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Getting Started with JMP Life Sciences

Software Basics

Introduction

Requirements

Text Conventions

The Life Sciences Main Menus

Important Differences between JMP and JMP Life Sciences Dialogs

Running a Process

Tabbed Reports

Stopping a Process

Saving and Loading Settings

SAS Variable Names and Labels

Files and Data Sets

SAS Data Sets

Experimental Design File (EDF)

Data Sets Used in JMP Genomics Processes

Experimental Design File

Genetic Marker Statistics

GWAS Testing

Other Association Testing

Comparison of Association Testing Processes

Spectral Preprocessing

Expression

Quality Control

Normalization

Normalization (Next-Gen)

Differential Expression

Predictive Modeling Utilities

SAS Data Set Utilities

Documentation and Help

Manage Genomics Studies

View Study Metadata

Assign Default Data Sets

Assign Wide Variable Roles

Import

Create a Design Data Set from an Existing Data Set

Create Design File Template

Create a Design File from MiniML

NetAffx Download Engine

Affymetrix Annotation CSV File Input Engine

Affymetrix ARR File Parser

Affymetrix Exon and Whole Transcript Expression CEL Input Engine

Affymetrix Expression CEL Input Engine

Affymetrix miRNA CEL Input Engine

Affymetrix SNP CEL Input Engine

Affymetrix Cytogenetics/CytoscanHD CEL Input Engine

Affymetrix Tiling CEL Input Engine

Affymetrix Exon CHP Input Engine

Affymetrix Expression CHP Input Engine

Affymetrix Tiling BAR Input Engine

Affymetrix SNP CHP Input Engine

Affymetrix Cytogenetics/CytoscanHD CHP Input Engine

Affymetrix Copy Number CHP Input Engine

Affymetrix CNAT Input Engine

Illumina Expression Input Engine

Illumina miRNA Input Engine

Illumina SNP Input Engine

Illumina Copy Number Input Engine

Illumina Methylation/Genotype IDAT Input Engine

Import Individual Text, CSV, or Excel Files

Import a Designed Experiment from Text, CSV, or Excel Files

Nanostring Input Engine

Reference Gene Normalization

Agilent Input Engine

ArrayTrack Input Engine

Bioconductor Expresso for Affymetrix Wrapper

GenePix Input Engine

QuantArray Input Engine

ScanAlyze Input Engine

Imputed SNP (Tall Format) Input Engine

Imputed SNP (Wide Format) Input Engine

Imputed SNP Import Tutorial

Arlequin Input Engine

NEXUS Input Engine

Pedigree Input Engine

PLINK Input Engine

PLINK Binary Input Engine

WinQTLCart Input Engine

OneMap Input Engine

Tassel-GBS Import Engine

ABI Analyst Input Engine

SAM Input Engine

BAM Input Engine

Eland Input Engine

Bin Intensities or Read Counts

Gene Model Summary

Call Variants with SAMtools

CLC Bio Input Engine

Complete Genomics Input Engine

VCF Input Engine

Import Feature-Barcode Matrices

Bioconductor QuasR Alignment Wrapper

Workflows

Basic Genetics Workflow

Basic Linkage Mapping Workflow

Basic Copy Number Workflow

Basic Exon/Alternative Splicing Workflow

Basic Expression Workflow

Basic miRNA/miRNA-Seq Workflow

Basic RNA-Seq Workflow

Basic Single Cell RNA-Seq Workflow

Basic Tiling Workflow

Genetics Rare Variants Workflow

Genetics Q-K Analysis Workflow

Expression QC Workflow

Expression Statistics Workflow

Verify Gender of Samples

Create Annotation Analysis Group Variable

Subset and Reorder Genetic Data

Recode Genotypes

Recode Missing Genotypes

Impute Missing Genotypes

Standardize Genotypes

Expand Multiallelic Genotypes

Collapse Multiallelic Genotypes

Calculate Square Root of Matrix

Missing Genotype by Trait Summary

LD Block Creation

Linkage Disequilibrium

LD tagSNP Selection

Malecot LD Map

Case-Control Association

PCA for Population Stratification

SNP-Trait Association

Imputed SNP-Trait Association

Survey SNP-Trait Association

Quantitative TDT

TDT

GWAS Meta-Analysis

Multiple SNP-Trait Association

Rare Variant Association

Pleiotropic Association

Marker-Trait Association

SNP-SNP Interactions

SNP Interaction Discovery

Haplotype Trend Regression

Haplotype Q-K Mixed Model

htSNP Selection

Affected Sib-Pair Tests

Haseman Elston Regression

Variance Components

Recombination and Linkage Groups

Linkage Map Order

Linkage Map Viewer

Compare Linkage Maps

Build Consensus Linkage Map

QTL Single Marker Analysis

Build QTL Genotype Probability Data Set

QTL IM, CIM and MIM Analysis

Copy Number/LOH Control Set Adjustment

Distribution Analysis

Data Standardize

Correlation and Principal Variance Component Analysis

Bin

One-Way ANOVA

Bivariate One-Way ANOVA

Partition

Spectral Preprocessing

Distribution Analysis

Correlation and Principal Variance Component Analysis

Correlation and Grouped Scatterplots

Filter Intensities

Feature Flagger

Effect Removal via PLS Normalization

Missing Value Imputation

Pseudo Image

Ratio Analysis

Surface Summary

Variable Gene Selection

Data Standardize

ANOVA Normalization

Mixed Model Normalization

Control Set Normalization

Batch Normalization

Batch Scoring

Loess Normalization

Factor Analysis Normalization

Partial Least Squares Normalization

Quantile Normalization

RPM Scaling

Upper Quartile Scaling

Allele Specific Expression Filter

Hierarchical Clustering

K-Means Clustering

Principal Components Analysis

Plot Intensities

Cross Correlation

Distance Matrix and Clustering

Multidimensional Scaling

Partial Correlation Diagram

Predictive Modeling

Introduction

Predictive Modeling Review

Discriminant Analysis

Distance Scoring

General Linear Model Selection

K Nearest Neighbors

Logistic Regression

Partial Least Squares

Partition Trees

Quantile Regression Selection

Radial Basis Machine

Ridge Regression

Life Regression

Proportional Hazards Regression

Genomic BLUP

Genomic Bayesian Regression

XG Boost Regression

Cross Validation Model Comparison

Learning Curve Model Comparison

Test Set Model Comparison

Model Summary and Ensemble

Merge Cross Validation Model Comparison Results

Merge Learning Curve Model Comparison Results

Recode Genotypes

Transpose Tall to Wide

Transpose Wide to Tall

Principal Component Scoring

Optimal Treatment Regime

P-Value Operations

P-Value Combination

P-Value Adjustment

P-Value Quantile Plotter

Meta-Analysis

Genome Views

JMP Genomics Browser

Chromosome Color Theme

UCSC Genome Browser Link

Affymetrix Integrated Genome Browser

Affymetrix Integrated Genome Browser

NetAffx Download Engine

Import Affymetrix Annotation CSV Files

Ingenuity Pathways Analysis Upload

Ingenuity Get Gene Pathways

Merge Gene Sets

Export to GSEA Format

Utilities

Missing Value Imputation

Transpose Rectangular

DATA Step

Compare Data Sets

Import SAS Transport Files

Import Text, CSV, or Excel Files

Export SAS Transport File

Statistics for Columns

Transform

Data Filter

Graph Builder

Create 0-1 Columns from a Class Variable

Create 0-1 Indicator for Selected Rows

Venn Diagram - Single Table

Venn Diagram - Multiple Tables

Open SAS Temporary Folder

Open Output Folder

Clear Parameter Defaults

Generate Clinical Dialogs

Stack

Subset

Transpose Tall to Wide

Transpose Wide to Tall

Unstack

Export to Affymetrix CHP Format

Rename

Reorder

Filter Wide Columns Based on Tall Rows

Combine Columns

Generate Genomics Dialogs

Save As SAS Data Set

Configure Life Sciences Settings

Set Life Sciences Preferences

Load Life Sciences Setting

Customize Clinical Starter

Customize Genomics Starter

Convert Character Date

Combine this Study with Study from Update Tab

Combine with:

Combined Study Name

Delete study

Default Annotation Data Set

Default Experimental Design Data Set

Default Tall Input Data Set

Default Wide Input Data Set

Dependent Variable

Experimental Design Data Sets

Folder of Annotation Study Data Sets

Folder of Experimental Design Study Data Sets

Folder of Tall Study Data Sets

Folder of Wide Study Data Sets

Input SAS Data Set

Label Variable

List-Style Specification of Lock-In Categorical Predictor Variables

List-Style Specification of Lock-In Class Predictor Variables

List-Style Specification of Lock-In Continuous Predictor Variables

List-Style Specification of Predictor Categorical Variables

List-Style Specification of Predictor Class Variables

List-Style Specification of Predictor Continuous Variables

Lock-In Categorical Predictor Variables

Lock-In Class Predictor Variables

Lock-In Continuous Predictor Variables

New Study Name

Output Folder

Predictor Categorical Variables

Predictor Class Variables

Predictor Continuous Variables

Server Output Directory

UNNAMED (Blank is a delimiter)

UNNAMED (Parse from left)

Additional New Design Variable Names

Affection Status Coding

Allele Delimiter

Allele Peaks Data Set

Alleles to Use for Genotypes

Allocate Memory Size

Alternate Phenotype File

Annotated Variants within Known Genes (gene)

Annotation Columns

Annotation Data Set

Annotation File

Annotation Gene Name Variable

Annotation Merge Variables

Annotation SAS Data Set

Apply log2 transformation to intensities

Apply original column names

ArrayTrack Annotation Output Data Set

ASM Files from Version of Assembly Pipeline prior to 2.0

Background Correction

Background Correction Method

Background Subtraction

Barcode File to Import

Baseline Reference Data Set

Baseline Reference SAS Data Set

Bin Summary Statistic

Binary PED File

Binding Density QC

By Variables

Cast Selected Columns into Roles

Cel Layout File

CGA Tools testvariants File

Channel Status

Check available disk space

Check uniqueness of column names

Choose a folder containing files listed in the File column

Chromosome

For the Gene Model Summary Process:

Chromosome Summary Data Set

Chromosome Variable

CN Columns to Include

CN Measurements to Include (100K or 500K Arrays)

Code genotypes numerically

Column Delimiter

Column Delimiter for Genotype Probability File(s)

Columns to Include

Columns to Include In Output Data Set

Combine multiple VCF files into a single data set

Compress output data sets

Compute Reference Scaling Factor

Continuous Variables to Summarize

Control Gene Normalization

Copy Number Annotation Data Set

Copy Number Annotation SAS Data Set

Copy Number Data Set

Copy Number File

Count Data

Count of Variations by Gene (geneVarSummary)

Count reads by strand

Covariate File

Covariate File has a Header Row

Create Combined Data Set

Create Final Report Data Set

Create Quality Flag Data Set

Create separate data sets for B_Allele_Freq and/or Log_R_Ratio

Create separate data set(s) for each chromosome when there are more variants than:

Create separate data sets for each selected measurement

Create separate data sets for SNP- and CN-summarized probesets

Create wide Output Genotype Data Sets

Cross Type

Custom File Filter Expression

Data Set Containing Probes to Remove

Data Start Row

Data Step Statements

Delimiters

Detection p-Value Cutoff for Setting Individual Intensities to Missing

Display marker genotype cell color plots

Drop SNPs with this percentage of samples that are below:

Encoding of Raw Array Files

End

Exclude rows with missing physical position

Filter to Exclude Chromosomes

Exon Count

Exon Ends

Exon Starts

Experimental Design Data Set Corresponding to Baseline Reference Data Set

Experimental Design File

Experimental Design SAS Data Set Corresponding to Baseline Reference SAS Data Set

Exponential Multiplier of Kernel Density

Expression

FAM File

Feature Identifier

Feature Identifier for Computing Counts

File Filter Expression

File Type

Files for Variants Call

Files in PBAT Format

Filter before:

Filter out genes with detection p-value above:

Filter to Include Annotation Rows

Filter to Include Gene Model Rows

Filter to Include Markers

Filter to Include Observations

Flag Filter Expression

Folder Containing a FASTA-formatted Reference Sequence

Folder Containing Other Library Files

Folder Containing Raw Data Files

Folder Containing Raw Sequence Files

Folder Containing Rterm.exe

Folder Containing the BPMAP Files

Folder Containing the CDF File

Folder Containing the Library Files

Folder Containing the Meta Probeset File

Folder Containing the RCC Data Files

Folder of BAM Files

Folder of Data Files from Eland

Folder of Feature-Barcode Matrices

Folder of Illumina Data File

Folder of Input Files

Folder of Raw Array Files

Folder of Raw BAM Data Files

Folder of Raw Data Files

Folder of Raw Files

Folder of Raw SAM Data Files

Folder of Samples File

Folder of SNP/DIP Detection Table CSV Files

Folder of Track Gene Text Settings Files

Folder of VCF Files

...for at least this percentage of the samples

Genotype Probability File(s)

Genotype Probability Threshold

Genotyping Calls Data Set

Genotyping Error Threshold

Get all column names from first row

Get Breadth of Coverage

Housekeeping Genes

How shall I get the information about your experiment

Import CEL intensities without merging annotation

Increase R Software Memory Limit

Include sequence data in output

Indicator of Different Column Names across Raw Data Files

Input WinQTLCart File

Intensity Variables to Bin

Keep single probes not associated to any probeset

Keep single-probe-sets not associated to any transcript cluster

Key Variable(s) to Merge with Input Data Set

Key Variable(s) to Merge with Input SAS Data Set

Length of Sample ID Column

LGEN File

List of Phenotype Variable Names

List of Variable Names

List of Variable Names and Lengths

List of Variable Names and Types

List-Style Specifications of Housekeeping Genes

List-Style Specification of Individual Variables

List-Style Specification of Intensity Variables to Bin

List-Style Specification of Marker Variables

List-Style Specification of Trait Variables

List-Style Specification of Variables to be Included for Normalization

LOH Columns to Include

LOH Measurements to Include (100K or 500K Arrays)

Master Variations (masterVarBeta)

Matrix File to Import

Maximum Column Length

Maximum Intron Bin Size

Measures to Include (Chromosomes Data)

Measures to Include (CopyNumber Data)

Measures to Include (SNP6 Array)

Merge Variables

Meta Probeset File

MiniML-formatted File

Minimum Number of Columns to Scan

Minimum Number of Probes to Summarize Probe-Set Level

Missing Covariate Value

Missing Genotype Value

Missing Individuals Threshold

Missing Phenotype Value

Missing Quantitative Trait Value

Name of Phenotype Variable

Names of Variables Preceding Genotype Probability Columns

NBeads Output Data Set

Negative Control Method

New Study

New Variable Names for Experimental Design

Normalize copy number data using autosomes

Normalize SNP data using autosomes

Number of CEL Files to Process at a Time

Number of Channels in Each File

Number of Data Files to Process at a Time

Number of Genotype Probability Columns

Number of Genotype Probability Columns per Individual

Number of Rows in Each Bin

Number of Rows to Scan

Include SNP variants only

Options

Order Annotation Data Set by SNP column order

Output Allele Intensity Data Set

For the Illumina Copy Number Input Engine:

