News from the group:
CAMDA'17 Conference
22-23 July 2017,
Prague, Czech Republic
(read more)
Outstanding Presentation Prize
for PP Łabaj
at CAMDA'15 (photo)
OeAW APART fellowship
for PP Łabaj

Sequencing Quality Control (SEQC) project,
MAQC Consortium 2011–2014 (read more)
Host–parasite interactions in biocontrol, WWTF grant 2010–2013 (read more)

Power and limitations of RNA-Seq,
FDA SEQC, Nature Biotechnology (read more)
Characterization and improvement of RNA-Seq precision,
Bioinformatics (read more)
Impact of heavy tails in microarray analysis, Bioinformatics (read more)
Novel conserved repeats in sorting signals,
FEBS Journal (read more)
Sound sensation gene,
Nature communications
(read more)
RNA interference in ageing research,
Gerontology (read more)

Impact of heavy tails in microarray analysis

Traditional microarray data analysis generally assumes Gaussian measurement noise – despite the sensitivity of the Normal Distribution to outliers. Especially for high-dimensional microarray data, Gaussian models are popular because of their computational efficiency in comparison to alternative approaches. We report on the first systematic study of the impact of noise model choice and its biological relevance.

Discrepancies in differential expression calls (follow link for full manuscript) A hierarchical Bayesian model allows the principled direct comparison of Gaussian models and robust alternatives. Interestingly, heavy-tailed distributions were the best fitting models for all the examined data sets, spanning a wide range of experiment types and measurement platforms. Moreover, application of an appropriately heavy-tailed t-distribution resulted in substantial changes for differential expression analysis, strongly affecting the functional categories implicated. Traditional microarray analyses relying on a Gaussian noise model thus not only distort results for individual genes but yield biased conclusions even at the higher level of functional categories. In contrast, experimental evidence strongly supports heavy tailed alternatives, and different robust approaches agree well with one another.


Posekany A, Felsenstein K, Sykacek P (2011) Assessing Robustness Issues in Microarray Data Analysis, Bioinformatics 27, 807-814. (read more | Supplement)

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