JMP® Genomics® Offers Solutions for Big Data Challenges Wenjun Bao, PhD; Tzu-Ming Chu, PhD; Russ Wolfinger, PhD JMP Life Sciences, SAS Institute Inc. Quality Factors Data Analysis Abstract The analysis and interpretation of “big data” presents the field of statistics with more challenges and opportunities than ever before. In this presentation, we discuss experimental design, data quality assessment, data normalization and significant signal identification as they apply to big data analysis. Microarray data is used as an example to demonstrate how JMP scripting and various interactive visualization graphics used in JMP Genomics can meet the challenges inherent in different stages of data collection, assessment and analysis and, finally, to ensure that the final results are reliable and trustworthy. Quality Assessment Normalization Models NGS Only Predictive Models Data Analysis Strategy Experimental Design After Batch Normalization 84 Models Controls Before Batch Normalization Conclusions Confounding To ensure reliable and trustable results, big data analysis needs to meet challenges in different data process stages with method library: Data generation Solid experimental design: need proper controls and avoid confounding Quality Assessment and Normalization: collected methods to handle different data Models and pattern discovery: multiple-ways ANOVA and various pattern discovery methods Predictive modeling: a range of methods with CVMC, LCMC and TSMC.

Data Analysis Genomics/Genetics Quality Control Predictive Modeling (CVMC, LCMC, TSMC) Normalization Understanding Discovery Modeling Hypothesis Testing Pattern Discovery Go Back to Poster

Experimental Design Confounding and Control issues Society of Toxicology 2010 Controls Go Back to Poster Confounding GenomicsResults\Desmond\exp_mtl3.jmp Go Back to Poster

Data Quality Evaluation Copyright © 2010, SAS Institute Inc. All rights reserved. Go Back to Poster

Normalization NGS Only Go Back to Poster Copyright © 2010, SAS Institute Inc. All rights reserved. NGS Only Go Back to Poster

ANOVA and Mixed Modeling Pathway Analysis In ANOVA, After results showed up. 1) Select a few genes and click Fit Model and Plot LSMeans button, highlight the probe_setid and run to show the change for each gene according to different treatments. 2) Select a few genes and click Construct Oneway Plots and then choose LN_CA2 to show box plot to compare the difference between CA and NCA for each genes Pattern Discovery Ingenuity Copyright © 2010, SAS Institute Inc. All rights reserved. Significant Genes Genome Browsers Go Back to Poster ANONA_ln158_mea_bnm_5k_amr

Predictive Modeling 84 Models Go Back to Poster Copyright © 2010, SAS Institute Inc. All rights reserved. Go Back to Poster

Predictive Modeling Cross Validation Model Comparison Summary After Batch Normalization Copyright © 2010, SAS Institute Inc. All rights reserved. Before Batch Normalization Go Back to Poster