FABIA 2.20.0 – Factor Analysis for Bicluster Acquisition

FABIA 2.20.0

:: DESCRIPTION

FABIA (Factor Analysis for Bicluster Acquisition) is a model-based technique for biclustering, that is clustering rows and columns simultaneously. FABIA is a multiplicative model that assumes realistic non-Gaussian signal distributions with heavy tails. FABIA utilizes well understood model selection techniques like variational approaches and applies the Bayesian framework. The generative framework allows FABIA to determine the information content of each bicluster to separate spurious biclusters from true biclusters. On 100 simulated data sets with known true, artificially implanted biclusters, FABIA clearly outperformed all 11 competitors. FABIA was tested on microarray data sets which known, biological verfified subclusters and performed on average best out of 11 biclustering approaches.

::DEVELOPER

Institute of Bioinformatics, Johannes Kepler University Linz

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

  FABIA

:: MORE INFORMATION

Citation

Sepp Hochreiter, Ulrich Bodenhofer, Martin Heusel, Andreas Mayr, Andreas Mitterecker, Adetayo Kasim, Tatsiana Khamiakova, Suzy Van Sanden, Dan Lin, Willem Talloen, Luc Bijnens, Hinrich W.H. Göhlmann, Ziv Shkedy, and Djork-Arné Clevert.
FABIA: Factor Analysis for Bicluster Acquisition,
Bioinformatics 2010, 26(12):1520-1527,

FARMS 1.38.0 – Factor Analysis for Robust Microarray Summarization

FARMS 1.38.0

:: DESCRIPTION

FARMS (Factor Analysis for Robust Microarray Summarization ) is a model-based technique for summarizing high-density oligonucleotide array data at probe level for Affymetrix GeneChips. It is based on a factor analysis model for which a Bayesian maximum a posteriori method optimizes the model parameters under the assumption of Gaussian measurement noise. The comparison on the Affymetrix spiked-in bechmark data shows the excellent sensitivity and specificity performance of FARMS.

::DEVELOPER

Institute of Bioinformatics, Johannes Kepler University Linz

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 FARMS

:: MORE INFORMATION

Citation

Sepp Hochreiter, Djork-Arné Clevert, and Klaus Obermayer.
A new summarization method for affymetrix probe level data.”
Bioinformatics 2006 22(8):943-949;

cn.FARMS 1.34.0 – Factor Analysis for Copy Number Estimation

cn.FARMS 1.34.0

:: DESCRIPTION

cn.FARMS is a latent variable model for detecting copy number variations in microarray data.

::DEVELOPER

Institute of Bioinformatics, Johannes Kepler University Linz

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / Mac OsX
  • R Package
  • BioConductor

:: DOWNLOAD

 cn.FARMS

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2011 Jul;39(12):e79. doi: 10.1093/nar/gkr197. Epub 2011 Apr 12.
cn.FARMS: a latent variable model to detect copy number variations in microarray data with a low false discovery rate.
Clevert DA1, Mitterecker A, Mayr A, Klambauer G, Tuefferd M, De Bondt A, Talloen W, G?hlmann H, Hochreiter S.