# Presentation: 950321

## MSCL Analyst’s Toolbox 

- Instructors:
- 	Jennifer Barb, Zoila G. Rangel, Peter Munson
- Jan 2008

- Mathematical and Statistical Computing Laboratory
- Division of Computational Bioscience

## Course Outline

- Day 1
- MSCL Analyst’s Toolbox and JMPTM overview
- MSCL Toolbox Concepts
- JMPTMfundamentals
- Lunch
- Affymetrix ExpressionConsoleTM, processing .cel files, exporting data
- MSCL Toolbox Demo
- Data input
- Basic Analysis (Master File, Final File, Data normalization, QC, PCA, )
- Gene selection, statistical tests (p-values, FDR)
- Annotation
- Day 2
- Statistical Topics (PCA, Data normalization, FDR)
- MSCL Analyst’sToolbox Demo (cont.)
- Complex Analysis (2-way ANOVA, blocked ANOVA)
- Data Visualization

## Topics not included

- Exon Array Analysis -- coming soon!
- SNP chip
- Resequencing analysis, ChIP-Chip, copy number
- 2-color or spotted cDNA array analysis
- complete JMP tutorial
- JMP on Mac, Linux
- JMP scripting language
- Data management commands in JMP:
-  Stack, Split, Concatenate, Sort

## Why use JMP?

- Interactive graphics facilitates data exploration, discovery of features
- Powerful,  > 2,00,000 rows by 100s of columns (currently, 2 GB limit)
- Scripting language -- object oriented, allows matrix manipulation
- Connects to database servers including NIHLIMS or local GCOS
*JMP* is also general purpose statistics pack
- Good technical support for *JMP *from: (919) 677-8008 or www.jmp.com
- No direct cost to individual NIH users* (centrally supported in most NIH ICs)
*MSCL Analyst's Toolbox* is FREE, adds tools for microarray studies

## MSCL Analyst’s Toolbox Features

- Menu driven
- Automated gene annotations
- Web link-out**
- Highly interactive, intuitive user interface
- Analysis pipeline, based on years of experience
- Familiar parametric analysis, e.g. ANOVA
- Exploratory Data Analysis
- Adaptable to new designs, analyses (e.g. Exon chips, SNP chips)
- Powerful, handles largest Affy chips, probe-level analysis
- Up to hundreds of chips at once
- PC, Mac or Linux desktops
- Support available through MSCL

## MSCL Analyst’s Toolbox Capabilities

- Connects to the central NIHLIMS database or local GCOS databases
- Reads in Pivot Tables from Affymetrix ECTM or GCOSTM
- Visualizes Principal Components
- Analyzes simple experiments (paired, unpaired T-tests)
- Analyzes complex experiments (multiple treatments, time series, linear trends, slope changes between treatments)
- Compensates for “batch” effects
- Selects and annotates significant genes
- Manages multiple gene lists (intersection, union, Venn diagrams)
- Multivariate, Cluster, Discriminant, Neural net analysis
- Uses dynamic visualization tools

## How to obtain:

**JMP**
- http://isdp.cit.nih.gov/downloads/stats.asp
- Find your desktop support person at http://isdp.cit.nih.gov/information/contact_lookup_nih.asp
- JMP technical support from (919) 677-8008
**The MSCL Analyst's Toolbox**
- Download from http://affylims.cit.nih.gov
- Help offered on collaborative basis by MSCL
- Email questions to: munson@helix.nih.gov

## NIH Bioinformatics Cooperativehttp://affylims.cit.nih.gov

## Input files or Fetch data
Transform and normalize
Principal Components Analysis
Create Master file, add treatment groups
Compute statistical test, get p-values
Correct for multiple comparisons or use FalseDiscoveryRate
Compute log fold-change
Visualize results
Select relevant genes

- Transform and normalize
- Principal Components Analysis
- Create Master file, add treatment groups
- Compute statistical test, get p-values
- Correct for multiple comparisons or use FalseDiscoveryRate
- Compute log fold-change
- Visualize results
- Select relevant genes

- files

- Xform

- PCA

- Master

- Final

**MSCL Toolbox Data Pipeline:**

## Data sources:

- NIHLIMS database via ODBC connection
- Local GCOS database via ODBC connection
- GCOS pivot table
- EC pivot table (NEW support for this option)
- Excel spread sheet
- Text files

## Data Input or data fetch

- DCEG/NCI
- Publish DB
- <username/password>

- MSCL
- Publish DB
- <username/password>

- client
- files

- client
- workstation
- Analyze (MAS)

