Sources Of Error In Microarray
These graphs can be used as the basis for modifying the process or for data normalization.Because our protein arrays feature fewer spots per array than do typical gene expression microarrays, a Error models are built to capture the predictable behavior of the variance. Downregulated data are marked with a black ‘x’ Data that are not differentially expressed are shown as gray dots. In (b) two repeated spots in each array are error-weighted and combined and then the two fluor-reversal arrays are error-weighted and combined based on Equation (16). navigate here
For comparison, the two overlapping black lines are modeled errors in the red and the green channels computed from Equation (7). Significance analysis of microarrays applied to the ionizing radiation response. The error-model approach has also been compared with some other published methods (Rajagopalan, 2003) where its advantages in improving detection sensitivity and specificity are clearly demonstrated. We can compute the P-value of the hypothesis test as (21) where Erf is the error function of a standard Gaussian distribution.
To determine the proper RANDOM, we examine the error model fit with data of technical replicates. Data are fluor-reversed two-color duplicates. The propagated error sets a lower bound or floor to prevent the error estimation from being smaller than the microarray technology can support. The plotted points are intrinsically symmetric across the diagonal line because a pair of points is plotted as both (x, y) and (y, x). (a) Numbers are extracted from the image
- GCRMA-EB had a particular disagreement with other methods when a t-test was used for group comparison, presumably because it might be more sensitive to the underlying statistical assumptions of a test
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- Previous SectionNext Section Conclusion We have outlined a method of obtaining reliable error estimates for spotted DNA microarray measurements.
- View larger version: In this window In a new window Download PPT Figure 2 A scatter plot of the data collected from the 4 × 1,152 dataset.
- Our layout features several distally separate replicates of each assay spot on each microarray to evaluate local processing effects.
- Typically, a histogram of many measurements will form a normal (i.e., Gaussian) distribution whose mean value is taken as the best estimate of the true value.
For this reason, we and others are developing ELISA microarray systems to evaluate 20 to 50 proteins using only a few microliters of sample in an efficient and quantitative manner [2,3].Processing When presented in a simple multi-panel visualization, the propagated errors provide valuable information about individual concentration estimates, the applicability of the estimated standard curve, quality of the experiment, and the conduct One such method is to compute a P value corresponding to the hypothesis that the mean values of the spots represent identical or distinct expression levels (9). Specifically, we explicitly modeled main error sources in microarray measurements and then apply the model-predicted measurement error as the error floor to help stabilize the variance estimation.
R. Results indicated that technical replicates at the hybridization step agree more closely (i.e. These small variances contribute partially to the overall false positive rate. By taking the logarithm, equal changes in up/down concentrations are represented by equal numerical values.
Figure 5 demonstrates false positives in a t-test example. In a following section, we will encounter similar standardization difficulties in the oligonucleotide platform. Google Scholar ↵ Tonouchi M. Department of Energy under Contract DE-AC05-76RL01830.ReferencesZangar RC, Varnum SM, Covington CY, Smith RD.
If differentially expressed genes are present, the number of small p-values will be increased. Department of Electrical Engineering and Computer Science; 2001. Each point represents a pair of different measurements of the same physical value. Each microarray spot is shown as one gray dot in the figure.
These intervals summarize the uncertainty in concentration estimates due to both the uncertainty in estimating the standard curve and the uncertainty in the sample spot intensity estimate. check over here This union defines an L-shaped region covering the standard curve segment and bound at its extremes by the intensity and concentration segments. These processing trends can be made more apparent with locally weighted regression, or loess, a statistical technique to fit a smooth curve through the scatterplot [22,23]. We used a computer algorithm to calculate the bootstrap median and confidence levels in the median.
It can be biased. In an ELISA microarray experiment, the standard data are collected by fixing a set of concentrations and measuring spot intensities via imagery of the treated arrays. After we find the proper value of this parameter for a given microarray technology during error model development, we fix RANDOM as a constant in later applications. http://nzbsites.com/sources-of/sources-of-error-in-hplc.html P.
A protein microarray ELISA for screening biological fluids. SAS Institute, Inc. (Cary, NC); 1999. ConclusionIdentification of sources of variation and their relative magnitudes, among other factors, is important for optimal experimental design and the development of quality control procedures.
Optimal design for ELISA and other forms of immunoassay.
J Comp Graph Stat 1996, 5(3):299–314.Google ScholarCopyright©Zakharkin et al; licensee BioMed Central Ltd.2005 This article is published under license to BioMed Central Ltd. Arrays were hybridized to mRNA from C2C12 and 10T1/2 cell lines. The propagated error from the error model is a conservative estimation of , which is not gene-specific and non-zero. Boston, Massachusetts: Birkhaser; 1996.
It is important to understand the meaning of the null hypothesis under a different number of replicates. This calculation provides a one-sided P-value. Loosely speaking, Lorentzian distributions have longer tails than Gaussian distributions. weblink Abstract/FREE Full Text ↵ Tusher V G, Tibshirani R, Chu G (2001) Proc Natl Acad Sci USA 98:5116–5121, pmid:11309499.
Home Chapter Home Jobs Conferences Fellowships Books Advertisement Molecular Pathology Microarray Common errors Author: Rodney E. doi: 10.1198/106186002317375640. [Cross Ref]Rocke DM, G J.
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