Biological variance

Normalization of cDNA microarray data is a very important step in the process of data analysis. With current technology, systematic hias is unavoidable and must he dealt with in a sensible manner. Furthermore, normalization methods need to be consistently apphed to all raw data. Using different normalization methods on different datasets may introduce bias and thereby decrease the validity of the data. Normahzed data should be free of systematic bias and should thereby provide a truer representation of the biological variance. Furthermore, normahzed data increases the validity of shde to shde comparisons. 

Current practice in microarray experimentation suggests that a balance design with adequate replication be used. Good experimental design and execution will produce data that minimize technical variance, allowing the statistical analyses to evaluate biological variance more effectively Still, the nature of the data requires that an estimate of the FDR be included in the statistical analysis. This enables the researcher to assess the reliability/validity of the results of the statistical analysis. As discussed earlier, cDNA microarray... 

The drawback of these approaches is their failure to compartmentalize sources of variation. Thus, in studies where biological and technical replicates both exist, there is the possibility that the normalization method will inappropriately remove biological variance, such as the treatment effect itself. This can lead to a reduction in the overall treatment related response. Other sources of variation include replicate effects, where the timing of hybridization and scanning, may cause persistent trends in the data. 

This equation is based on Abraham s solvation equation which uses five molecular descriptors excess molar refraction (F), solute polarity/polarizability (S), McGowan characteristic volume (V), solute overall acidity (A) and basicity (B). The steric (size/shape) descriptors E, S and V have a positive effect on oral absorption, while the descriptors related to H-bonding, A and B, have a negative effect. The model accounts for 74% of the variance (r ) in the data and the predictions have a 14% standard error (i). This is nearly as good as it gets, since the experimental biological variance is ca. 15%. 

The ratio of biological variance to the technical variance. Only when this ratio is large, that is, the biological error is much bigger than the technical error, does the benefit become substantial. 

Optionally use PCA or HC at this level to get an idea about biological variance observed in the data set, on whether or not the tissue classes are separated based on the overall variance in the data set, and to correlate patient clusters with clinical meta-information ( ggNote 24). 

Ji2 was the most significant contributor to herbicidal activity, explaining over 70% of the biological variance. Including ic and L improved the equation to over 90%. No significant correlations were found between herbicidal activity and any of the electronic parameters (a, F). 

The relevance of the results of Delves et al (1982) for some of the major surveys of the effects of lead exposure in children, employing tooth lead analysis, remains unclear. For example, in the study of Needleman et al (1979), dentine zone analysis was carried out using concordance criteria for acceptability of replicate measurements. The relative impact of variation in lead level within tooth type may be increased at very low levels of concentration and decreased with higher concentration. There is no evidence to indicate that the relative biological variance of the type seen by Delves et al persists with increasing concentration. As Delves et al have acknowledged, their mean and median lead levels are lower than those noted elsewhere in... 

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