Evaluating concentration estimation errors in ELISA microarray experiments
Enzyme-linked immunosorbent assay (ELISA) is a standard immunoassay to predict a protein concentration in a sample. Deploying ELISA in a microarray format permits simultaneous prediction of the concentrations of numerous proteins in a small sample. These predictions, however, are uncertain due to processing error and biological variability. Evaluating prediction error is critical to interpreting biological significance and improving the ELISA microarray process. Evaluating prediction error must be automated to realize a reliable high-throughput ELISA microarray system. Methods: In this paper, we present a statistical method based on propagation of error to evaluate prediction errors in the ELISA microarray process. Although propagation of error is central to this method, it is effective only when comparable data are available. Therefore, we briefly discuss the roles of experimental design, data screening, normalization and statistical diagnostics when evaluating ELISA microarray prediction errors. We use an ELISA microarray investigation of breast cancer biomarkers to illustrate the evaluation of prediction errors. The illustration begins with a description of the design and resulting data, followed by a brief discussion of data screening and normalization. In our illustration, we fit a standard curve to the screened and normalized data, review the modeling diagnostics, and apply propagation of error.
- Research Organization:
- Pacific Northwest National Lab., Richland, WA (US)
- Sponsoring Organization:
- US Department of Energy (US)
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 15016360
- Report Number(s):
- PNNL-SA-42198; TRN: US200513%%262
- Journal Information:
- BMC Bioinformatics, Vol. 6, Issue 17; Other Information: PBD: 26 Jan 2005
- Country of Publication:
- United States
- Language:
- English
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