- Process DB
- <experiment>
- .dat files
- .cel files
- .chp files
- .rpt files

- Import(LM)
- Export(LM)

- Import

- Publish(MAS)

- ODBC
- access

- DMT

- Partek

- GeneSpring

- archive(LM)
- delete(LM)
- assume ownership(LM)

- Fluidics Platform

- Scanner

- CCMD
- Publish DB

- A-SCAN

- NIHLIMS database

- ECTM or
- GCOSTM

- MAS5TM

- .txt

**Notes:**
This shot shows an overview of the possible data flow throughout our system and where the toolbox fits in.

## Gene Expression Data Matrix

- Expression
- Matrix

- Gene Annotations

- Sample information

**Notes:**
I wanted to go over the idea of a gene expression matrix and what format the toolbox expects the data.  

## Annotations for each gene

- Probe Set ID
- Genbank ID
- Unigene ID, Title
- Entrez Gene ID
- Cytogenetic map location
- Physical map location
- HUGO gene symbol, synonyms
- Functional relevance
- Associated literature references ...
- GO terms for molecular process, biological function or cellular component

- Gene
- Annotations

## Annotation Files:

**Affymetrix annotations for each probeset have been downloaded and formatted for MSCL Toolbox, available at *****affylims.cit.nih.gov***
**Annotations are updated quarterly**
**Annotation tables may be JOINed by *****ProbeSetID ***
- Probe Set ID
- Gene Title
- Gene Symbol
- UnigeneID
- Transcript ID
- Ensembl
- Entrez Gene
- Representative Public ID
- First SwissProt
- Genome Alignment Chromosome
- Genome Alignment Start Address
- Genome Alignment Stop Address
- Genome Alignment Strand
- Chromosomal Location

- Final

- Annot.

- Final-Annot

## Annotating Genes

- Netaffx, reformatted

- Your data file

- “JOIN” on ProbeSetID

## Information about the Sample(transposed into MasterFile)

- Information about
- each Sample

- Clinical information (human)
- Diagnosis
- Demographic information
- Treatment (in vivo, in vitro) in designed experiment
- Tissue of origin
- Cell culture, strain, passage
- Sampling date/time
- RNA preparation protocol
- Operator/batch/lot/laboratory information
- QC information (rawQ, scale factor, 3/5-actin, 3/5-GAPDH, etc)

## Table formats 

- JMP usually deals with a single Table, but...
- TWO tables are needed for MSCL Analyst’s Toolbox:
**1. "Master File" layout**
- Each ROW represents a chip
- Columns define treatment, replicate number, etc.
**2. "Final" layout**
- COLUMNs correspond to chips (rows in Master File)
- Each ROW is a probe set, unique identifier is probe set ID
- Tables are LINKED by “Shortnames” field in Master

## Linked Table Formats

## Naming Convention for Final File Columns (prefixes)

- Data type: AD-, SG-, PA-
- Data transform: L-, Lmed-, GL-, S10-
- Statistical results: p-, FDR-, mean-, SFC-
- Column Naming Tips:
- Avoid punctuation, hyphen, period, slash, etc.
- Avoid spaces, use underscore “_” instead
- Shorter is better
- Toolbox utility available for trimming column names

- Column Name
- ITEM_NAME
- SG-33NH
- SG-33TH
- S10-33NH
- S10-33TH
- PA-33NH
- PA-33TH
- SFC-7
- SFC-11
- p-slope&cent2
- FDR slope&cent2

## Input files or Fetch data
Transform and normalize
Principal Components Analysis
Create Master file, add treatment groups
Compute statistical test, get p-values
Correct for multiple comparisons or use FalseDiscoveryRate
Compute log fold-change
Visualize results
Select relevant genes

- Transform and normalize
- Principal Components Analysis
- Create Master file, add treatment groups
- Compute statistical test, get p-values
- Correct for multiple comparisons or use FalseDiscoveryRate
- Compute log fold-change
- Visualize results
- Select relevant genes

**Data Pipeline:**

- files

- Xform

- PCA

- Master

- Final

## Data Transformation and Normalization

## Log(x/median x) transform (“Lmed”)

## Input files or Fetch data
Transform and normalize
Principal Components Analysis
Create Master file, add treatment groups
Compute statistical test, get p-values
Correct for multiple comparisons or use FalseDiscoveryRate
Compute log fold-change
Visualize results
Select relevant genes

