The use of size-normalised procedures in the analysis of organic contaminants in estuarine sediments

Sediments are being used increasingly for monitoring the aquatic environment because of their ability to integrate contaminants over time, and because they provide valuable information on source, dispersion and accumulation of toxicants. However, as the majority of contaminants are usually associated with the fine fraction of the sediment, interpretation of spatial distributions is often confounded by variable grain size. The confounding effects of variable grain size can be reduced by separating the fine fraction of the sediment and analysing the contaminant concentration of this material. This approach is commonly used in heavy metal studies, but it is rare in the analysis of organochlorine compounds because of an absence on information on possible contaminant loss to the sieve water during the separating process, and possible removal of contaminants with the coarse fraction. Results from the current study indicate such losses to be minimal and examples are presented to illustrate the superiority of size-normalised data in the identification of source and in the determination of dispersion and accumulation of contaminants.     

Gaussian process regression model for normalization of LC-MS data using scan-level information

Background

Differences in sample collection, biomolecule extraction, and instrument variability introduce bias to data generated by liquid chromatography coupled with mass spectrometry (LC-MS). Normalization is used to address these issues. In this paper, we introduce a new normalization method using the Gaussian process regression model (GPRM) that utilizes information from individual scans within an extracted ion chromatogram (EIC) of a peak. The proposed method is particularly applicable for normalization based on analysis order of LC-MS runs. Our method uses measurement variabilities estimated through LC-MS data acquired from quality control samples to correct for bias caused by instrument drift. Maximum likelihood approach is used to find the optimal parameters for the fitted GPRM. We review several normalization methods and compare their performance with GPRM.

Results

To evaluate the performance of different normalization methods, we consider LC-MS data from a study where metabolomic approach is utilized to discover biomarkers for liver cancer. The LC-MS data were acquired by analysis of sera from liver cancer patients and cirrhotic controls. In addition, LC-MS runs from a quality control (QC) sample are included to assess the run to run variability and to evaluate the ability of various normalization method in reducing this undesired variability. Also, ANOVA models are applied to the normalized LC-MS data to identify ions with intensity measurements that are significantly different between cases and controls.

Conclusions

One of the challenges in using label-free LC-MS for quantitation of biomolecules is systematic bias in measurements. Several normalization methods have been introduced to overcome this issue, but there is no universally applicable approach at the present time. Each data set should be carefully examined to determine the most appropriate normalization method. We review here several existing methods and introduce the GPRM for normalization of LC-MS data. Through our in-house data set, we show that the GPRM outperforms other normalization methods considered here, in terms of decreasing the variability of ion intensities among quality control runs.

     

Effects of MRI image normalization techniques in prostate cancer radiomics

The variance in intensities of MRI scans is a fundamental impediment for quantitative MRI analysis. Intensity values are not only highly dependent on acquisition parameters, but also on the subject and body region being scanned. This warrants the need for image normalization techniques to ensure that intensity values are consistent within tissues across different subjects and visits. Many intensity normalization methods have been developed and proven successful for the analysis of brain pathologies, but evaluation of these methods for images of the prostate region is lagging.In this paper, we compare four different normalization methods on 49 T2-w scans of prostate cancer patients: 1) the well-established histogram normalization, 2) the generalized scale normalization, 3) an extension of generalized scale normalization called generalized ball-scale normalization, and 4) a custom normalization based on healthy prostate tissue intensities. The methods are compared qualitatively and quantitatively in terms of behaviors of intensity distributions as well as impact on radiomic features.Our findings suggest that normalization based on prior knowledge of the healthy prostate tissue intensities may be the most effective way of acquiring the desired properties of normalized images. In addition, the histogram normalization method outperform the generalized scale and generalized ball-scale methods which have proven superior for other body regions.     

Evaluation of normalization methods in mammalian microRNA-Seq data

Simple total tag count normalization is inadequate for microRNA sequencing data generated from the next generation sequencing technology. However, so far systematic evaluation of normalization methods on microRNA sequencing data is lacking. We comprehensively evaluate seven commonly used normalization methods including global normalization, Lowess normalization, Trimmed Mean Method (TMM), quantile normalization, scaling normalization, variance stabilization, and invariant method. We assess these methods on two individual experimental data sets with the empirical statistical metrics of mean square error (MSE) and Kolmogorov-Smirnov (K-S) statistic. Additionally, we evaluate the methods with results from quantitative PCR validation. Our results consistently show that Lowess normalization and quantile normalization perform the best, whereas TMM, a method applied to the RNA-Sequencing normalization, performs the worst. The poor performance of TMM normalization is further evidenced by abnormal results from the test of differential expression (DE) of microRNA-Seq data. Comparing with the models used for DE, the choice of normalization method is the primary factor that affects the results of DE. In summary, Lowess normalization and quantile normalization are recommended for normalizing microRNA-Seq data, whereas the TMM method should be used with caution.     

