icaSetCarbayo


  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 bladder cancer
  expression data measured on HG-U133A Affymetrix
  microarrays. The original expression data were normalized
  with MAS5 by the authors of the paper followed by
  log2-transformation. ICA was run on the dataset
  restricted to the 10000 most variable probe sets (based
  on IQR values). 10 components were computed. Only probe
  sets/genes having an absolute projection higher than 3
  are stored in this object.


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

icaSetCarbayo 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 bladder cancer
  expression data measured on HG-U133A Affymetrix
  microarrays. The original expression data were normalized
  with MAS5 by the authors of the paper followed by
  log2-transformation. ICA was run on the dataset
  restricted to the 10000 most variable probe sets (based
  on IQR values). 10 components were computed. Only probe
  sets/genes having an absolute projection higher than 3
  are stored in this object.

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

Description

Arguments

References