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quantile normalization for combining gene-expression datasets

BIOTECHNOLOGY & BIOTECHNOLOGICAL EQUIPMENT 2018年第3期32卷 751-758页

作者： Pan, Meng Zhang, Jie Jinan Univ Coll Sci & Engn Dept Optoelect Engn Guangzhou Guangdong Peoples R China Jinan Univ Coll Sci & Engn Dept Phys Guangzhou Guangdong Peoples R China

Our research aimed to improve survival prediction by combining gene expression datasets, and to apply molecular signatures across different datasets. Many methods have previously been developed to remove unwanted variations among datasets and maintain the wanted factor variations. However, for inter-study validation (ISV) research, a whole dataset is set aside for testing, and the statuses of wanted factors are assumed unknown for the whole dataset;thus, regression cannot be used to determine the unwanted variations for this dataset. In this study, quantile normalization (QN) was utilized to remove the unwanted dataset variations, after which the adjusted datasets were used for classification. It was observed that the datasets formed by QN combination in the study of ISV had superior prediction performance compared to the datasets combined by other methods. Combining datasets using QN could improve the prediction performance for the study of ISV.

关键词： quantile normalization gene expression dataset combination prediction inter-study validation

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Enhanced quantile normalization of microarray data to reduce loss of information in gene expression profiles

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BIOMETRICS 2007年第1期63卷 50-59页

作者： Hu, Jianhua He, Xuming Univ Texas MD Anderson Canc Ctr Dept Biostat & Appl Math Houston TX 77030 USA Univ Illinois Dept Stat Champaign IL 61820 USA

In microarray experiments, removal of systematic variations resulting from array preparation or sample hybridization conditions is crucial to ensure sensible results from the ensuing data analysis. For example, quantile normalization is routinely used in the treatment of both oligonucleotide and cDNA microarray data, even though there might be some loss of information in the normalization process. We recognize that the ideal normalization, if it ever exists, would aim to keep the maximal amount of gene profile information with the lowest possible noise. With this objective in mind, we propose a valuable enbancenjent to quantile normalization, and demonstrate through three Affymetrix experiments that the enhanced normalization can result in better performance in detecting and ranking differentially expressed genes across experimental conditions.

关键词： quantile normalization residue right singular vectors Shannon entropy singular value

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Smooth quantile normalization

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BIOSTATISTICS 2018年第2期19卷 185-198页

作者： Hicks, Stephanie C. Okrah, Kwame Paulson, Joseph N. Quackenbush, John Irizarry, Rafael A. Bravo, Hector Corrada Dana Farber Canc Inst Dept Biostat & Computat Biol 450 Brookline Ave Boston MA 02215 USA Harvard TH Chan Sch Publ Hlth Dept Biostat 677 Huntington Ave Boston MA 02115 USA Genentech Inc Prod Dev Biostat 1 DNA Way San Francisco CA 94080 USA Univ Maryland Dept Comp Sci College Pk MD 20742 USA Univ Maryland Inst Adv Comp Studies Ctr Bioinformat & Computat Biol 8314 Paint Branch Dr College Pk MD 20742 USA

Between-sample normalization is a critical step in genomic data analysis to remove systematic bias and unwanted technical variation in high-throughput data. Global normalization methods are based on the assumption that observed variability in global properties is due to technical reasons and are unrelated to the biology of interest. For example, some methods correct for differences in sequencing read counts by scaling features to have similar median values across samples, but these fail to reduce other forms of unwanted technical variation. Methods such as quantile normalization transform the statistical distributions across samples to be the same and assume global differences in the distribution are induced by only technical variation. However, it remains unclear how to proceed with normalization if these assumptions are violated, for example, if there are global differences in the statistical distributions between biological conditions or groups, and external information, such as negative or control features, is not available. Here, we introduce a generalization of quantile normalization, referred to as smooth quantile normalization (qsmooth), which is based on the assumption that the statistical distribution of each sample should be the same (or have the same distributional shape) within biological groups or conditions, but allowing that they may differ between groups. We illustrate the advantages of our method on several high-throughput datasets with global differences in distributions corresponding to different biological conditions. We also perform a Monte Carlo simulation study to illustrate the bias-variance tradeoff and root mean squared error of qsmooth compared to other global normalization methods. A software implementation is available from https://***/stephaniehicks/gsmooth.

