a microarray based screening procedure for detecting differentially represented yeast mutants
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A Microarray-Based Screening Procedure for Detecting Differentially Represented Yeast Mutants. Rafael A. Irizarry Department of Biostatistics, JHU [email protected] http://biostat.jhsph.edu/~ririzarr. CEN/ARS. aatt. ttaa. URA3. NHEJ Defective. A. DOWNTAG. kanR. UPTAG. CEN/ARS. B. URA3.

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a microarray based screening procedure for detecting differentially represented yeast mutants

A Microarray-Based Screening Procedure for Detecting Differentially Represented Yeast Mutants

Rafael A. Irizarry

Department of Biostatistics, JHU

[email protected]

http://biostat.jhsph.edu/~ririzarr

slide3
CEN/ARS

aatt

ttaa

URA3

NHEJ Defective

A

DOWNTAG

kanR

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

which mutants are nhej defective
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)
slide6
5718 mutants

3 replicates on each slide

5 Haploid slides, 4 Diploid slides

Arrays are divided into 2 downtags, 3 uptag (2 of which replicate uptags)

Data
improvement to usual approach
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 ratio’s we lose information about R and G separately
  • Look at them separately (absolute analysis)
using model we can attach uncertainty to tests
Using model we can attach uncertainty to tests

For example posterior z-test,

weighted average of z-tests with weights obtained using the posterior probability (obtained from EM)

Is Normal(0,1)

resultstable
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

ResultsTable
acknowledgements
Siew Loon Ooi

Jef Boeke

Forrest Spencer

Jean Yang

Acknowledgements
summary
Simple data exploration useful tool for quality assessment

Statistical thinking helpful for interpretation

Statistical models may help find signals in noise

Summary
acknowledgements1
Acknowledgements

Biostatistics

Karl Broman

Leslie Cope

Carlo Coulantoni

Giovanni Parmigiani

Scott Zeger

MBG (SOM)

Jef Boeke

Siew-Loon Ooi

Marina Lee

Forrest Spencer

PGA

Tom Cappola

Skip Garcia

Joshua Hare

UC Berkeley Stat

Ben Bolstad

Sandrine Dudoit

Terry Speed

Jean Yang

Gene Logic

Francois Colin

Uwe Scherf’s Group

WEHI

Bridget Hobbs

Natalie Thorne

warning
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
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

Correlation between replicates
correlation between red green haploid diplod uptag downtag
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

slide34
The mean squared error across slides is about 3 times bigger than the mean squared error within slidesBTW
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

Mixture Model
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

Random effect justification
what can we do now that we couldn t 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

What can we do now that we couldn’t do before?
slide45
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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