Consensus for experimental design in electromyography (CEDE) project: Amplitude normalization matrix

The general purpose of normalization of EMG amplitude is to enable comparisons between participants, muscles, measurement sessions or electrode positions. Normalization is necessary to reduce the impact of differences in physiological and anatomical characteristics of muscles and surrounding tissues. Normalization of the EMG amplitude provides information about the magnitude of muscle activation relative to a reference value. It is essential to select an appropriate method for normalization with specific reference to how the EMG signal will be interpreted, and to consider how the normalized EMG amplitude may change when interpreting it under specific conditions. This matrix, developed by the Consensus for Experimental Design in Electromyography (CEDE) project, presents six approaches to EMG normalization: (1) Maximal voluntary contraction (MVC) in same task/context as the task of interest, (2) Standardized isometric MVC (which is not necessarily matched to the contraction type in the task of interest), (3) Standardized submaximal task (isometric/dynamic) that can be task-specific, (4) Peak/mean EMG amplitude in task, (5) Non-normalized, and (6) Maximal M-wave. General considerations for normalization, features that should be reported, definitions, and “pros and cons” of each normalization approach are presented first. This information is followed by recommendations for specific experimental contexts, along with an explanation of the factors that determine the suitability of a method, and frequently asked questions. This matrix is intended to help researchers when selecting, reporting and interpreting EMG amplitude data.     

Evaluation of normalization methods for microarray data

Background

Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. This novel technique helps us to understand gene regulation as well as gene by gene interactions more systematically. In the microarray experiment, however, many undesirable systematic variations are observed. Even in replicated experiment, some variations are commonly observed. Normalization is the process of removing some sources of variation which affect the measured gene expression levels. Although a number of normalization methods have been proposed, it has been difficult to decide which methods perform best. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization.

Results

In this paper, we use the variability among the replicated slides to compare performance of normalization methods. We also compare normalization methods with regard to bias and mean square error using simulated data.

Conclusions

Our results show that intensity-dependent normalization often performs better than global normalization methods, and that linear and nonlinear normalization methods perform similarly. These conclusions are based on analysis of 36 cDNA microarrays of 3,840 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells. Simulation studies confirm our findings.

     

EMG signal amplitude normalization technique in stretch-shortening cycle movements

Analysis of functional movements using surface electromyography (EMG) often involves recording both eccentric and concentric muscle activity during a stretch-shorten cycle (SSC). The techniques used for amplitude normalization are varied and are independent of the type of muscle activity involved. The purpose of this study was: (i) to determine the effect of 11 amplitude normalization techniques on the coefficient of variation (CV) during the eccentric and concentric phases of the SSC; and (ii) to establish the effect of the normalization techniques on the EMG signal under variable load and velocity. The EMG signal of the biceps brachii of eight normal subjects was recorded under four SSC conditions and three levels of isometric contraction. The 11 derived normalization values were total rms, mean rms and peak rms (100 ms time constant) for the isometric contractions and the mean rms and peak rms values of the ensemble values for each set of isotonic contractions. Normalization using maximal voluntary isometric contractions (MVIC), irrespective of rms processing (total, mean or peak), demonstrated greater CV above the raw data for both muscle actions. Mean ensemble values and submaximal isometric recordings reduced the CV of concentric data. No amplitude normalization technique reduced the CV for eccentric data under loaded conditions. An ANOVA demonstrated significant (P < 0.01) main effects for load and velocity on concentric raw data and an interaction (P < 0.05) for raw eccentric data. No significant effects were demonstrated for changes in velocity when the data were normalized using mean rms values. The reduction of the CV should not be at the expense of true biological variance and current normalization techniques poorly serve the analysis of eccentric muscle activity during the SSC.     

