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Microarrays Tzu Lip Phang, Ph.D. Lawrence Hunter, Ph.D. Associate Professor of Bioinformatics Director, Computational Bioscience Program Division of Pulmonary Sciences and Critical Care Medicine University of Colorado School of Medicine University of Colorado School of Medicine [email protected] [email protected] http://compbio.uchsc.edu/Hunter Data Science AKA BIG DATA The Devils is in the Details Workshop The Central Dogma Genome Transcriptome Microarrys in the Literature 7000 Number of papers 6000 5000 4000 3000 2000 1000 0 Year Microarray: Primer Basic Statistical Analysis Basic Statistical Analysis Power Analysis • How many biological replication? • My experience; at least 3, preferably 5, even 7 • Bioconductor: SSPA Basic Statistical Analysis QC • Including image analysis, normalization, • and data transformation Data normalization: – Remove systematic errors introduced in labeling, hybridization and scanning procedures – Correct these errors while preserve biological variability / information Why normalization? To normalize or not to … Basic Statistical Analysis Statistical Testing • Hypothesis Testing: Is the means of two groups different from each other – Fold Change – Student-T Test Student-T Test What is Multiple Comparison Testing??! Genes Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6 Gene 7 Gene 8 Gene 9 Gene 10 Alpha level = 0.05 P-values 0.0001 0.0002 0.008 0.009 0.005 0.09 0.05 0.09 0.2 0.3 <= <= <= <= <= <= <= <= <= <= Critical level 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 Ho 1 1 1 1 1 0 0 0 0 0 When large number of tests … Genes Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6 … … Gene 999 Gene 1000 P-values 0.0001 0.0002 0.008 0.009 0.005 0.09 … … 0.2 0.3 Alpha level = 0.05 50 wrong genes … <= <= <= <= <= <= … … <= <= Critical level 0.05 0.05 0.05 0.05 0.05 0.05 … … 0.05 0.05 Ho 1 1 1 1 1 0 … … 0 0 Correction … Bonferroni Genes Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6 … … Gene 999 Gene 1000 P-values 0.0001 0.0002 0.008 0.009 0.005 0.09 … … 0.2 0.3 <= <= <= <= <= <= … … <= <= Alpha level = 0.05 / 1000 = 0.00005 Critical level 0.00005 0.00005 0.00005 0.00005 0.00005 0.00005 0.00005 0.00005 0.00005 0.00005 Ho 0 0 0 0 0 0 … … 0 0 Strike the balance … Most Conservative Bonferroni Most Lenient False Discovery Rate No correction The False Discovery Rate (FDR) of a set of predictions is the expected percent of false predictions in the set of predictions. Example: If the algorithm returns 100 genes with false discovery rate of 0.3, then we should expect 70 of them to be correct Put them together Basic Statistical Analysis Biological Interpretation