JMP Genomics Starter | Pattern Discovery

Click on a button corresponding to a pattern discovery process. Refer to the table below for guidance.

Creating a tree of observation (row) relationships with the option of clustering variables to create a two-way organization, choosing from a variety of methods Creating optimally separated groups of observations (rows), resulting in groups with similar membersTip: Consider running Data Standardize before clustering to ensure that the columns are all comparable. Examining relationships among many quantitative variables, using orthogonal transformation to reduce potentially correlated variables into uncorrelated variables known as principal components Visualizing row-level intensity measurements for individual samples via Parallel Plots, with the option of placing measurements and plots into groups as defined in an Experimental Design Data Set (EDDS) Computing and testing the significance of all pairwise correlations between two sets of numeric variables, and visualizing these correlations using a Heat Map and Dendrogram Computing and plotting distance or dissimilarity measures between observations (rows), and storing these measures in a square matrix output data set that can be used as input for the Multidimensional Scaling process Estimating the coordinates of a set of objects in a space of specified dimensionality (using distance matrix input) and creating a 2-D or 3-D Scatterplot of these coordinatesTip: Input data for this process can be generated using the Distance Matrix and Clustering process. Inferring association and potential causal relationships between a set of variables, plotting variables as nodes connected with line segments that vary in appearance based on partial correlationsTip: A wide variety of plot types and interactive options are available. You are encouraged to explore them all.See the JMP Genomics Starter main page for other process categories.