Output Allele Intensity Experimental Design Data Set

Output Annotation Data Set

Output Copy Number Intensity Data Set

Output Data Set

Output Data Set Name

Output Data Set of Normalization Factors

Output Data Set of Selected Stable Genes

Output Expected Genotype Data Set

Output Experimental Design Data Set

Output File Name

Output File Prefix

Output Folder

Output Genotype by Chromosomes

Output Genotype Data Set

Output Genotype Probabilities Data Set

Output Genotype Threshold Data Set

Output Map Data Set

Output Probe-level Intensity Data Set

Output Sequence Data Set

Output SNP-summarized Copy Number Intensity Data Set

Output Wide Data Set

p-Value Cutoff for Segregation Test

Package or Individual Genome Folder Containing ASM Results

Parent 1 ID Variable

Parent 2 ID Variable

Parse Associated Gene Column

PDF or RTF Output File

Percentage of Data to Be Included in Training Data

Percentage of Samples Below Cut-off(s) for Dropping SNPs

Percentage of samples for dropping SNPs

Perform log2 transform

Perform log2 transform after scaling

Phenotype File has a Header Row

Positive Control Limit of Detection QC

Positive Control Linearity QC

Positive Control Method

Prefix for New Columns

Prefix for Output Data Set Names

Prefix for Output Experimental Design Data Set Names

Prefix for SNP Names

Prefix to Append to SNP Column Names

Prefix to Append to SNP Column Names (for Full Data Table)

Prefixes of SNPs to Include in Data Sets

Probe Coordinates Output Data Set

Probe Group File

Probe Normalization

Probe Normalization Method

QC Probe Output Data Set

Quality Flag Output Data Set

Quantitative Variables

Reference File

Reference Gene Normalization

Reference Genes to Use

Reference Genome File

Remove AFFX Control SNPs from output data set(s)

Remove Control Genes

Remove PCR duplicates

Remove Reference Genes in Output

Row Number of Variable Names

Sample File

Sample Files

Sample Variable

SAS Code for Customized Flagging Rule

SAS Code to Create Columns

SAS Code to Create New Design Variables

Segments CNNeutralLOH Data Set

Segments LOH Data Set

Segments Mosaicism Data Set

Segments NormalDiploid Data Set

Select an array

Select Column to Parse

Select Files

Select key variable to merge files

Select the folder containing .ARR files

Select the folder containing pairs of ARR and data files

Select the type of file(s) to input

Selected Column

Server Output Directory

Set flagged data to missing

Set heterozygous to missing

Set individual genotypes to missing that are below:

Shifting Factor

Smoothing Bandwidth Multiplier

SNP Annotation Data Set

SNP Annotation SAS Data Set

SNP Map File

Sort genotype columns and map rows

Source of Files

Specify the maximum number of missing genotypes for a locus to be included in the output data set

Specify your Input File

Spot Coordinates Output Data Set

For Gene Model Summary:

Summary Level

Summary Method

Tab-Formatted Probe Sequence File

Track Gene Text Setting File

Trait Variables

Transformation Method

Type of Array

Type of File

Type of File(s)

Type of File Conversion

Type of Map Files

Type of Other Files

Type of PED/FAM and Other Files

Type of Phenotype Variable

Unit for Genetic Distance

Value of Chromosome Variable Indicating Non-autosomes

Value of Columns Above to Be Associated with Non-autosomes

Variables By Which to Merge Annotation Data

Variables Containing Primary Data

Variables to be Included for Normalization

Variables to Keep in Output Data Set

Variations (var)

Variations at Known db SNP Loci (dbSNPAnnotated)

Weighted with Kernel Density

What kind of data files do you have

What type of Experiment is this

Width of Positional Bin

Workflows

Accession Number Variable

Add Fold Change Filter to Select Significant Tests

Add Marker Genes to Explore

Add Mean Difference Filter to select significant tests

Additional Bandwidth on Each Side of Tracks

Additional Fixed Effects

Additional Random Effects

Adjust Variability for these Random Effects

Adjustment Effects

Adjust Model with Additional Fixed Effects

Allele Characters for A (P1 line) and B (P2 line)

Alpha

Alpha value for Beta distribution

Annotation Analysis Group Variable to Use

Annotation Analysis Group Variable

Annotation Analysis Group Variable for Collapsing Rare Variants

Annotation Analysis Group (Gene e.g.) Variable

Annotation Chromosome Variable

Annotation Group Variable

Annotation Input SAS Data Set

Annotation Label Variable

Annotation Location Variable

Annotation Merge Variables

Annotation Plotting Group Variable

Annotation Position Variable

Annotation SAS Data Set

Apply a Shifted log2 Transformation for QC Analysis

Association Tests

Automated Linkage Group Clustering Method

Bandwidth

Beta value for Beta distribution

Binary Trait Variable

Binary Trait Variables

Break linkage groups based on:

Break linkage groups between markers with large ordered distances

By Variables

Calculate trend odds ratios

Categorical Variables

Categorical Variables Defining Groups

Categorical Variables for Model

Cells with Maximum Features Detected

Cells with Minimum Features Detected

Change Output Folder to Workflow Folder in settings moved to right panel

Chromosome Variable

Class Variables

Cluster significant LSMean profiles

Cluster significant Mean profiles

Cluster Significant Results

Clustering Method

Collapse rare variants within analysis group

Compress the K matrix

Compression Rate

Compute Q Variables from PCA

Compute results for annotated rows only

Compute results for exon annotated rows only

Compute sandwich (empirical) estimator of covariance matrix

Control Levels for Difference Comparisons

Control Levels for Differential Expression Comparisons

Conversion for P-Values

Correlation and Grouped Scatterplots

Correlation and Principal Variance Components Analysis

Create add-in package

Cross Type

Cumulative Proportion of Variation to Explain with Principal Components

Current Study

Data Set Containing LSMeans Differences to Include

Data Set of Differences to Include from Comparison Set

Define linkage groups based on the:

Delete rows:

Delete rows with Interquartile Range satisfying this expression

Delete rows with Mean satisfying this expression

Delete rows with Median satisfying this expression

Delete rows with Number of Missing Values satisfying this expression

Delete rows with Percentile satisfying this expression

Delete rows with Standard Deviation satisfying this expression

Denominator Degrees of Freedom Method

Direction of the Cutoff

Direction of the Fold Change Cutoff

Direction of the Mean Difference Cutoff

Display marker genotype cell color plots

Display principal components plots

Distribution Analysis

Estimate LSMeans for these Fixed Effects

Event Trait Value

Exon (Probeset) ID Variable

Exon Annotation Chromosome Variable

Exon Annotation Label Variable

Exon Annotation Merge Variables

Exon Annotation Position Variable

Exon Annotation SAS Data Set

Exon Annotation Transcript Variable

Expected Segregation Ratios for AA AB BB

Experimental Design SAS Data Set

Features Detected in Minimum Cells

File Containing Estimate Statements

Filter Data with Zero or Missing Values

Filter Rows Whose Proportion of Zero/Missing Values Exceeds this Cutoff

Filter to Include Data in Analysis

Filter to Include Exon Annotation Rows for ANOVA

Filter to Include Individuals

Filter to Include Markers

Filter to Include miRNA Annotation Rows

Filter to Include Observations

Filter to Include Transcript Annotation Rows for ANOVA

Filter to Select Markers for Computing the K Matrix

Filter to Select Markers for PCA

Fix covariance parameters

Fixed Effects

Fixed Effects for Differential Expression

Fixed Effect Interactions with Exon ID Variable (Alternative Splicing)

Fold Change Filter Cutoff

Folder of Available Processes

Folder of Available Settings

Folder of Track Settings Files

Format of Marker Variables

Framework Linkage Group Variable

Framework Map Data Set

Framework Marker Name Variable

Framework Order Variable

GenBank Accession Variable

Gene Description Variable

Genetic Distance Break Value

Genotype Delimiter

Genotyping Generation (n)

Group Percentage for Deletion

Grouping Method

Grouping Recombination Fraction Threshold

Hierarchical Clustering

Hotelling’s T-squared Test

ID Variables

Include 3D plots

Include adjusted p-values in addition to -log10(p-values)

Include Fold Changes in Addition to log Fold Changes

Include fold changes in addition to log2 fold changes

Include input data in package

Include p-values in addition to -log10(p-values)

Include study data in package

Individuals Minimum Proportion of Nonmissing Genotypes

Input data is log-transformed

Input Genotype SAS Data Set

Input SAS Data Set

Intensity Columns to Filter

Interaction Effects

JMP Journal Output File

K-Means Clustering

Kernel Function

Label Variable

Launch ANOVA for Differential Expression Analysis

Launch ANOVA Interface

List every model fit

List-Style Specification of Intensity Columns to Filter

List-Style Specification of Marker Variables

List-Style Specification of Trait Variables

List-Style Specification of Variables Whose Rows are to be Clustered

-log10(p-value) Cutoff

LSMeans Control Levels

LSMeans Difference Set for Volcano Plots

MAF Threshold for Rare Variants

Map Function

Marker Name Variable

Marker Variables

Max Iteration of t-SNE

Maximum Dimension of K Matrix

Maximum Dispersion to Filter Genes

Maximum Mean to Filter Genes

Maximum Number of Chromosomes Per Row in 3D Display

Maximum Number of Clusters for K-Means

Maximum Number of Principal Components

Maximum Number of Principal Components to Model

Mean Difference Filter Cutoff

Means Control Levels

Means Difference Set for Volcano Plots

Method

Minimum Dimension of K Matrix

Minimum Dispersion to Filter Genes

Minimum Distance of UMAP

Minimum Mean to Filter Genes

Minimum Number of Clusters for K-Means

Minimum Number of Observations Required for a Branch

Minimum Proportion of Nonmissing Genotypes

Minimum X Chromosome Heterozygosity for Females

Minor allele frequency at Marker Locus

Minor Allele Frequency Threshold

Minor Allele Frequency Threshold for Including SNPs

miRNA ID Variable

Model interactions of these Fixed Effects with the Exon ID Variable (Screen for possible alternative splicing)

Model Data As:

Modeling Distribution

Model these Fixed Effects

Multiple-Locus Regression Model

Multiple-Locus Radial Basis Machine (Kernel Method)

Multiple Testing Correction

Multiple Testing Method

Multiple Testing Method for Segregation Tests

Normalization Method

Number of Clusters

Number of Clusters Expected

Number of Epochs of UMAP

Number of Linkage Groups

Number of Markers in Each Group

Number of Neighbors of UMAP

Number of Principal Components

Number of Principal Components to Use

Number of Rows in Input Data for Testing Run

Number of SNPs to Test

Number of the First Principal Component to Model

Number of Variable Genes to Keep

Analyze rare variants only

Organism

Output Data Set

Output Data Set Containing Filtered Data

Output Dimension of Embedding

Output File Prefix

Output Folder

Output genotype LS means and diffs

Output residuals from every model

P-Value Adjustment

p-Value Cutoff for Plots

p-Value Cutoff for Segregation Test Plots

PARMS Statement Values and/or Options

PC Regression Model

Pedigree ID

Percentage of Mitochondria Genes Allowed

Percentile to Compute for PCTL Statistic

Perform Case-Control Association Tests

Perform Missing Genotype by Trait Analysis

Perform multiallelic analysis on multiallelic markers

Perform shifted log2 transformation:

Perplexity of t-SNE

Plot Markers with significant p-values only

Position Variable

Prefix of Marker Genotype Variables

Principal Component Analysis

Principal Variance Component Effects for QC

PROC GLIMMIX Estimation Method

Random Effects

Random Mating Generation (t) Prior to Inbreeding

Random Statement Options

Recode genotypes numerically (2,1,0)

Recombination Fraction Break Value

Recombination Fraction Cutoff

Reference Trait Value

Remove Mitochondrial Genes from Analysis

Remove Ribosomal Genes from Analysis

Replace Cluster Means with representative observations

Replace highest values

Replace intensities falling above this column percentile

Replace intensities falling above this value

Replace intensities falling at least this many standard deviations above the column mean

Replace intensities falling at least this many standard deviations below the column mean

Replace intensities falling below this column percentile

Replace intensities falling below this value

Replace lowest values

Report SNP x Interaction Effect tests only

Results to Include in the Review

Review Output File

Run Analyses Above...

Run QC analyses:

Run Subset and Reorder Genetic Data process to order marker data for QTL analysis

Run t-SNE and UMAP (Appropriate R Packages Required)

Scale columns

Scale rows

Select Comparison Set for Differential Expression Tests

Select Comparison Set for Mean Differences Tests

Select Method

Separate and journal results by chromosome

Sequence Kernel Association Test (SKAT)

Server Output Directory

Shifted log2 Transformation for ANOVA

Shifted log2 Transformation for QC

Shifting Factor

Shifting Factor of log2 Transform for QC

Shifting Factor of log2 Transform before Normalization or ANOVA

Show Only:

Single-Locus Genotype Tests Pearson chi-square and Fisher’s exact

Single-Locus Regression Model

SNP ID Variable

SNPs: Minimum HWE p-Value

SNPs Minimum Minor Allele Frequency

SNPs Minimum Missing Genotype by Trait p-Value

SNPs Minimum Proportion of Nonmissing Genotypes

Template Study Output Folder

Terminate further processes when an error occurs

Test on a subset of SNPs

Track Settings Files

Trait Value of Individuals to Include in HWE Test

Trait Variables

Transcript Annotation Chromosome Variable

Transcript Annotation Label Variable

Transcript Annotation Merge Variables

Transcript Annotation Position Variable

Transcript Annotation SAS Data Set

Transcript Annotation Variables to Keep

Transcript Cluster ID Variable

Two Way Clustering

Type of Trait

Use lower boundary constraint of 0 for K matrix covariance parameter

Use QTL data numeric coding from JMP Genomics versions prior to 5.1

Value Ordering for Nominal Color Variable

Value to Use to Replace Highest Values

Value to Use to Replace Lowest Values

Variable Containing Names of Marker Variables

Variable Gene Selection Method

Variables By Which to Merge Exon Annotation Data

Variables By Which to Merge Tx Annotation Data

Variables Defining Blocks

Variables Defining Groups

Variables Defining Plotting Groups

Variables Defining the One-Way Classification

Variables for QC Plotting Groups

Variables to Keep in Linkage Map Data Set

Variables to Keep in Output

Variables to Keep in Output or By Which to Merge Annotation Data

Variables to Retain in Linkage Map Data Set

Variables Whose Rows are to be Clustered

Variance Component Effects

Variant Weights

Where Clause for Subsetting Input Data Set in Test Run

Width of Positional Group

Absolute Mean Difference Cutoff for Continuous Predictors

Absolute Proportion Difference Cutoff for Class Predictors

Accession Number Variable

Add markers dropped out from marker reduction analysis back to the linkage groups

