MIPHENO: data normalization for high throughput metabolite analysis
1 Quantitative Biology Program, Michigan State University, East Lansing, MI, USA
2 Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
3 National Health and Environmental Effects Research Laboratory, Biostatistics and Bioinformatics Research Core, United States Environmental Protection Agency, Research Triangle Park, NC, USA
4 Department of Plant Biology, Michigan State University, East Lansing, MI, USA
BMC Bioinformatics 2012, 13:10 doi:10.1186/1471-2105-13-10Published: 13 January 2012
High throughput methodologies such as microarrays, mass spectrometry and plate-based small molecule screens are increasingly used to facilitate discoveries from gene function to drug candidate identification. These large-scale experiments are typically carried out over the course of months and years, often without the controls needed to compare directly across the dataset. Few methods are available to facilitate comparisons of high throughput metabolic data generated in batches where explicit in-group controls for normalization are lacking.
Here we describe MIPHENO (Mutant Identification by Probabilistic High throughput-Enabled Normalization), an approach for post-hoc normalization of quantitative first-pass screening data in the absence of explicit in-group controls. This approach includes a quality control step and facilitates cross-experiment comparisons that decrease the false non-discovery rates, while maintaining the high accuracy needed to limit false positives in first-pass screening. Results from simulation show an improvement in both accuracy and false non-discovery rate over a range of population parameters (p < 2.2 × 10-16) and a modest but significant (p < 2.2 × 10-16) improvement in area under the receiver operator characteristic curve of 0.955 for MIPHENO vs 0.923 for a group-based statistic (z-score). Analysis of the high throughput phenotypic data from the Arabidopsis Chloroplast 2010 Project (http://www.plastid.msu.edu/ webcite) showed ~ 4-fold increase in the ability to detect previously described or expected phenotypes over the group based statistic.
Results demonstrate MIPHENO offers substantial benefit in improving the ability to detect putative mutant phenotypes from post-hoc analysis of large data sets. Additionally, it facilitates data interpretation and permits cross-dataset comparison where group-based controls are missing. MIPHENO is applicable to a wide range of high throughput screenings and the code is freely available as Additional file 1 as well as through an R package in CRAN.