Biomedical subjects

Kevin K Anderson

Publications and source records attributed to Kevin K Anderson.

4 recordsLinked to original sources

PubMed2006

ProMAT: protein microarray analysis tool.

SUMMARY: ProMAT is a software tool for statistically analyzing data from enzyme-linked immunosorbent assay microarray experiments. The software estimates standard curves, sample protein concentrations and their uncertainties for multiple assays. ProMAT generates a set of comprehensive figures for assessing results and diagnosing process quality. The tool is available for Windows or Mac, and is distributed as open-source Java and R code. AVAILABILITY: ProMAT is available at http://www.pnl.gov/statistics/ProMAT. ProMAT requires Java version 1.5.0 and R version 1.9.1 (or more recent versions). ProMAT requires either Windows XP or Mac OS 10.4 or newer versions.

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PubMed2006

Estimating probabilities of peptide database identifications to LC-FTICR-MS observations.

BACKGROUND: The field of proteomics involves the characterization of the peptides and proteins expressed in a cell under specific conditions. Proteomics has made rapid advances in recent years following the sequencing of the genomes of an increasing number of organisms. A prominent technology for high throughput proteomics analysis is the use of liquid chromatography coupled to Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR-MS). Meaningful biological conclusions can best be made when the peptide identities returned by this technique are accompanied by measures of accuracy and confidence. METHODS: After a tryptically digested protein mixture is analyzed by LC-FTICR-MS, the observed masses and normalized elution times of the detected features are statistically matched to the theoretical masses and elution times of known peptides listed in a large database. The probability of matching is estimated for each peptide in the reference database using statistical classification methods assuming bivariate Gaussian probability distributions on the uncertainties in the masses and the normalized elution times. RESULTS: A database of 69,220 features from 32 LC-FTICR-MS analyses of a tryptically digested bovine serum albumin (BSA) sample was matched to a database populated with 97% false positive peptides. The percentage of high confidence identifications was found to be consistent with other database search procedures. BSA database peptides were identified with high confidence on average in 14.1 of the 32 analyses. False positives were identified on average in just 2.7 analyses. CONCLUSION: Using a priori probabilities that contrast peptides from expected and unexpected proteins was shown to perform better in identifying target peptides than using equally likely a priori probabilities. This is because a large percentage of the target peptides were similar to unexpected peptides which were included to be false positives. The use of triplicate analyses with a "2 out of 3" reporting rule was shown to have excellent rejection of false positives.

Journal Article↗

PubMed2005

Evaluating concentration estimation errors in ELISA microarray experiments.

BACKGROUND: Enzyme-linked immunosorbent assay (ELISA) is a standard immunoassay to estimate a protein's concentration in a sample. Deploying ELISA in a microarray format permits simultaneous estimation of the concentrations of numerous proteins in a small sample. These estimates, however, are uncertain due to processing error and biological variability. Evaluating estimation error is critical to interpreting biological significance and improving the ELISA microarray process. Estimation error evaluation must be automated to realize a reliable high-throughput ELISA microarray system. In this paper, we present a statistical method based on propagation of error to evaluate concentration estimation errors in the ELISA microarray process. Although propagation of error is central to this method and the focus of this paper, it is most 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 concentration estimation errors. RESULTS: We use an ELISA microarray investigation of breast cancer biomarkers to illustrate the evaluation of concentration estimation 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. We summarize the results with a simple, three-panel diagnostic visualization featuring a scatterplot of the standard data with logistic standard curve and 95% confidence intervals, an annotated histogram of sample measurements, and a plot of the 95% concentration coefficient of variation, or relative error, as a function of concentration. CONCLUSIONS: This statistical method should be of value in the rapid evaluation and quality control of high-throughput ELISA microarray analyses. Applying propagation of error to a variety of ELISA microarray concentration estimation models is straightforward. Displaying the results in the three-panel layout succinctly summarizes both the standard and sample data while providing an informative critique of applicability of the fitted model, the uncertainty in concentration estimates, and the quality of both the experiment and the ELISA microarray process.

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PubMed2005

The utility of accurate mass and LC elution time information in the analysis of complex proteomes.

The combination of mass and normalized elution time (NET) of a peptide identified by liquid chromatography-mass spectrometry (LC-MS) measurements can serve as a unique signature for that peptide. However, the specificity of an LC-MS measurement depends upon the complexity of the proteome (i.e., the number of possible peptides) and the accuracy of the LC-MS measurements. In this work, theoretical tryptic digests of all predicted proteins from the genomes of three organisms of varying complexity were evaluated for specificity. Accuracy of the LC-MS measurement of mass-NET pairs (on a 0 to 1.0 NET scale) was described by bivariate normal sampling distributions centered on the peptide signatures. Measurement accuracy (i.e., mass and NET standard deviations of +/-0.1, 1, 5, and 10 ppm, and +/-0.01 and 0.05, respectively) was varied to evaluate improvements in process quality. The spatially localized confidence score, a conditional probability of peptide uniqueness, formed the basis for the peptide identification. Application of this approach to organisms with comparatively small proteomes, such as Deinococcus radiodurans, shows that modest mass and elution time accuracies are generally adequate for confidently identifying most peptides. For more complex proteomes, more accurate measurements are required. However, the study suggests that the majority of proteins for even the human proteome should be identifiable with reasonable confidence by using LC-MS measurements with mass accuracies within +/-1 ppm and high efficiency separations having elution time measurements within +/-0.01 NET.

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