Additional Covariates

Additional Fixed Effects

Additional PROC CLUSTER Options

Additional PROC TREE Options

Additional Random Effects

Adjust permutation p-values for multiple testing

Affected Offspring Variables

Affected Sib-Pair Tests

Affected Value of Trait Variable

Algorithm

All markers are biallelic

Allele Characters for A (P1 line) and B (P2 line)

Allele Variables

Alpha

Alpha for Dprime Confidence Limits

Alpha for LSMeans Confidence Intervals

Alpha for Selecting PCs

Alpha Level for Covariates

Alpha Level for Empirical LOD Thresholds

Alpha value for Beta distribution

Annotation Accession Variable

Annotation Analysis Group Variable for Collapsing Rare Variants

Annotation Analysis Group Variable

Annotation By Group Variable

Annotation Column Name Variable

Annotation Input SAS Data Set

Annotation SAS Data Set

Annotation Group Variable

Annotation Label Variable

Annotation Location Variable

Annotation Location Variable Units

Annotation MAF Variable

Annotation Major Allele Variable

Annotation Minor Allele Variable

Annotation Plotting Group Variable

Annotation Variables to Drop

Annotation Variables to Keep

Annotation Weight Variable

Append markers dropped out from marker reduction analysis to the output data sets

Append prefix to current marker name

Apply adaptive weights

Apply mean-correction to covariates

Apply stopping rules for map order optimization

Apply VIF for genomic control

Association Tests

Assume Hardy-Weinberg equilibrium at all loci

Asymmetric Loss Fitting Proportion

Automated Linkage Group Clustering Method

Average Pool Size

Backcross Parental Lines Variable

Bandwidth

Base Input SAS Data Set

Baseline Unit

Beta value for Beta distribution

Bias correction for the additive relationship matrix

Binary Trait Variable

Binary Trait Variables

Block Partition Variable

Bootstrap Confidence Interval Alpha

Bootstrap Samples

Break linkage groups based on:

Break linkage groups between markers with large ordered distances

Build a Combined Wide Data Set

By Variables

Calculate allele odds ratios

Calculate trend odds ratios

Calculate p-values for F statistics

Categorical Covariates

Categorical Variables

Category Variable

Censor Limit (-log10 scale)

Choose a linkage grouping method

Choose a method for RIL simulation

Choose the first-generation mating type

Choose the mating type

Choose the multi-generation mating type

Choose the selection direction for the index

Choose the selection direction for the trait

Chromosome

Chromosome ID

Chromosome Label

Chromosome Label from Base Input SAS Data Set

Cluster Center Variable

Color of Bars ({r,g,b})

Heat Map Color Theme

Color Variable

Color Variable Type

Columns of Q Matrix sum to 1

Columns of Q Matrix sum to 1 in Q1 Model

Columns of Q Matrix sum to 1 in Q2 Model

Columns of Q Matrix sum to 1 in Q3 Model

Compress output data set

Compression Method

Compression Rate

Compute Cholesky Root of Matrix

Compute sandwich (empirical) estimator of covariance matrix

Compute selection index

Compute the root of the matrix by SVD

Constrain negative IBD estimates to 0

Continuous Covariates

Continuous Trait Variables

Control Marker Data Set - Sorted Marker List

Control Marker Number

Control Marker Selection Method

Convergence Criterion

Conversion for F Statistics p-Values

Conversion for p-Values

Correlation Radius for Clustering

Correlation Value for Clustering

Corresponding Key Chromosome Number Variable from Merge Input SAS Data Set (1-12)

Corresponding Key Grid Variable from Merge Input SAS Data Set (1-12)

Corresponding Key Marker Label from Merge Input SAS Data Set (1-12)

Corresponding Key Testing Location Variable from Merge Input SAS Data Set (1-12)

Covariates

Create cell plot

Create data set containing haplotype frequency estimates

Create data set of covariance parameter estimates for every model fit

Create data set with numerically coded genotypes

Create data sets of CorrCoeff2 in matrix format

Create frequency charts

Create haplotype frequency charts

Create Hierarchical Cluster of SNPs

Create HTML output

Create merged PCA output data set

Create output data set containing allelic transmissions

Create Parallel Plot of SNPs

Create PARMS statement from optimal model covariance parameter estimates

Create Phase Assignment Data Set

Create SAS data set containing htSNP indicator variable

Create Significance Indicator Columns

Create Stability Point Plot

Create subset data set

Create tagSNP subset indicator variable

Criterion for Evaluating Sets of htSNPs

Criterion for Optimal Compression Level

Criterion for Stopping Model Selection

Cross Type

Cumulative Proportion of Variation to Explain with Principal Components

Current Value(s) Denoting Missing Genotypes or Alleles

Cutoff Level of Tree Axis

CV Partitioning Method

D’ between Marker and Disease Locus

Data Step Statements

Define linkage groups based on the:

Delete Nonmatching Rows

Delete Rows Not in Base Input SAS Data Set

Denominator Degrees of Freedom Method

Dependent Categorical Variable

Dependent Class Variable

Display cell plot of Mendelian errors

Display column attributes

Display marker genotype cell color plots

Display markers

Display principal components plots

Display QTL location

Distance to Include Downstream and Upstream of Gene

Distance Unit

Distance Unit for Defining Maximum Range of LD Blocks

Dprime Lower Confidence Limit greater than:

Dprime Upper Confidence Limit greater than:

Dprime Upper Confidence Limit less than:

Drop Alleles with frequency below:

Drop Last Allele for Each Marker

Effect Estimate or Direction Variable

Effect Estimate or Direction Variable is the odds ratio

EigenCore Multiple Testing Method

Environment Variables

Estimate Rho and K Rho

Estimate the Number of Founding Populations

Estimated Frequency Cutoff for Combining Rare Haplotypes

Estimation Method

Evaluate individuals in the Input SAS Data Set without making crosses

Event Trait Value

Exclude single-SNP genes

Expanded Genotype Recoding

Expected Segregation Ratios for AA AB BB

Extreme Sampling Percentile

Family Association Tests

Family Test Options

Feature Selection Criterion

File Filter Expression

Filter Defining First SNP in Interaction

Filter Defining Second SNP in Interaction

Filter to Include Annotation Rows

Filter to Include Linkage Groups

Filter to Include Markers

Filter to Include Markers (applies to both data sets)

Filter to Include Observations

Filter to Include Observations for HWE Test

Filter to Include Predictor Categorical Variables

Filter to Include Predictor Class Variables

Filter to Include Predictor Continuous Variables

Filter to Include Rows in Annotation SAS Data Set

Filter to Include Windows

Filter to Select Null SNPs

Filter Variables

First Column to Display

First Row to Display

Fit Square Root of Recombination Fractions in MDS Algorithm

Fix covariance parameters

Fixed Effects

Fixed Threshold

Flip alleles for A/T or C/G SNPs with major/minor alleles reversed from reference

Folder Containing Scoring Code Files

Folder of Input SAS Data Sets

Folder of Linkage Map Files

Force all markers into linkage groups

Format of Marker Variables

Format of SNP Variables

Framework Linkage Group Variable

Framework Map Data Set

Framework Marker Name Variable

Framework Order Variable

Frequency Cutoff

Frequency Cutoff for Combining Haplotypes

Frequency Initialization

Frequency Variable

Function of Covariates

Function of Trait Variable

Gene By Variables

Gene Input SAS Data Set

Gene Label

Gene Start Variable

Gene Stop Variable

Genetic Distance Break Value

Genetic Distance Unit for Results Plots

Genotype Data Set

Genotype Delimiter

Genotype Probability Data Set

Genotype Probability Variables

Genotype Recoding

Genotype Recoding for Breeding: Additive (1 0 -1) and Dominant (0 1 0)

Genotype to code as 2 for the Trend Test

Genotype to code as 2 for the Trend Test ORs

Genotype Variable

Genotyping Generation (n)

Grouping LOD Threshold

Grouping LOD Score Threshold

Grouping Method

Grouping Recombination Fraction Threshold

Grouping Variable

Haplotype Estimation Method

Haplotype Frequency Data Set

High-risk Allele Frequency

Hold L at starting value

Hold M at starting value

Hold-Out Method

Hold-Out Size, Specify as:

Hotelling’s T-squared Test

IBD Data Set

IBS sharing counted for:

ID Variables

Identity By Descent Threshold

Identity By State Threshold

Impute zeros for missing continuous predictor values

Impute zeros for missing values

Inbreeding Coefficient

Include 3D plots

Include direction of variant effect in test

Include framework markers in the K-Means clustering analysis

Include self crosses

Increase R Software memory limit

Increment

Individual ID

Individual ID Variable

Individuals to Include in Matrix

Initial Number of Linkage Groups

Input data contains means of genotypes on each environment

Input SAS Data Set

Input Genotype SAS Data Set

Input K Matrix Data Set

Input Linkage Map SAS Data Set

Interaction Block Variables

Interaction Effects

Interactive Clustering Method

Interactive Linkage Group Clustering Method

Italics Value

Italics Variable

JMP Script Output File Name

K for K-Fold CV

K for K-Fold or 1/K Hold-Out

K Matrix is compressed

K Matrix is compressed in K1 Model

K Matrix is compressed in K2 Model

K Matrix Square Root Variables

K Matrix Square Root Variables in K1 Model

K Matrix Square Root Variables in K2 Model

K-Means Clustering Method

Keep original marker variables

Kernel Function

Key Chromosome Number Variable from Base Input SAS Data Set

Key Chromosome Variable from Base Input SAS Data Set

Key Grid Variable from Base Input SAS Data Set

Key Marker Label from Base Input SAS Data Set

Key Testing Location Variable from Base Input SAS Data Set

Kinship Matrix Diagonal

Krho

L for Leave-L-Out

Label Variable

Linear-Bilinear Model

Linkage Group Hierarchical Clustering Method

Linkage Group Variable

Linkage Group Variable 1

Linkage Group Variable 2

Linkage Map Files

Linkage Map Weight List

Linkage Phase Data Set

Linkage Phase of Adjacent Markers

List every model fit

Listing for Each Model Fit

List of optimization direction for progeny selection

List of threshold direction for progeny selection

List of threshold values for progeny selection

List of optimization sense for progeny selection

List of weight values for the selection index

List-Style Specification of K Matrix Square Root Variables

List-Style Specification of K Matrix Square Root Variables in K1 Model

List-Style Specification of K Matrix Square Root Variables in K2 Model

List-Style Specification of LOD Score Variables

List-Style Specification of Marker Variables

List-Style Specification of Matrix Variables

List-Style Specification of Predictor Categorical Variables

List-Style Specification of Predictor Class Variables

List-Style Specification of Predictor Continuous Variables

List-Style Specification of Q Matrix Variables

List-Style Specification of Q Matrix Variables in Q1 Model

List-Style Specification of Q Matrix Variables in Q2 Model

List-Style Specification of Q Matrix Variables in Q3 Model

List-Style Specification of Recombination Rate Variables

List-Style Specification of SNP Variables

List-Style Specification of Numeric SNP Variables

List-Style Specification of SNP Variables to Keep in Output Data Set

List-Style Specification of SNP Variables to Retain in Output Data Set

List-Style Specification of Trait Variables

List-Style Specification of Variables to Be Standardized

List-Style Specification of Variables to Keep in Output Data Set

List-Style Specification of Variables to Retain in Output Data Set

Location Variable

LOD Score Break Value

LOD Score ID Variable

LOD Score SAS Data Set

LOD Score Variables

LOD Threshold [1,10]

LOD Threshold for Entry into the MIM Model

LOD Threshold for Staying in the MIM Model

-log10(p-Value) Cutoff

log10 Regularization Parameter

MAF Threshold for Rare Variants

Main QTL Test Step in cM

Major Allele Variable

Marker Genotype Variables

Marker ID Variable

Marker ID Merge Variable

Marker Label

Marker Location Unit

Marker locus is disease locus

Marker Name Variable

Marker Name Variable 1

Marker Name Variable 2

Marker Names Variable

Marker Order Variable

Marker Physical Position

Numeric Marker Variables

Maternal Value of the Sex Variable

Matrix Variables

Max Number of Categories Allowed in a Predictor

Max Number of Effects in the Model

Max Number of Variables to Consider for Splitting a Node

Maximum

Maximum Convergency Tolerance

Maximum Dimension of K Matrix

Maximum Depth of Tree

Maximum Distance

Maximum Interval Window List

Maximum Iterations

Maximum Number of Chromosomes Per Row in 3D Display

Maximum Number of Clusters

Maximum Number of Filtered Predictors

Maximum Number of Intervals to Fit Together as a Region

Maximum Number of Iterations

Maximum Number of K-Means Clusters / Predictors

Maximum Number of Principal Components

Maximum Number of Steps

Maximum Number of Trees

Maximum Number of Variables to Select with Model Averaging

Maximum Number of Variables to Select with Pooling

Maximum Order of Interactions

Maximum Range of LD Blocks

Maximum Recombination Fraction Threshold

Maximum Subset Size

Maximum Time per Algorithm Iteration (in minutes)

Maximum X Chromosome Heterozygosity for Males

Mean Square Blocks or Replicates within Environments

Mean Square Environment

Mean Square Error

Mean Square Error Degrees of Freedom

Measure for LD Contour Plot

Measure for LD Decay Plot

Measure of Genetic Distance

Merge Individual ID Variable

Merge Input SAS Data Set (1-12)

Merge Key Variables

Method to Order Populations

Method to Use

Minimum

Minimum Dimension of K Matrix

Minimum LOD Threshold

Minimum Number of Linkage Groups

Minimum Number of Observations Required for a Branch

Minimum Number of Offspring Sharing for Computing Runs

Minimum Proportion of Nonmissing Genotypes

Minimum Run Length for Including in Output

Minimum X Chromosome Heterozygosity for Females

Minor Allele Frequency at Marker Locus

Minor Allele Frequency Threshold

Minor Allele Frequency Threshold for Including SNPs

Model Selection Method

Multi-Marker Model Selection

Multiple-Locus Regression Model

Multiple Testing Correction

Multiple Testing Method

Multiple Testing Method for Segregation Tests

Nearby LOD Score Constraint

Nearby Marker Recombination Constraint

Nominalize Continuous Dependent Variables

Null SNP Variables

Number of Backcross Generations

Number of Blocks or Replicates

Number of Clusters

Number of Clusters for Automated Compression

Number of Columns

Number of Contours

Number of Distinct Genotypes

Number of Founding Populations

Number of Generations

Number of Intervals to Overlap in Consecutive Regions

Number of Legend Decimals

Number of Legend Levels

Number of Linkage Groups

Number of Markers in Each Group

Number of model averaging samples

Number of Nearest Neighbor Markers

Number of Nearest Neighbor Samples

Number of Permutations

Number of Permutations or Simulations to Perform

Number of Permutations to Compute

Number of Permutations to Perform

Number of Principal Components

Number of Random Hold-Out Iterations

Number of Reconfigurations

Number of Representative Markers

Number of Rounds of Selection

Number of Rows

Number of Selections to Display

Number of Selfing Generations

Number of Shuffles

Number of Simulated Progeny for Each Cross

Number of Simulations for MAX Test

Number of Starts

Number of Steps

Number of Variables to Process at a Time

Numerical Parameter for Advanced Standardization Methods

Observed Frequency Cutoff for Dropping Rare Haplotypes

Output most probable haplotype pair only

Optimize the continuous predictor set

Optimized Automated Clustering Method

Options to Define Cluster Membership

Order Algorithm

Order Data Set

Other Accession Number Variables

Other Chromosome Variables

Other Effect Estimate Variables

Other Location Variables

Other Major Allele Variables

Other p-Value Variables

Other Sample Size Variables

Other SNP ID Variables

Other Standard Error Variables

Other Variables to Keep in Output Data Set

Other Variables to Retain in Output Data Set

Output Annotation Data Set

Output covariance parameter estimates from every model

Output Data Set

Output Data Set Prefix

Output File Name

Output File Prefix

Output fitness statistics for every model

Output Folder

Output Genotype Data Set

Output genotype LS means

Output genotype LS means and diffs

Output M and L Data Set

Output Map Data Set

Output markers with significant p-values only

Output parameter estimates from every model

Output predicted values for the AMMI models

Output predicted values from every model

Output predicted values from every model from global test

Output R-Square from the logistic regression of binary, nominal, and ordinal traits

