Computational Laboratory: aCGH Data Analysis

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. 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 Workflow of aCGH Analysis

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

7. S – Mean of S S’ = STD of S S’ ~ N(0,1 ) after Log transformation Log(S) before Log transformation S Normalization • Normalization • Reduces technical variation between chips • Before After S : Probe raw intensity; S’ : Log transformation, S’ = log2(S) CN = S’tumor - S’normal = log2(Stumor/Snormal) • Log Transformation

8. Segmentation/Smoothing CN Clone/Chromosome

9. Segmentation/Smoothing CN Clone/Chromosome

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 Segmentation/Smoothing

11. Segmentation Methods • 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.

12. 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 Workflow of aCGH Data Analysis

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. 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 Flowchart of Data Analysis

16. Ruby: Mapping Probes

17. Ruby: Mapping Probes

18. Ruby: Mapping Probes 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 • ruby script from step 1 that creates your lff file • ruby script from step 5 that parses your table • two-column final output from step 5