- Transform and normalize
- Principal Components Analysis
- Create Master file, add treatment groups
- Compute statistical test, get p-values
- Correct for multiple comparisons or use FalseDiscoveryRate
- Compute log fold-change
- Visualize results
- Select relevant genes

**Data Pipeline:**

- files

- Xform

- PCA

- Master

- Final

## Principal Components Analysis

- PC 1(38%)

- PC 2(12%)

**Notes:**
"The principal components analysis provides a quick overview of the complete dataset. 
Each data point on the plot represents an individual chip measuring expression in one of four experimental conditions. PC1 <point to x-axis> is a particular linear combination of the expression levels for all ~10,000 gene expression levels, chosen to represent the largest possible fraction of total variation (in this case 38%) between the individual chips.PC2 <point> is an orthogonal combination representing the next highest fraction (12%) of total variation.In this case, you see the chips separate on PCA according to the presence of Interferon (IF), with those receiving Interferon treatment on the left <indicate> and those without on the right <indicate>. There is at most a small separation with respect to the second treatment, Dexamethasone <indicate>.You can also see that the separation along PC2 is associated color, Red or Green.  We have color-coded the the Probe Array Lot number.  This suggests that Probe Array Lot is an important variable in measuring gene-expression.

The PCA plot is a very useful tool for recognizing the overall outcome of an experiment, and can also help you detect outliers or bad quality chips."

## Input files or Fetch data
Transform and normalize
Principal Components Analysis
Create Master file, add treatment groups
Compute statistical test, get p-values
Correct for multiple comparisons or use FalseDiscoveryRate
Compute log fold-change
Visualize results
Select relevant genes

- Transform and normalize
- Principal Components Analysis
- Create Master file, add treatment groups
- Compute statistical test, get p-values
- Correct for multiple comparisons or use FalseDiscoveryRate
- Compute log fold-change
- Visualize results
- Select relevant genes

**Data Pipeline:**

- files

- Xform

- PCA

- Master

- Final

## Analysis Scripts

**ANOVA1**
**T-test, unequal variance**
**Paired t-test**
**Consistency test**
**ANOVA1 with blocking**
**ANOVA2 with interaction terms (unbalanced data allowed)**
**ANOVA2 with blocking**
**Linear regression**
**ANCOVA with blocking (balanced data case)**
**ANCOVA2 with blocking (balanced data case)**
**Other tests are easily added (requires scripting)**

## Input files or Fetch data
Transform and normalize
Principal Components Analysis
Create Master file, add treatment groups
Compute statistical test, get p-values
Correct for multiple comparisons or use FalseDiscoveryRate
Compute log fold-change
Visualize results
Select relevant genes

- Transform and normalize
- Principal Components Analysis
- Create Master file, add treatment groups
- Compute statistical test, get p-values
- Correct for multiple comparisons or use FalseDiscoveryRate
- Compute log fold-change
- Visualize results
- Select relevant genes

**Data Pipeline:**

- files

- Xform

- PCA

- Master

- Final

## Log(FoldChange)=“LFC”

- FoldChange = treated / control

- Log(FoldChange) = Log(treated / control)
-                               = Log(treated) - Log(control)

- _Rule of Thumb for Base10 Logarithms:_
- 	Log10(2-fold change) = 0.3
- 	Log10(10-fold change) = 1
- 	Log10(0.1-fold change) = -1

## Input files or Fetch data
Transform and normalize
Principal Components Analysis
Create Master file, add treatment groups
Compute statistical test, get p-values
Correct for multiple comparisons or use FalseDiscoveryRate
Compute log fold-change
Visualize results
Select relevant genes

- Transform and normalize
- Principal Components Analysis
- Create Master file, add treatment groups
- Compute statistical test, get p-values
- Correct for multiple comparisons or use FalseDiscoveryRate
- Compute log fold-change
- Visualize results
- Select relevant genes

**Data Pipeline:**

- files

- Xform

- PCA

- Master

- Final

## Volcano Plot

- Selection
- Regions

## Interpreting Gene Lists

- Final

- Annot.

- Filter
- (FDR<10%)

- GeneList

- Significant Terms

- IngenuityTM,
- GeneGoTM

## GO-SCAN- Gene Ontology Annotations

*Gene Ontology for Significant Collection of Annotations*: GO-SCAN is a bioinformatics
- tool that selects and presents relevant Gene Ontology (GO) annotations for a gene "hit"
- list from an Affymetrix microarray experiment.     http://goscan.cit.nih.gov/

## Ingenuity Pathway Analysis(Doug Joubert, NIH Library)