Output residuals from every model

Output residuals from every model from global test

Output SAS Data Set from the Genomic BLUP Process

Output SAS data set of between and within family component variables for the O-QTDT

Output survival function estimates for viewing survival curves

Output the additive relationship matrix

Output the root of the additive relationship matrix by SVD

P for P-Percent-Hold-Out

p-Value Adjustment

p-Value Combination Method

p-Value Cutoff for Segregation Test Plots

PARMS Statement Values and/or Options

Pedigree ID Variable in K1 Model

Pedigree ID Variable in K2 Model

Percent Cut Off

Perform 2-sided Test

Perform association tests on selected markers

Perform association tests on unselected markers conditional on selected markers

Perform EigenCorr to select PCs

Perform F statistics calculations

Perform genetic distance matrix calculations

Perform LD calculations for all pairs within annotation groups

Perform model averaging

Perform poolwise selection

Perform Principal Components Analysis

Perform recoding with respect to the:

Perform SAS-based clustering on the genetic distance matrix

Permutations

Phase Assignment ID Variables

Phase Assignment Probability Cutoff

Phased Haplotypes Data Set

Phenotyping Generation (m)

Ploidy Number

Plot haplotypes with significant p-values only

Plot heatmaps for linkage groups

Plot Markers with significant p-values only

Plot Markers with significant F ST p-values only

Plot Relationship Matrix heat map

Population Prevalence

Predictor Categorical Variables

Predictor Class Variables

Predictor Continuous Variables

Prefix

Prefix for Column Names of Expanded Genotypes

Prefix for Column Names of Recoded Genotypes

Prefix for Naming Distance Output Variables

Prefix for Naming Output Variables

Preliminary Delimiter Separating Annotation Categories (Enclose in Quotes)

Prior Probabilities / Prevalences

PROC GENESELECT Statement Options

PROC GLIMMIX Estimation Method

PROC MIXED Estimation Method

Progeny Selection Method

Proportion of Alleles Identical by State Threshold

Proportion of Informative Pairs in Strong LD greater than:

Proximity to Optimal Mapping Order

Q and K Data Set

Q Matrix Variables

Q Matrix Variables in Q1 Model

Q Matrix Variables in Q2 Model

Q Matrix Variables in Q3 Model

QTDT Tests

QTL Effect Size Variables from Base Input SAS Data Set

QTL Effect Size Variables from Merge Input SAS Data Set (1-12)

QTL Indicator Variable from Base Input SAS Data Set

QTL Indicator Variable from Merge Input SAS Data Set (1-12)

QTL Mapping Method

QTL Mapping Model Algorithm

QTL Test Size Variables from Base Input SAS Data Set

QTL Test Size Variables from Merge Input SAS Data Set (1-12)

QTL Test Step in cM

QTL Test Step in cM (1-D Genomewide Scan)

QTL Test Step in cM (2-D Genomewide Scan)

Quantile Level

Quantitative Trait Variables

Quantitative Variables

R Software Memory Size (Mb)

R-squared Threshold

Radial Basis Machine (Kernel Method)

Random Effects

Random Mating Generation (t) Prior to Inbreeding

Random Number Seed

Random Number Seed for Forest

Random Number Seed for Testing F Statistics

Random Statement Options

Range of Markers Variable

Rare Variant Recoding

RATE= Option

Recombination Fraction Break Value

Recombination Fraction Cutoff

Recombination Rate Variables

Reference Annotation SAS Data Set

Reference Annotation Label Variable

Reference Annotation Major Allele Variable

Reference Annotation Minor Allele Variable

Reference Population for FST

Reference Trait Value

Relationship Matrix to Compute

Risk of Observing the Trait in the Heterozygous Genotype (A/a) Relative to the Homozygous Recessive Genotype (a/a)

Risk of Observing the Trait in the Homozygous Dominant Genotype (A/A) Relative to the Homozygous Recessive Genotype (a/a)

Remove markers not found in Annotation SAS Data Set

Reorder Variables

Replicate Variable

Report SNP x Interaction Effect tests only

Reverse Color Theme

Rho

Sample Proportion of Cases

Sample Size Variable

Sample Sizes

SAS Data Set Indicating Which Crosses to Simulate

SAS Data Sets

Save Data Set for Each Window

Scoring Code Files

Search Method

Select best simulated progenies to cross

Select Neighbor Markers with strongest LD within Baseline Unit

Selection Criterion for QTL Main Effect Search and Drop

Selection Criterion for Two-way QTL Interaction Search and Drop

Selection Index Cut Off

Sequence Kernel Association Test (SKAT)

Server Output Directory

Sex Variable

Shuffle Markers

Shuffling LOD Threshold

Shuffling Window Size

Significance Level for Entry into the MIM Model

Significance Level for Staying in the MIM Model

Significance Level for Entry into the Model

Significance Level for Staying in the Model

Similarity Measure

Simulate data from all crosses

Simulate multiple generations

Simulate only markers present in the score files

Simulate progenies

Simulate RIL progenies

SL for Adding Variables

SL for Keeping Variables

SL for Keeping Variables in the Selected Model

Sliding Window Size

Sliding Window Variable

SNP Data Set

SNP ID Variable

SNP Variables

SNP Variables (Coded Numerically)

Numeric SNP Variables

SNP Variables to Keep in Output Data Set

SNP Variables to Retain in Output Data Set

Specification of Regularization Parameter (Lambda)

Standard Error Estimate

Standard Error Variable

Standardization Method for Predictor Continuous Variables

Standardize genotypes

Standardize Predictors Row-Wise

Starting Value for E, the Exponential Decline of Rho with Physical Distance

Starting Value for L, the Bias at Large Distance

Starting Value for M, the Proportion of the Youngest Haplotype that is Monophyletic

Statistical Testing Method for Continuous Predictors

Suppress all graphical and HTML output

Tau Value for TPM

Temperature

Temperature Reduction Factor

Test allelic association (LD)

Test Data Set

Test each marker individually

Test for HWE

Test Individual Haplotypes

Test Statistic

Test Window Size in cM

Tests

Top Cross Tester Lines Indicator Variable

TOTAL= Option

Trait Variable

Trait Variables

Transformation for Predictor Continuous Variables

Transformation of Input p-Values

Transformation of Output p-Values

Transpose data for cell plot

Treat missing genotypes as:

Type I Error Rate

Type of Coefficients to Calculate

Type of Kernel to Use

Type of Model

Type of Tests to Perform

Type of Trait

Type of Weight to Use

Usage of K-Means Clusters

Use a variable threshold for including variants

Use Annotation Label Variable for Variable Prefixes

Use Automated Hierarchical Clustering to Assign Linkage Groups

Use bias corrected recombination formula for RIL

Use dominant coding for trend test

Use Forest to create interaction indicators

Use Forest to filter predictors

Use genetic map to simulate linkage

Use grid computing

Use K-Means clustering to reduce marker number

Use K-Means clustering to reduce number of markers

Use K-Means clustering to reduce predictors

Use lower boundary constraint of 0 for K matrix covariance parameter

Use Monte Carlo simulations for KBAC p-values

Use QTL data numeric coding from JMP Genomics versions prior to 5.1

Use rank-based statistic for tests

Use reported value of sex variable for genetic sex when ambiguous

Use statistical testing to filter predictors

Value of the Backcross Parental Lines Variable Indicating the Parental Line 1

Value of the Backcross Parental Lines Variable Indicating the Parental Line 2

Value Ordering for Nominal Color Variable

Value Ordering for Nominal Variables

Value Representing Cases

Value to Impute for Missing Genotypes

Variable By Which to Merge Annotation Data

Variable Containing Names of Marker Variables

Variable to Define Tree Axis

Variables to Be Standardized

Variables to Drop

Variables to Keep in PCA Data Set

Variables to Keep in Linkage Map Data Set

Variables to Retain in Linkage Map Data Set

Variables to Keep in Output

Variables to Keep in Output Data Set

Variables to Retain in Output Data Set

Variant Weights

Weight the MDS Fit Based on Recombination Fractions

Weight Variable

Weighting Method for Sibships

Width of Positional Group

Window Overlap

Window Size in cM

Window Size Unit

X-linked Marker Clause

X-linked Markers Clause

Copy Number

-log(Alpha) for Stopping Rule

-log10(p-value) Cutoff

Add Mean Difference filter to select significant tests

Additional Bandwidth on Each Side of Tracks

Adjustment Effects

Alpha

Annotation Chromosome Variable

Annotation Label Variable

Annotation Merge Variables

Annotation Position Variable

Annotation SAS Data Set

Annotation Variables to Keep

Annotation Variables to Keep in Output Data Set

Annotation Variables to Retain in Output Data Set

Bin Method

Bin Summary Statistic

By Variables

Calculate the absolute value of the differences

Categorical Variables

Categorical Variables Defining Groups

Center rows

Chromosome Variable

Class Variables

Cluster significant Mean profiles

Cluster Significant Results

Color Theme

Color Variables

Compute Pearson covariances instead of correlations

Compute results for annotated rows only

Control Levels

Control Levels for Difference Comparisons

Control Reference Value

Control Set Summary Statistic

Cumulative Proportion of Variation to Explain

Data Set to Use for Filter

Density Smoothing Bandwidth Computation Method

Density Smoothing Bandwidth Multiplier

Direction of the Cutoff

Display detailed distribution statistics and histograms

Display Format for Density Grid Points

Display side-by-side box plots

Display Standard Deviation versus Mean plots

Exclude rows with missing physical position

Experimental Design SAS Data Set

Experimental Factor Variables

File Containing Estimate Statements

Filter to Include Annotation Rows

Filter to Include Observations

Filter to Include Rows

Folder of Track Settings Files

GenBank Accession Variable

Gene Description Variable

Include adjusted p-values in addition to -log10(p-values)

Include p-values in addition to -log10(p-values)

Include Standard Errors

Include t-Statistics

Input SAS Data Set

Intensity Variables to Bin

JMP Script Output file Name

JMP Script Output File Name

JSL Output File

K for Circular B S Minor Arc

Key Variable(s) to Merge with Input SAS Data Set

Label Variable

List every model fit

List-Style Specification of Intensity Variables to Bin

List-Style Specification of Variables

List-Style Specification of Variables for Which to Display Distributions

List-Style Specification of Variables to be Standardized

List-Style Specification of Variables to Partition Intensity Values

List-Style Specification of With Variables

Maximum Depth of Recursion

Maximum Number of Principal Components to Apply

Mean Difference Filter Cutoff

Means Control Value(s)

Means Difference Set for Volcano Plots

Means Standardization Method

Merge Annotation into Residuals Data Set

Merge Key Variables to Associate with Input SAS Data Set

Minimum Segment Size

Multiple Testing Method

Negative Mean Intensity Maximum [<=0]

Number of Density Grid Points

Number of Markers in Each Bin

Number of Permutations

Number of the First Principal Component for VCA

Numerical Parameter for Advanced Standardization Methods

Organism

Output Data Set

Output Eigenvalues Data Set

Output Experimental Design Data Set

Output Folder

Output experimental samples only

Output Prefix

Output Statistics Data Set

Output Test Data Set

Output Variance Components Data Set

Overlay density estimates

Perform log2 transform after standardization

Perform LOH targeted subtraction

Plot standardized residuals

Position Variable

Positive Mean Intensity Minimum [>=0]