关键词： Global normalization methods quantile normalization

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Feature-specific quantile normalization and feature-specific mean-variance normalization deliver robust bi-directional classification and feature selection performance between microarray and RNAseq data

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BMC BIOINFORMATICS 2024年第1期25卷 136页

作者： Skubleny, Daniel Ghosh, Sunita Spratlin, Jennifer Schiller, Daniel E. Rayat, Gina R. Univ Alberta Fac Med & Dent Dept Surg Edmonton AB T6G 2R3 Canada Univ Alberta Fac Med & Dent Dept Oncol Edmonton AB T6G 2R3 Canada Univ Alberta Fac Sci Dept Math & Stat Sci Edmonton AB T6G 2R3 Canada

Background Cross-platform normalization seeks to minimize technological bias between microarray and RNAseq whole-transcriptome data. Incorporating multiple gene expression platforms permits external validation of experimental findings, and augments training sets for machine learning models. Here, we compare the performance of Feature Specific quantile normalization (FSQN) to a previously used but unvalidated and uncharacterized method we label as Feature Specific Mean Variance normalization (FSMVN). We evaluate the performance of these methods for bidirectional normalization in the context of nested feature *** FSQN and FSMVN provided clinically equivalent bidirectional model performance with and without feature selection for colon CMS and breast PAM50 classification. Using principal component analysis, we determine that these methods eliminate batch effects related to technological platforms. Without feature selection, no statistical difference was identified between the performance of FSQN and FSMVN of cross-platform data compared to within-platform distributions. Under optimal feature selection conditions, balanced accuracy was FSQN and FSMVN were statistically equivalent to the within-platform distribution performance in multivariable linear regression analysis. FSQN and FSMVN also provided similar performance to within-platform distributions as the number of selected genes used to create models *** In the context of generating supervised machine learning classifiers for molecular subtypes, FSQN and FSMVN are equally effective. Under optimal modeling conditions, FSQN and FSMVN provide equivalent model accuracy performance on cross-platform normalization data compared to within-platform data. Using cross-platform data should still be approached with caution as subtle performance differences may exist depending on the classification problem, training, and testing distributions.

关键词： Cross-platform normalization Molecular classification Microarray RNAseq Feature selection quantile normalization Mean Variance FSQN FSMVN

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The impact of quantile and rank normalization procedures on the testing power of gene differential expression analysis

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BMC BIOINFORMATICS 2013年第1期14卷 1-10页

作者： Qiu, Xing Wu, Hulin Hu, Rui Univ Rochester Dept Biostat & Computat Biol Rochester NY 14642 USA

Background: quantile and rank normalizations are two widely used pre-processing techniques designed to remove technological noise presented in genomic data. Subsequent statistical analysis such as gene differential expression analysis is usually based on normalized expressions. In this study, we find that these normalization procedures can have a profound impact on differential expression analysis, especially in terms of testing power. Results: We conduct theoretical derivations to show that the testing power of differential expression analysis based on quantile or rank normalized gene expressions can never reach 100% with fixed sample size no matter how strong the gene differentiation effects are. We perform extensive simulation analyses and find the results corroborate theoretical predictions. Conclusions: Our finding may explain why genes with well documented strong differentiation are not always detected in microarray analysis. It provides new insights in microarray experimental design and will help practitioners in selecting proper normalization procedures.

关键词： normalization Procedure Rank normalization Testing Power Differential Expression Analysis quantile normalization

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Feature selection and hybrid CNNF deep stacked autoencoder for botnet attack detection in IoT

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 122卷

作者： Kalidindi, Archana Arrama, Mahesh Babu VNR Vignana Jyothi Inst Engn & Technol Dept CSE AIML & IoT Hyderabad India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Hyderabad Telangana India

Botnet attack is a severe cyber security issue, which occurs in the Internet of Things (IoT). These attacks are carried out by hackers to acquire control of various IoT devices and carry out illegal activities. Though several methods have been proposed to overcome these issues, the rapidly evolving nature of botnet makes attack detection complicated. Hence, in this paper a Deep Learning (DL) model is introduced for identifying botnets in IoT. Initially, the IoT network is simulated, and the detection of attack is established using the log data. Afterwards, the log data is fed into data pre-processing, in which the data is pre-processed by quantile normalization. Then, feature selection is effectuated by employing Information Gain (IG), and City Block Distance. Once the feature selection is performed, data augmentation is done with the use of oversampling to increase the samples. Lastly, the Botnet attack detection is carried out by using the proposed Convolutional Neural Network Fused with Deep stacked Autoencoder (CNN-FDSA), which is formed by fusing Deep Stacked Autoencoder (DSA) and Convolutional Neural Network (CNN). Furthermore, the proposed CNN-FDSA attained the highest recall, precision, f-measure, and accuracy of 90.3 %, 91.6 %, 90.9 %, and 92.4 %, and then the lowest False Positive Rate (FPR) of 8.2 %.

