icaSetRiester


  Object of class <code><a rd-options="" href="/link/IcaSet?package=MineICA&version=1.12.0" data-mini-rdoc="MineICA::IcaSet">IcaSet</a></code> containing an ICA
  decomposition calculated by the FastICA algorithm
  (through matlab function "icasso") on gene expression
  data of urothelial tumors. measured on a HG-U133-plus2
  Affymetrix microarrays. It contains 20 independent
  components. The original expression data contain 93 tumor
  samples, were normalized with GCRMA with
  log2-transformation, and are restricted to the 10000 most
  variable probe sets.


datasets

The goal of MineICA is to perform Independent Component
Analysis (ICA) on multiple transcriptome datasets, integrating
additional data (e.g molecular, clinical and pathological).
This Integrative ICA helps the biological interpretation of the
components by studying their association with variables (e.g
sample annotations) and gene sets, and enables the comparison
of components from different datasets using correlation-based
graph.

Anne Biton

MineICA

Analysis of an ICA decomposition obtained on genomics data

icaSetRiester function

Object of class <code><a href="IcaSet" rd-options="">IcaSet</a></code> containing an ICA
  decomposition calculated by the FastICA algorithm
  (through matlab function "icasso") on gene expression
  data of urothelial tumors. measured on a HG-U133-plus2
  Affymetrix microarrays. It contains 20 independent
  components. The original expression data contain 93 tumor
  samples, were normalized with GCRMA with
  log2-transformation, and are restricted to the 10000 most
  variable probe sets.

icaSetRiester: IcaSet-object containing a FastICA decomposition of gene expression microarrray-based data of bladder cancer samples.

Description

Arguments

References