Optimizing signal / noise ratios in expression profiling : Project-specific algorithm selection and detection p value weighting in Affymetrix microarrays

Motivation: The most commonly utilized microarrays for mRNA profiling (Affymetrix) include “probe sets” of a series of perfect match and mismatch probes (typically 22 oligonucleotides per probe set). There are an increasing number of reported “probe set algorithms” that differ in their interpretation of a probe set to derive a single normalized “signal” representative of expression of each mRNA. These algorithms are known to differ in accuracy and sensitivity, and optimization has been done using a small set of standardized control microarray data. We hypothesized that different mRNA profiling projects have varying sources and degrees of confounding noise, and that these should alter choice of a specific probe set algorithm. Also, we hypothesized that use of the Microarray Suite (MAS) 5.0 probe set detection p value as a weighting function would improve the performance of all probe set algorithms. Results: We built an interactive visual analysis software tool (HCE2W) to test and define parameters in Affymetrix analyses that optimize the ratio of signal (desired biological variable) versus noise (confounding uncontrolled variables). Five probe set algorithms were studied with and without statistical weighting of probe sets using the Microarray Suite (MAS) 5.0 probe set detection p values. The signal/noise optimization method was tested in two large novel microarray datasets with different levels of confounding noise; a 105 sample U133A human muscle biopsy data set (11 groups; mutationdefined; extensive noise), and a 40 sample U74A inbred mouse lung data set (8 groups; little noise). Performance was measured by the ability of the specific probe set algorithm, with and without detection p value weighting, to cluster samples into the appropriate biological groups (unsupervised agglomerative clustering with F-measure values). 50% random sampling analyses showed a highly statistically significant difference between probe set algorithms by ANOVA (F(4,10)>14, p<0.0001), with weighting by MAS 5.0 detection p value showing significance in the mouse data by ANOVA (F(1,10)>9, p<0.013) and paired t-test (t(9)=-3.675, p=0.005). Probe set detection p-value weighting had the greatest positive effect on performance of dChip difference model, ProbeProfiler, and RMA algorithms. Importantly, probe set algorithms did indeed perform differently depending on the specific project, likely due to degree of confounding noise. Our data indicates that significantly improved data analysis of mRNA profile projects can be achieved by optimizing the choice of probe set algorithm with the noise levels intrinsic to a project, with dChip difference model with MAS 5.0 detection p value continuous weighting showing the best overall performance in both projects. Furthermore, both existing and newly developed probe set algorithms should incorporate a detection p value weighting to improve performance. * To whom correspondence should be addressed, ehoffman@cnmcresearch.org Bioinfor matics © Oxford University Press 2004; all rights reserved. Bioinformatics Advance Access published April 29, 2004 at Penylvania State U niersity on Feruary 3, 2013 httpioinform atics.oxjournals. org/ D ow nladed from