Probe or SNP ID Variable

PROC GENESELECT Statement Options

Sample Set(s) to Use for Subtraction

Scale rows

Select Comparison Set for Mean Differences Tests

Separate and journal results by chromosome

Server Output Directory

Shifting Factor

Shrink variances using Empirical Bayes

SNP ID Variable

Standardization Method

Standardization Method for Comparing Means

Standardization Statistics Input SAS Data Set

Standardize

Standardized Residuals Output Data Set Name

Study

Subset Data Set to Use for Normalization

Track Settings Files

Type of Correlation

Uniformly scale histograms in detail plots

Uniformly scale y-axes in Volcano and Chromosome Position plots

Uniformly scale y-axes in Volcano plots

Variable Distinguishing Bivariate Observations

Variables

Variables By Which to Merge Annotation Data

Variables By Which to Merge Filter Data Set

Variables Defining Analysis Groups

Variables Defining Blocks

Variables Defining Control Sets

Variables Defining Groups for Mean Processing

Variables Defining the One-Way Classification

Variables for Which to Display Distributions

Variables to be Standardized

Variables to Keep in Output

Variables to Keep in Output or By Which to Merge Annotation Data

Variables to Partition Intensity Values

Variance Component Effects

Width of Positional Bin

With Variables

Spectral Preprocessing

Alpha

Bandwidth

Between-Group Overlay Jitter

Bin Method

Bin Size

Bin Summary Statistic

Bin Width

By Variable

By Variables

Filter to Include Observations

ID Variables to Keep

Input SAS Data Set

JMP Script Output file Name

JMP Script Output File Name

Label Variable

List-Style Specification of y Variables

List-Style Specification of y Variables Group A

List-Style Specification of y Variables Group B

List-Style Specification of y Variables to Bin

List-Style Specification of y Variables to Detrend

log2 transform areas or heights

Lower and Upper x-Axis Values for Noise Intervals

Maximum Number of Peaks to Compute

Noise Baseline Fitting Option

Noise Cutoff

Output Data Set

Output Details Data Set

Output Folder

Peak Cutoff

Peak Quantity to Output

Percentage of Maximum Signal Below Which is Noise

Plot raw data

Response Density SAS Statements

Scale the area under the curve

Server Output Directory

Within-Group Overlay Jitter

x Smoothing Bandwidth Multiplier

x Variable

x Variable Lower Bound

x Variable Number of Grid Points

x Variable Upper Bound

y Smoothing Bandwidth Multiplier

y Variable

y Variable Lower Bound

y Variable Number of Grid Points

y Variable Upper Bound

y Variables

y Variables to Bin

y Variables to Detrend

y Variables Group A

y Variables Group B

z Variable

z Variable Lower Bound

z Variable Upper Bound

z Variables

Expression Parameters

Accumulative Proportion of Variation

Add Mean Difference filter to select significant tests

Additional Bandwidth on Each Side of Tracks

Additional Covariates

Additional Fixed Effects

Additional PROC MIXED Statements

Adjust Counts by Total Counts

Annotation Chromosome Variable

Annotation Label Variable

Annotation Merge Variables

Annotation Merging Variable

Annotation Position Variable

Annotation SAS Data Set

Annotation Title Variables

Annotation Variables to Keep

Apply Certain Samples as Reference

Apply data from autosomes for normalization

Apply kernel density estimation for MA plot

Apply normalized data for MA plot

Apply reference baseline from previously input data set

Array Standardization Method

Apply weighting approach for trimmed mean

Array Variables to Plot

Bandwidth Multiplier of x-Dimension

Bandwidth Multiplier of y-Dimension

Baseline

Baseline in Reference SAS Data Set

Baseline Quantification

Baseline reference data is log-transformed

Baseline Reference SAS Data Set

Baseline Value

Baseline Variable

Batch Profile Input SAS Data Set

By Variables for Loess Normalization

Variables by Which to Merge Annotation Data 1

Variables by Which to Merge Annotation Data 2

Calculate the absolute value of the differences

Catalog Number

Categorical Variables

Categorical Variables Defining Groups

Cells with Maximum Features Detected

Cells with Minimum Features Detected

Cluster significant LSMean profiles

Cluster significant Mean profiles

Cluster Significant Results

Column to Identify Control Rows in the EDDS

Columns by Which to Merge with Input SAS Data Set

Columns Defining Batches

Component Output Data Set Name

Compute Component Fixed-Effect Tests

Compute Multiple Testing Adjustment Separately for Each Test

Compute Pearson covariances instead of correlations

Compute results for annotated rows only

Compute results for secondary annotated rows only

Constant to Apply

Constants for Each Variance Component

Contributors

Control Levels

Control Levels for Difference Comparisons

Control Levels for Differential Expression Comparisons

Control Set Design

Control Set Summary Statistic

Coordinate Data Set

Coordinate Merge Variables

Covariates

Covariates for Differential Expression

Create JMP Surface plot

Cumulative Proportion of Variation to Explain

Data Processing

Data Set Containing LSMeans Differences to Include

Data Set of Differences to Include from Comparison Set

Data Set to Use for Filter

Delete nonmatching rows

Delete rows

Delete rows not in First Tall Data Set

Delete rows with at least this percentage of Missing values

Delete rows with Interquartile Range satisfying this expression (use keyword IQR)

Delete rows with Mean satisfying this expression (use keyword MEAN)

Delete rows with Median satisfying this expression (use keyword MEDIAN)

Delete Rows with number of missing values satisfying this expression (use keyword NMISS)

Delete rows with Percentile satisfying this expression (use keyword PCTL)

Delete rows with Standard Deviation satisfying this expression (use keyword STD)

Density Smoothing Bandwidth Computation Method

Density Smoothing Bandwidth Multiplier

Description

Design-Level By Variables

Design-Level Grouping Variables

Differential Expression Effects to Keep in the Normalized Data

Direction of the Cutoff

Display Cumulative Percentage of Summary plots

Display detailed distribution statistics and histograms

Display Format for Density Grid Points

Display side-by-side box plots

Display Scatterplot matrices

Display Standard Deviation versus Mean plots

Distribution

DNA data is log-transformed

DNA Design Variables to be Added in Output Data Sets

DNA Intensity Input SAS Data Set

DNA Matching Variables

Effect Sizes

Experimental Design SAS Data Set

Experimental Design Data Set Corresponding to Baseline Reference Data Set

Experimental Design SAS Data Set of DNA Intensity

Experimental Design SAS Data Set of RNA Intensity

Experimental Factor Variables

Exponential Multiplier of Kernel Density

Extract Protocol

Feature Variable

Features Detected in Minimum Cells

Fifth Experimental Design Data Set

Fifth Tall Data Set

File Containing Estimate Statements

Filter data with Zero or Missing values

Filter Rows Whose Proportion of Zero/Missing Values Exceeds this Cutoff

Filter to Include Annotation Rows

Filter to Include Observations

Filter to Include Rows

Filter to Include Secondary Annotation Rows

Filtered Output Data Set Name

Filtration Method for Data Points with Large Residuals

First Experimental Design Data Set

First Tall Data Set

Fixed Effects

Fixed Effects for Differential Expression

Flagging Output Data Set Name

Folder of Raw Data Files

Folder of Track Settings Files

Format

Fourth Experimental Design Data Set

Fourth Tall Data Set

GenBank Accession Variable

Gene Description Variable

Gradient Convergence Criterion [1,1000000]

Group Percentage for Deletion

Growth Protocol

Hyb Protocol

ID Variable

In Mean plots, size Error Bars using:

Include adjusted p-values in addition to -log10 (p-values)

Include exponentiated Estimates and Differences

Include Fold Changes in Addition to Log Fold Changes

Include Fold Changes in Addition to Log2 Fold Changes

Include group statistics in Output Data Set

Include simple LSMean Differences only

Include p-values in addition to -log10 (p-values)

Include simple Differences only

Include Standard Errors

Include t-Statistics

Input data is log-transformed

Input SAS Data Set

Input x and y Data Set

Input z Data Set

Intensity Columns to Filter

JMP Journal Output File

JMP Script Output File Name

JSL Output File

Keep 0 Values without normalizing

Kenward-Roger Degrees of Freedom Method

Key Variable(s) to Associate with Input SAS Data Set

Key Variable(s) to Merge with Input SAS Data Set

Key Variables for Merging

List-Style Specification of Intensity Columns to Filter

List-Style Specification of Variables

List-Style Specification of Variables for Which to Display Distributions

List-Style Specification of Variables to Be Standardized

List-Style Specification of With Variables

Loess Weight Data Set

-log10(p-Value) Cutoff

Log Transformation of Measurements

Log transformed Output Data Set

LSMeans Control Levels

LSMeans Difference Set

LSMeans Difference Set for Volcano Plots

LSMeans Effects

LSMeans Effect to Keep in the Normalized Data

LSMeans Effect to Retain in the Normalized Data

LSMeans Standardization Method

Manufacturer

Manufacturer protocol

Match case for key variables

Maximum Dispersion to Filter Genes

Maximum Filtering Loops

Maximum Mean to Filter Genes

Maximum Number of Principal Components to Apply

Maximum z Value

Mean Difference Filter Cutoff

Means Control Levels

Means Difference Set for Volcano Plots

Means Standardization Method

Merge Annotation into Residuals Data Set

Merge Key Variables

Merge Key Variables to Associate with Input SAS Data Set

Minimum Dispersion to Filter Genes

Minimum Distance Between Genotyping Groups

Minimum Mean to Filter Genes

Minimum Ratio Range Between 2 Homozygous Groups

Minimum z Value

Mixed Model Expression Index Output Data Set Name

Mixed Model Output Data Set Name

Mixed Model Output Data Set Prefix

MMEI Output Data Set Name

Model Data As:

Model Distribution

Molecule

Multiple Testing Fixed Effects

Multiple Testing Method

Multipliers of Design Size

New Batches from Previous Batches

Normalized Response Filter Expression

Number of By Groups to Process at a Time

Number of Clusters

Number of Density Grid Points

Number of Discrete Classes to Use in Plots

Number of Factors to Subtract from the Data

Number of the First Principal Component for VCA

Number of First Principal Component to Model

Number of Last Principal Component to Model

Number of LOESS Iterations

Number of PLS Components

Number of Principal Components

Number of Random Splits

Number of Rows to Process at a Time

Number of Standard Deviations in Error Bars

Number of Variable Genes to Keep

Number of x Blocks

Number of x Grid Points

Number of y Blocks

Number of y Grid Points

Numerical Parameter for Advanced Standardization Methods

Only compute overall F-Test for Fixed Effects for Differential Expression

Organism

Organism(s)

Origin Corner

Other Columns to Include

Output Batch Profile Data Set

Output Coordinates Data Set

Output Data Set

Output Data Set Containing Filtered Data

Output Data Set for Graphs

Output Data Set Name

Output Data Set for MA Plot

Output Data Set of KDMM Factors

Output Data Set of Rescaling Factors

Output Data Set of RPM Factors

Output Data Set of TMM Factors

Output Data Set of TPM Factors

Output Data Set of Upper Quartile Factors

Output Data Set Prefix

Output Eigenvalues Data Set

Output Experimental Design Data Set

Output File Prefix

Output File Primary Title

Output File Secondary Title

Output Folder

Output HTML File

Output Normalized Data Set

Output experimental samples only

Output Plotting Data Set

Output Ratio Data Set

Output Statistics Data Set

Output Surface Data Set

Output Tall Data Set

Output Test Data Set

Output Updated Batch Profile Data Set

Output Variance Components Data Set

Overall Design

Overlay density estimates

PCA Plot Grouping Variables

PDF or RTF Output File

PDF or RTF Output File Name

Percentage of Data to Be Included in Training Data

Percentage of Mitochondria Genes Allowed

Percentile to Compute for PCTL Statistic

Percentage of Data on A Component to Be Trimmed

Percentage of Data on M Component to Be Trimmed

Percentage of Data to Be Trimmed before Summary [0,50]

Percentage of Nonmissing and Nonzero Data to Be in Training Subset

Percentile to Impute

Perform log2 transform after standardization

Perform within-array Loess normalization

Platform

Platform Contributors

Plot standardized residuals

Plot Variable 1 Axis Label

Plot Variable 2 Axis Label

Plotting Variables

Position Variable

Prefix for Experimental Design Output Data Set Names

Prefix for Tall Output Data Set Names

Primary Title to use in Plots

PROC FACTOR Options

PROC GLIMMIX Options

PROC MIXED Options

PROC MIXED/GLIMMIX Statements

PROC PLS Options

PROC MIXED Statements

Pseudo-Likelihood Convergence Criterion

Random Number Seed for Picking Random Subset

Randomly split the data

Ratio Output Data Set

Raw Data File(s)

Reference Variable

Reference Variable to Be Applied as the Baseline

Remove duplicate columns

Repeats

Replace intensities falling at least this many Standard Deviations above the Column Mean

Replace intensities falling at least this many Standard Deviations below the Column Mean

Replace intensities falling above this column percentile

Replace intensities falling above this value

Replace intensities falling below this Column Percentile

Replace intensities falling below this value

Replace highest values

Replace lowest values

Rescale the data based on the total measurements in the Training data set

Residual Cutoff

Residual False Positive Rate

Response Filter Expression

Response Variable Axis Label

Result Data Set

RNA data is log-transformed

RNA Design Variables to be Added in Output Data Sets

RNA Intensity Input SAS Data Set

RNA Matching Variables

RNA Output Data Set

Round normalized count data to integer

Row Index Variable

Row-Level Categorical Variables

Row-Level Class Variables

Second Experimental Design Data Set

Second Tall Data Set

Secondary Annotation Chromosome Variable

Secondary Annotation Label Variable

Secondary Annotation Merge Variables

Secondary Annotation Position Variable

Secondary Annotation SAS Data Set

Select Comparison Set for Mean Differences Tests

Select Comparison Set for Differential Expression Tests

Select Training subset based on Missing status

Separate and journal results by chromosome

Separate results based on Design-Level By Variables

Series Title

Series Variables

Server Output Directory

Set flagging data to Missing

Set negative values to Missing before imputing

Shifting Factor

Shrink variances using Empirical Bayes

Smoothing Bandwidth Multiplier

Smoothing Parameter

SNP ID Variable

Sort VCA Plots by Weighted Average Proportion of Variation

Source Name

Split by these variables

Standardization Method

Standardization Method for Comparing Means

Standardization Method for Difference Tests

Standardization Statistics Input SAS Data Set

Standardize

Standardize Batch profile

Standardize KDMM factor

Standardize TMM Scaling Factor

Standardized Residuals Output Data Set Name

Subset Data Set to Use for Normalization

Third Experimental Design Data Set

Third Tall Data Set

Threshold

Time to Event Variable

Title

Track Settings Files

Transposed Output Data Set

Treatment Protocol

Type of Correlation

Unbounded Variance Component Estimates

Uniformly scale histograms in Detail plots

Uniformly scale y-axes in Volcano and Chromosome Position plots

Uniformly scale y-axes in Volcano plots

Use pooled Estimate of Variance

Value Definition

Value of Columns above to Be Associated with Control

Value of Columns above to Be Associated with Non-autosomes

Value of Columns above to Be Associated with Reference

Value of the Censor Variable that Indicates Censoring

Value of Variable Above to Be Used as Denominator

Value to Use to Replace Highest Values

Value to Use to Replace Lowest Values

Values of ID Variable to Plot

Variable Containing Names of Loess Weight Data Set Columns

Variable Defining the Ratio

Variables

Variables by Which to Merge Annotation Data

Variables By Which to Merge Filter Data Set

Variables by Which to Merge Primary Annotation Data

Variables by Which to Merge Secondary Annotation Data

Variables Defining Analysis Groups

Variables Defining Blocks

Variables Defining Control Sets

Variables Defining Groups

Variables Defining Groups for Mean Processing

Variables Defining the One-Way Classification

Variables for Which to Display Distributions

Variables to Be Normalized

Variables to Be Standardized

Variables to Keep in Output

Variables to Keep in Output or By Which to Merge Annotation Data

Variance Component Effects

Variable Gene Selection Method

Variables to Include in the Imputation Process

Weblink

Weighted With Kernel Density

Where Clause for Subsetting Input Data Set in Test Run

Winsor Rate

With Variables

Within Sub-Feature Median

x-Coordinate Variable

y-Coordinate Variable

z-Coordinate Variables

Pattern Discovery

Additional PROC CLUSTER Options

Additional PROC DISTANCE Options

Additional PROC TREE Options

Alpha

Annotation Chromosome Variable

Annotation Column Name Variable

Annotation Group Variable

Annotation Label Variable

Annotation Merge Variables

Annotation Position Variable

Annotation SAS Data Set

Annotation Variables to Keep

By Variables

Variables by Which to Merge Annotation Data

Categorical Variables

Color row profiles by:

