Processes | Copy Number | Correlation and Principal Variance Component Analysis

Correlation and Principal Variance Component Analysis
The Correlation and Principal Variance Component Analysis process computes correlations between numeric variables, principal components of the correlation matrix, and an accompanying outlier analysis. It also optionally computes a variance components decomposition that helps you determine the major sources of overall variability in your experiment.
What do I need?
One data set is required for this process: the Input Data Set that contains all of the numeric data to be analyzed. The drosophilaaging_norm.sas7bdat data set is a normalized data set derived from the Drosophila Aging experiment described in Drosophila Aging Experimental Data, and serves as an example. This data set is partially shown below. It has 49 columns and 100 rows. Note that this is a tall data set; each probe corresponds to one row whereas each column corresponds to a separate experimental condition.
Two data sets are optional:
The Experimental Design Data Set (EDDS). The drosophilaaging_exp.sas7bdat EDDS, which is used in the example that follows, is shown below. This data set tells how the experiment was performed, providing information about the columns in the input data set. Note that one column in the EDDS must be named ColumnName and the values contained in this column must exactly match the column names in the input data set.
The Data Set to Use for Filter. This data set is used to establish filtering criteria for determining those input observations to include in the correlation. It must contain at least one merging key variable to merge with the input data set. The drosophila_annotation.sas7bdat file serves as an example, and is shown below.
The example experiment consisted of 24 two-color cDNA microarrays, six for each experimental combination of two lines (Oregon and Samarkand), two sexes (Female and Male), and two ages (1 week and 6 weeks). The Cy3 and Cy5 dyes were flipped for two of the six replicates for each genotype and sex combination. The design is a split-plot design, with Age and Dye as subplot factors, and Line and Sex as whole-plot factors. A total of 4256 clones were spotted on the arrays, but for this example, we use a subset containing 100 randomly selected genes. Raw data from this experiment was normalized to the mean to generate the drosophilaaging_norm.sas7bdat data set.
For detailed information about the files and data sets used or created by JMP Life Sciences software, see Files and Data Sets.
The output generated by this process is summarized in a Tabbed report. Refer to the Correlation and Principal Variance Component Analysis output documentation for detailed descriptions and guides to interpreting your results.