Selecting Relevant Genes with a Spectral Approach

Array technologies have made it possible to record simultaneously the expression pattern of thousands of genes. A fundamental problem in the analysis of gene expression data is the identification of highly relevant genes that either discriminate between phenotypic labels or are important with respect to the cellular process studied in the experiment: for example cell cycle or heat shock in yeast experiments, chemical or genetic perturbations of mammalian cell lines, and genes involved in class discovery for human tumors. In this paper we focus on the task of unsupervised gene selection. The problem of selecting a small subset of genes is particularly challenging as the datasets involved are typically characterized by a very small sample size ?? the order of few tens of tissue samples ??d by a very large feature space as the number of genes tend to be in the high thousands. We propose a model independent approach which scores candidate gene selections using spectral properties of the candidate affinity matrix. The algorithm is very straightforward to implement yet contains a number of remarkable properties which guarantee consistent sparse selections. To illustrate the value of our approach we applied our algorithm on five different datasets. The first consists of time course data from four well studied Hematopoietic cell lines (HL-60, Jurkat, NB4, and U937). The other four datasets include three well studied treatment outcomes (large cell lymphoma, childhood medulloblastomas, breast tumors) and one unpublished dataset (lymph status). We compared our approach both with other unsupervised methods (SOM,PCA,GS) and with supervised methods (SNR,RMB,RFE). The results clearly show that our approach considerably outperforms all the other unsupervised approaches in our study, is competitive with supervised methods and in some case even outperforms supervised approaches.

Self-Assembly of Stimuli-Responsive Water-Soluble [60]Fullerene End-Capped Ampholytic Block Copolymer

Well-defined, water-soluble, pH and temperature stimuli-responsive [60]fullerene (C₆₀) containing ampholytic block copolymer of poly((methacrylic acid)-block-(2-(dimethylamino)ethyl methacrylate))-block–C₆₀ (P(MAA-b-DMAEMA)-b-C₆₀) was synthesized by the atom transfer radical polymerization (ATRP) technique. The self-assembly behaviour of the C₆₀ containing polyampholyte in aqueous solution was characterized by dynamic light scattering (DLS), and transmission electron microscopy. This amphiphilic mono-C₆₀ end-capped block copolymer shows enhanced solubility in aqueous medium at room and elevated temperatures and at low and high pH but phase-separates at intermediate pH of between 5.4 and 8.8. The self assembly of the copolymer is different from that of P(MAA-b-DMAEMA). Examination of the association behavior using DLS revealed the co-existence of unimers and aggregates at low pH at all temperatures studied, with the association being driven by the balance of hydrophobic and electrostatic interactions. Unimers and aggregates of different microstructures are also observed at high pH and at temperatures below the lower critical solution temperature (LCST) of PDMAEMA. At high pH and at temperatures above the LCST of PDMAEMA, the formation of micelles and aggregates co-existing in solution is driven by the combination of hydrophobic, electrostatic, and charge-transfer interactions.