Creating the Hierarchical Tree

After combining Signals from each GeneChip™ with Microarray Suite 5, the invariant set normalization method was used (see Hoffman, Seidl, and Dugas 2002) in dChip software (see Li and Wong 2001). This normalization method chooses the probesets that have small rank differences in the signal to serve as the basis for fitting a non-linear regression curve. Statistical filtering was applied using Significance Analysis of Microarrays (SAM) v1.13. SAM is a statistical technique for finding significant genes in a set of Microarray experiments. SAM identifies genes with statistically significant changes in expression by assimilating a set of gene-specific t-tests. Each gene is assigned a score on the basis of its change in gene expression relative to the standard deviation of repeated measurements for that gene. Genes with scores greater than threshold delta (see Tusher, Tibshirani and Chu 2001 for the detailed description) are deemed potentially significant. The percentage of such genes identified by chance is the false discovery rate (FDR). To estimate the FDR, nonsense genes are identified by analyzing permutation of the measurements. The threshold can be adjusted to identify smaller or larger sets of genes, and FDRs are calculated for each set.

After statistical filtering described above, the results are converted and imported to Cluster v 2.11 (Eisen et al, 1998) using Samster Software. After log transformation and normalization, complete linkage hierarchical clustering was done. Pearson correlation metrics were used. The results were shown by TreeView v 1.50.

Statistical filtering

Using the clusters

An overview of the results of the cluster analysis is displayed in "Broad view of all clusters". If looking for a particular gene you must go to the individual tree clusters. They are Adobe Acrobat files (.pdf) that open in the web browser and can be searched by going to EDIT in the top menu bar > FIND. There is a shortcut on your browser that will also work: click on the binocular icon and type in the gene name or abbreviation you want to query and it will be highlighted in the text. To see where else the word is found click on the binocular+arrow icon.

NOTE: Red indicates an increase and green indicates a decrease from the mean which is displayed in black.

Literature

Eisen,M.B., Spellman,P.T., Brown,P.O. and Botstein,D. (1998). Cluster Analysis and Display of Genome-Wide Expression Patterns. Proc Natl Acad Sci USA, 95:14863-8. [Full Text]

Hoffmann,R., Seidl,T., and Dugas,M. (2002) Profound effect of normalization on detection of differentially expressed genes in oligonucleotide microarray data analysis. Genome Biology 3(7):research0033.1-0033.11 [Full Text]

Li,C. and Wong,W.H. (2001) Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc Natl Acad Sci USA, 98:31-36 [Full Text]

Tusher,V.G., Tibshirani,R., and Chu,G. (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA, 98: 5116-5121 [Full Text]

Page last modified: 05-Aug-2003