microarray normalization using proportional transcript

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1. Microarray Normalization Using Proportional TranscriptAbundance Steven Eschrich and Craig Beam Moffitt Cancer Center and Research Institute University of South Florida December 18, 2004

2. Overview Single-Channel Microarray Normalization Proportional Transcript Abundance Results: Affymetrix Spikein Study

3. Microarray Normalization Normalization between microarray chips is crucial for differential expression analysis. Assumption is that few genes change dramatically. Shifted curves represent experimental variability.

4. Normalization Goals Reduce variability due to experimental noise. Preserve existing signal (differentially expressed genes).

5. Affymetrix Linear Normalization

6. RMA Quantile Normalization Probe-level normalization. Set Q-Q plot to be a line. Set intensity histograms to be at the same location and the same shape. Disadvantage: all chips needed for normalization.

7. Proportional Transcript Abundance Represent individual gene expression as proportion of total expression on the chip. Self normalization relative to total hybridized RNA. Independent of total amount of RNA hybridized (varying concentrations). Universal comparability (no extra processing needed).

8. Advantages Self-normalization technique Can combine data from multiple sites without renormalizing. Efficient (does not require all chips in memory). Expression is independent of total amount of RNA hybridized on the chip. Cross-hybridization may not be linear at all concentrations.

9. Testing Normalization Affycomp (Irizarry) software for testing normalization http://affycomp.biostat.jhsph.edu Or available as an R/Bioconductor package. Provides a series of tests on several controlled datasets to demonstrate effectiveness.

10. Affymetrix SpikeIn Experiment Dataset of microarrays designed to detect predetermined gene spike-in concentrations. Goals Detect spikein genes as differentially expressed. Ignore all other probes. Preserve the relative proportions of spikein concentrations.

11. Measuring Variance in Technical Replicates Normalization should remove differences that are not real Experimental noise. Addition of different amounts of RNA. SpikeIn study provides three technical replicates across many different genes. We measure the variance in each of the gene signal values.

12. SpikeIn Study: Variance Scatter

13. Variance Analysis Vi = ViMAS - ViPTA V is assumed to be a normally distributed random variable (n=22K). Null Hypothesis Mean of V = 0 Rejected, pvalues too small to be reported by R.

14. SpikeIn Study: Reproducing Spikes Reducing variability should not change the relationship of spikeins. Easy to sacrifice signal in low-range for lower variability. Normalization does not affect spikein probeset signal.

15. Identifying Significant Genes Higher levels of noise leads to less precise identification of significant genes. ROC demonstrates the tradeoff between accepting false positives and identifying true positives.

16. ROC Continuous Detection

17. ROC Greater than 2-fold only

18. Conclusion Normalization is crucial for gene expression studies (well-published fact). Linear scaling based on the mean intensity is not always enough. Proportional Transcript Abundance Self-normalization technique. Preserves known signals. Reduces overall variance due to experimental noise in technical replicates.

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