Color Theme for Heat Map

Color Variable

Compare Variables

Compute Covariances Instead of Correlations

Compute Pearson covariances instead of correlations

Compute results for annotated rows only

Continuous Variables

Correlation Radius for Clustering

Covariates for Partial Correlations

Cutoff Level of Tree Axis

Design Color Variables

Design Label Variable

Display clustered heat map

Distance Metric

Distance Variables

Double center multiplicatively

Experimental Design SAS Data Set

Filter to Include Annotation Rows

Filter to Include Observations

Filter to Include Observations from the Primary Data Set

Filter to Include Observations from the Secondary Data Set

Filter to Include Primary Variables

Filter to Include Secondary Variables

GenBank Accession Variable

Gene Description Variable

Gene ID Variable

Gene Symbol Variable

Group Percentage for Row Inclusion

Hierarchical Clustering Method

ID Variable

ID Variables

Impute missing values for clustering

Include 3D plots

Include rows if:

Include rows with Interquartile Range satisfying this expression

Include rows with Mean satisfying this expression

Include rows with Median satisfying this expression

Include rows with Percentile satisfying this expression

Include rows with Standard Deviation satisfying this expression

Increment Between Lower and Upper Values

Input SAS Data Set

Input Data Set is a Distance Matrix

Intensity Columns to Plot

JMP Script Output File Name

JSL Output File

K-Means Clustering Method

Label Variable

Level of Measurement

List every model fit

List-Style Specification of Categorical Variables

List-Style Specification of Class Variables

List-Style Specification of Continuous Variables

List-Style Specification of Covariates for Partial Correlations

List-Style Specification of Distance Variables

List-Style Specification of Intensity Columns to Plot

List-Style Specification of Primary Set of Variables

List-Style Specification of Secondary Set of Variables

List-Style Specification of Variables to Compute Distances/Clustering Across

List-Style Specification of Variables to Plot

List-Style Specification of Variables Whose Rows Are to Be Clustered

-log10(p-Value) Cutoff

-log10(p-Value) Cutoff for Output Test Data Set

Lower Number of Dimensions to Fit

Maximum Number of Clusters

Merge Key Variables

Multiple Testing Method for Output Test Data Set

Number of Clusters

Number of Primary Set of Variables to Process at a Time (log10 Scale)

Number of Principal Components

Number of Secondary Set of Variables to Process at a Time (log10 Scale)

Number of Variables to Process at a Time

Numerical Parameter for Advanced Standardization Methods

Options to Define Cluster Membership

Output Data Set Prefix

Output File Prefix

Output Folder

Output Means Data Set

Output Test Data Set

Percentile to Compute for PCTL Statistic

Perform SAS-based clustering on the Distance Matrix

Plot

Plot clusters

Prefix for Naming Output Variables

Primary Annotation SAS Data Set

Primary Input SAS Data Set

Primary Set of Variables

PROC CORR Statement Options

PROC FASTCLUS Options

PROC MDS Options

PROC PLS Options

Process Group Size for Primary Variables

Process Group Size for Secondary Variables

Replace cluster means with representative observations

Scale columns

Scale rows

Secondary Annotation Column Name Variable

Secondary Annotation SAS Data Set

Secondary Annotation Variables to Keep

Secondary Input SAS Data Set

Secondary Set of Variables

Server Output Directory

SNP ID Variable

Standardization Method

Standardize variables before clustering

Study

Two Way Clustering

Type of Correlation

Upper Number of Dimensions to Fit

Use lower boundary constraint of 0 for K matrix covariance parameter

Variable to Define Tree Axis

Variables By Which to Merge Annotation Data

Variables Defining Groups

Variables to Compute Distances/Clustering Across

Variables to Keep in Output

Variables to Plot

Variables to Keep in Output Data Set

Variables to Retain in Output Data Set

Variables Whose Rows are to Be Clustered

Weight Variable

Predictive Modeling

Absolute Mean Difference Cutoff for Continuous Predictors

Absolute Proportion Difference Cutoff for Class Predictors

Add reflected y variable to binary model

Algorithm

Annotation SAS Data Set

Apply adaptive weights

Asymmetric Loss Evaluation Proportion

Asymmetric Loss Fitting Proportion

Automated Model Type

Average Pool Size

Binary Dependent Variable Event Value

Binary Target Variable Event Value

Block Partition Variable

Boosted Fraction of Training Observations in a Single Tree

Boosted Maximum Number of Iterations

Boosted or Forest Random Number Seed

Boosted Shrinkage Factor

Categorical Covariates

Categorical Variables

Censor Variable

Centroid or Distance Summarization Method

Change output folder in Settings moved to right panel

Combined Output Data Set Name

Compute Individual Model Fits

Compute near optimal low-rank randomized SVD for the additive and dominance matrices

Continuous Covariates

Continuous Variables

Correlation Radius for Clustering

Criterion for Stopping Model Selection

Current Review

Custom Costs

Custom Prior Probabilities

CV Partitioning Method

Data Columns to be Transposed

Data Step Statements

Degrees of Freedom Hyper-parameter

Dependent Categorical Variable

Dependent Class Variable

Dependent Variable

Dependent Variables

Display Frobenius Measure

Distance Metric for Analysis

Distance Metric

Distribution

Early Stopping Threshold

Elastic Net L2 Penalty

Expected Proportion of Variance Explained by the Regression Model

Experimental Design Data Set

Experimental Design SAS Data Set

Feature Selection Criterion

Filter for BLUP Coefficients

Filter for PLS Coefficients

Filter to Include Markers

Filter to Include Observations

Filter to Include Predictor Categorical Variables

Filter to Include Predictor Class Variables

Filter to Include Predictor Continuous Variables

Fixed Effects

Fixed Test Data Set

Fixed Test Set

Folder of CVMC Results 1

Folder of CVMC Results 2

Folder of CVMC Results 3

Folder of CVMC Results 4

Folder of CVMC Results 5

Folder of Predictive Modeling Settings

Folder of Settings Files

Folder of Test Data Sets

Folder of Test Sets

For Nominal Dependent Variables, compute distances to:

For Nominal Target Variables, compute distances to:

Forest Max Number of Variables to Compute Predictor Importance

Forest Max Number of Variables to Consider for Splitting a Node

Forest Maximum Number of Trees

Forest: Output filtered predictors list

Heterogeneous Variance Components

Hold-Out Method

Hold-Out Size, Specify as:

ID Variable

ID Variables

Impute zeros for missing continuous predictor values

Impute zeros for missing values

Include 3D plots

Include Dominance Effects

Increase R Software memory limit

Iteration Start Number

JMP Script Output File Name

K for K-Fold CV

K for K-Fold or 1/K Hold-Out

Kernel Function

Kernel Function for Computing Posterior Probabilities

L for Leave-L-Out

L1 Regularization Parameter

L2 Regularization Parameter

Label Variable

Link Function

List every model fit

List SAS Output for all model fits

List-Style Specification of Continuous Variables

List-Style Specification of Data Columns to be Transposed

List-Style Specification of Lock-In Categorical Predictor Variables

List-Style Specification of Lock-In Class Predictor Variables

List-Style Specification of Lock-In Continuous Predictor Variables

List-Style Specification of Lock-In Marker Variables

List-Style Specification of Marker Variables

List-Style Specification of Pedigree Relationship Matrix Variables

List-Style Specification of Predictor Categorical Variables

List-Style Specification of Predictor Class Variables

List-Style Specification of Predictor Continuous Variables

List-Style Specification of Variables to Be Standardized

Lock-In Categorical Predictor Variables

Lock-In Class Predictor Variables

Lock-In Continuous Predictor Variables

Lock-In Marker Variables

-log10(p-Value) Cutoff

log10 Regularization Parameter

Lower Bound for Target Variable

Marker Label

Marker Physical Position

Marker Position

Marker Variables

Max Number of Categories Allowed in a Predictor

Max Number of Effects in the Model

Max Number of Variables to Consider for Splitting a Node

Maximum Depth of Tree

Maximum Number of Buckley-James Iterations

Maximum Number of Filtered Predictors

Maximum Number of Grid Nodes to Use

Maximum Number of K-Means Clusters / Predictors

Maximum Number of Predictors to Use

Maximum Number of Steps

Maximum Number of Trees

Maximum Number of Variables to Select with Model Averaging

Maximum Number of Variables to Select with Pooling

Maximum Order of Interactions

Maximum Size of Training Set

Maximum Time for Area under Survival Curves

Method

Metric

Minimum Number of Observations Required for a Branch

Minimum Number of Observations Required for a Categorical Value

Minimum Size of Training Set

Minimum Time for Area under Survival Curves

Mode

Model Selection Method

Multiple Testing Method

Nominalize Continuous Dependent Variables

Number of BLUPs to Use in Prediction

Number of Burn In Samples

Number of Generations

Number of Grid Points for Each Learning Curve

Number of Iterations

Number of model averaging samples

Number of Nearest Neighbors

Number of PLS Components

Number of power iterations (q) for the randomized SVD

Number of Predictor Variables to Select

Number of Predictors Included in Model

Number of Principal Components

Number of Random Hold-Out Iterations

Number of Random Iterations

Number of Rounds of Selection

Number of Rows in Input Data to Use in Test Run

Number of Subsets Containing a Particular Variable

Numerical Parameter for Advanced Standardization Methods

Optimize the continuous predictor set

Outer Loop Test Set

Output Data Set

Output Data Set Name

Output Data Set Prefix

Output Experimental Design Data Set

Output Folder

Output Tall Data Set

Output Wide Data Set

P for P-Percent-Hold-Out

Pedigree Relationship Matrix Variables

Perform Buckley-James Estimation

Perform Cross Validation Model Comparison

Perform Learning Curve Model Comparison

Perform model averaging

Perform poolwise selection

Perform recoding with respect to:

Plot Separate Charts

Plot weights for predictor variables

Predictor Categorical Variables

Predictor Class Variables

Predictor Continuous Variables

Prefix for Tall Column Names

Prefix for Wide Column Names

Primary Input SAS Data Set

Primary Output Data Set Name

Prior Probabilities / Prevalences

PROC DISCRIM Options

PROC DISTANCE Options

PROC GENESELECT Statement Options

PROC GLIMMIX Additional Statements

PROC GLIMMIX Class Variables

PROC GLIMMIX Fixed Effects

PROC GLIMMIX MODEL Statement Options

PROC GLIMMIX Statement Options

PROC GLMSELECT Modeling Options

PROC GLMSELECT Statement Options

PROC HPMIXED Additional Statements

PROC HPMIXED Class Variables

PROC HPMIXED Fixed Effects

PROC HPMIXED MODEL Statement Options

PROC HPMIXED Statement Options

PROC LIFEREG Modeling Options

PROC LIFEREG Statement Options

PROC LOGISTIC MODELING Options

PROC LOGISTIC Response Options

PROC LOGISTIC Statement Options

PROC MIXED Additional Statements

PROC MIXED Class Variables

PROC MIXED Fixed Effects

PROC MIXED MODEL Statement Options

PROC MIXED Statement Options

PROC PHREG Modeling Options

PROC PHREG Statement Options

PROC PLS Statement Options

PROC QUANTSELECT Modeling Options

PROC QUANTSELECT Statement Options

Quantile Level

Quantile Level for Quantile Regression

R Software Memory Size (Mb)

Random Number Seed

Random Number Seed for Forest

Random Number Seed for Randomize SVD

Reference Time for Comparing Survival Curves

Reference Times for Comparing Survival Curves

Regard missing values as valid for prediction

Regression Model

Root Mean Square Error Convergence Tolerance

Rule Mix Maximum Number of Initial Rules

Rule Mix Maximum Number of Secondary Rules

SAS Procedure

Scale Hyper-parameter

Secondary Input SAS Data Set

Secondary Output Data Set Name

Separate Bar Charts for Each Model

Separate Bar Charts for Each Test Set

Separate Charts for Each Model

Separate Charts for Each Test Set

Server Output Directory

Settings Files

Settings for Which to Construct Learning Curves

Settings to Cross Validate

Settings to Use for Test Data Set Evaluation

Settings to Use for Test Set Evaluation

Significance Level for Adding Variables

Significance Level for Keeping Variables

Significance Level for Retaining Variables

Similarity Measure

SL for Adding Variables

SL for Keeping Variables

SL for Retaining Variables

Standardization Method for Predictor Continuous Variables

Standardize Predictors Row-Wise

Statistical Testing Method for Continuous Predictors

Study

Target Categorical Variable

Target rank (k) of the low-rank randomized SVD

Time to Event Variable

Transformation for Predictor Continuous Variables

Type of Dependent Variable

Type of Target Variable

Upper Bound for Target Variable

Usage of K-Means Clusters

Use Forest to create interaction indicators

Use Forest to filter predictors

Use Grid Computing

Use K-Means clustering to reduce predictors

Use Leave-One-Out error rate as the Fitness function

Use life regression to filter predictors

Use PROC HPLOGISTIC

Use simple Cox Proportional Hazards function to filter predictors

Use statistical testing to filter predictors

Validation Data Set

Value of the Censor Variable that Indicates Censoring

Values of the Censor Variable that Indicate Censoring

Variable Selection Method

Variables Defining Tall Column Names

Variables Defining Wide Column Names

Variables to Be Standardized

Weight for Censored Observations

Weight Variable

Weighting Function

Where Clause for Subsetting Input Data in Test Run

Subgroup Analysis

Alpha

Apply adaptive weights

Categorical Clustering Variables

Class Clustering Variables

Cluster Effect Type

Continuous Clustering Variables

Criterion for Stopping Model Selection

CV Partitioning Method

Dependent Variable

Direction of Enhanced Treatment Effect

Fixed Main Effects

Input SAS Data Set

JMP Script Output File Name

K for K-Fold CV

List-Style Specification of Categorical Clustering Variables

List-Style Specification of Class Clustering Variables

List-Style Specification of Continuous Clustering Variables

List-Style Specification of Predictor Categorical Variables

List-Style Specification of Predictor Class Variables

List-Style Specification of Predictor Continuous Variables

Main Effects Selection Method

Max Number of Variables to Consider for Splitting a Node

Maximum Depth of Tree

Maximum Number of Steps

Maximum Number of Trees

Minimum Number of Observations Required for a Branch

Minimum Number of Observations Required for a Categorical Value

Maximum Order of Interactions

Model Selection Method

Number of Bins for Continuous Variables

Number of Principal Components to Use for Propensity Scoring

Number of Resamples to Use for Computing Significance

Output Data Set Prefix

Output Folder

Predictor Categorical Variables

Predictor Class Variables

Predictor Continuous Variables

Random Number Seed

Random Number Seed for Forest

Regard missing values as valid for prediction

Relaxation Factor for Alpha

SL for Adding Variables

SL for Keeping Variables

Study

Target Variable

Treatment Control Level

Treatment Variable

Type of Dependent Variables

Use Interactions between treatment and predictor variables

Variable Selection Method

Variability Assumption

Weight Variable

P-Value Operations

Adjust all p-value variables together

Alpha

Annotation SAS Data Set

Annotation Merge Variables

Category Variable

Censor Limit (-log10 scale)

Censor Limit (-log or -log10 scale)

