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Getting Started with JMP Life Sciences
Software Basics
Introduction
Requirements
Text Conventions
The Life Sciences Main Menus
The File Menu
The Genomics Starter
Studies
Analytical Process
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
Sample Case Studies
JMP Genomics Starter
Studies
Using Studies
Import
Experimental Design File
Affymetrix
Illumina
Nanostring
Other Expression
Other Genetics
Proteomics
Next-Gen Sequencing
Text
Summarize
Workflows
Basic
Advanced
Genetics
Genetics Utilities
Relatedness Measures
Genetic Marker Statistics
GWAS Testing
Other Association Testing
Comparison of Association Testing Processes
Haplotype Analysis
Model-free Linkage
Linkage Maps and QTL
Breeding Analysis
Copy Number
Spectral Preprocessing
Expression
Quality Control
Normalization
Normalization (Next-Gen)
Differential Expression
Expression Utilities
Pattern Discovery
Predictive Modeling
Main Methods
Model Comparisons
Predictive Modeling Utilities
Subgroup Analysis
P-Value Operations
Genome Views
Genome Browser
Track Creation
Annotation Analysis
General
Affymetrix
Ingenuity
GSEA / MSigDB
SAS Data Set Utilities
Tables
Rows
Columns
Import/Export
General Utilities
Documentation and Help
The Menu Bar
Current Study
Processes
Studies
Add Study
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
Parse a Column
Create Array Index
Create ColumnName
Check File Names
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
Workflow Builder
Journal Builder
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
Flip Strand
SNP Power
Kinship Matrix
Relationship Matrix
K Matrix Compression
Calculate Square Root of Matrix
Population Admixture
IBS Sharing Regions
Population Measures
Phenotype Summary
Marker Properties
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
Q-K Model Fitness
Q-K Mixed Model
Genomic Heritability
Haplotype Estimation
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
Compare QTL Plots
Phenotype Summary
GxE Interaction
Cross Evaluation
Progeny Simulation
Copy Number
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
2D Bin
2D Detrend
2D Peak Find
2D Plot
3D Align
3D Plot
Expression
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
TMM Normalization
TPM Normalization
KDMM Normalization
One-Way ANOVA
ANOVA
Mixed Model Analysis
Survival Analysis
Allele Specific Expression Filter
Estimate Builder
Difference Chooser
Two-Way Plotter
Combine Experiments
Split Experiment
Mixed Model Power
Pattern Discovery
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
Survival Residuals
Subgroup Analysis
Interaction Trees
Local Control
Virtual Twins
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
Track Bar Chart
Track Color Map
Track Gene Text
Track Gene Web
Track Gene GFF
Track SNP Web
Annotation Analysis
Gene Set Enrichment
Gene Set Scoring
List Enrichment
Create Web Links
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
Estimate Builder
Mixed Model Power
Column Contents
Append
Merge
Merge and Transform
Transpose Rectangular
DATA Step
Compare Data Sets
Import SAS Transport Files
Import Text, CSV, or Excel Files
Export SAS Transport File
Export
Rank Rows
Sort Rows
Statistics for Rows
Change Labels
Change Lengths
Data Standardize
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
Register Add-ins
R Package Manager
Parameters
Studies
Annotation Data Sets
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
Study
Study Name
Tall Data Sets
Weight Variable
Wide Data Sets
Import
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
Baseline Variable
BIM File
Bin Method
Bin Size
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
Customer Array
Cut-off for DP
Cut-off for GQ
Data File
Data File Type
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
GC Score Cutoff
Gene Identifier
Gene Model Text File
Gene File to Import
Genotype Data Set
Genotype File
Genotype Files
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
ID Variables
ID Variables to Keep
Illumina Data File
Imaging QC
Import CEL intensities without merging annotation
Increase R Software Memory Limit
Individual Variables
Input Data Set
Input SAS Data Set
Include intron bins
Include MM in output
Include sequence data in output
Indicator of Different Column Names across Raw Data Files
Input Arlequin File
Input NEXUS File
Input OneMap File
Input Pedigree File
Input Phenotype File
Input SAS Data Set
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)
Map Data Set
