copyright © 2008, sas institute inc. all rights reserved. discovering meaningful patterns in...

Copyright © 2008, SAS Institute Inc. All rights reserved.

Discovering Meaningful Patterns in Genomics Data with JMP Genomics

Jordan HillerJMP Genomics Application Scientist

2/19/2010


About SAS

SAS founded in 1976• About 10,000 employees worldwide• Over $2 billion in revenue

SAS provides statistical analysis solutions to pharma, financial and retail businesses, among others• Provides robust statistical and data mining algorithms• SAS programming language is powerful, but complex


About JMP

We are a division of SAS

Founded in 1989 by John Sall, co-founder of SAS

Originally Mac only, ported to PC in the 90s

Desktop software, highly visual

Provides easy-to-use statistical analysis software for non-SAS users


JMP Genomics SAS on the back-end

• Using established analytic tools: SAS/STAT, IML, SAS/Genetics

• Powerful and flexible language for data manipulation and statistical analysis

• Can handle large genomic datasets with file-based analysis

JMP up front• Point-and-click interface to SAS analytics• Dynamic, interactive visual display of results

Best of both worlds• Choose from JMP or SAS tools


Philosophy of JMP Genomics

We provide pre-built workflows for biologist users

…while retaining statistical flexibility and transparency demanded by analysts

We offer many options for exploring and visualizing complex data sets


JMP Genomics Architecture

“Run” Submits

Program to

to Local SAS

SAS Macros

Generate

Scripts and

Output Tables

New in 4.1:Option to Submit Job to SAS Server

New in 4.1:JMP Genomics64 bit Edition


Highlights of Expression Workflows Import, QC, and analyze large datasets

• Traditional arrays, summaries from next-gen • Simplified workflows, advanced options for statisticians


Highlights of Genetics Workflows

GWAS (up to 1.5M SNPs x 15,000 for 4.0)

Adjustments for population structure, relatedness

QC to filter markers and samples

Association Tests Population Structure Assessment/Correction

Linkage Disequilibrium

Relationship Matrices Individual and Group


Highlights of Copy Number Workflows Import, QC, Normalization and ANOVA

Support SNP6, Illumina 1M SNP

Group vs. Group Individual vs. Group

Copy Number Partition ( Faster CBS)

ANOVA:


Support for Next-Gen Data

Secondary:

Sequence

Alignment

Primary:

Image analysis

Base-calling

Tertiary:

Statistical Analysis of

Summarized Data

National Center for Genome Resources

Mudge et al, PloS One, 2008.


Check our Code

All SAS code and JSL (JMP Scripting Language) code underlying Genomics analytic procedures is accessible

Key Benefits• Transparency: Examine underlying code to understand

procedures• Reproducibility: Use workflows to document analysis

steps, standardize across data sets• Extensibility: Adapt existing code or write your own• Flexibility: Using our analytic toolkit, run analyses we

didn’t think of


What’s New in JMP Genomics 4.1

Support for miRNA, Tiling Arrays, and imputed SNP data

New Population Measures and Relationship Matrix

Q-K Mixed Model process to test for SNP association while adjusting for family relatedness and population structure simultaneously

Manhattan Plots

Improved P-Value Browser visualizes statistical results and genomic information together


Today’s Demonstration

Gene Expression Analysis• Basic Expression Workflow

− QC Analysis

− ANOVA


Today’s Dataset

Affymetrix MPRO hourly dataset

MPRO cell line stimulated with retinoic acid

5 timepoints: 0, 1, 2, 4, and 8 hours

4 technical replicates at each timepoint

Murine U74A arrays


Thank you

Contact Info:

Jordan Hiller

[email protected]

919-531-9809

mailto:[email protected]

copyright © 2008, sas institute inc. all rights reserved. discovering meaningful patterns in...

Documents

sas institute

sas code

jmp genomics sas

sas tools slide

local sas sas macros

revenue sas

division of sas

sas server new