Create significance indicator columns

Effect Estimate or Direction Variable

Effect Estimate or Direction Variable is the odds ratio

Effect ID Variable

Filter to Include Annotation Rows

Filter to Include Observations

Folder of Input SAS Data Sets

ID Variable

Input SAS Data Set

Method to Use

Multiple Testing Method

Output rows with a significant p-value only

Open Output Data Set

Other Effect Variables

Other ID Variables

Other p-Value Variables

Other Sample Size Variables

Other Standard Error Variables

Output Data Set

Output File Prefix

Output Folder

p-Value Combination Method

p-Value Variable

p-Value Variables

Primary Delimiter Separating Annotation Categories

Sample Size Variable

SAS Data Sets

Secondary Delimiter

Server Output Directory

Standard Error Variable

Study

Study ID Variable

Tau Value for TPM

Transformation of Input p-Values

Transformation of Output Adjusted p-Values

Transformation of Output p-Values

Variables By Which to Merge Annotation Data

Genome Views

-log10(p-value) Cutoff

Additional Bandwidth on Each Side of Tracks

Alpha

Annotation Merge Variables

Annotation SAS Data Set

Annotation Variables to Keep

Bin Method

Bin Summary Statistic

Chromosome

Chromosome Color Theme Settings File

Chromosome Text Data Set

Chromosome Text File

Chromosome Variable

Chromosome Variable 1

Chromosome Variable 2

Circular Display

Coding Region End

Coding Region Start

Color of Bars ({r,g,b})

Color Theme

Color Variable

Color Variable Type

Construct Color Theme using:

Continuous Variables to Plot

Create separate plots for each variable

Custom Track Variable

Description

Direction of Other Variables

Filter to Include Annotation Rows

Filter to Include Observations

Folder of Chromosome Color Theme Settings Files

Folder of Track Settings Files

GenBank Accession Variable

Gene Description Variable

Gene Name or Identifier

Initial Cutoff for Displaying Bars

Input GFF File

Input SAS Data Set

Label for Other Variables

Links Input SAS Data Set

Multiple Testing Method

Name

Number of Legend Decimals

Number of Legend Levels

Number of Rows in Each Bin

Plot markers with significant p-values only

Position

Position Variable

Position Variable 1 Start

Position Variable 1 Stop

Position Variable 2 Start

Position Variable 2 Stop

Server Output Directory

Summary Statistic Across Continuous Variables

Text Data Set

Text File

Threshold Indicator

Title of Custom Track

Track Settings Files

Transformation of Input p-Values

Transformation of Output p-Values

Transparency

Two Column Threshold

Type of Continuous Variables to Plot

Value at Which to Censor High Values

Value at Which to Censor Low Values

Value Ordering for Nominal Variables

Values at Which to Draw Horizontal Reference Lines

Variables By Which to Merge Annotation Data

Variables to Keep in HTML Data Set

Variables to Retain in HTML Data Set

Width of Bars

Width of Positional Bin

-log10(p-Value) Cutoff

Affymetrix Array Type

Affymetrix GeneChip Array

Annotation Merge Variables

Annotation SAS Data Set

Annotation Variables to Keep

Applied BioSystems Array Type

Categorical Phenotype Variable

Category Variable

Category Variables

Check uniqueness of column names

Chromosomal Location

Chromosome

Class Phenotype Variables

Codelink Array Type

Compress output data sets

Continuous Phenotype Variables

Control Levels

Custom Track Variable

Direction of Significance Variables

EC Database Link

End

Enrichment Tests

Ensembl Database Link

Ensembl ID

Entrez Gene ID

Entrez Databases Link

Entrez Gene Link

Entry Delimiter

Entry Id Delimiter

Enzyme ID (EC number)

Experimental Design SAS Data Set

Files to Import

Filter to Include Annotation Rows

Filter to Include Observations

Gene Description Variable

Gene ID

Gene Identifier Variable

Gene Name or Identifier

Gene Name Variable

Gene/Protein Identifier

Gene Set Summary Method

Gene Symbol

Genome

GO ID

HGNC (Gene Symbol) Database Link

High Color RGB

High Hit Total Threshold

Homologene

ID Variable

Illumina Array Type

Ingenuity Canonical Pathway Membership Data (CSV file)

Ingenuity knowledge Base

Ingenuity Server

Input GMT File of Gene Sets

List Description File

Low Color RGB

Low Hit Threshold

Map Viewer Database Link

Maximum Category Length

Maximum Column Length

Method for Computing Reference Level for Each Gene

Middle Color RGB

Multiple Testing Method for Adjusting p-Values

Number of Bins for Cochran-Armitage Tests

Number of Bootstrap or Permutation Samples

Number of Genes to Process at a Time

Number of Rows to Scan

OMIM Database Link

OMIM ID

Organism

Organism-specific code

Output Data Set

Output Data Set Containing Categories

Output Data Set Containing Category Indicators

Output Data Set Containing Statistics

Parse associated gene column

Percentile to Use as the Highest Color Value

Percentile to Use as the Lowest Color Value

Platform

Prefix for Output Data Set Names

Primary Delimiter Separating Annotation Categories

Rank Transform Continuous Significance Variables for PAGE Tests

Reference Sequence Database Link

RefSeq ID

Row Reference Summary Statistic

Secondary Delimiter

Server Output Directory

Set window size

Significance Cutoff

Significance Input SAS Data Set

Significance Variable

Significance Variable Cutoff for Fisher Exact Tests

Significance Variables

Specify up to 20 IPA observations to upload

Standardize Rows based on Reference Group Standard Deviation

Start

Study

Swiss-Prot Database Link

Swiss-Prot ID

Title of Custom Track

Type

Type of Gene Identifier

UniGene ID

UniGene Link

Use a proxy server to access the Web

Variable Containing IDs

Variables by Which to Color Pathways

Variables By Which to Merge Annotation Data

Variables Defining Analysis Groups

Variables Defining Control Sets

Variables to Keep in Gene-Level Output

Variables to Keep in Output

Variables to Keep in Output or by Which to Merge Annotation Data

Variables to Retain in HTML Data Set

Window Size

Utilities

0-1 Variables

Access Documentation from the:

Add Filter Columns

Additional Fixed Effects

Additional PROC MIXED Statements

Additional SAS Data Step Statements

Affymetrix Array Type

Agilent Array Type

Alpha Values

Append Input Data Set

Append Input SAS Data Set

Applied BioSystems Array Type

Apply Original Column Names

Archive the resolved SAS macro code

Base Input SAS Data Set

By Variables

Categorical Variables

Channel Variable

Check uniqueness of column names

Class Variables

Codelink Array Type

Column Name Variable

Column Order Data Set

Column Variables

Compare Parameters Data Set

Compress output data set

Compress output data sets

Constant to Apply

Constants for Each Variance Component

Control Levels for Differential Expression Comparisons

Corresponding Key Variables from Merge Input Data Set

Corresponding Key Variables from Merge Input SAS Data Set

Data Columns to be Transposed

Data Set Containing LSMeans Differences to Include

Data Set of Differences to Include from Comparison Set

Data Start Row

Data Step Statements

Default Annotation Folder

Default Input Folder

Default Library Folder

Default Narrative Template Folder

Default Output Folder

Default Server Output Directory

Default Settings Folder

Default Template Name

Delete nonmatching rows

Delete rows not in Base Input Data Set

Delete rows not in Base Input SAS Data Set

Delete rows satisfying this expression

Delete rows with at least this percentage of Missing values

Display Pre-Study Variable Requirements in terms of:

Drop numeric variables used to compute statistics

Drop Variables

Effect Sizes

Enable access to SAS Drug Development study data

Experimental Design Data Set

Experimental Design SAS Data Set

File Containing Estimate Statements

File Filter Expression

File Type

Files to Import

Filter to Include Observations

Filter to Include Observations (1)

Filter to Include Observations (2)

Filter to Include Observations (3)

Filter to Include Observations (4)

Filter to Include Tall Rows (and the corresponding Wide Variables)

Filter to Keep Observations

First Folder of Data Sets to Compare

Fixed Effects

Fixed Effects for Differential Expression

Folder of Non-UTF-8 Files

Folder for UTF-8 Files after Converting

Gene/Protein Identifier

Hide reports with unsatisfied study requirements from Starter

Hide unchosen dialog options from requirements report

Hide unsatisfying studies from report dialogs

ID Variables

Illumina Array Type

Include

Include observations that meet:

Ingenuity Knowledge Base

Ingenuity Server

Input Data Set

Input Data Step Statements

JMP Script Output File Name

Keep labels

Key Variables from Base Input Data Set

Key Variables from Base Input SAS Data Set

List of Variable Names and Lengths

List of Variable Names and Types

List-Style Specification of Data Columns to Be Transposed

List-Style Specification of Numeric Variables

List-Style Specification of Transpose Variables

List-Style Specification of Variables to Be Standardized

List-Style Specification of Variables to Be Summarized

List-Style Specification of Variables to Be Transformed

List-Style Specification of Variables to Reorder

List-Style Specification of Wide Variables Corresponding to Tall Rows

LSMeans Control Levels

LSMeans Difference Set

LSMeans Effects

Match case for key variables

Maximum Column Length

Maximum Length for All Other (Unselected) Variables

Merge Input Data Set

Merge Input SAS Data Set

Merge Key Variables

Merge Key Variables (Venn Diagram - Multiple Tables)

Merge Key Variables to Associate with Input SAS Data Set

Metadata Server Name

Method for Handling Ties

Minimize lengths of selected variables

Minimum Number of Columns to Scan

Multipliers of Design Size

Name

Name of Response Variable

Name of this Settings Profile

New Label Specifications

New Length for Selected Variables

New Variable Names

Number of Columns to Use in Subset

Number of Groups for Rank Method GROUPS

Number of Rows to Scan

Numeric Variables over Which to Compute Row Statistics

Numerical Parameter for Advanced Standardization Methods

Output Data Set

Output Data Set for Graphs

Output Data Set Name

Output Data Step Statements

Output Experimental Design Data Set

Output Experimental Design Data Set Name

Output File

Output File Name

Output Folder

Output Stack Data Set

Output Statistics Data Set

Output Tall Data Set

Output Wide Data Set

Password

Percentile to Compute for PCTL Statistic

Percentile to Impute

Perform log2 transform after standardization

Platform

Port Number

Prefix

Prefix for Column Names in Tall Data Set

Prefix for Output Data Set Names

Prefix for Tall Column Names

Prefix for Wide Column Names

Pre-SET Data Step Statements

Pre-SET Output Data Step Statements

Print Options

PROC MIXED Options

Prompt about closing all associated graphics and tables when closing tabbed reports

Proportional Areas

Proportional Surrounding Area (applies only for 1-way, 2-way, and 3-way diagrams)

Remove labels from these variables

Remove rows with duplicate values of Sort Variables

Remove variables not found

Replace value of counts with proportion of total counts

Repository Name

Response Variable

Row Number of Variable Names

Row Variables

SAS Code for Transformation

SAS Data Set Folder

SAS Drug Development Server URL

SAS File Folder

Save and Restore Current Row States

Second Folder of Data Sets to Compare

Select

Select Columns

Select Comparison Set for Differential Expression Tests

Select Missing

Server Output Directory

Set negative values to Missing before imputing

Shared Grid Folder (UNC path recommended)

Shared Grid Folder - Client Version

Shared Grid Folder - Server Version

Shifting Factor

Show

Show Starter Customization buttons

Show Variable Requirements and Usage link on report dialogs

Sort

Sort Variables

Specify a name for the 0-1 column

Specify up to 20 IPA observations to upload

Standardization Method

Standardization Statistics Input SAS Data Set

Standardize

Starting Values for 3-Way Optimization

Statistic to Compute

Statistic to Impute

Statistics to Compute

Study

Subset Data Set to Use for Normalization

Summary Statistic to Compute for Each Variable

Tall SAS Data Set

Transform Expression

Transport File Folder

Transport File Name

Transpose Variables

Transposed Output Data Set

Type

Type of Transformation

Use a subset of columns

Use Only Selected Rows when Comparing Tables

User Name

Variable Containing Current Column Names

Variable Containing Names of Wide Variables

Variable Containing New Column Names

Variable to Identify Individual Probe Set

Variables By Which to Summarize

Variables Defining Groups

Variables Defining Tall Column Names

Variables Defining Wide Column Names

Variables to Be Standardized

Variables to Be Summarized

Variables to Be Transformed

Variables to Drop

Variables to Exclude from the Output Stack Data Set

Variables to Include in the Imputation Process

Variables to Keep in Output Data Set

Variables to Reorder

Variables to Retain in Output Data Set

Variables Whose Length is to be Changed

Version of SDD instance:

Weight Variable

Wide SAS Data Set

Wide Variables Corresponding to Tall Rows

Servers Parameters

Authentication Domain

Delete server

Machine

Machine Name or Server ID

Server Output Directory

Server Type

User ID

User Name

Output Graphics and Action Buttons

Hazard Ratio Event Plot

Heat Map and Dendrogram

Histogram

Hy’s Law Time Course Plot

LD Decay Plot

MA Plot

Mahalanobis Distances

Manhattan Plot

Matched Pairs Analysis

Principal Components Analysis Plot

Q-Q Plot

Receiver Operating Characteristics (ROC) Curves

Reliability Diagram

Scatterplot

Scree Plot

Segmentation Summary Plot

Shift Plot

Standardized Residual Plots

Add Notes (Disproportionality Analysis)

Add Record-Level Notes

Annotation Summary

Apply Subject Filter

Box Plots

Change Significance Criterion

Check Variable Requirement and Usage

Close All

Cluster Domains

Cluster Subjects

Column Switcher for TreeMap Tabs

Construct One-way Plots

Create static report for Review...

