Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.
-
Upload
elfreda-little -
Category
Documents
-
view
216 -
download
1
Transcript of Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.
![Page 1: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/1.jpg)
Computational Laboratory: aCGH Data Analysis
Feb. 4, 2011
Per Chia-Chin Wu
![Page 2: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/2.jpg)
Today’s Topics
• Review aCGH and its data analysis
• Homework of aCGH data analysis using tools in Genboree and ruby
![Page 3: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/3.jpg)
Chromosomal Aberrations
REF: Albertson et al
![Page 4: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/4.jpg)
Array CGHLabel
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)
![Page 5: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/5.jpg)
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
![Page 6: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/6.jpg)
• Background Adjustment/CorrectionReduces unevenness of a single chip
Before adjustment After adjustment
Corrected Intensity (S’) = Observed Intensity (S) – Background Intensity (B)
Eliminates non-specific hybridization signal
Normalization
![Page 7: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/7.jpg)
• NormalizationReduces 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’ = log2(S)CN = S’tumor - S’normal = log2(Stumor/Snormal)
![Page 8: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/8.jpg)
Segmentation/Smoothing
CN
Clone/Chromosome
![Page 9: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/9.jpg)
CN
Clone/Chromosome
Segmentation/Smoothing
![Page 10: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/10.jpg)
Segmentation/Smoothing
• 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
![Page 11: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/11.jpg)
• 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
![Page 12: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/12.jpg)
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
![Page 13: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/13.jpg)
Homework: Analyze TCGA Data
![Page 14: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/14.jpg)
The Cancer Genome Atlas Project (TCGA)
• Goal: find genomic alterations that cause cancer (mutations, CNA, methylation, …)
• Pilot project1. brain (glioblastoma multiforme): 186 pairs of tumor and normal samples2. lung (squamous)3. ovarian (serous cystadenocarcinoma )
![Page 15: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/15.jpg)
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
![Page 16: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/16.jpg)
Ruby: Mapping Probes
![Page 17: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/17.jpg)
Ruby: Mapping Probes
![Page 18: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/18.jpg)
Ruby: Mapping Probes
LFF format
![Page 19: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/19.jpg)
Upload Data
![Page 20: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/20.jpg)
Data Analysis: Segmentation
![Page 21: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/21.jpg)
Data Analysis: Combine Tracks
![Page 22: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/22.jpg)
Data Analysis: Annotation Selector
![Page 23: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/23.jpg)
Data Analysis: Mapping Genes
![Page 24: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/24.jpg)
Data Analysis: Recurrent Genes
![Page 25: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/25.jpg)
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)
![Page 26: Computational Laboratory: aCGH Data Analysis Feb. 4, 2011 Per Chia-Chin Wu.](https://reader036.fdocuments.us/reader036/viewer/2022062518/5697bf881a28abf838c89af5/html5/thumbnails/26.jpg)
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