Transformed data

Preprocessing is the operation which precedes the extraction of latent vectors from the data. It is an operation which is carried out on all the elements of an original data table X and which produces a transformed data table Z. We will discuss six common methods of preprocessing, including the trivial case in which the original data are left unchanged. The effects of each of these six types of preprocessing will be illustrated numerically by means of the small 4x3 data table from the study of trace elements in atmospheric samples which has been used in previous sections (Table 31.1). The various effects of the transformations can be observed from the two summary statistics (mean and norm). These statistics include the vector of column-means m and the vector of column-norms of the transformed data table Z ... 

In the context of data analysis we divide by n rather than by (n - 1) in the calculation of the variance. This procedure is also called autoscaling. It can be verified in Table 31.5 how these transformed data are derived from those of Table 31.4. 

The Chi-square distance can be seen as a weighted Euclidean distance on the transformed data ... 

The global weighted sum of squares c of the transformed data Z can be shown to be equal to the global interaction 52 between rows and columns ... 

Unipolar and bipolar axes have been discussed in Section 31.2. Briefly, a unipolar axis is defined by the origin and the representation of a row or column. A bipolar axis is drawn through the representations of two rows or through the representations of two columns. Projections upon unipolar axes reproduce the values in the transformed data table. Projections upon bipolar axes reproduce the contrasts (i.e. differences) between values in the data table. 

Transforming Data and Creating Analysis Data Sets... 

Also, if conversion of drug to active metabolite shows significant departure from linear pharmacokinetics, it is possible that small differences in the rate of absorption of the parent drug (even within the 80-125% range for log transformed data) could result in clinically significant differences in the concentration/ time profiles for the active metabolite. When reliable data indicate that this situation may exist, a requirement of quantification of active metabolites in a bioequivalency study would seem to be fully justified. 

For PyMS to be used for (1) routine identification of microorganisms and (2) in combination with ANNs for quantitative microbiological applications, new spectra must be comparable with those previously collected and held in a data base.127 Recent work within our laboratory has demonstrated that this problem may be overcome by the use of ANNs to correct for instrumental drift. By calibrating with standards common to both data sets, ANN models created using previously collected data gave accurate estimates of determi-nand concentrations, or bacterial identities, from newly acquired spectra.127 In this approach calibration samples were included in each of the two runs, and ANNs were set up in which the inputs were the 150 new calibration masses while the outputs were the 150 old calibration masses. These associative nets could then by used to transform data acquired on that one day to data acquired at an earlier data. For the first time PyMS was used to acquire spectra that were comparable with those previously collected and held in a database. In a further study this neural network transformation procedure was extended to allow comparison between spectra, previously collected on one machine, with spectra later collected on a different machine 129 thus calibration transfer by ANNs was affected. Wilkes and colleagues130 have also used this strategy to compensate for differences in culture conditions to construct robust microbial mass spectral databases. 

Data shown as examples in this review were typically acquired as 2K X 128 or 2K x 160 point files. Data were processed with linear prediction or zero-filling prior to the first Fourier transform. Data were uniformly linear predicted to 512 points in the second dimension followed by zero-filling to afford final data matrices that were 2K x IK points. 

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