CGH as an Alternative to PCR-based Detection of Deletions in C ...
CGH as an Alternative to PCR-based Detection of Deletions in C ...
CGH as an Alternative to PCR-based Detection of Deletions in C ...
- No tags were found...
Create successful ePaper yourself
Turn your PDF publications into a flip-book with our unique Google optimized e-Paper software.
Normalization <strong>of</strong> High-Density Array <strong>CGH</strong>Data with M<strong>an</strong>y OutliersChi K<strong>in</strong> HoKenneth LoJay Mayd<strong>an</strong>
Array Comparative Genomic Hybridization (<strong>CGH</strong>)
M<strong>an</strong>y <strong>Deletions</strong> <strong>in</strong> Stra<strong>in</strong> from HawaiiLog 2 (Hawaii<strong>an</strong> / N2)I II IIIChromosome Coord<strong>in</strong>ate Chromosome Coord<strong>in</strong>ate Chromosome Coord<strong>in</strong>ateLog 2 (Hawaii<strong>an</strong> / N2)IV V X
MA plot (Hawaii<strong>an</strong> CB4856 / Bris<strong>to</strong>l N2) – raw dataBefore Lowess NormalizationNon-Robust Lowess NormalizationM (log 2 ratio)M (log 2 ratio)A (<strong>in</strong>tensity)A (<strong>in</strong>tensity)
Our ProjectF<strong>in</strong>d a better way <strong>to</strong> normalize this data• robust lowess normalization (iterative approach)• subset <strong>of</strong> probes for normalization <strong>to</strong> decre<strong>as</strong>e the proportion <strong>of</strong> outliers(probes <strong>to</strong> the X chromosome only; probes <strong>to</strong> genes for which mut<strong>an</strong>ts arelethal)• “Supersmoother” variable-sp<strong>an</strong> smooth<strong>in</strong>g (not very super)• r<strong>an</strong>k <strong>in</strong>vari<strong>an</strong>t method• sequence <strong>in</strong>formation
Non-Robust vs. Robust Implementation <strong>of</strong> LowessLowess Normalization(Non-robust; All probes)Lowess Normalization(Robust; All probes)M (log 2 ratio)M (log 2 ratio)A (<strong>in</strong>tensity)A (<strong>in</strong>tensity)
Robust Lowess vs SupersmootherLowess Normalization(Robust; All probes)Supersmoother(All probes)M (log 2 ratio)M (log 2 ratio)A (<strong>in</strong>tensity)A (<strong>in</strong>tensity)Not <strong>as</strong> robust <strong>to</strong> outliers.
Tak<strong>in</strong>g subsets <strong>of</strong> the data <strong>to</strong> reduce the proportion <strong>of</strong> outliersWhole genome453 “lethal” genesX chromosome
Genes with Larval Lethal RNAi Phenotypes are not Miss<strong>in</strong>g
Genes with Larval Lethal RNAi Phenotypes are not Miss<strong>in</strong>g(More work for Jay’s thesis)
Normaliz<strong>in</strong>g the whole genome with a subset <strong>of</strong> probesAll probes453 “lethal” genesX chromosomeNot a big difference if arobust normalization isperformed.
R<strong>an</strong>k <strong>in</strong>vari<strong>an</strong>t method vs Sequence <strong>in</strong>formationR<strong>an</strong>k Invari<strong>an</strong>t method(Iterative)Sequence <strong>in</strong>formation(All probes)M (log 2 ratio)M (log 2 ratio)A (<strong>in</strong>tensity)A (<strong>in</strong>tensity)
EBarrays (LNN model) – Posterior Probabilities
EBarrays (LNN model)– Posterior Prob. smoothed by Mov<strong>in</strong>g Averages
EBarrays (LNN model)– identified regions <strong>of</strong> deletions / amplifications
EBarrays (LNN model)– identified regions <strong>of</strong> deletions / amplifications
EBarrays (LNN model)– identified regions <strong>of</strong> deletions / amplifications
Conclusions (so far)• Robust Lowess Normalization appears <strong>to</strong> be the best. This method is very<strong>to</strong>ler<strong>an</strong>t <strong>to</strong> outliers.• Further improvements by us<strong>in</strong>g a subset <strong>of</strong> the data with a lower proportion<strong>of</strong> outliers are m<strong>in</strong>imal.Possible extensions:• Compare the identification <strong>of</strong> deletions before <strong>an</strong>d after the improvements<strong>in</strong> our normalization (difficult <strong>to</strong> qu<strong>an</strong>tify).• Further normalization strategies.
Change language