JMP Genomics Starter

Category	Contains processes for...	Input data requirement ¹
Studies	Adding, managing, renaming, combining, and deleting studies. A study is a construct that helps you organize and track the data sets and settings associated with a particular study or project. See the Studies page for more information.	All data sets of the same type (for example, wide ) per study must be found in the same folder.
Import	Converting and summarizing input data in formats from other applications, programs, and platforms into data usable by JMP Genomics processes.	Varies considerably by process. Experimental Design File (EDF) s are required by some processes.
Workflows	Running and building workflows . A workflow is a sequence of processes to run in a specific order -- an analytical pipeline. Once built, a workflow can be repeatedly called, eliminating the need to repeatedly set parameters for each of the individual processes. See the Workflows page for more information.	Tall or wide Input Data Set s , Experimental Design Data Set (EDDS) s , and/or Annotation Data Set s , depending on the underlying processes.
Genetics	Examining and converting genetic data, determining relatedness measures and marker statistics, performing genome-wide and other association testing, conducting haplotype analyses, working with linkage and associated maps, and analyzing breeding data.	Tall or wide data, depending on the process. Annotation data use varies by process.
Copy Number	Quality control, data manipulation, and modeling processes particularly useful for genomic copy number analysis.	Tall data. EDDS use varies considerably by process.
Spectral Preprocessing	Analyzing, plotting, binning, and detrending two- or three-dimensional spectra.	Tall or stacked data.
Expression	Quality assessment, normalization , modeling, manipulation, and submission of expression data.	Generally, a tall data set and EDDS are required. There are a few exceptions.
Pattern Discovery	Investigating the nature, magnitude, and causality of relationships between observations . Tip : Explore data quality and evaluate the need for normalization before using these processes.	Tall, wide, rectangular, or square, depending on the process.
Predictive Modeling	Construction of continuous or categorical outcome predictors using data from genetic markers, microarrays , or proteomics as predictor variables . Model comparison and data preparation utilities are also available.	Wide data.
Subgroup Analysis	Analyzing subgroups of rows with differentiated or enhanced treatment effects.	Wide data.
P-Value Operations	Viewing, adjusting, and combining p-values in preparation for more detailed analyses.	Must contain at least one column containing p -values (or effects).
Genome Views	Generating a graphical representation of genes and other features of a genome superimposed on statistical graphics.	Varies by process. Many require the presence of annotation or identifier columns.
Annotation Analysis	Incorporation of biological meaning into statistical results. Gene set and list enrichment, merging of identifier and pathway data from public data sources, annotation report creation, and uploading data to public repositories is supported.	Varies by process, but typically some combination of significance, annotation, or identifier columns should be present.
SAS Data Set Utilities	Managing and modifying SAS data sets. Appending, merging, transforming, stacking, unstacking. subsetting, and transposing tables, ranking and sorting rows, and changing labels, lengths, and orders of columns are just some of the operations available.	Varies considerably by process. Most require one data set; some require two. A few require an EDDS.
General Utilities	Interactive utilities, as well as those for clearing parameter defaults, customizing the Starter , and rebuilding dialogs .	Some require a JMP table to be open and in focus. Others do not use data sets.
Documentation and Help	Accessing the online JMP Genomics User Guide, other JMP Genomics material, and SAS documentation. Applications for contacting the JMP Life Sciences development team and Technical Support are also available.	Not applicable.

Adding, managing, renaming, combining, and deleting studies. A study is a construct that helps you organize and track the data sets and settings associated with a particular study or project. See the Studies page for more information.

All data sets of the same type (for example, wide ) per study must be found in the same folder.

Import

Converting and summarizing input data in formats from other applications, programs, and platforms into data usable by JMP Genomics processes.

Varies considerably by process. Experimental Design File (EDF) s are required by some processes.

Workflows

Running and building workflows . A workflow is a sequence of processes to run in a specific order -- an analytical pipeline. Once built, a workflow can be repeatedly called, eliminating the need to repeatedly set parameters for each of the individual processes. See the Workflows page for more information.

Tall or wide Input Data Set s , Experimental Design Data Set (EDDS) s , and/or Annotation Data Set s , depending on the underlying processes.

Genetics

Examining and converting genetic data, determining relatedness measures and marker statistics, performing genome-wide and other association testing, conducting haplotype analyses, working with linkage and associated maps, and analyzing breeding data.

Tall or wide data, depending on the process. Annotation data use varies by process.

Copy Number

Quality control, data manipulation, and modeling processes particularly useful for genomic copy number analysis.

Tall data. EDDS use varies considerably by process.

Spectral Preprocessing

Analyzing, plotting, binning, and detrending two- or three-dimensional spectra.

Tall or stacked data.

Expression

Quality assessment, normalization , modeling, manipulation, and submission of expression data.

Generally, a tall data set and EDDS are required. There are a few exceptions.

Pattern Discovery

Investigating the nature, magnitude, and causality of relationships between observations .

Tip : Explore data quality and evaluate the need for normalization before using these processes.

Tall, wide, rectangular, or square, depending on the process.

Predictive Modeling

Construction of continuous or categorical outcome predictors using data from genetic markers, microarrays , or proteomics as predictor variables . Model comparison and data preparation utilities are also available.

Wide data.

Subgroup Analysis

Analyzing subgroups of rows with differentiated or enhanced treatment effects.

Wide data.

P-Value Operations

Viewing, adjusting, and combining p-values in preparation for more detailed analyses.

Must contain at least one column containing p -values (or effects).

Genome Views

Generating a graphical representation of genes and other features of a genome superimposed on statistical graphics.

Varies by process. Many require the presence of annotation or identifier columns.

Annotation Analysis

Incorporation of biological meaning into statistical results. Gene set and list enrichment, merging of identifier and pathway data from public data sources, annotation report creation, and uploading data to public repositories is supported.

Varies by process, but typically some combination of significance, annotation, or identifier columns should be present.

SAS Data Set Utilities

Managing and modifying SAS data sets. Appending, merging, transforming, stacking, unstacking. subsetting, and transposing tables, ranking and sorting rows, and changing labels, lengths, and orders of columns are just some of the operations available.

Varies considerably by process. Most require one data set; some require two. A few require an EDDS.

General Utilities

Interactive utilities, as well as those for clearing parameter defaults, customizing the Starter , and rebuilding dialogs .

Some require a JMP table to be open and in focus. Others do not use data sets.

Documentation and Help

Accessing the online JMP Genomics User Guide, other JMP Genomics material, and SAS documentation. Applications for contacting the JMP Life Sciences development team and Technical Support are also available.

Not applicable.

Following the import of data (an Experimental Design File (EDF) is sometimes required), Tall and Wide Data Sets (which are SAS Data Sets ) are the most common primary Input Data Set s required throughout JMP Genomics. Less common are stacked , square , and other specialized varieties. (See Other Types of Data Sets .) In many cases, an additional Experimental Design Data Set (EDDS) , Annotation Data Set , or settings file is either specifiable or required. See Data Sets Used in JMP Genomics Processes and the documentation for individual processes for additional information.