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