Bias in normalization: Causes,consequences, detection and remedies

Introduction Normalization is an optional step in LCIA that is used to better understand the relative importance and magnitude of the impact category indicator results. It is used for error checking, as a first step in weighting, and for standalone presentation of results. A normalized score for a certain impact category is obtained by determining the ratio of the category indicator result of the product and that of a reference system, such as the world in a certain year or the population of a specific area in a certain year. Biased Normalization In determining these two quantities, the numerator, the denominator, or both can suffer from incompleteness due to a lack of emission data and/or characterisation factors. This leads to what we call a biased normalization. As a consequence. the normalized category indicator result can be too low or too high. Some examples from hypothetical and real case studies demonstrate this. Consequences of Biased Normalization Especially when for some impact categories the normalized category indicator result is right, for others too low, and for others too high, severe problems in using normalized scores can show up. It is shown how this may affect the three types of usage of normalized results: error checking, weighting and standalone presentation. Detection and Remedies of Biased Normalization Some easy checks are proposed that at least alert the LCA practitioner of the possibility of a biased result. These checks are illustrated for an example system on hydrogen production. A number of remedies of this problem is possible. These are discussed. In particular, casedependent normalization is shown to solve some problems, but on the expense of creating other problems. Discussion It appears that there is only one good solution: databases and tables of characterisation factors must be made more completely, so that the risk of detrimental bias is reduced. On the other hand, the use of the previously introduced checks should become a standard element in LCA practice, and should be facilitated with LCA software. ESS-Submission Editor: Duane A. Tolle (tolled@battelle.org)     

A normalization strategy for comparing tag count data

Background

High-throughput sequencing, such as ribonucleic acid sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) analyses, enables various features of organisms to be compared through tag counts. Recent studies have demonstrated that the normalization step for RNA-seq data is critical for a more accurate subsequent analysis of differential gene expression. Development of a more robust normalization method is desirable for identifying the true difference in tag count data.

Results

We describe a strategy for normalizing tag count data, focusing on RNA-seq. The key concept is to remove data assigned as potential differentially expressed genes (DEGs) before calculating the normalization factor. Several R packages for identifying DEGs are currently available, and each package uses its own normalization method and gene ranking algorithm. We compared a total of eight package combinations: four R packages (edgeR, DESeq, baySeq, and NBPSeq) with their default normalization settings and with our normalization strategy. Many synthetic datasets under various scenarios were evaluated on the basis of the area under the curve (AUC) as a measure for both sensitivity and specificity. We found that packages using our strategy in the data normalization step overall performed well. This result was also observed for a real experimental dataset.

Conclusion

Our results showed that the elimination of potential DEGs is essential for more accurate normalization of RNA-seq data. The concept of this normalization strategy can widely be applied to other types of tag count data and to microarray data.     

Comparative analysis of creatinine and osmolality as urine normalization strategies in targeted metabolomics for the differential diagnosis of asthma and COPD

Introduction

Urine is an ideal matrix for metabolomics investigation due to its non-invasive nature of collection and its rich metabolite content. Despite the advancements in mass spectrometry and ¹H-NMR platforms in urine metabolomics, the statistical analysis of the generated data is challenged with the need to adjust for the hydration status of the person. Normalization to creatinine or osmolality values are the most adopted strategies, however, each technique has its challenges that can hinder its wider application. We have been developing targeted urine metabolomic methods to differentiate two important respiratory diseases, namely asthma and chronic obstructive pulmonary disease (COPD).

Objective

To assess whether the statistical model of separation of diseases using targeted metabolomic data would be improved by normalization to osmolality instead of creatinine.

Methods

The concentration of 32 metabolites was previously measured by two liquid chromatography-tandem mass spectrometry methods in 51 human urine samples with either asthma (n?=?25) or COPD (n?=?26). The data was normalized to creatinine or osmolality. Statistical analysis of the normalized values in each disease was performed using partial least square discriminant analysis (PLS-DA). Models of separation of diseases were compared.

Results

We found that normalization to creatinine or osmolality did not significantly change the PLS-DA models of separation (R²Q²?=?0.919, 0.705 vs R²Q²?=?0.929, 0.671, respectively). The metabolites of importance in the models remained similar for both normalization methods.

Conclusion

Our findings suggest that targeted urine metabolomic data can be normalized for hydration using creatinine or osmolality with no significant impact on the diagnostic accuracy of the model.

