Array Platforms 16K Agilent inkjet printed cDNA arrays –The recently developed inkjet printing method (Agilent Technologies) produces more uniform spots than pin spotting techniques –Array includes cDNAs selected from the RIKEN FANTOM collection supplemented by cDNAs from AfCS protein list Affymetrix GeneChip system –U74A v.2 chip (represents approx. 13,000 mouse genes) 16k Agilent inkjet printed Oligonucleotide arrays (in preparation) –Operon 70mers (13,443) and Compugen 65mers (2,304)

Ligand Screen Transcript Analysis B cell samples prepared by Cell Lab. Cultured for different time periods (.5, 1, 2, and 4 hr) in the presence or absence of ligands before harvesting for total RNA isolation. Treated and untreated time-course samples hybridized against a spleen reference. After removing the common spleen denominator, comparison to 0 time point data reflects the changes in mRNA levels due to ligand treatment and/or time in culture. All of the experiments were done in triplicate. Including in controls >450 arrays

Molecular Biology Laboratory Microarray & Analysis Sangdun Choi Xiaocui Zhu Rebecca Hart Anna Cao Mi Sook Chang Jong Woo Kim Sun Young Lee

a. Calculate gene expression value: Compute log2(Treated/0hr) = log2(Treated/Spleen) – log2(0hr/Spleen) using processedSignalIntensity b. Hierarchical cluster: with genes showing >= 2 fold change in at least one condition while keeping ligands in alphabetical/time course order: Gene 1 Gene 2 Gene 3 …….. 30min 1hr 2hr 4hr 30min 2MA 1hr 2MA 2hr 2MA 4hr 2MA 30min AIG 1hr AIG 2hr AIG 4hr AIG …. Average of triplicates Average of 6-23 replicates 5281 genes 132 conditions Clustering Analysis of Gene Expression Profile Using log2Ratio (Treated/0hr)

Genes, clustered Ligands, time course ( i.e. medium- 30 min, 1hr, 2hr, 4hr; 2MA- 30 min, 1hr, 2hr, 4hr…)

Genes up regulated in AIG, CD40L, IL4, LPS and CpG IL4 LPS CD40L AIG CpG 317 features Ccnd2 Cdk4 Caspase 4 Bax Ak2 Hk2 Atf cdk6 Ifrd2 Image contrast: 1.07 None

Genes down regulated in AIG, CD40L, IL4, LPS and CpG IL4 LPS CD40L AIG CpG 319 features id3 Bnip3l Gnai2 Gprk6 Bcap31 Image contrast: 1.07 cAMP-GEFII None

IL4 LPS CD40LAIG CpG Genes showing AIG & CD40L specific changes 235 features Par-6 Gadd45b Dagk1 Mapk12 Image contrast: 1.16 IL3ra IL10ra None

Genes up regulated in IL4 IL4LPSCD40LAIG CpG 42 features Image contrast: 1.14 None Socs1 Caspase 6 Xbp1 Rgs14 Dapp1

IL4LPS CD40L AIG CpG Genes showing AIG specific changes 65 features Stress induced protein Bak1 Image contrast: 1.54 apolipoprotein E Bcl2l11 LTb None

Madhusudan Natarajan Rama Ranganathan

basal Observed value Clustering Analysis of Gene Expression Profile Using Z Score Z score: a measurement of the distance between an observed value and the mean of a population

a.Calculate gene expression metric, x : For each gene i on a given chip j: x ij ={rMedianIntensity (treated) / gMedianIntensity (spleen) }/ x j, where x j is the mean of intensity ratio of all genes on chip j c.Calculate the mean and standard deviation of gene expression in 27 sets of 0hr untreated data: For each gene i, calculate the mean(  i ) and the standard deviation (  i ) of expression on 27 0hr chips; d.Calculate Z score as a measurement of differential expression from 0hr condition For each gene i on a given chip j, Z ij = ( x ij –  i ) /  i f.Cluster genes and ligands using Z-score: with genes whose Z > 2 in any of the ligands Clustering Analysis of Gene Expression Profile Using Z Score

Clustering ligand based on Z scores

AfCS Data Analysis- Microarray Dennis Mock UC Principal Statistician University of California, San Diego Director: Shankar Subramaniam Acknowledgment: Eugene Ke, Bob Sinkovits, Brian Saunders

Two-way hierarchical clustering –unsupervised- Ligands (n=33) (0hr,.5h, 1h, 2h, 4h) Note: the ligand cluster according early –late conditions with % accuracy (metrics: sample = Euclidean; gene = Pearson) late 2-4 hr early.5-1 hr 0 hrearly.5-1 hr (non-mitogenic) late 2-4 hr mitogenic Interleukins Dennis Mock - UCSD

Significance analysis of microarrays * (SAM) (R. Tibshirani, G. Chu 2002) Objective: The replicated expression for each gene is taken for the 4hr time condition (untreated vs ligand) to determine whether the gene is statistically differentially up- or down- regulated. The t-statistics for all the genes are ordered and noted. The labels are then permutated and the t-statistic is calculated again. After many iterations, the cumulative t-statistics is averaged for each gene. Finally, for a given false positive rate, [called “False Discovery Rate” or FDR], the significant genes are selected. For each gene, define the adjusted “t-statistic” as follows:  treated -  untreated  + adjustment factor   mean of replicates   standard deviation for the gene Dennis Mock - UCSD

Concordance of significantly up (+) or down (-) regulated genes mitogenic ligands (FDR = 1%) 756 (-) 1082 (+) 337 (-) 135 (-) 553 (-) 147 (-) “down-regulated” matches “up-regulated” matches 3 (-) 446 (-) 887 (+) 96 (-) Mosaic plot 578 (+) 73 (+) 597 (+) 117 (+) 47 (+) 477 (+) 117 (+) 4 (+)6 (+)3 (+) 796 (-) 854 (+) 5 (+)4 (+) 3 (-) 10 (+) 1 (-) 3 (-) 2 (-) 3 (-) 72 (+) 18 (+) 341 (-) 143 (-) 152(-) 80(+) 108 (+) 171 (-) 163 (+) 151 (-) 119 (-) Discordance matrix Example: CD40L had 756 down-regulated and 1082 up-regulated genes. Those which were similarly regulated in AIG: 337 down 578 up. 72 (-)

Beyond Clustering How can we obtain biological information from array data at the level of individual genes and correlations in expression between genes? Can we use the correlations to build a connection network that reflects correlations in expression? Is there biological significance to this?

Two-way hierarchical cluster: mean ratio (vs control) of phosphoprotein levels and ligand Note: the ligands that elicit an ERK response (chemokines + AIG, CD40L) clustered together.

Transcription factor encoded by fos is stabilized by ERK and continues to affect other IE genes such as jun from Nature Cell Biology august 2002 v 4 issue 8

A clear lesson that we must implement as soon as possible is to decrease the cycle time from experimental design - data collection - data analysis - conclusions, models - to experimental redesign. In the past the rate limiting step has been data analysis

Input Signals Signal Processing Translocation Gene Expression Cytoskeleton Transcription Translation Transcription Translation