1. Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu

2. Today’s Topics Review aCGH and its data analysis Homework of aCGH data analysis using tools in Genboree and ruby

3. Chromosomal Aberrations REF: Albertson et al

4. Array CGH Label Patient DNA with Cy3 Label Control DNA with Cy5 Hybridize DNA to genomic clone microarray Analyze Cy3/Cy5 fluorescence ratio of patient to control (log of Cy3/Y5)

5. Workflow of aCGH Analysis Finished chips (scanner) Raw image data (experiment info ) (image processing software) Probe level raw intensity data Background adjustment, Normalization, transformation Raw copy number (CN) data [log ratio of tumor/normal intensities] Segmentation and boundary determination Estimation of CN Characterizing individual genomic profiles

6. Background Adjustment/Correction Reduces unevenness of a single chip Before adjustment After adjustment Corrected Intensity (S’) = Observed Intensity (S) – Background Intensity (B) Eliminates non-specific hybridization signal Normalization

7. Reduces technical variation between chips Before After S – Mean of S S’ = STD of S S’ ~ N(0,1 ) Normalization Log Transformation before Log transformation S after Log transformation Log(S) S : Probe raw intensity; S’ : Log transformation, S’ = log 2 (S) CN = S’ tumor - S’ normal = log 2 (S tumor /S normal )

8. Segmentation/Smoothing CN Clone/Chromosome

9. CN Clone/Chromosome Segmentation/Smoothing

10. Goal:To partition the clones into sets with the same copy number and to characterize the genomic segments. Noise reduction Detection of Loss, Normal, Gain, Amplification Breakpoint analysis Biological model: genomic rearrangements lead to gains or losses of sizable contiguous parts of the genome. Recurrent (over tumors) aberrations may indicate an oncogene or a tumor suppressor gene

11. AWS - Adaptive Weights Smoothing CBS - Circular Binary Segmentation HMM - Hidden Markov Model partitioning Many more All existing methods amount to unsupervised, location- specific partitioning and operating on individual chromosomes. Segmentation Methods

12. Workflow of aCGH Data Analysis Finished chips (scanner) Raw image data (experiment info ) (image processing software) Probe level raw intensity data Background adjustment, Normalization, transformation Raw copy number (CN) data [log ratio of tumor/normal intensities] Segmentation and boundary determination Estimation of CN Characterizing individual genomic profiles

13. Homework: Analyze TCGA Data

14. The Cancer Genome Atlas Project (TCGA) Goal: find genomic alterations that cause cancer (mutations, CNA, methylation, …) Pilot project 1. brain (glioblastoma multiforme): 186 pairs of tumor and normal samples 2. lung (squamous) 3. ovarian (serous cystadenocarcinoma )

15. Flowchart of Data Analysis Raw copy number (CN) data [log ratio of tumor/normal intensities] Segmenttion and boundary determination Estimation of CN Characterizing individual genomic profiles Annotation Identify Recurrent Genes

16. Ruby: Mapping Probes

18. LFF format

19. Upload Data

20. Data Analysis: Segmentation

21. Data Analysis: Combine Tracks

22. Data Analysis: Annotation Selector

23. Data Analysis: Mapping Genes

24. Data Analysis: Recurrent Genes

25. Overview of Data Analysis Raw copy number (CN) data [log ratio of tumor/normal intensities] Data Preprocessing (Ruby) and uploading data to Genboree Segmentation (Segmentation Tool) Characterizing individual genomic profiles Combing data Annotation (Annotation Selector; Attribute Lifter) Identify Recurrent Genes (Ruby)

26. You Need To Submit 1.ruby script from step 1 that creates your lff file 2.ruby script from step 5 that parses your table 3.two-column final output from step 5