     

Batch Leaching Tests: Colloid Release and PAH Leachability

The Toxicity Characteristic Leaching Procedure (TCLP) was developed by the U.S. Environmental Protection Agency to assess leaching potential of contaminants from waste, and to provide a test to classify hazardous waste. It is a batch leaching test where a waste (such as contaminated soil) and an extraction fluid are agitated for a predetermined time. Since TCLP employs an aggressive mixing technique, it is possible that hydrophobic contaminant-laden colloidal fractions may appear as “dissolved” constituents. In this study, TCLP was employed to determine the leachability of PAH contamination from a coal tar contaminated site. Generated colloids and the apparent aqueous concentrations of naphthalene and phenanthrene were measured at various mixing times in the extraction fluid.

A mathematical model was developed that predicted the apparent aqueous contaminant concentration in the filtrate. This model accounted for the presence of colloids in the filtrate, and quantified contaminant desorption from colloids. The fraction of colloid-bound contaminant was predicted to be negligible for naphthalene. However, phenanthrene was predicted to have a significant fraction of the total contaminant in the colloidal phase, while naphthalene was primarily dissolved. The desorption model and PAH desorption data are presented here to determine the extent of colloid-facilitated desorption during leaching tests.     

A systematic assessment of normalization approaches for the Infinium 450K methylation platform

The Illumina Infinium HumanMethylation450 BeadChip has emerged as one of the most popular platforms for genome wide profiling of DNA methylation. While the technology is wide-spread, systematic technical biases are believed to be present in the data. For example, this array incorporates two different chemical assays, i.e., Type I and Type II probes, which exhibit different technical characteristics and potentially complicate the computational and statistical analysis. Several normalization methods have been introduced recently to adjust for possible biases. However, there is considerable debate within the field on which normalization procedure should be used and indeed whether normalization is even necessary. Yet despite the importance of the question, there has been little comprehensive comparison of normalization methods. We sought to systematically compare several popular normalization approaches using the Norwegian Mother and Child Cohort Study (MoBa) methylation data set and the technical replicates analyzed with it as a case study. We assessed both the reproducibility between technical replicates following normalization and the effect of normalization on association analysis. Results indicate that the raw data are already highly reproducible, some normalization approaches can slightly improve reproducibility, but other normalization approaches may introduce more variability into the data. Results also suggest that differences in association analysis after applying different normalizations are not large when the signal is strong, but when the signal is more modest, different normalizations can yield very different numbers of findings that meet a weaker statistical significance threshold. Overall, our work provides useful, objective assessment of the effectiveness of key normalization methods.     

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

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 enhancement 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.     

Comparing normalization methods and the impact of noise

Introduction

Failure to properly account for normal systematic variations in OMICS datasets may result in misleading biological conclusions. Accordingly, normalization is a necessary step in the proper preprocessing of OMICS datasets. In this regards, an optimal normalization method will effectively reduce unwanted biases and increase the accuracy of downstream quantitative analyses. But, it is currently unclear which normalization method is best since each algorithm addresses systematic noise in different ways.

Objective

Determine an optimal choice of a normalization method for the preprocessing of metabolomics datasets.

Methods

Nine MVAPACK normalization algorithms were compared with simulated and experimental NMR spectra modified with added Gaussian noise and random dilution factors. Methods were evaluated based on an ability to recover the intensities of the true spectral peaks and the reproducibility of true classifying features from orthogonal projections to latent structures—discriminant analysis model (OPLS-DA).

Results

Most normalization methods (except histogram matching) performed equally well at modest levels of signal variance. Only probabilistic quotient (PQ) and constant sum (CS) maintained the highest level of peak recovery (>?67%) and correlation with true loadings (>?0.6) at maximal noise.

Conclusion

PQ and CS performed the best at recovering peak intensities and reproducing the true classifying features for an OPLS-DA model regardless of spectral noise level. Our findings suggest that performance is largely determined by the level of noise in the dataset, while the effect of dilution factors was negligible. A minimal allowable noise level of 20% was also identified for a valid NMR metabolomics dataset.

     

A reproducibility-based evaluation procedure for quantifying the differences between MS/MS peak intensity normalization methods

The identification of peptides and proteins from fragmentation mass spectra is a very common approach in the field of proteomics. Contemporary high-throughput peptide identification pipelines can quickly produce large quantities of MS/MS data that contain valuable knowledge about the actual physicochemical processes involved in the peptide fragmentation process, which can be extracted through extensive data mining studies. As these studies attempt to exploit the intensity information contained in the MS/MS spectra, a critical step required for a meaningful comparison of this information between MS/MS spectra is peak intensity normalization. We here describe a procedure for quantifying the efficiency of different published normalization methods in terms of the quartile coefficient of dispersion (qcod) statistic. The quartile coefficient of dispersion is applied to measure the dispersion of the peak intensities between redundant MS/MS spectra, allowing the quantification of the differences in computed peak intensity reproducibility between the different normalization methods. We demonstrate that our results are independent of the data set used in the evaluation procedure, allowing us to provide generic guidance on the choice of normalization method to apply in a certain MS/MS pipeline application.     