关键词： Botnet attack Convolutional neural network quantile normalization Oversampling Deep stacked Autoencoder

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How cyanobacteria pose new problems to old methods: challenges in microarray time series analysis

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BMC BIOINFORMATICS 2013年第1期14卷 1-16页

作者： Lehmann, Robert Machne, Rainer Georg, Jens Benary, Manuela Axmann, Ilka Steuer, Ralf Humboldt Univ Inst Theoret Biol D-10115 Berlin Germany Charite Inst Theoret Biol D-10115 Berlin Germany Univ Vienna Inst Theoret Chem A-1090 Vienna Austria Univ Freiburg Fac Biol D-79104 Freiburg Germany Univ Freiburg Freiburg Initiat Syst Biol D-79104 Freiburg Germany Acad Sci Czech Republ Global Change Res Ctr CzechGlobe CZ-37333 Nove Hrady Czech Republic

Background: The transcriptomes of several cyanobacterial strains have been shown to exhibit diurnal oscillation patterns, reflecting the diurnal phototrophic lifestyle of the organisms. The analysis of such genome-wide transcriptional oscillations is often facilitated by the use of clustering algorithms in conjunction with a number of pre-processing steps. Biological interpretation is usually focussed on the time and phase of expression of the resulting groups of genes. However, the use of microarray technology in such studies requires the normalization of pre-processing data, with unclear impact on the qualitative and quantitative features of the derived information on the number of oscillating transcripts and their respective phases. Results: A microarray based evaluation of diurnal expression in the cyanobacterium Synechocystis sp. PCC 6803 is presented. As expected, the temporal expression patterns reveal strong oscillations in transcript abundance. We compare the Fourier transformation-based expression phase before and after the application of quantile normalization, median polishing, cyclical LOESS, and least oscillating set (LOS) normalization. Whereas LOS normalization mostly preserves the phases of the raw data, the remaining methods introduce systematic biases. In particular, quantile-normalization is found to introduce a phase-shift of 180 degrees, effectively changing night-expressed genes into day-expressed ones. Comparison of a large number of clustering results of differently normalized data shows that the normalization method determines the result. Subsequent steps, such as the choice of data transformation, similarity measure, and clustering algorithm, only play minor roles. We find that the standardization and the DTF transformation are favorable for the clustering of time series in contrast to the 12 m transformation. We use the cluster-wise functional enrichment of a clustering derived by LOS normalization, clustering using flowClust, and DFT tra

关键词： normalization Method Discrete Fourier Transformation quantile normalization Circadian Time Adjusted Rand Index

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Adaptive Single-Channel EEG Artifact Removal With Applications to Clinical Monitoring

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2022年 30卷 286-295页

作者： Dora, Matteo Holcman, David Ecole Normale Super PSL BENS Grp Appl Math & Computat Biol F-75005 Paris France Churchill Coll Cambridge CB3 0DS England

Electroencephalography (EEG) has become very common in clinical practice due to its relatively low cost, ease of installation, non-invasiveness, and good temporal resolution. Portable EEG devices are increasingly popular in clinical monitoring applications such as sleep scoring or anesthesia monitoring. In these situations, for reasons of speed and simplicity only few electrodes are used and contamination of the EEG signal by artifacts is inevitable. Visual inspection and manual removal of artifacts is often not possible, especially in real-time applications. Our goal is to develop a flexible technique to remove EEG artifacts in these contexts with minimal supervision. We propose here a new wavelet-based method which allows to remove artifacts from single-channel EEGs. The method is based on a data-driven renormalization of the wavelet components and is capable of adaptively attenuate artifacts of different nature. We benchmark our method against alternative artifact removal techniques. We assessed the performance of the proposed method on publicly available datasets comprising ocular, muscular, and movement artifacts. The proposed method shows superior performances on different kinds of artifacts and signal-to-noise levels. Finally, we present an application of our method to the monitoring of general anesthesia. We show that our method can successfully attenuate various types of artifacts in single-channel EEG. Thanks to its data-driven approach and low computational cost, the proposed method provides a valuable tool to remove artifacts in real-time EEG applications with few electrodes, such as monitoring in special care units.

