Data interpretation, planning experiments

This book focuses on statistical data evaluation, but does so in a fashion that integrates the question—plan—experiment—result—interpretation—answer cycle by offering a multitude of real-life examples and numerical simulations to show what information can, or cannot, be extracted from a given data set. This perspective covers both the daily experience of the lab supervisor and the worries of the project manager. Only the bare minimum of theory is presented, but is extensively referenced to educational articles in easily accessible journals. 

Table 1 is a list of textbooks and their characteristics in which the reader can find the physical background about the XRD technique. The following section includes a brief introduction to the physics (Langford and Louer, 1996 Alexander and Klug, 1974) of the diffraction experiment, intended to familiarize the reader with basic facts of the technique from a practical viewpoint that must be observed when planning experiments and interpreting data obtained under catalytic reaction conditions. 

However, because of constraint of resources, sometimes toxicogenomics is not necessary if the sole purpose is to run a study in preclinical safety assessment it is more likely to piggyback on other toxicology conducts. At other times, a microarray study happens to run because of leftover of interested frozen tissues, a mere afterthought rather than a planned experiment. Such practice might confound the data interpretation and limit the future reference value of the study. As discussed earlier, the incremental value of toxicogenomics lies in the ever-expanding reference data of quality. 

It is also extremely important to determine in so far as possible the conditions under which the data presented have been determined and for which they will be used. A recently identified example of major uncertainty is that of gender differences between male and female rats and mice [56]. Planning and interpreting animal experiments are primary applications of allometry, but they must be used with care. 

The flow reactor is used primarily in the study of the kinetics of heterogeneous reactions. Planning of experiments and interpretation of data obtained in flow reactors are considered in later chapters. 

This section deals with interpretive optimization methods. In these. methods, the extent of chromatographic separation is predicted indirectly from the retention behaviour of the individual solutes. The data are interpreted to locate the optimum in terms of the complete chromatogram. The interpretive methods may involve a limited number of experiments according to a pre-planned experimental design (section 5.5.1) or may start with a minimum number of experiments in order to try and locate the optimum by an iterative process (section 5.5.2). 

In this section we will describe several optimization procedures which are simultaneous in the sense that all experiments are performed according to a pre-planned experimental design. However, unlike the methods described in section 5.2, the experimental data are now interpreted in terms of the individual retention surfaces for all solutes. The window diagram is the best known example of this kind of procedure. 

Therefore, we have to analyse the variation of the rate of permeation according to the temperature (zj), the trans-membrane pressure difference (Z2) and the gas molecular weight (Z3). Then, we have 3 factors each of which has two levels. Thus the number of experiments needed for the process investigation is N = 2 = 8. Table 5.13 gives the concrete plan of the experiments. The last column contains the output y values of the process (flow rates of permeation). Figure 5.8 shows a geometric interpretation for a 2 experimental plan where each cube corner defines an experiment with the specified dimensionless values of the factors. So as to process these statistical data with the procedures that use matrix calculations, we have to introduce here a fictive variable Xq, which has a permanent +1 value (see also Section 5.4.4). 

In fact, it is probably fair to say that very few problems involving real momentum, heat, and mass flow can be solved by mathematical analysis alone. The solution to many practical problems is achieved using a combination of theoretical analysis and experimental data. Thus engineers working on chemical and biochemical engineering problems should be familiar with the experimental approach to these problems. They have to interpret and make use of the data obtained from others and have to be able to plan and execute the strictly necessary experiments in their ovm laboratories. In this chapter, we show some techniques and ideas which are important in the planning and execution of chemical and biochemical experimental research. The basic considerations of dimensional analysis and similitude theory are also used in order to help the engineer to understand and correlate the data that have been obtained by other researchers. 

Chemometrics has been defined as the chemical discipline that uses mathematical and statistical methods to design or select optimal measurement procedures and experiments and to provide maximum chemical information by analysing chemical data (Kowalski, 1978). It is a relatively new discipline that assists with (i) the planning of experiments, and (ii) the manipulation and interpretation of large data sets. Some aspects of chemometrics can be done using an appropriate speadsheet but the majority of applications require the use of dedicated software. The fundamental principles of most of the processes involved in chemometrics are those of statistics. You are therefore advised to become familiar with the material in Chapters 40 and 41 before proceeding. 

Thus, a second key strategy in drug discovery is to improve the planning and interpretation of discovery experiments using pharmaceutical-profiling property data. Drug-discovery researchers of all disciplines should have access to compound property data and be aware of the potential effects on the experiments they are performing. 

Figure 15.2 illustrates some of the discovery bioactivity experiments in which a test compound must be successful to advance. If erroneous activity or selectivity data are generated or misinterpreted, the SAR will mislead the project team. SAR is a central strategy of drug-discovery research. If the activity assays are affected by properties in addition to target protein interaction, then the SAR will be a composite of multiple variables. Table 15.1 lists some of the potential effects on SAR from lack of property data application in planning and interpretation of drug-discovery bioassays. 

The questions related to the secondary equipment selection, calculation of the number of points (cross-sections), and number of the cycles of tests and their sequence are worked out at the planning stage of an experiment Let s consider the sequence of the zirconia gas sensor test and calculate its errors based on the results of the experimental data without the detailed consideration of the questions related to the planning of these tests (their interpretation will be done in Chapter 7). 

In reading a manuscript, I appreciate a concise introductory section that places the subject matter in perspective and provides relevant background information. The Experimental section should be sufficiently detailed to allow an experienced chemist to assess the quality of the data. I prefer comprehensive descriptions of experimental techniques and procedures rather than cursory accounts that leave the reader uncertain about how experiments were performed. Discretion must be exercised, however, and repetitive information such as syntheses of closely related compounds and lengthy descriptions of instrumentation already published should be minimized. The Results and Discussion section should present data in well-planned tables and figures and provide logical, well-supported, and adequately referenced interpretations. Unfounded claims and excessive speculation should be avoided. 

From the results of such rate experiments (either chemical or biochemical) and preliminary interpretation, one hopefully will have gained sufficient insight into the reaction system to permit intelligent planning of further experiments from which the best values of the constants of correlating equations (or rival correlating equations) can be obtained. The same sort of overall comparison of the individual equations based on goodness of fit to the experimental data (and chemical reasonableness), can... 

Compare to other analysis techniques, it must suffice here to add that fluorescence measurements are rapid, accurate and require only very small quantities of sample (nanomole or less). Fluorescence instrumentation is also relatively inexjjensive and easy to use. In general, fluorescence experiments are relatively easy to jjerform as in many fields, it is the planning of appropriate experiments, the analysis, and accurate interpretation of the data that require more extensive exp>erience. 

---