Gene mention normalization and interaction extraction with context models and sentence motifs

Background:

The goal of text mining is to make the information conveyed in scientific publications accessible to structured search and automatic analysis. Two important subtasks of text mining are entity mention normalization - to identify biomedical objects in text - and extraction of qualified relationships between those objects. We describe a method for identifying genes and relationships between proteins.

Results:

We present solutions to gene mention normalization and extraction of protein-protein interactions. For the first task, we identify genes by using background knowledge on each gene, namely annotations related to function, location, disease, and so on. Our approach currently achieves an f-measure of 86.4% on the BioCreative II gene normalization data. For the extraction of protein-protein interactions, we pursue an approach that builds on classical sequence analysis: motifs derived from multiple sequence alignments. The method achieves an f-measure of 24.4% (micro-average) in the BioCreative II interaction pair subtask.

Conclusion:

For gene mention normalization, our approach outperforms strategies that utilize only the matching of genes names against dictionaries, without invoking further knowledge on each gene. Motifs derived from alignments of sentences are successful at identifying protein interactions in text; the approach we present in this report is fully automated and performs similarly to systems that require human intervention at one or more stages.

Availability:

Our methods for gene, protein, and species identification, and extraction of protein-protein are available as part of the BioCreative Meta Services (BCMS), see http://bcms.bioinfo.cnio.es/.

     

A systematic assessment of normalization approaches for the Infinium 450K methylation platform

The Illumina Infinium HumanMethylation450 BeadChip has emerged as one of the most popular platforms for genome wide profiling of DNA methylation. While the technology is wide-spread, systematic technical biases are believed to be present in the data. For example, this array incorporates two different chemical assays, i.e., Type I and Type II probes, which exhibit different technical characteristics and potentially complicate the computational and statistical analysis. Several normalization methods have been introduced recently to adjust for possible biases. However, there is considerable debate within the field on which normalization procedure should be used and indeed whether normalization is even necessary. Yet despite the importance of the question, there has been little comprehensive comparison of normalization methods. We sought to systematically compare several popular normalization approaches using the Norwegian Mother and Child Cohort Study (MoBa) methylation data set and the technical replicates analyzed with it as a case study. We assessed both the reproducibility between technical replicates following normalization and the effect of normalization on association analysis. Results indicate that the raw data are already highly reproducible, some normalization approaches can slightly improve reproducibility, but other normalization approaches may introduce more variability into the data. Results also suggest that differences in association analysis after applying different normalizations are not large when the signal is strong, but when the signal is more modest, different normalizations can yield very different numbers of findings that meet a weaker statistical significance threshold. Overall, our work provides useful, objective assessment of the effectiveness of key normalization methods.     

Improving the genetic representation of rare taxa within complex microbial communities using DNA normalization methods

Complex microbial communities typically contain a large number of low abundance species, which collectively, comprise a considerable proportion of the community. This ‘rare biosphere’ has been speculated to contain keystone species and act as a repository of genomic diversity to facilitate community adaptation. Many environmental microbes are currently resistant to cultivation, and can only be accessed via culture‐independent approaches. To enhance our understanding of the role of the rare biosphere, we aimed to improve their metagenomic representation using DNA normalization methods, and assess normalization success via shotgun DNA sequencing. A synthetic metagenome was constructed from the genomic DNA of five bacterial species, pooled in a defined ratio spanning three orders of magnitude. The synthetic metagenome was fractionated and thermally renatured, allowing the most abundant sequences to hybridize. Double‐stranded DNA was removed either by hydroxyapatite chromatography, or by a duplex‐specific nuclease (DSN). The chromatographic method failed to enrich for the genomes present in low starting abundance, whereas the DSN method resulted in all genomes reaching near equimolar abundance. The representation of the rarest member was increased by approximately 450‐fold. De novo assembly of the normalized metagenome enabled up to 18.0% of genes from the rarest organism to be assembled, in contrast to the un‐normalized sample, where genes were not able to be assembled at the same sequencing depth. This study has demonstrated that the application of normalization methods to metagenomic samples is a powerful tool to enrich for sequences from rare taxa, which will shed further light on their ecological niches.