Map File
MAP Files
Marker Data Set
Marker ID Variable
Marker Label
Marker Name Variable
Marker Type
Marker Variables
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
No Family ID Column
No Parent ID Columns
No Phenotype Column
No Sex Column
Normalization
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
PDF Output File
PDF Output File Name
PED or FAM Files
Pedigree 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
Ploidy Level
Ploidy Type
PM Correction
Position
Position Variable
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
Probeset Variable
Probe Variable
Probeset File
QC Control File
QC Output Data Set
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 File
Sample Files
Sample Variable
SAS Code for Customized Flagging Rule
SAS Code to Create Columns
SAS Code to Create New Design Variables
Save as SAS Data Set
Scaling Factor
SD Output Data Set
Segments CN Data Set
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
Start
Strand
Study
Summarization Method
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
Censor Values
Censor Variable
Center columns
Center rows
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
Color Theme
Color Variable
Color Variable Type
Color Variables
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
Gene Length Variable
Gene ID Variable
Gene Symbol Variable
Genes of Interest
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
Sort settings by:
Strata Variables
Study
Study Name
Study Output Folder
Tau Value for TPM
Template Study
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
Workflow Folder
Workflow Output Name
Workflow to Journal
Workflow to Run
Genetics
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)
Censor Limit
Censor Values
Censor Variable
Censor Variables
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
Chromosome Number
Chromosome Variable
Class Covariates
Class Variables
Cluster Center Variable
Cluster Variable
Cluster Variables
Clustering Method
Co-factor Variables
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
MANOVA Statistic
Map 1 Label
Map 2 Label
Map Function
Marker 1 Label
Marker 2 Label
Marker 1 Location
Marker 2 Location
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
Marker Position
Marker Type
Marker Units
Marker Variables
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
p-Value Variable
Parent 1 ID
Parent 2 ID
Parent Variables
PARMS Statement Values and/or Options
PC Regression Model
PCA Data Set
Pedigree Data Set
Pedigree ID
Pedigree ID Variable
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
Population Variable
Position Variable
Position Variable 1
Position Variable 2
Power Values
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
Stepwise EM Cutoff
Strata Variable
Strata Variables
Study
Subset Size
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
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
Gene Variable
Gene Symbol Variable
ID Variable
ID Variables to Keep
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
Statistical Test
Study
Trim below zero
Trim Peak Maximum
Trim Valley Minimum
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
Adjustment Effects
Allele Variable
Alpha
Alpha Values
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
Biomaterial Provider
Block Variable
By Variable
By Variables
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
Censor Variable
Center rows
Characteristics
Chromosome Variable
Class Variables
Cluster significant LSMean profiles
Cluster significant Mean profiles
Cluster Significant Results
Coating
Color Scale
Color Variable
Color Variables
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
Gene Length Data Set
Gene Length Variable
Gene ID Variable
Gene Symbol Variable
Gradient Convergence Criterion [1,1000000]
Group Percentage for Deletion
Growth Protocol
Hyb Protocol
ID
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
Label
Label Protocol
Label Variable
Lambda Number
Link Function
List all model fits
List every model fit
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
Platform Data Set
Platform Organism
Platform Title
Plot Rate
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
PubMed ID
QC Data Set
Random Effects
Random Number Seed
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
Sample Description
Sample Name
Scale rows
Scaling Approach
Scan Protocol
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
Statistic to Impute
Study
Sub-Feature Variable
Subject Variable
