1. 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.

2. Data Analysis Genomics/Genetics
Quality Control
Predictive Modeling (CVMC, LCMC, TSMC)
Normalization
Understanding
Discovery
Modeling
Hypothesis Testing
Pattern Discovery
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3. Experimental Design Confounding and Control issues
Society of Toxicology 2010
Controls
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Confounding
GenomicsResults\Desmond\exp_mtl3.jmp
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4. Data Quality Evaluation
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5. Normalization NGS Only Go Back to Poster
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NGS Only
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6. 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
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ANONA_ln158_mea_bnm_5k_amr

7. Predictive Modeling 84 Models Go Back to Poster
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8. Predictive Modeling Cross Validation Model Comparison Summary
After Batch Normalization
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Before Batch Normalization
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