Methods for normalization of experimental data

Methods for normalization of experimental data with experiment-to-experiment variability. The experimental data may include biotechnology data or other data where experiment-to-experiment variability is introduced by an environment used to conduct multiple iterations of the same experiment. Deviations in the experimental data are measured between a central character and data values from multiple indexed data sets. The central character is a value of an ordered comparison determined from the multiple indexed data sets. The central character includes zero-order and low order central characters. Deviations between the central character and the multiple indexed data sets are removed by comparing the central character to the measured deviations from the multiple indexed data sets, thereby reducing deviations between the multiple indexed data sets and thus reducing experiment-to-experiment variability. Preferred embodiments of the present invention may be used to reduce intra-experiment and inter-experiment variability. When experiment-to-experiment variability is reduced or eliminated, comparison of experimental results can be used with a higher degree of confidence. Experiment-to-experiment variability is reduced for biotechnology data with new methods that can be used for bioinformatics or for other types of experimental data that are visual displayed (e.g., telecommunications data, electrical data for electrical devices, optical data, physical data, or other data). Experimental data can be consistently collected, processed and visually displayed with results that are accurate and not subject to experiment-to-experiment variability. Thus, intended experimental goals or results (e.g., determining polynucleotide sequences such as DNA, cDNA, or mRNA sequences) may be achieved in a more efficient and effective manner.