Deriving quantitative conclusions from microarray expression data

Deriving quantitative conclusions from microarray expression data

MOTIVATION The last few years have seen the development of DNA microarray technology that allows simultaneous measurement of the expression levels of thousands of genes. While many methods have been developed to analyze such data, most have been visualization-based. Methods that yield quantitative conclusions have been diverse and complex. RESULTS We present two straightforward methods for id...

Microarray Data Analysis: From Hypotheses to Conclusions Using Gene Expression Data

We review several commonly used methods for the design and analysis of microarray data. To begin with, some experimental design issues are addressed. Several approaches for pre-processing the data (filtering and normalization) before the statistical analysis stage are then discussed. A common first step in this type of analysis is gene selection based on statistical testing. Two approaches, per...

cDNA Microarray Data Normalization

Quantitative trait associated microarray gene expression data analysis.

Selection on phenotypes may cause genetic change. To understand the relationship between phenotype and gene expression from an evolutionary viewpoint, it is important to study the concordance between gene expression and profiles of phenotypes. In this study, we use a novel method of clustering to identify genes whose expression profiles are related to a quantitative phenotype. Cluster analysis ...

Deriving conclusions from non-monotonic cause-effect relations

We present an extension of Logic Programming (under stable models semantics) that, not only allows concluding whether a true atom is a cause of another atom, but also deriving new conclusions from these causal-effect relations. This is expressive enough to capture informal rules like “if some agent’s actions A have been necessary to cause an event E then conclude atom caused(A, E),” something t...