Copy Number

This menu gathers together those quality control, data manipulation, and modeling processes that are particularly useful for copy number analysis. These processes require tall data sets. (See Tall and Wide Data Sets.) Experimental Design Data Set (EDDS) use varies by process. Refer to their individual descriptions for more information.

Process	Choose this process for...
Copy Number/LOH Control Set Adjustment	Adjusting copy number intensity or count data by subtracting control sample intensities or counts, either within-subject or across sets
Distribution Analysis	Displaying univariate distribution results for variables, with the option of computing and overlaying density estimates
Data Standardize	Standardizing values of numeric variables
Correlation and Principal Variance Component Analysis	Computing correlations between numeric variables, principal components of the correlation matrix, an outlier analysis, and a variance components decomposition
Bin	Binning observations into groups, reducing the total number of rows in a data set
One-Way ANOVA	Fitting a one-way repeated-measures ANOVA linear model to rows of the input data set, using all combinations of different effects as distinct groups Tip: To examine all possible effects and their interactions separately, choose the ANOVA process instead.
Bivariate One-Way ANOVA	Fitting a one-way repeated-measures analysis-of-variance linear model to pairs of input data set rows Important: The bivariate observations are assumed to have the same variance, and a correlation is modeled between them. If the mean or covariance structure is more complex than this, then you should run the Mixed Model Analysis process instead. Note: This process can be computationally intensive for large data sets, but runs faster than Mixed Model Analysis for identical models.
Partition	Finding potential breakpoints of copy number changes across a chromosome using recursive partitioning

See the The JMP Genomics Starter main page for other process categories.