Cluster analysis and display of genome-wide expression patterns

A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.

On the belief that arthritis pain is related to the weather.

There is a widespread and strongly held belief that arthritis pain is influenced by the weather; however, scientific studies have found no consistent association. We hypothesize that this belief results, in part at least, from people's tendency to perceive patterns where none exist. We studied patients (n = 18) for more than I year and found no statistically significant associations between their arthritis pain and the weather conditions implicated by each individual. We also found that college students (n = 97) tend to perceive correlations between uncorrelated random sequences. This departure of people's intuitive notion of association from the statistical concept of association, we suggest, contributes to the belief that arthritis pain is influenced by the weather.