Subset Data Set to Use for Normalization
Summary
Summary Statistic
Support
Technology
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
Center Columns
Center Rows
Class Variables
Clustering Method
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
Ordering Variable
Organism
Output Data Set
Output Data Set Name
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
Chromosome ID
Chromosome Label
Chromosome Number
Class Covariates
Color Variable
Color Variables
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
Generate HTML Output
Group Variable
Groups Variable
Grouping Variable
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
Inner Loop Algorithm
Input SAS Data Set
Input Tall Data Set
Input Wide Data Set
Iteration End Number
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
Target Variable
Target Variables
Test Data Set
Test Data Sets
Test Sets
Thinning Rate
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
Display output as a:
Draw indicator line
End
Exon Count
Exon Ends
Exon Starts
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
Gene ID Variable
Gene Symbol Variable
Genome
Initial Color Theme
Initial Cutoff for Displaying Bars
Input GFF File
Input SAS Data Set
Label for Other Variables
Label
Label Variable
Lightness Scale
Links Color Theme
Links Color Variable
Links Input SAS Data Set
Multiple Testing Method
Name
Number of Legend Decimals
Number of Legend Levels
Number of Rows in Each Bin
Organism
Output Data Set
Output Folder
Plot bars
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
Primary Color
Reverse Color Theme
Saturation Scale
Search Type
Secondary Color
Server Output Directory
Set window size
SNP ID Variable
Start
Strand
Study
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
Window Size
y Variable
y Variables
Annotation Analysis
-log10(p-Value) Cutoff
Affymetrix Array Type
Affymetrix GeneChip Array
Affymetrix Link
Agilent Array Type
Alpha
AmiGO Database Link
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
Data Step Statements
dbSNP ID
dbSNP Link
Description
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
Folder of List Files
Folder of Raw Files
GenBank Accession
GenBank Link
Gene Description
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
Input SAS Data Set
IPA Entry Point
IPA Project Name
JSL Output File
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
Output Data Set Name
Output File Name
Output File Type
Output Folder
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
Probe ID
Probe Set or Gene ID
PubMed Database Link
Random Number Seed
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
Archive the SAS code
Array Variable
Auto clear
Base Input Data Set
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
Folder of Raw Files
Force Append
Format
Frequency Variable
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
Input SAS Data Set
Input Tall Data Set
Input Wide Data Set
IPA Entry Point
IPA Project Name
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)
Proxy Port Number
Proxy Server Name
Random Effects
Random Number Seed
Rank Method
Rank Order
Rank Variables
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
New Connection Type
New Port Number
New Server ID
New Server Name
New User ID
New User Password
Output Folder
Password
Port
Port Number
Profile Name
Server
Server ID
Server Name
Server Output Directory
Server Type
User ID
User Name
Output Graphics and Action Buttons
Graphics
Autocorrelation Plot
Bar Chart
Box Plot
Bubble Plot
Cell Plot
Chart
Contingency Plot
Contingency Table
Contour Plot
Correlogram
Dendrogram
Distance Graph
Distribution
Forest Plot
Geographical Map
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
Mosaic Plot
One-way Plot
Overlay Plot
Parallel Plot
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
Surface Plot
Survival Curves
Survival Plot
Time Series Graph
Trace Plot
Tree Map
Trellis Plot
Volcano Plot
Waterfall Plot
Action Buttons
AceView Database
Add Comment
Add Notes
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
Contingency
Contingency Analysis
Create Animal Filter
Create Narratives
Create Static Report
All Other Reports:
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
dbSNP
Demographic Counts
Describe Output
Dot Plot
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
GenBank Nucleotide
Gene List
Generate Report
Genotype
GO Stat
Graph Time Profiles
Graph Time Trends
Graph Trellis Plot
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
Manage Tabs...
