Use%20of%20Mixture%20Model%20in%20a%20genome-wide%20DNA%20microarray-based%20genetic%20screen%20for%20components%20of%20the%20NHEJ%20Pathway%20in%20Yeast

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Use of Mixture Model in a genome-wide DNA
microarray-based genetic screen for components of
the NHEJ Pathway in Yeast

• Rafael A. Irizarry
• Department of Biostatistics, JHU
• rafa_at_jhu.edu

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Damaged DNA
Yku70p/Yku80p (DNA-PK )
DNA end binding
Nucleolytic processing
Rad50p/Mre11p/Xrs2p
Ligation
Lig4p/Lif1p
Repaired DNA
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A
kanR
DOWNTAG
UPTAG
CEN/ARS
B
URA3
MCS
Circular pRS416
EcoRI linearized PRS416
Transformation into deletion pool
Select for Ura transformants Genomic DNA
preparation
PCR
Cy5 labeled PCR products
Cy3 labeled PCR products
Oligonucleotide array hybridization
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Data

• 5718 mutants
• 3 replicates on each slide
• 5 Haploid slides, 4 Diploid slides
• Haploids are divided into 2 downtags, 3 uptag (2
  of which replicate uptags)
• Diploids are divided into 3 uptags (2 of which
  are replicates) and 2 uptags

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Which mutants are NHEJ defective?

• Find mutants defective for transformation with
  linear DNA
• Dead in linear transformation (green)
• Alive in circular transformation (red)
• Look for spots with large log(R/G)

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Improvement to usual approach

• Take into account that some mutants are dead and
  some alive
• Use a statistical model to represent this
• Mixture model?
• With ratios we lose information about of R and G
  separately
• Look at them separately (absolute analysis)

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Warning

• Absolute analyses can be dangerous for
  competitive hybridization slides
• We must be careful about spot effect
• Big R or G may only mean the spot they where on
  had large amounts of cDNA
• Look at some facts that make us feel safer

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Correlation between replicates

• R1 R2 R3 G1 G2 G3
• R1 1.00 0.95 0.95 0.94 0.90 0.90
• R2 0.95 1.00 0.96 0.90 0.95 0.91
• R3 0.95 0.96 1.00 0.91 0.92 0.95
• G1 0.94 0.90 0.91 1.00 0.96 0.96
• G2 0.90 0.95 0.92 0.96 1.00 0.97
• G3 0.90 0.91 0.95 0.96 0.97 1.00

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Correlation between red, green, haploid, diplod,
uptag, downtag

• RHD RHU RDD RDU GHD GHU
  GDD GDU
• RHD 1.00 0.59 0.56 0.32 0.95 0.58 0.54 0.37
• RHU 0.59 1.00 0.38 0.56 0.58 0.95 0.40 0.58
• RDD 0.56 0.38 1.00 0.58 0.54 0.39 0.92 0.64
• RDU 0.32 0.56 0.58 1.00 0.33 0.53 0.58 0.89
• GHD 0.95 0.58 0.54 0.33 1.00 0.62 0.56 0.39
• GHU 0.58 0.95 0.39 0.53 0.62 1.00 0.41 0.58
• GDD 0.54 0.40 0.92 0.58 0.56 0.41 1.00 0.73
• GDU 0.37 0.58 0.64 0.89 0.39 0.58 0.73 1.00

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BTW

• The mean squared error across slides is about 3
  times bigger than the mean squared error within
  slides

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Mixture Model

• We use a mixture model that assumes
• There are three classes
• Dead
• Marginal
• Alive
• Normally distributed with same correlation
  structure from gene to gene

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Random effect justification

• Each x (r1,,r5,g1,,g5) will have the
  following effects
• Individual effect same mutant same expression
  (replicates are alike)
• Genetic effect same genetics same expression
• PCR effect expect difference in uptag, downtag

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Does it fit?
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Does it fit?
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What can we do now that we couldnt do before?

• Define a t-test that takes into account if
  mutants are dead or not when computing variance
• For each gene compute likelihood ratios comparing
  two hypothesis
• alive/dead vs.dead/dead or alive/alive

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QQ-plot for new t-test
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Better looking than others
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• 1 YMR106C 9.5 47 69.2 a
  a 100
• 2 YOR005C 19.7 35 44.9 a
  d 100
• 3 YLR265C 6.1 32 35.8 a
  m 100
• 4 YDL041W 10.4 32 35.6 a
  m 100
• 5 YIL012W 12.2 31 21.7 a
  a 100
• 6 YIL093C 4.8 29 30.8 a
  a 100
• 7 YIL009W 5.6 29 -23.5 a
  a 100
• 8 YDL042C 12.9 29 32.1 a
  d 100
• 9 YIL154C 1.8 28 91.3 m
  m 82
• 10 YNL149C 1.7 27 93.4 m
  d 71
• 11 YBR085W 2.5 26 -15.8 a
  a 84
• 12 YBR234C 1.7 26 87.5 m
  d 75
• 13 YLR442C 6.1 26 -100.0 a
  a 100

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Acknowledgements

• Siew Loon Ooi
• Jef Boeke
• Forrest Spencer
• Jean Yang