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## Google Scholar ↵ Rajagopalan D.

We provide **comparisons mainly** to illustrate the compatibility of several algorithms. This union defines an L-shaped region covering the standard curve segment and bound at its extremes by the intensity and concentration segments. Abstract/FREE Full Text ↵ Lee M-L T, Kuo F C, Whitmore G A, Sklar J (2000) Proc Natl Acad Sci USA 97:9834–9839, pmid:10963655. These replicated arrays come from the same RNA sample, and are processed (inverse-transcription, amplification, labeling, hybridization and so forth) separately, so the data contain most of the technical variations in the click site

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 If differentially expressed genes are present, the number of small p-values will be increased. Their estimated intensity errors are σ1(i,j) and σ2(i,j). These estimates, however, are uncertain due to processing error and biological variability.

The first is called the propagated error, which is the population error of derived from the individual measurement errors by invoking the formula for : (17) The second is called the Proc. The additional information gained from using the error model opens many new opportunities for us to improve the quality of microarray data analysis.

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. This segment generally corresponds to the linear segment of a standard curve. When P-value is small, e.g. <0.05, we reject the null hypothesis and accept the alternative hypothesis that the sequence transcript is present. To that **end, we examined two plots. **

In a t-test example of two replicates of expression ratios, we only have 2 − 1 = 1 degree-of-freedom for the within-group variance estimation, and the result is unreliable. doi: 10.1021/pr025506q. [PubMed] [Cross Ref]Racine-Poon A, Weihs C, Smith AFM. New York, New York: Springer-Verlag; 2000. As demonstrated previously, these spots are typically uniform in shape with a reasonable homogenous distribution of protein across the spot [1-3].

This term is not covered in the modeled error in Equation (7). Google Scholar **↵ Theilhaber J., et** al . Each microarray spot is shown as one gray dot in the figure. This provides an alternative view of the error in concentration estimation over the concentration range covered by the concentration estimation equation.

- doi: 10.1198/106186002317375640. [Cross Ref]Rocke DM, G J.
- Cellular processes that are decoupled from translation, such as responses to micronutri-ents, drugs, or physiologic conditions might be poorly represented by changes in RNA levels.
- Google Scholar ↵ Schadt E.E., et al .

But there is no harm to the hypothesis test because those high-intensity sequences already have small-enough P-values and are clearly present in the transcripts. 3.3 Differential expression calls Traditionally, people use The two lines become separated at low intensities because the red and the green channels have different levels of additive noise. ROC curves closer to the upper-left corner of the plot have higher statistical powers in terms of sensitivity and specificity. Including this error information in data analysis increases statistical power (higher sensitivity and higher specificity) for a given small number of replicates, as indicated in the elevated ROC curves.

two replicates, by chance some sequences may have differences between two repeated measurements close to zero. http://phabletkeyboards.com/sources-of/sources-of-error-using-spectrometer.php It describes spot imperfections caused by dust, physical damage and contaminations. Biostatistics 2003, 4(3):465–477. 10.1093/biostatistics/4.3.465View ArticlePubMedGoogle ScholarAllison DB, Allison RL, Faith MS, Paultre F, Pi-Sunyer FX: Power and money: designing statistically powerful studies while minimizing financial costs. High correlation would be expected for the small number of genes with direct relevance to the disease process of interest, with weaker correlations for genes indirectly related to that metabolic state.

Images were scanned on a HP 2500 scanner. Figure 6 Distributions of p-values for the paired t-test for hybridization effect. We define CL and CU to be CL = C(IL) and CU = C(IU), respectively. Whereas expression array data require considerable effort to compare one study to another (a point we will return to later), RNA Isolated from Sample J Reverse Transcribe DS DNA J Link navigate to this website The line indicates the mean value of the four measurements for each gene.

It should also be apparent whether the variability in spot intensity is increasing with mean spot intensity. Downregulated data are marked with a black ‘x’. This indicates that approximately one in five of the genes that appear to have significant changes in expression level do not; they are statistical outliers that are an artifact of the

On the importance of standardisation in life sciences. Application of experimental design Techniques to optimize a competitive ELISA. It should be noted that in our case biological variation could be confounded by technical variation arising during tissue isolation and preparation of mRNA samples. Then we apply statistical hypothesis tests to analyze the change in measured intensities.

Bioinformatics 2003, 19(14):1817–1823. 10.1093/bioinformatics/btg245View ArticlePubMedGoogle ScholarThygesen HH, Zwinderman AH: Comparing transformation methods for DNA microarray data. 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. Although we know that DNA sequences are not random and that related genes might often share sequence homology, such events are rare enough to allow SAGE to be useful. http://phabletkeyboards.com/sources-of/sources-of-error-in-ac-circuits.php BMC Bioinformatics 2004, 5(1):77. 10.1186/1471-2105-5-77PubMed CentralView ArticlePubMedGoogle ScholarQin LX, Kerr KF, Contributing Members of the Toxicogenomics Research Consortium: Empirical evaluation of data transformations and ranking statistics for microarray analysis.

A protein microarray ELISA for screening biological fluids. Although both are expected (the first due to the randomness generally observed when counting photons, and the second due to the use of a concentration dilution series in the design), each The error of the averaged measurement in (16) is usually smaller than individual error σx(i). each individual measurement comes from an individual test subject (e.g.

They may be indeed biologically absent. Pixel standard deviations are provided from microarray feature extraction software. Cell Biochem. 2002;84:120-125. It has been demonstrated that the hybridization intensity is approximately proportional to the RNA abundance (Lockhart et al., 1996).

HER-2 belongs to the family of epidermal growth factor receptors and has been used as a serum biomarker for the detection of breast cancer. PNAS. 2000;97:9834–9839. This follows from general statistical principles, where the standard error in a measurement is proportional to the square root of the number of independent measurements made. Controlling the false discovery rate: a practical and powerful approach to multiple testing.

A 30% correlation does not mean that each gene is 30% correlated, but, rather, that some are highly correlated and some not at all. Evaluation of relative magnitudes of different sources of variation The relative magnitudes of different sources of variation were estimated using a general linear model in PROC VARCOMP procedure of SAS 9.1 Image processing Each of the low-level *.cel data files was processed using four popular image analysis algorithms: DNA Chip Analyzer (dChip) [21], MAS 5.0 [22], RMA [23], and GCRMA-EB [24]. GPP and DBA supervised and coordinated the project and assisted with the interpretation.

The specific role of concentration error estimates and the general role of statistical diagnostics is to reveal process accuracy and precision. Propagation of error provides a straightforward approach to estimating concentration estimation errors in ELISA microarray experiments. 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 In summary, the Rosetta error model described in this paper provides us with prediction about microarray measurement errors.

With either of these parametric models, concentration estimation errors may be estimated using propagation of error, also known as the delta method.To choose between competing candidate models, a number of measures