Missing Bar Charts
Odds Ratio Plot
Onto-Express
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
Profile Subjects
PubMed
Related AE
Related CM
Related Labs
Related Vitals
Relative Risk Plot
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
Shift Plots
Show All Records
Show Domain Keys
Show Dropped Records
Show Duplicates
Show Events
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
Subset and Transpose
Subset Clustering
Tabulate
Time Trend
Trend
Trend Plots
UCSC Genome Browser
UniGene Database
Uniques Occurrence Subject Counts
Venn Diagram
View Chromosome Segmentation Details
View Comments
View Data
View Domain
View Domain Notes
View Notes
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
Parse a Column
Create Array Index
Create ColumnName
Check File Names
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
Flip Strand
SNP Power
Kinship Matrix
Relationship Matrix
K Matrix Compression
Calculate Square Root of Matrix
Population Admixture
IBS Sharing Regions
Population Measures
Phenotype Summary
Marker Properties
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
Q-K Model Fitness
Q-K Mixed Model
Genomic Heritability
Haplotype Estimation
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)
Compare QTL Plots
GxE Interaction
Cross Evaluation
Progeny Simulation
Copy Number
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
2D Bin
2D Detrend
2D Peak Find
2D Plot
3D Align
3D Plot
Expression
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
KDMM Normalization
RPM Scaling
TMM Normalization
TPM Normalization
Upper Quartile Scaling
One-Way ANOVA
ANOVA
Mixed Model Analysis
Survival Analysis
Allele Specific Expression Filter
Estimate Builder
Difference Chooser
Two-Way Plotter
Combine Experiments
Split Experiment
Mixed Model Power
Pattern Discovery
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
Survival Residuals
Subgroup Analysis
Interaction Trees
Local Control
Virtual Twins
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
Track Bar Chart
Track Color Map
Track Gene Text
Track Gene Web
Track Gene GFF
Track SNP Web
Annotation Analysis
Gene Set Enrichment
Gene Set Scoring
List Enrichment
Create Web Links
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
Estimate Builder
Mixed Model Power
Column Contents
Append
Merge
Merge and Transform
Transpose Rectangular
Data Step
Compare Data Sets
Import SAS Transport Files
Import Text, CSV, or Excel Files
Export SAS Transport File
Export
Rank Rows
Sort Rows
Statistics for Rows
Change Labels
Change Lengths
Data Standardize
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
Clustering
Data Overview
Embedding
Feature Screening
Variable Gene Selection
Gene Expression Visualization
Genetics
2D Biplots
2D Linkage Map
3D Biplot
3D Linkage Map
Accuracy
All Distributions
All F-st Results
All P-Value Plots
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)
Block Summary
Cell Plot
Cell Plot (TDT)
Chromosome Results
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)
PC1 x Mean Trait
PCA 2D Row Scores
PCA 3D Row Scores
P-Value Plot Global
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
Plot of ...
Plot of Mass by Time
Volcano Plot
Expression
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)
Cumulative Plots
Distribution Details
Fixed Effects Tests
Goodness of Fit
Grouped Correlation
KDMM Scaling Factors
Kernel Density Estimates
MA Plots (KDMM Normalization)
MA Plots (Loess Normalization)
MA Plots (Ratio Analysis)
MA Plots (TMM Normalization)
PCA Plot
Pseudo Image
Residual Plots
Results
RPM Factors
Scree Plot (Correlation and Principal Components)
Standardized Residual Plots
Surface Summary
Summary
TMM Factors
TPM Scaling Factors
UQS Factors
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
K-Means Clusters
MDS Results
Model History
PCA 2D Column Scores
PCA 2D Row Scores
PCA 3D Column Scores
PCA 3D Row Scores
Pearson Correlation Heat Map Results
Results
Test Results
Predictive Modeling
3D Scatterplot
Accuracy
AUC
Canonical Scores
Charts by Test Set
Correlations
Distributions
Harrell’s C
Model Effects
Model 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
Tree Diagram
Variable Importance
Variable Weights
Subgroup Analysis
Interaction Tree
Model Results
Subtree Assessment
Tree Diagram
Variable Importance
P-Value Operations
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
FastCCMacros
GAMacros
GenMacros
IBDMacros
InputEngineMacros
LoessModelMacros
MixedModelMacros
MultipleTestingAdjustmentMacros
PartitionTreesMacros
PCA_VCA
PLS_PCA
PredMacros
qtlUtilityMacros
RMAMacros
SeedMacros
SNPTraitAssocMacro
SurfacePlot
TestSetModelComparisonMacros
UtilityMacros
WebSearchJSL
Writing High Quality Processes
Audience
Development Tips
Code Reuse
Improving Your SAS Macro Language Skills
Line and Macro Length Limits
Collaboration
Additional Resources
Appendixes
Introduction
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