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Evaluation of Affymetrix array normalization procedures based on spiked cRNAs Andrew Hill Expression Profiling Informatics Genetics Institute/Wyeth-Ayerst Research Outline • The GI/Harvard C. elegans array dataset as a normalization testbed • Some general challenges of array data reduction • GeneChip Scaled Average Difference (ADs) – the constant mean assumption • A purely spike-based normalization strategy (Frequency) • A hybrid normalization (Scaled Frequency) • Conclusions October 11, 2001 2 GI/Harvard C. elegans dataset • • • This data set used to evaluate several normalization procedures Experiments: – 8 developmental stages of the worm C. elegans were profiled, ranging from egg to adult worm – n=2-4 replicate hybridizations for most array designs at most stages – 52 total arrays Arrays: – Three custom worm GeneChip designs (A, B, and C) – Each array monitors between 5700-6700 ORFs, in aggregate ~98% of the worm genome – Chip A: ORFs with cDNA/EST matches in AceDB – Chips B/C: other ORFs – Several worm ORFs tiled on all 3 arrays for across-array-design comparisons Science 290 809-812; Genome Biology (in the press) October 11, 2001 3 Some challenges of Affymetrix GeneChip data reduction • Array data from Affymetrix GeneChip sofware (pre-MAS 5.0): – negative low intensity signals – lack of across-design normalization standard – limited QC information • Spike-based normalization methods can help to address each of these challenges Normalization: array scaling of average difference data from multiple arrays/designs to minimize technical noise among arrays • Current “standard” normalization procedure is a global scaling procedure: the GeneChip scaled average difference (ADs) October 11, 2001 4 GeneChip Scaled Average Difference (ADs) • The trimmed (2%) mean intensity of all probesets on all arrays is scaled to a constant target level. • Works well in many cases (e.g. replicates) • Some obvious situations where the “constant mean assumption” may not be well supported. October 11, 2001 5 Constant mean assumption: problematic cases •Chips monitoring a “small” fraction of transcriptome •Non-random gene selection on arrays (e.g. C. elegans A vs. B/C) •Large biological variation in expression October 11, 2001 6 A cRNA spike-based normalization procedure (Frequency) • Add 11 biotin-labeled cRNA spikes to each hybridization cocktail • Construct a calibration curve • Use the Absent/Present calls for the spikes to estimate array sensitivity • Dampen AD signals below the sensitivity level to eliminate negative AD values. October 11, 2001 7 Eleven spiked cRNAs Spiked Transcript ATCC Accession Affymetrix Gene Qualifier Final concentration (pMol) Final concentration (ppm) DAPM 87826 AFFX-DapX-M_at 30 950 DAP5 87827 AFFX-DapX-5_at 10 317 CRE5 87832 AFFX-CreX-5_at 5 158 BIOB5 87825 AFFX-BioB-5_at 2.5 79 BIOD3 87830 AFFX-BioDn-3_at 1.2 38 BIOB3 87828 AFFX-BioB-3_at 0.6 19 CRE3 87835 AFFX-CreX-3_at 0.4 13 BIOC5 87833 AFFX-BioC-5_at 0.3 10 BIOC3 87834 AFFX-BioC-3_at 0.2 6 DAP3 87831 AFFX-DapX-3_at 0.15 5 BIOBM 87829 AFFX-BioB-M_at 0.1 3 October 11, 2001 8 Response to spikes over 2.5 log range Figure 2 •Fit response with S-plus GLM, gamma error model, zero intercept. •Power law fit AD=kFn yields n=0.93 •cRNA mass, scanner PMT gain are important determinants of response October 11, 2001 9 1.0 Chip sensitivity calculation P P P P P P P 0.6 0.4 0.2 0.0 A/P call 0.8 P A A 0 1 A 2 3 4 log(frequency) October 11, 2001 5 6 7 •Consider A/P calls as binary response against log(known frequency) •Compute sensitivity as 70% likelihood level by either interpolation or logistic regression •“Dampen” computed frequencies below sensitivity: •F < 0: F’ = avg(0,S) •0<F<S: F’=avg(F,S) 10 How well does it work? October 11, 2001 11 Reproducibility of F metric (A array) 0h 36h 1 1 AD ADs F 0 1 MEDACV F 0.5 October 11, 2001 AD ADs F F Absent Present F AD 1 AD ADs F 0.5 0 Absent Present 48h AD ADs 0 MEDACV 0.5 MEDACV MEDACV AD ADs 0.5 ADs Absent Present 60h AD ADs F AD F ADs 0 Absent Present 12 Example of spike-skewed hybridization (36 hr sample) 2000 1800 Worm Genes cRNA spikes •cRNA spikes are well normalized at the expense of worm genes 1600 frequency 36h 1400 1200 • Suggests inconsistency between ratio of spikes to worm cRNA across samples: spike skew 1000 800 600 400 200 0 0 October 11, 2001 500 1000 frequency 36h 1500 2000 13 Sources of spike skew • Actual concentration of spikes may not be nominal due to variation in cRNA “purity” • Causes: liquid handling of small microlitre volumes, side reactions in cDNA/IVT process produce UVabsorbing, non-hybridizable contaminants • Result: random per-hybe noise term introduced into normalized frequencies October 11, 2001 14 An alternative hybrid normalization: Scaled frequency (Fs) • Need to reduce or eliminate spike skew as a source of experimental variation in normalized frequencies • Average the globally scaled spike response over a complete set of arrays October 11, 2001 15 Scaled frequency description • Define a set of arrays • Compute ADs for all arrays • Pool spike responses and fit single model to pooled response • Calibrate all arrays with single calibration factor • Compute array sensitivity and dampen frequencies as in the frequency approach. October 11, 2001 16 A pooled, scaled spike response fitted slope: 0.146162419368372 P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P 3 P 2 log10 average_difference 4 P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P •Fit response with S-plus GLM, gamma error model, zero intercept. 1 P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P 1 2 3 log10 ppm October 11, 2001 17 Reproducibility of Fs metric (A array) October 11, 2001 18 Scaled frequency: cross design reproducibility (A,B,C arrays) Three messages tiled on all array designs and called Present on all 0h arrays October 11, 2001 19 Conclusions • Array response to spiked cRNAs can be close to linear over 2.5 logs of concentration. • A chip sensitivity metric can be computed from Absolute Decisions associated with spikes; a very useful QC metric. • Normalization based only on spikes performs inconsistently in some cases due to ill-quantitation of cRNAs, but can still be valuable when constant-mean assumption is violated. Better cRNA quantitation and process control will help. • A hybrid approach based on global scaling and spikes performs the same as global AD scaling for single designs, and also allows cross-design comparisons October 11, 2001 20 Acknowledgements • • • • • • Donna Slonim Maryann Whitley Yizheng Li Bill Mounts Scott Jelinsky Gene Brown October 11, 2001 Harvard University: •Craig Hunter •Ryan Baugh 21 Extra slides follow ( not part of presentation) October 11, 2001 22 Simulations (description) • Simulations were performed • Governing equation: ADij bij October 11, 2001 ADB i a j mij sij rij 23 Figure 4 CV characteristics of simulated data October 11, 2001 24 Simulations: spike skew degrades reproducibility of frequency (A array) October 11, 2001 25 Figure 7 Simulations: spike skew degrades accuracy of frequency October 11, 2001 26