Relevant Gene Selection Using Normalized Cut Clustering with Maximal Compression Similarity Measure

Bala, Rajni; Agrawal, R. K.; Sardana, Manju

doi:10.1007/978-3-642-13672-6_9

Cited by 2 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The biggest advantage of this technique, the comprehensive gene set, is also its main pitfall as the imbalance between a large number of genes and a handful of biological samples transforms the analysis step into the bottleneck of the microarray technology . Furthermore, technical variations (dye bias and array variability) and biological noise (signal variations) challenge accurate analysis (Bala et al 2010). …”

Section: Introductionmentioning

confidence: 99%

An improved analysis methodology for translational profiling by microarray

Sbarrato¹,

Spriggs²,

Wilson³

et al. 2017

RNA

View full text Add to dashboard Cite

Translational regulation plays a central role in the global gene expression of a cell, and detection of such regulation has allowed deciphering of critical biological mechanisms. Genome-wide studies of the regulation of translation (translatome) performed on microarrays represent a substantial proportion of studies, alongside with recent advances in deep-sequencing methods. However, there has been a lack of development in specific processing methodologies that deal with the distinct nature of translatome array data. In this study, we confirm that polysome profiling yields skewed data and thus violates the conventional transcriptome analysis assumptions. Using a comprehensive simulation of translatome array data varying the percentage and symmetry of deregulation, we show that conventional analysis methods (Quantile and LOESS normalizations) and statistical tests failed, respectively, to correctly normalize the data and to identify correctly deregulated genes (DEGs). We thus propose a novel analysis methodology available as a CRAN package; Internal Control Analysis of Translatome (INCATome) based on a normalization tied to a group of invariant controls. We confirm that INCATome outperforms the other normalization methods and allows a stringent identification of DEGs. More importantly, INCATome implementation on a biological translatome data set (cells silenced for splicing factor PSF) resulted in the best normalization performance and an improved validation concordance for identification of true positive DEGs. Finally, we provide evidence that INCATome is able to infer novel biological pathways with superior discovery potential, thus confirming the benefits for researchers of implementing INCATome for future translatome studies as well as for existing data sets to generate novel avenues for research.

show abstract

Section: Introductionmentioning

confidence: 99%