In general this fold profile will have high positive correlation with the EF profiles from the treatment set and high negative correlations with the control set. In cases where there is no obvious way of separating selleck chem samples into control and treatment sets, as with samples from multiple organ types or cell types, the EF representation can be viewed as a normalized expression value. In searching SPIED with a query profile one is not deriving any biological sig nificance for non correlating profiles as lack of correla tion can be attributed to multiple factors such as bad experimental data or genuine lack of biological relevance. Rather significantly correlating or anti correlating pro files are posited as having biological significance.
The next objective was to reduce the expression profiles to non redundant EF gene profiles by associating each gene with just one probe ID, so that the database can then be searched with gene set data alone. Here, for a given chip platform the distribution of each probe ID EF value across the totality of series was compiled and each gene was then assigned to the probe having the highest average fold magnitude. The gene names were unam biguously associated with the Entrez human gene list consisting of 24,764 genes and these were matched to probe IDs by inspection of the given platform annotation files. The final form of SPIED consists of individual files for each chip platform and these files are formatted starting with a gene list fol lowed by the sample ID and corresponding EF profiles.
This format lends itself to rapid searching in an analo gous fashion to FASTA formatted sequence databases. In contrast to the KS query score scheme, which requires generating random reference gene list data, we adopted a simple regression scoring scheme with corresponding statistic. Searches can be performed on a standard desk top PC and take 10 minutes per query. Although, the present database consisting of expression data for over 100,000 samples from five platforms covering three spe cies is all from Affymetrix expression array chips, the methodology is truly platform independent and it is a straight forward matter to include data based on other array technologies. Other species and platform technologies will be added to SPIED in the future. For the present study Affymetrix was chosen because of the relatively large number of available sam ples.
Further details are presented in the methods section below. Results Drug treatment based profile SPIED queries The CMAP contains expression change profiles as ranked array probe IDs for 6,100 individual treatments Batimastat corresponding to 1,309 distinct drug like compounds. Statistically filtered response profiles can be defined for 1,218 of the drugs as these have at least three instances in the database.
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