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Microarray Data Analysis Day 2

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Presentation on theme: "Microarray Data Analysis Day 2"— Presentation transcript:

1 Microarray Data Analysis Day 2

2 Microarray Data Process/Outline
Experimental Design Image Analysis – scan to intensity measures (raw data) Normalization – “clean” data More “low level” analysis-fold change, ANOVA, (Z-score) --data filtering Data mining-how to interpret > 6000 measures Databases Software Techniques-clustering, pattern recognition etc. Comparing to prior studies, across platforms? Validation *

3 Today we will be using Spotfire software to filter and search your data records in Spotfire S. pombe specific Affy controls S. cerevisiae specific

4 6603 4377 1407 819 The Affy detection oligonucleotide sequences are frozen at the time of synthesis, how does this impact downstream data analysis?

5 Biology and Data Mining

6 Subcellular Localization, Provides a simple goal for genome-scale functional prediction
Determine how many of the ~6000 yeast proteins go into each compartment

7 Subcellular Localization, a standardized aspect of function
Cytoplasm Nucleus Membrane ER Extra- cellular [secreted] Golgi Mitochondria

8 "Traditionally" subcellular localization is "predicted" by sequence patterns
Cytoplasm NLS Nucleus Membrane TM-helix ER HDEL Extra- cellular [secreted] Golgi Import Sig. Mitochondria Sig. Seq.

9 [Expression Level in Copies/Cell]
Subcellular localization is associated with the level of gene expression [Expression Level in Copies/Cell] Cytoplasm Nucleus Membrane ER Extra- cellular [secreted] Golgi Mitochondria

10 [Expression Level in Copies/Cell]
Combine Expression Information & Sequence Patterns to Predict Localization [Expression Level in Copies/Cell] Cytoplasm NLS Nucleus Membrane TM-helix ER HDEL Extra- cellular [secreted] Golgi Import Sig. Mitochondria Sig. Seq.

11 The central dogma of molecular biology???
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??? Epigenetics RNA editing Post-translational modification Translational regulation Proteins are very complicated molecules with 20 different amino acids.

12 Biology Application Domain
Validation Data Analysis Microarray Experiment Image Analysis Data Mining Experiment Design and Hypothesis Data Warehouse Artificial Intelligence (AI) Knowledge discovery in databases (KDD) Statistics

13 Higher Level Microarray 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

14 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.

15 Types of Clustering Herarchical Self Organizing Maps (SOM)
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)

16 Cluster by color/expression difference

17 Self Organizing Maps

18 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)

19 The 3 Gene Ontologies 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

20 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…

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