Normalization Using Weighted Negative Second Order Exponential Error Functions(NeONORM) Provides Robustness Against Asym-metries in Comparative Transcriptome Profiles and Avoids False Calls

Studies on high-throughput global gene expression using microarray technology have generated ever larger amounts of systematic transcriptome data. A major challenge in exploiting these heterogeneous datasets is how to normalize the expression profiles by inter-assay methods. Different non-linear and linear normalization methods have been developed, which essentially rely on the hypothesis that the true or perceived logarithmic fold-change distributions between two different assays are symmetric in nature. However, asymmetric gene expression changes are frequently observed, leading to suboptimal normalization results and in consequence potentially to thousands of false calls. Therefore, we have specifically investigated asymmetric comparative transcriptome profiles and developed the normalization using weighted negative second order exponential error functions （NeONORM） for robust and global inter-assay normalization. NeONORM efficiently damps true gene regulatory events in order to minimize their misleading impact on the norrealization process. We evaluated NeONORM＇s applicability using artificial and true experimental datasets, both of which demonstrated that NeONORM could be systematically applied to inter-assay and inter-condition comparisons.

High-Sensitivity Transcriptome Data Structure and Implications for Analysis and Biologic Interpretation

Novel microarray technologies such as the AB1700 platform from Applied Biosysterns promise significant increases in the signal dynamic range and a higher sensitivity for weakly expressed transcripts. We have compared a representative set of AB1700 data with a similarly representative Affymetrix HG-U133A dataset. The AB1700 design extends the signal dynamic detection range at the lower bound by one order of magnitude. The lognormal signal distribution profiles of these highsensitivity data need to be represented by two independent distributions. The additional second distribution covers those transcripts that would have gone undetected using the Affymetrix technology. The signal-dependent variance distribution in the AB1700 data is a non-trivial function of signal intensity, describable using a composite function. The drastically different structure of these highsensitivity transcriptome profiles requires adaptation or even redevelopment of the standard microarray analysis methods. Based on the statistical properties, we have derived a signal variance distribution model for AB1700 data that is necessary for such development. Interestingly, the dual lognormal distribution observed in the AB1700 data reflects two fundamentally different biologic mechanisms of transcription initiation.

Quality Assessment of Transcriptome Data Using Intrinsic Statistical Properties

In view of potential application to biomedical diagnosis, tight transcriptome data quality control is compulsory. Usually, quality control is achieved using labeling and hybridization controls added at different stages throughout the processing of the biologic RNA samples. These control measures, however, only reflect the performance of the individual technical manipulations during the entire process and have no bearing as to the continued integrity of the RNA sample itself. Here we demonstrate that intrinsic statistical properties of the resulting transcriptome data signal and signal-variance distributions and their invariance can be identified independently of the animal species studied and the labeling protocol used. From these invariant properties we have developed a data model, the pa- rameters of which can be estimatei：l from individual experiments and used to compute relative quality measures based on similarity with large reference datasets. These quality measures add supplementary, non-redundant in- formation to standard quality control estimates based on spike-in and hybridization controls, and are exploitable in data analysis. A software application for analyzing datasets as well as a reference dataset for AB 1700 arrays are provided. They should allow AB 1700 users to easily integrate this method into their analysis pipeline, and might instigate similar developments for other transcriptome platforms.

Generation of Synthetic Transcriptome Data with Defined Statistical Properties for the Development and Testing of New Analysis Methods

We have previously developed a combined signal/variance distribution model that accounts for the particular statistical properties of datasets generated on the Applied Biosystems AB1700 transcriptome system. Here we show that this model can be efficiently used to generate synthetic datasets with statistical properties virtually identical to those of the actual data by aid of the JAVA application ace.map creator 1.0 that we have developed. The fundamentally different structure of AB1700 transcriptome profiles requires re-evaluation, adaptation, or even redevelopment of many of the standard microarray analysis methods in order to avoid misinterpretation of the data on the one hand, and to draw full benefit from their increased specificity and sensitivity on the other hand. Our composite data model and the ace.map creator 1.0 application thereby not only present proof of the correctness of our parameter estimation, but also provide a tool for the generation of synthetic test data that will be useful for further development and testing of analysis methods.

CDS:A Fold-change Based Statistical Test for Concomitant Identification of Distinctness and Similarity in Gene Expression Analysis

The problem of identifying differential activity such as in gene expression is a major defeat in biostatistics and bioinformatics. Equally important, however much less frequently studied, is the question of similar activity from one biological condition to another. The fold- change, or ratio, is usually considered a relevant criterion for stating difference and similarity between measurements. Importantly, no statistical method for concomitant evaluation of similarity and distinctness currently exists for biological applications. Modern micro- array, digital PCR （dPCR）, and Next-Generation Sequencing （NGS） technologies frequently provide a means of coeff^cient of variation estimation for individual measurements. Using fold-change, and by making the assumption that measurements are normally distributed with known variances, we designed a novel statistical test that allows us to detect concomitantly, thus using the same formalism, differ- entially and similarly expressed genes （http：]]cds.ihes.fr）. Given two sets of gene measurements in different biological conditions, the probabilities of making type I and type II errors in stating that a gene is differentially or similarly expressed from one condition to the other can be calculated. Furthermore, a confidence interval for the fold-change can be delineated. Finally, we demonstrate that the assumption of normality can be relaxed to consider arbitrary distributions numerically. The Concomitant evaluation of Distinctness and Similarity （CDS） statistical test correctly estimates similarities and differences between measurements of gene expression. The imple- mentation, being time and memory efficient, allows the use of the CDS test in high-throughput data analysis such as microarray, dPCR, and NGS experiments. Importantly, the CDS test can be applied to the comparison of single measurements （N = 1） provided the var- iance （or coefficient of variation） of the signals is known, making CDS a valuable tool also in biomedical analysis where typically a single measurement per subject is available.