Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects

Abstract

We consider the problem of comparing the gene expression levels of cells grown under two different conditions using cDNA microarray data. We use a quality index, computed from duplicate spots on the same slide, to filter out outlying spots, poor quality genes and problematical slides. We also perform calibration experiments to show that normalization between fluorescent labels is needed and that the normalization is slide dependent and non-linear. A rank invariant method is suggested to select non-differentially expressed genes and to construct normalization curves in comparative experiments. After normalization the residuals from the calibration data are used to provide prior information on variance components in the analysis of comparative experiments. Based on a hierarchical model that incorporates several levels of variations, a method for assessing the significance of gene effects in comparative experiments is presented. The analysis is demonstrated via two groups of experiments with 125 and 4129 genes, respectively, in Escherichia coli grown in glucose and acetate.

Placental expression profiling in preeclampsia: local overproduction of hemoglobin may drive pathological changes

“…The oligo-set co-printed with our cDNA-clones were used in the normalization procedure to improve its reliability. Replicate ratios were quality controlled as previously described (28). Briefly, the mean ratio, standard deviation (SD), and the coefficient of variation (CV) were calculated for the replicates.…”

Gene expression-based, individualized outcome prediction for surgically treated lung cancer patients

“…We employed a rankinvariant scaling method to select genes (Tseng et al, 2001), which were then used for fitting of a nonlinear normalization curve. After normalization, scatter plots of the 50 pairs of replicate data points for each of the genes were generated, the reproducibility of expression between the replicate pairs was assessed, and genes showing a Pearson correlation coefficient higher than 0.85 were selected.…”

A review of statistical methods for preprocessing oligonucleotide microarrays

“…However, when the up- and down-regulation is not symmetric across arrays, most methods that normalize to a common distribution would fail. An alternative is to use only the probes that are expected to show no difference across arrays in normalization (28; 29). Since there is often limited number of control genes to cover the range of observed data and some control genes (such as housekeeping genes) actually show biological variations, an “ invariant set ” of probes is often selected to be the normalizing elements.…”