srbct

This data set from Khan et al. 2001 gives the expression measure of 2308 genes measured on 63 samples.

datasets

The package supplies two efficients methodologies:
        regularized CCA and sparse PLS to unravel relationships between
        two heterogeneous data sets of size (nxp) and (nxq) where the p
        and q variables are measured on the same samples or individuals
        n. These data may come from high throughput technologies, such
        as omics data (e.g. transcriptomics, metabolomics or proteomics
        data) that require an integrative or joint analysis. However,
        mixOmics can also be applied to any other large data sets where
        p + q >> n. rCCA is a regularized version of CCA to deal with
        the large number of variables. sPLS allows variable selection
        in a one step procedure and two frameworks are proposed:
        regression and canonical analysis. Numerous graphical outputs
        are provided to help interpreting the results.

Kim-Anh Cao

mixOmics

Integrate Omics data project

srbct function

encoding

<url>http://research.nhgri.nih.gov/microarray/Supplement</url>

srbct: Small version of the small round blue cell tumors of childhood data

Description

Usage

Arguments

encoding

source

References