Abstract
Reverse-phase protein arrays (RPPAs) are widely used in biological and biomedical fields of study. One of the most popular analytic methods in RPPA data analysis is the SuperCurve method, which requires estimation of the background fluorescence level. This estimation is usually not accurate and has sample bias and spatial bias. Here, we propose a taking-the-difference method to overcome this problem. Briefly, for each two consecutive RPPA cycles, we subtract the later cycle from the earlier cycle, transforming the m-cycle data into m-1 cycle of data. This removes most of the background fluorescence noise. We then use the m-1 cycle of data to fit a new model accordingly derived from the SuperCurve model. To evaluate our proposed method, we compare the accuracy and precision between our proposed model and the original SuperCurve model by testing them on both real and simulated datasets. For both situations, our modified model shows improved results. The modified SuperCurve method is easy to perform and the taking-the-difference idea is recommended for application to all current methods of RPPA data analysis.
Original language | English (US) |
---|---|
Pages (from-to) | 765-769 |
Number of pages | 5 |
Journal | Journal of Computational Biology |
Volume | 22 |
Issue number | 8 |
DOIs | |
State | Published - Aug 1 2015 |
Keywords
- Background subtraction
- SuperCurve model
- regression model
- reverse-phase protein array
ASJC Scopus subject areas
- Modeling and Simulation
- Molecular Biology
- Genetics
- Computational Mathematics
- Computational Theory and Mathematics
MD Anderson CCSG core facilities
- Bioinformatics Shared Resource
- Biostatistics Resource Group
- Functional Proteomics Reverse Phase Protein Array Core