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Cytomics, a Practical Approach

G.Valet



1. Enrichment of Predictive Parameters by Repetitive Data Sieving

Data sieving (L1) represents an inductive approach for the exhaustive information extraction from large multi-parametric data spaces in view of predictive or diagnostic goals. Hypothesis driven data collection (fig.1a) is followed by data sieving (fig.1b) and interpretation (fig.1c) of the resulting predictive data patterns for individualized classification of unkown patients such as for the pretherapeutic identification of risk patients or for individualized pretherapeutic risk assessment.
The data pattern may serve in repetitive rounds as starting point for new multi-parametric experiments followed by data sieving and interpretation of the resulting new data pattern (top-down molecular reverse engineering).
Large multi-parametric data spaces can be investigated in this way in a comparatively short time. Besides the predictive data pattern the vast majority of excluded non informative parameters are equally of interest because they are positively excluded from further hypothesis or concept development. This limits substantially the amount of thinking possibilities which may otherwise very quickly become an innovation limiting problem.

principle of data sieving

2. Practical Determination of Predictive Data Patterns
predictive data patterns

The ultimately desired high statistical significance of results for clinical applications is initially in conflict with the search for individually predictive parameter patterns through the collection of large amounts of multi-parametric information from flow cytometry of heterogeneous cellular suspensions, bead arrays or DNA and protein expression arrays. A two phase strategy (L2) is therefore appropriate (fig.2). The initial pilot phase study (fig.2 phase 1) is performed at an acceptable minimum of statistical stringency such as a significance level of P<0.05 or P<0.10. The majority of uninformative parameters can be eliminated at this stage by data sieving.
In the second phase (fig.2 phase 2), the remaining discriminatory parameters for disease course prediction are analysed in statistically large patient groups (L3). This provides exact numbers for the reliability of individualized disease course predictions and eliminates pseudo-informative parameters which have slipped for random statistical reasons into the group of informative parameters during the first phase. Informative parameters may likewise have been lost for random statistical reasons into the group of non-informative parameters during the initial phase. They my be recoverable during the later deductive hypothesis and concept forming phase from the molecular context of the final predictive parameter pattern.


3. Evolution of Concept ( Purdue CD-Series):

external link CD7 2003
ISBN: 0-97117498-8-4
CD8 2004
ISBN: 1-890473-C6-5

4. Literature References
L1. G.Valet Predictive Medicine by Cytomics: Potential and Challenges. J.Biol.Regulators 16:164-167(2002)
L2. G.Valet, A.Tárnok: Cytomics in predictive medicine. Cytometry 53B:1-3 (2003) (external link PDF)
L3. G.Valet, R.Repp, H.Link, A.Ehninger, M.Gramatzki and SHG-AML study group: Pretherapeutic identification of high risk acute myeloid leukemia (AML) patients from immunophenotype, cytogenetic and clinical parameters. Cytometry 53B:4-10 (2003) (external link PDF)
Ln. further readings

© 2017 G.Valet
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Last update: Dec 07,2017 First display: Mar 06,2003