Acidification and Nutrient Cycling

Additions of acid anions can alter the cycling of other nutrients and elements within an ecosystem. As strong acid ions move through a forest, they may increase the concentrations of nitrogen (N) and sulfur (S) in the soil solution and stream water. Such treatments also may increase or decrease the availability of other anions, cations and metal ions in the soil. A number of studies in Europe and North America have documented increases in base cation concentrations such as calcium (Ca) and magnesium (Mg) with increased N and S deposition (Foster and Nicolson 1988, Feger 1992, Norton et al. 1994, Adams et al. 1997, Currie et al. 1999, Fernandez et al. 2003). Experiments in Europe also have evaluated the response of forested watersheds to decreased deposition (Tietema et al. 1998, Lamersdorf and Borken 2004). In this chapter, we evaluate the effects of the watershed acidification treatment on the cycling of N, S, Ca, Mg and potassium (K) on Fernow WS3.

Extensions to Gene Set Enrichment

Motivation: Gene Set Enrichment Analysis (GSEA) has been developed recently to capture moderate but coordinated changes in the expression of sets of functionally related genes. We propose number of extensions to GSEA, which uses different statistics to describe the association between genes and phenotype of interest. We make use of dimension reduction procedures, such as principle component analysis to identify gene sets containing coordinated genes. We also address the problem of overlapping among gene sets in this paper. Results: We applied our methods to the data come from a clinical trial in acute lymphoblastic leukemia (ALL) [1]. We identified interesting gene sets using different statistics. We find that gender may have effects on the gene expression in addition to the phenotype effects. Investigating overlap among interesting gene sets indicate that overlapping could alter the interpretation of the significant results.

GENE SET ENRICHMENT ANALYSIS MADE SIMPLE

Among the many applications of microarray technology, one of the most popular is the identification of genes that are differentially expressed in two conditions. A common statistical approach is to quantify the interest of each gene with a p-value, adjust these p-values for multiple comparisons, chose an appropriate cut-off, and create a list of candidate genes. This approach has been criticized for ignoring biological knowledge regarding how genes work together. Recently a series of methods, that do incorporate biological knowledge, have been proposed. However, many of these methods seem overly complicated. Furthermore, the most popular method, Gene Set Enrichment Analysis (GSEA), is based on a statistical test known for its lack of sensitivity. In this paper we compare the performance of a simple alternative to GSEA.We find that this simple solution clearly outperforms GSEA.We demonstrate this with eight different microarray datasets.