BMC Genomics 2007, 8:117 http://www.biomedcentral.com/1471-2164/8/117 Saver (Genisphere) and scanned with Axon 4000B Gene- Pix scanners (Axon) at wavelengths of 532 nm for Cy3 and 635 nm for Cy5. Additional file 5 provides the day of hybridization, print set, sample number, and cluster den- drogram for all microarray experiments. cDNA microarray data analysis Microarray images were processed using GenePix 4.0 soft- ware (Axon). After aligning the settings file and collecting the pixel intensity, the data were uploaded to the Long- horn Array Database (LAD) [40] for spot filtering and nor- malization. Significance testing was performed with Acuity 4.0 (Axon). In LAD, spots that were flagged during manual gridding or spots that had less than a median intensity of 150 were excluded from further analysis. After log2-transformation and background subtraction, data were normalized to a median intensity ratio of one. Only 8,338 genes with expression measurements on at least 80% of arrays were analyzed further. Averaged linked clus- tering and data centering was performed with LAD. Calculation of the Pearson correlation between cell lines and tissue included the same data filtering described above; in addition, genes were also excluded if they were not present in at least 2 out of 3 cell line replicates. Repli- cates were averaged before calculating the correlation. Two separate SVD analyses were performed, first on all samples and second solely on the cervical tissue to iden- tify genes differentially expressed between normal cervix and cervical cancer. In both cases, SVD was performed on the 8,338 genes used for hierarchical clustering where the non-missing row average for a gene replaced any missing data for that gene. The columns (cell lines or tissue biop- sies) were normalized. 499 genes were identified as differ- entially expressed between cervical cancer and normal cervix by rank ordering the genes according to fold change, and selecting genes with a greater than 2-fold change. A Student's t-test was performed on the 8,338 genes described above to identify differentially expressed genes between cervical cancer and normal cervix. 434 genes were identified using a Benjamini-Hochberg multiple hypothesis correction (p < 0.01) with a minimum 2-fold expression change. The overlap of the SVD and t-test data produced a highly confident gene list of 140 genes. Addi- tionally, a t-test was also used to identify 77 differentially expressed genes between SiHa cells grown as a monolayer versus an organotypic environment (p < 0.001). The Pearson correlation coefficient was calculated between each cell line and, individually, against cervical cancer and normal cervix. Genes were subjected to the same filters used in the SVD analysis. Tissue to tissue com- parisons were calculated by averaging each replicate and then splitting the patient samples into two groups. The same data used to calculate the global correlation were used to calculate the pathway specific correlation. Replicates were averaged and the clone identifiers were mapped to LocusLink identifiers using SOURCE [41]. Clone IDs with more than 4 LocusLink identifiers were removed; the rest were annotated to Gene Ontology, Bio- logical Process (levels 5–11) using LocusLink. A strict Bon- ferroni multiple hypothesis correction was used (p < 0.0005, t-test) based on the database size of GO. Authors' contributions MWC carried out the cell culture, tissue procurement, mRNA isolation/amplification, and microarray hybridiza- tions. VRI provided the construction of the microarrays and tools for the statistical analysis. MWC and EMM con- ceived the study and participated in its design and helped draft the manuscript. All authors read and approved the final manuscript. Additional material Additional file 1 The list of 140 genes differentially regulated between normal cervix and invasive cervical cancer. The intersection of a t-test using the Benjamini- Hochberg multiple hypothesis correction (p < 0.01) and the SVD results, along with a 2-fold minimum expression change, was used to identify 140 differentially expressed genes. 19 genes marked with an (*)were also reported in the cervical cancer literature. 30 genes marked with (**)were homologs that were reported in the previous cervical cancer literature. Click here for file [http://www.biomedcentral.com/content/supplementary/1471- 2164-8-117-S1.xls] Additional file 2 Cell line culture conditions. Cell lines were cultured as monolayers in ATCC recommended media. Also, SiHa and HeLa cells were cultured in DMEM as well as in an organotypic environment. Click here for file [http://www.biomedcentral.com/content/supplementary/1471- 2164-8-117-S2.xls] Additional file 3 Potential biomarkers derived from cell lines cluster normal cervix from cervical cancer in a manner comparable to the tissue-derived expression profiles. We identified a cell line with the highest correlation to cervical cancer (C4-I) and generated a list of 196 differentially expressed genes between the C4-I and primary normal cell lines. This list was used to hier- archical cluster the normal cervix and cervical cancer biopsies. The result- ing dendrogram was similar to the dendrogram in Figure 2A. Click here for file [http://www.biomedcentral.com/content/supplementary/1471- 2164-8-117-S3.pdf] Page 11 of 13 (page number not for citation purposes)