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include three well studied treatment outcomes (large cell lymphoma, childhood medulloblastomas, breast tumors) and one unpublished dataset (lymph status).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.

Variability in the Stability and Productivity of Transfected Genes in Chinese Hamster Ovary (CHO) cells

In the field of biologics production, productivity and stability of the transfected gene of interest are two very important attributes that dictate if a production process is viable. To further understand and improve these two traits, we would need to further our understanding of the factors affecting them. These would include integration site of the gene, gene copy number, cell phenotypic variation and cell environment. As these factors play different parts in the development process, they lead to variable productivity and stability of the transfected gene between clones, the well-known phenomenon of “clonal variation”. A study of this phenomenon and how the various factors contribute to it will thus shed light on strategies to improve productivity and stability in the production cell line. Of the four factors, the site of gene integration appears to be one of the most important. Hence, it is proposed that work is done on studying how different integration sites affect the productivity and stability of transfected genes in the development process. For the study to be more industrially relevant, it is proposed that the Chinese Hamster Ovary dhfr-deficient cell line, CHO-DG44, is used as the model system.