Create Subject Filter

Create Subset Experimental Design Data Set

Create Subset Experimental Design Data Set, Excluding Selected Curves

Create Subset Genotype and Annotation Data Sets

Create Subset with Mean Difference and P-value Criteria

Enter new -log10(p) cutoff

Entrez Cross

Entrez Gene

Exclude Markers and Rerun Analysis

Fit Incidence Density Model

Fit Model and Plot LS Means

Fit Survival Model to Input Data for Selected Rows

Forest Plot

Forest Plots of Credible Intervals

Hierarchical for Drug

Hierarchical for Event

IPA Upload

iPathwayGuide Output Data

JMP Clinical Variable Report

K-Means for Drug

K-Means for Event

Kaplan-Meier and Hazard Plots

Launch Interactive LD Plots for R^2

Launch JMP Genomics Browser

Launch Plot Intensities

Linkage Group X

Liver Lab Shift Plots

Malecot LD Map

Manage Data Templates

Manage Display Templates

Open Subset in Tall Format

Open Subset in Wide Format

Overall Bubble

Overall Bubble Across Time

Partial Correlation Diagram

Plot Fixed Effect Components

Plot Oneway Means by Chromosome Position and Overlay Tracks

Plot Survival Curves for Stratified Data

Plot Trait by Genotype

Remove Stable Records

Reopen Dialog

Report Actions

Reverse Linkage Group Marker Orders

Revert Clustering

Save Current Linkage Group Membership

Select Markers in Graphs or Linked Tables, then Click to View Survival Curves

Show Events Leading to SMQs

Show New or Modified Records

Show Records with Missing Test Results

Show Rows in Heat Map

Show SMQs

Show Subjects

Strata Bubble

Study Variable Report

Uniques Occurrence Subject Counts

Venn Diagram

View Chromosome Segmentation Details

View Notes (Disproportionality Analysis)

View Offspring Genotypes

View plot of input data for the selected interaction(s)

View Record-Level Notes

View Venn Diagram of Significant Markers by Trait for the Test Below

Visits Standard Report

Output

Import

Create a Design Data Set from an Existing Data Set

Create Design File Template

Create a Design File from MiniML

NetAffx Download Engine

Affymetrix Annotation CSV File Input Engine

Affymetrix ARR File Parser

Affymetrix Exon and Whole Transcript Expression CEL Input Engine

Affymetrix Expression CEL Input Engine

Affymetrix miRNA CEL Input Engine

Affymetrix SNP CEL Input Engine

Affymetrix Cytogenetics CEL Input Engine

Affymetrix Tiling CEL Input Engine

Affymetrix Exon CHP Input Engine

Affymetrix Expression CHP Input Engine

Affymetrix Tiling BAR Input Engine

Affymetrix SNP CHP Input Engine

Affymetrix Cytogenetics/CytoscanHD CHP Input Engine

Affymetrix Copy Number CHP Input Engine

Affymetrix CNAT Input Engine

Illumina Expression Input Engine

Illumina miRNA Input Engine

Illumina SNP Input Engine

Illumina Copy Number Input Engine

Illumina Methylation/Genotype IDAT Input Engine

Import Individual Text, CSV, or Excel Files

Import a Designed Experiment from Text, CSV, or Excel Files

Nanostring Expression Input Engine

Reference Gene Normalization

Agilent Input Engine

ArrayTrack Input Engine

BioConductor Expresso for Affymetrix Wrapper

GenePix Input Engine

QuantArray Input Engine

ScanAlyze Input Engine

Imputed SNP (Tall Format) Input Engine

Imputed SNP (Wide Format) Input Engine

Imputed SNP Import Tutorial

Arlequin Input Engine

NEXUS Input Engine

Pedigree Input Engine

PLINK Input Engine

PLINK Binary Input Engine

WinQTLCart Input Engine

OneMap Input Engine

Tassel-GBS Import Engine

ABI Analyst Input Engine

SAM Input Engine

BAM Input Engine

Eland Input Engine

Bin Intensities or Read Counts

Gene Model Summary

Call Variants with SAMtools

CLC Bio Input Engine

Complete Genomics Input Engine

VCF Input Engine

Import Feature-Barcode Matrices

Bioconductor QuasR Alignment Wrapper

Workflows Output Overviews

Basic Single Cell RNA-Seq Workflow

Genetics

Check Data Contents

Verify Gender of Samples

Create Annotation Analysis Group Variable

Subset and Reorder Genetic Data

Recode Genotypes

Recode Missing Genotypes

Impute Missing Genotypes

Standardize Genotypes

Expand Multiallelic Genotypes

Collapse Multiallelic Genotypes

Calculate Square Root of Matrix

Missing Genotype by Trait Summary

LD Block Creation

Linkage Disequilibrium

LD tagSNP Selection

Malecot LD Map

Case-Control Association

PCA for Population Stratification

SNP-Trait Association

Imputed SNP-Trait Association

Survey SNP-Trait Association

Quantitative TDT

TDT

GWAS Meta-Analysis

Multiple SNP-Trait Association

Rare Variant Association

Pleiotropic Association

Marker-Trait Association

SNP-SNP Interactions

SNP Interaction Discovery

Haplotype Trend Regression

Haplotype Q-K Mixed Model

htSNP Selection

Affected Sib-Pair Tests

Haseman-Elston Regression

Variance Components

Recombination and Linkage Groups

Linkage Map Order

Linkage Map Viewer

Compare Linkage Maps

Build Consensus Linkage Map

QTL Single Marker Analysis

Build Genotype Probability Data Set

QTL IM, CIM and MIM Analysis

Results (IM/CIM Analysis)

Results (MIM Analysis)

Copy Number/LOH Control Set Adjustment

Distribution Analysis

Data Standardize

Correlation and Principal Variance Component Analysis

Bin

One-Way ANOVA

Bivariate One-Way ANOVA

Partition

Spectral Preprocessing

Distribution Analysis

Correlation and Principal Variance Component Analysis

Correlation and Grouped Scatterplots

Filter Intensities

Feature Flagger

Missing Value Imputation

Pseudo Image

Ratio Analysis

Surface Summary

Variable Gene Selection

Data Standardize

ANOVA Normalization

Mixed Model Normalization

Control Set Normalization

Batch Normalization

Batch Scoring

Loess Normalization

Factor Analysis Normalization

Partial Least Squares Normalization

Quantile Normalization

Evaluation of Normalization Methods

Upper Quartile Scaling

Allele Specific Expression Filter

Hierarchical Clustering

K-Means Clustering

Principal Components Analysis

Plot Intensities

Cross Correlation

Distance Matrix and Clustering

Multidimensional Scaling

Partial Correlation Diagram

Predictive Modeling

Predictive Modeling Review

Discriminant Analysis

Distance Scoring

General Linear Model Selection

K Nearest Neighbors

Logistic Regression

Partial Least Squares

Partition Trees

Quantile Regression Selection

Radial Basis Machine

Ridge Regression

Life Regression

Proportional Hazards Regression

Genomic BLUP

Genomic Bayesian Regression

XG Boost Regression

Cross Validation Model Comparison

Learning Curve Model Comparison

Test Set Model Comparison

Model Summary and Ensemble

Merge CVMC Results

Merge LCMC Results

Recode Genotypes

Transpose Tall to Wide

Transpose Wide to Tall

Principal Component Scoring

Optimal Treatment Regime

P-Value Operations

P-Value Combination

P-Value Adjustment

P-Value Quantile Plotter

Meta-Analysis

Genome Views

JMP Genomics Browser

Chromosome Color Theme

UCSC Genome Browser

Affymetrix Integrated Genome Browser

Affymetrix Integrated Genome Browser

Download NetAffx Files

Import Affymetrix Annotation CSV Files

IPA Upload

Ingenuity Get Gene Pathways

Merge Gene Sets

Export to GSEA Format

Utilities

Missing Value Imputation

Transpose Rectangular

Data Step

Compare Data Sets

Import SAS Transport Files

Import Text, CSV, or Excel Files

Export SAS Transport File

Statistics for Columns

Transform

Data Filter

Graph Builder

Create 0-1 Columns from a Class Variable

Create 0-1 Indicator for Selected Rows

Venn Diagram - Single Table

Venn Diagram - Multiple Tables

Open SAS Temporary Folder

Open Output Folder

Clear Parameter Defaults

Generate Clinical Dialogs

Check Translations

Stack

Subset

Transpose Tall to Wide

Transpose Wide to Tall

Unstack

Export to Affymetrix CHP Format

Rename

Reorder

Filter Wide Columns Based on Tall Rows

Combine Columns

Generate Genomics Dialogs

Save As SAS Data Set

Configure Life Sciences Settings

Set Life Sciences Preferences

Load Life Sciences Setting

Change Clinical User Role

Change Genomics User Role

Import Output Tabs

Genotype Color Plots

Marker Type Counts Plot

P-Value Plots of Test I

P-Value Plots of Test II

Selected Marker Counts Plot

Workflows Output Tabs

Variable Gene Selection

Gene Expression Visualization

All P-Value Plots (Multiple SNP-Trait Association)

All P-Value Plots (Pleiotropic Association)

All Results

All Results (htSNP Selection)

All Trends Odds Ratio Plots

Annotation Group Results

Annotation Group Results (Marker Properties)

Annotation Group Results (Missing Genotypes by Trait Summary)

Annotation Group Plots/All Markers Plots (Linkage Disequilibrium)

Annotation Group Results (Pleiotropic Association)

Annotation Group Results (htSNP Selection)

Annotation Plotting Group Results

AUC

Bivariate Plots (Malecot LD Map)

Distributions (Verify Gender)

Estimate Plots (IM and CIM Analysis)

Fitness Information Plot

First Generation Expected Trait Statistics

G LSMeans

GxE LSMeans

Genetic Distance Results

Genomic Heritability

Genomic Variance

Genotype Color Plots

Haplotype Frequency Charts

Heat Map Results

Heterogeneity Statistics

IBD/IBS Pairs Results

Individual Admixture

Individual Cluster

Individual Missing Genotype Proportion

LD Decay

Linkage Group Results

Segregation and Linkage Groups

Linkage Map

Linkage Map Comparisons

Linkage Order Results

Manhattan Plot

Manhattan Plot (GWAS Meta-Analysis)

Manhattan Plot (Multiple SNP-Trait Analysis)

Manhattan Plot (Pleiotropic Association)

Map Length

Marker Frequencies

Model Effects

Model Fitness Plot Global

Model Fitness Plot Global (Haplotype Trend Regression)

Model Fitness Plot Single

Model Fitness Plot Single (Haplotype Trend Regression)

Multi-Generations Simulated Trait Means Ordered

Multi-Generations Simulated Trait Ordered

Multi-Generations Simulated Trait Statistics

Overlay Plots (Malecot LD Map)

P-Value Plot Global (Haplotype Trend Regression)

P-Value Plot Single

P-Value Plot Single (Haplotype Trend Regression)

Plots of Runs (Chromosome)

Q & K Model

R2 Plots

Reliability Diagrams

Results (Compare QTL Plots)

Results (IM and CIM Analysis)

Results (K Matrix Compression)

Results (MIM Analysis)

Results (Phenotype Summary)

Results (SNP Power)

Results (SNP-SNP Interactions)

Results by BY Variable

RMSE

RMSE (Build Consensus Linkage Map)

Run Distribution

Scree Plot

Scree Plot (GxE Interaction)

Scree Plot (Population Admixture)

Scree Plot (Relationship Matrix)

Simulated Trait Distribution

Simulated Trait Means Ordered

Simulated Trait Means Ordered (Cross Evaluation)

Simulated Trait Ordered

Simulated Trait Statistics

Simulated Trait Statistics (Cross Evaluation)

SNP Cluster

SNP Genotype Frequency

SNP Status Distribution

Stability

Summary Chart

Summary Chart (GWAS Meta-Analysis)

Summary Chart (Haplotype Estimation)

Summary Chart (Rare Variant Association)

Summary Chart (Pleiotropic Association)

Summary Chart (Marker Properties)

Summary Chart (Multiple SNP-Trait Association)

Trait Statistics

Volcano Plot Single

Volcano Plot Single (Haplotype Trend Regression)

Volcano Plot(s)

Volcano Plot(s) (QTL Single Marker Analysis)

Volcano Plot(s) (Survey SNP-Trait Association)

Copy Number

2D PCA Plots (Correlation and Principal Components)

3D PCA Plot (Correlation and Principal Components)

Ave Sig Index Chromosome Plots

Box Plots

Chromosome Position Plots

Chromosome Segmentation Plots

Chromosome Segmentation Summary

Correlation Distributions (Correlation and Principal Components)

Correlation Heat Map (Correlation and Principal Components)

Distribution Details

Kernel Density Estimates

Results

Scree Plot (Correlation and Principal Components)

Variability Estimates

Variance Components Charts (Correlation and Principal Components)

Spectral Preprocessing

Cell Plot

Mean Spectra

Peak Finding Statistics

2D PCA Plots (Correlation and Principal Components)

3D PCA Plot (Correlation and Principal Components)

Box Plots

Chromosome Position Plots

Correlation Distributions (Correlation and Principal Components)

Correlation Heat Map (Correlation and Principal Components)

Kernel Density Estimates

MA Plots (KDMM Normalization)

MA Plots (Loess Normalization)

MA Plots (Ratio Analysis)

MA Plots (TMM Normalization)

Scree Plot (Correlation and Principal Components)

Standardized Residual Plots

Variability Estimates

Variance Components Chart (Batch Normalization)

Variance Components Charts (Correlation and Principal Components)

Volcano Plot

Pattern Discovery

Candidates for Deleting/Adding

Diagram

Distributions

Hierarchical Clustering

Intensity Parallel Plots

Pearson Correlation Heat Map Results

Model Results (General Linear Model Selection)

Model Results (Genomic Bayesian Regression)

Model Results (Life Regression)

Node Statistics

Reliability Diagrams

Results

Results (Proportional Hazards Regression)

RMSE

ROC

ROC (Genomic Bayesian Regression)

Row Scores

SAS Output

SAS Output (Life Regression)

SAS Output (Proportional Hazards Regression)

SAS Output Frame

Simulated Trait Ordered

Subtree Assessment

Test Set Average Accuracy Range

Test Set Average and Individual RMSE

Test Set Average AUC Range

Test Set Average RMSE Range

Heterogeneity Statistics

Results

Volcano Plot(s)

Annotation Analysis Output Tabs

ProbF Trt

JMP Life Sciences Programming Guide

Getting Started

The Column Contents Process

The Anatomy of a JMP Life Sciences Analytical Process

Adding and Deleting JMP Life Sciences Analytical Processes

JMP Life Sciences XML Tags, Attributes, and Values

The Column Contents Process

JMP Life Sciences XML tags, Attributes, and Values

Symbols

Writing your XML Code

Example: A Process for Creating a SAS Data Set

The Import Individual Text, CSV or Excel Files Process

SAS Code for the Import Individual Text, CSV, or Excel Files Process

XML Code for the Import Individual Text, CSV, or Excel Files Process

Creating a JSL File for Dynamic Graphics and Analyses

The Distribution Analysis Process

SAS Code for Generating JSL

Macros Available for JMP Life Sciences Processes

Macro Descriptions

ASEFMacros

Bin2D

BinaryResponseEffectSelectionMacros

Class Vars

CopyNumberMacros

CrossValidationModelComparisonMacros

MultipleTestingAdjustmentMacros

TestSetModelComparisonMacros

UtilityMacros

WebSearchJSL

Writing High Quality Processes

Audience

Development Tips

Code Reuse

Improving Your SAS Macro Language Skills

Line and Macro Length Limits

Using JMP Graphics in Presentations and Publications

Server Profiles

Delete SAS Server Profile

Select SAS Server Profile

Specifying Folders, Files, and Data Sets

Specifying Files or Data Sets

Examining Folders, Files, and Data Sets

Examining Folders, Files, and Data Sets When JMP Is Connected to SAS on Your Local Machine

Examining Folders, Files, and Data Sets When JMP Is Connected to SAS on a Server

List-Style Specification

The SAS WHERE Expression

Estimate Statements and the Estimate Builder

Rules for Study Names

Rules for Paths

Rules for Review Package Names

JMP Life Sciences Files are Identified by Suffixes

JMP Life Sciences Processes Call SAS PROCS

Glossary

Trouble Shooting

References