cDNA-Project cDNA project Julia Brettschneider (UCB Statistics)

cDNA project

Biological question What are the underlying molecular cascades that are activated following brain injury and lead the cells into and through cell death?

Experiments Platform: Incyte mouse cDNA arrays Twelve cDNA dye swap pairs 9024 genes total 4 print-tip groups (48 x 47 spots each) Controls: The last 24 spots in each print-tip group

Find differentially expressed genes… injured (i) vs. sham (s) hypocamp. subfield CA3 (C) vs. upper blade Dentate (D) between different marker status (+,>,-) would also be interesting…

The 24 comparisons #01 iC+ sC- #02 iC- sC- #03 iD+ sD- #04 iD- sD- #05 iC+ iC- #06 iD+ iD- #07 sC- iC+ #08 iC- iC+ #09 sC- iC- #10 sD- iD+ #11 iD- iD+ #12 sD- iD- #13 iC+ iC> #14 iC> iC- #15 iC> sC- #16 iD+ iD> #17 iD> iD- #18 iD> sD- #19 iC> iC+ #20 iC- iC> #21 sC- iC> #22 iD> iD+ #2C iD> iD- #24 sD- iD>

Dye swap pair (injured +) vs (sham -)

Dye swap pairs (injured >) vs (sham -)

Incyte’s Flags Between 22% and 91% of the genes flagged, in 15 chips more than 50%, in 8 chips more than 75%. Incyte’s criterion for flagging: - signal to bg ratio smaller than 2.5, or - spot area smaller than 40% in both dyes Flagged genes are not used in normalization

Problems Design (regions CA3 and dentate never hybridized together) “Flagged genes” Low quality chips Inconsistent dye swap pairs Normalization

Image analysis and normalization Image analysis using “Spot” (Yang, Buckley, and Speed 1999, Program written by CSIRO Mathematics and Information Science) Lowess normalization (Dudoit et al. 2000) for print-tip groups

Summary cDNA project Image analysis redone Normalization Gene rank lists

Affymetrix Technoloy Statistics Drosophila project

Technology of oligonucleotide arrays Each gene is interrogated by a number (16 - 20) of probe pairs Each probe pair consists of two oligonucleotides (typically 25 bp long): Perfect Match (PM) fits the target exactly Missmatch (MM) has a middle base flipped

Chip Probes PM MM

PM MM …TGCTGGGTTACGCAGTCTTACTGACCATACACGCACTGCT… PM: AATGCGTCAGAATGACTGGTATGTGCGTCACGA MM: AATGCGTCAGAAAGACTGGTATGTGCGTCACGA ^ missmatch position

Steps in the analysis Converting .cel file into probe pair data Normalization Calculation of an expression value for each gene based on the intensities of its 20 probe pairs Comparing treatment/control, WT/mutant etc. Hypothesis testing Clustering etc.

Gene expression estimation Genechip (Affymetrix): Average difference between the probe pairs Average log ratio Now: robust/resistent version

Gene expression estimation Li and Wong (2000) fit a model expression in chip i (j probe pair index)

Gene expression estimation Irizarry, Speed et al. (2002): Average log (PM - BG), where BG global background Now: robust version using median polish

Example Scatters

Mixture Model fits

Fits for significant genes

Significance of regression

Residual Plots

Axon guidance in the Drosophila CNS How do neurons find their correct targets, make appropriate synaptic connections, and set and adjust their size and strength? Find genes which regulate these mechanisms by comparing different mutants Loss Of Function (LOF) and Gain Of Functions (GOF) mutants for a number of proteins

Robo, Slit & Comm Robo: transmembrane protein, receptor for slit, negatively controlled by Comm Slit: extracellular protein, expressed by midline glia, ligand for Robo receptor Comm: surface protein, expressd on midline cells, transferred to commissural neurons

Phenotypes

Experiments 12 (plus…) different mutants, plus WT 4 replicates for each Affymetrix Drosophila chips about 13 thousand genes in total

Comparisons Formulate particular questions/hypothesis for subgroups of comparisons, - Slit and Robo, Robo2 mutants have similar phenotypes, compare their gene expression - Compare Robo1, Robo2, and Robo1,2 - etc.

Data Analysis Using Affy R-package (Bolstad, Irizarry) Quantile normalization (Bolstad 2001) Currently implementing an additiv model on the probe pair level: - simultanously for mutant/WT - incorporates replicates

Acknowledgement Ben Bolstad Sandrine Dudoit Phil Spector Terry Speed Jean Yee Hwa Yang (UCB Statistics) Tiago Magelhaes (Goodman lab, UCB) Paolo Marciano (Pennsylvania) Francois Collin (Genlogic) Rafael Irizarry (Johns Hopkins) www.stat.berkeley.edu/users/terry/zarray/Html/index.html