Microarrays are “cutting edge” technology

• You now have experience doing a technique that most Ph.D.s have never done

• Looks great on a resume…