breast.tumors

This data set contains the expression of 1753 genes in 47 surgical specimens of
human breast tumours from 17 different individuals before and after chemotherapy treatment.

datasets

We provide statistical integrative techniques and
        variants to analyse highly dimensional data sets: regularized
        Canonical Correlation Analysis ('rCCA') and sparse Partial Least Squares variants ('sPLS') 
        to unravel relationships between two heterogeneous data sets of size (n times p) and (n times q) 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 Canonical Correlation Analysis 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. Recent methodological developments include: sparse PLS-Discriminant Analysis ('sPLS-DA'), Independent 
        Principal Component Analysis ('IPCA'), multilevel analysis using variance decomposition of the data and integration 
        of multiple data sets with regularized Generalised Canonical Correlation Analysis ('rGCCA') and variants (sparse 'GCCA'). More details  can be found 
        on our website.

Kim-Anh Cao

mixOmics

Omics Data Integration Project

breast.tumors function

encoding

The Human Breast Tumors dataset is a companion resource for the paper of
Perou <em>et al.</em> (2000), and was downloaded from the Stanford Genomics Breast 
Cancer Consortium Portal 
<url>http://genome-www.stanford.edu/breast_cancer/molecularportraits/download.shtml</url>

breast.tumors: Human Breast Tumors Data

Description

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