关键词： Monitoring Electroencephalography Art Licenses Reactive power Discrete wavelet transforms Anesthesia Electroencephalography (EEG) artifact removal wavelet transform quantile normalization

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Comparison of Affymetrix data normalization methods using 6,926 experiments across five array generations

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BMC BIOINFORMATICS 2009年第Sup1期10卷 1-12页

作者： Autio, Reija Kilpinen, Sami Saarela, Matti Kallioniemi, Olli Hautaniemi, Sampsa Astola, Jaakko Tampere Univ Technol Dept Signal Proc FIN-33101 Tampere Finland VTT Tech Res Ctr Turku Finland Univ Turku Turku Finland Univ Helsinki Inst Mol Med Finland FIMM Helsinki Finland Univ Helsinki Computat Syst Biol Lab Inst Biomed Helsinki Finland Univ Helsinki Genome Scale Biol Res Program Helsinki Finland

Background: Gene expression microarray technologies are widely used across most areas of biological and medical research. Comparing and integrating microarray data from different experiments would be very useful, but is currently very challenging due to the experimental and hybridization conditions, as well as data preprocessing and normalization methods. Furthermore, even in the case of the widely-used, industry-standard Affymetrix oligonucleotide microarrays, the various array generations have different probe sets representing different genes, hindering the data integration. Results: In this study our objective is to find systematic approaches to normalize the data emerging from different Affymetrix array generations and from different laboratories. We compare and assess the accuracy of five normalization methods for Affymetrix gene expression data using 6,926 Affymetrix experiments from five array generations. The methods that we compare include 1) standardization, 2) housekeeping gene based normalization, 3) equalized quantile normalization, 4) Weibull distribution based normalization and 5) array generation based gene centering. Our results indicate that the best results are achieved when the data is normalized first within a sample and then between-samples with Array Generation based gene Centering (AGC) normalization. Conclusion: We conclude that with the AGC method integrating different Affymetrix datasets results in values that are significantly more comparable across the array generations than in the cases where no array generation based normalization is used. The AGC method was found to be the best method for normalizing the data from several different array generations, and achieve comparable gene values across thousands of samples.

关键词： Housekeeping Gene normalization Method Weibull Distribution Array Generation quantile normalization

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normalization and centering of array-based heterologous genome hybridization based on divergent control probes

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BMC BIOINFORMATICS 2011年第1期12卷 1-10页

作者： Darby, Brian J. Jones, Kenneth L. Wheeler, David Herman, Michael A. Kansas State Univ Div Biol Ecol Genom Inst Manhattan KS 66506 USA Univ Colorado Dept Biochem & Mol Genet Sch Med Aurora CO 80045 USA

Background: Hybridization of heterologous (non-specific) nucleic acids onto arrays designed for model-organisms has been proposed as a viable genomic resource for estimating sequence variation and gene expression in non-model organisms. However, conventional methods of normalization that assume equivalent distributions (such as quantile normalization) are inappropriate when applied to non-specific (heterologous) hybridization. We propose an algorithm for normalizing and centering intensity data from heterologous hybridization that makes no prior assumptions of distribution, reduces the false appearance of homology, and provides a way for researchers to confirm whether heterologous hybridization is suitable. Results: Data are normalized by adjusting for Gibbs free energy binding, and centered by adjusting for the median of a common set of control probes assumed to be equivalently dissimilar for all species. This procedure was compared to existing approaches and found to be as successful as Loess normalization at detecting sequence variations (deletions) and even more successful than quantile normalization at reducing the accumulation of false positive probe matches between two related nematode species, Caenorhabditis elegans and C. briggsae. Despite the improvements, we still found that probe fluorescence intensity was too poorly correlated with sequence similarity to result in reliable detection of matching probe sequence. Conclusions: Cross-species hybridizations can be a way to adapt genome-enabled tools for closely related non-model organisms, but data must be appropriately normalized and centered in a way that accommodates hybridization of nucleic acids with diverged sequence. For short, 25-mer probes, hybridization intensity alone may be insufficiently correlated with sequence similarity to allow reliable inference of homology at the probe level.

关键词： Control Probe Nematode Species Probe Intensity quantile normalization Signal Intensity Ratio

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