Current Data Analysis Methods Research Articles

Harnessing data science to improve molecular structure elucidation from tandem mass spectrometry

Compound structural identification for non-targeted screening of organic molecules in complex mixtures is commonly carried out using liquid chromatography coupled to tandem mass spectrometry (UHPLC-HRMS/MS and related techniques). Instrumental developments in recent years have increased the quality and quantity of data available; however, using current data analysis methods, structures can be assigned to only a small fraction of compounds present in typical mixtures. We present a new data analysis pipeline, “MSEI”, that harnesses data science methodologies to improve structural identification capabilities from tandem mass spectrometry data. In particular, feature vectors for fingerprint calculation are found directly from tandem mass spectra, strongly reducing computational costs, and fingerprint comparison uses an optimised methodology accounting for uncertainty to improve distinction between matching and non-matching compounds. MSEI builds on the identification of a small number of compounds through current state-of-the-art data analysis on UHPLC-HRMS/MS measurements and uses targeted training and tailored molecular fingerprints to focus identification to a particular molecular space of interest. Initial compound identifications are used as training data for a set of random forests which directly predict a custom 75-digit molecular fingerprint from a vectorised MS/MS spectrum. Kendrick mass defects (KMDs) for peaks as well as “lost” fragments removed during fragmentation were found to be useful information for fingerprint prediction. Fingerprints are then compared to potential matches from the PubChem structural database using Euclidean distance, with fingerprint digit weights determined using an SVM to maximise distance between matching and non-matching compounds. Potential matches are additionally filtered for hydrophobicity based on measured retention time, using a newly developed machine learning method for retention time prediction. MSEI was able to correctly assign > 50% of structures in a test dataset and showed > 10% better performance than current state-of-the-art methods, while using an order of magnitude less computational power and a fraction of the training data.

Diagnosis of Schizophrenia Based on the Data of Various Modalities: Biomarkers and Machine Learning Techniques (Review).

Schizophrenia is a socially significant mental disorder resulting frequently in severe forms of disability. Diagnosis, choice of treatment tactics, and rehabilitation in clinical psychiatry are mainly based on the assessment of behavioral patterns, socio-demographic data, and other investigations such as clinical observations and neuropsychological testing including examination of patients by the psychiatrist, self-reports, and questionnaires. In many respects, these data are subjective and therefore a large number of works have appeared in recent years devoted to the search for objective characteristics (indices, biomarkers) of the processes going on in the human body and reflected in the behavioral and psychoneurological patterns of patients. Such biomarkers are based on the results of instrumental and laboratory studies (neuroimaging, electro-physiological, biochemical, immunological, genetic, and others) and are successfully being used in neurosciences for understanding the mechanisms of the emergence and development of nervous system pathologies. Presently, with the advent of new effective neuroimaging, laboratory, and other methods of investigation and also with the development of modern methods of data analysis, machine learning, and artificial intelligence, a great number of scientific and clinical studies is being conducted devoted to the search for the markers which have diagnostic and prognostic value and may be used in clinical practice to objectivize the processes of establishing and clarifying the diagnosis, choosing and optimizing treatment and rehabilitation tactics, predicting the course and outcome of the disease. This review presents the analysis of the works which describe the correlates between the diagnosis of schizophrenia, established by health professionals, various manifestations of the psychiatric disorder (its subtype, variant of the course, severity degree, observed symptoms, etc.), and objectively measured characteristics/quantitative indicators (anatomical, functional, immunological, genetic, and others) obtained during instrumental and laboratory examinations of patients. A considerable part of these works has been devoted to correlates/biomarkers of schizophrenia based on the data of structural and functional (at rest and under cognitive load) MRI, EEG, tractography, and immunological data. The found correlates/biomarkers reflect anatomic disorders in the specific brain regions, impairment of functional activity of brain regions and their interconnections, specific microstructure of the brain white matter and the levels of connectivity between the tracts of various structures, alterations of electrical activity in various parts of the brain in different EEG spectral ranges, as well as changes in the innate and adaptive links of immunity. Current methods of data analysis and machine learning to search for schizophrenia biomarkers using the data of diverse modalities and their application during building and interpretation of predictive diagnostic models of schizophrenia have been considered in the present review.

Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research.

Research in environmental health is becoming increasingly reliant upon data science and computational methods that can more efficiently extract information from complex datasets. Data science and computational methods can be leveraged to better identify relationships between exposures to stressors in the environment and human disease outcomes, representing critical information needed to protect and improve global public health. Still, there remains a critical gap surrounding the training of researchers on these in silico methods. We aimed to address this gap by developing the inTelligence And Machine lEarning (TAME) Toolkit, promoting trainee-driven data generation, management, and analysis methods to “TAME” data in environmental health studies. Training modules were developed to provide applications-driven examples of data organization and analysis methods that can be used to address environmental health questions. Target audiences for these modules include students, post-baccalaureate and post-doctorate trainees, and professionals that are interested in expanding their skillset to include recent advances in data analysis methods relevant to environmental health, toxicology, exposure science, epidemiology, and bioinformatics/cheminformatics. Modules were developed by study coauthors using annotated script and were organized into three chapters within a GitHub Bookdown site. The first chapter of modules focuses on introductory data science, which includes the following topics: setting up R/RStudio and coding in the R environment; data organization basics; finding and visualizing data trends; high-dimensional data visualizations; and Findability, Accessibility, Interoperability, and Reusability (FAIR) data management practices. The second chapter of modules incorporates chemical-biological analyses and predictive modeling, spanning the following methods: dose-response modeling; machine learning and predictive modeling; mixtures analyses; -omics analyses; toxicokinetic modeling; and read-across toxicity predictions. The last chapter of modules was organized to provide examples on environmental health database mining and integration, including chemical exposure, health outcome, and environmental justice indicators. Training modules and associated data are publicly available online (https://uncsrp.github.io/Data-Analysis-Training-Modules/). Together, this resource provides unique opportunities to obtain introductory-level training on current data analysis methods applicable to 21st century science and environmental health.

The Application of Internet of Things Data Analysis in the Development of International Trade.

There are some problems in the application of current data analysis methods in international economy and trade, such as low service efficiency, low data utilization, and low degree of intelligence. Based on this, this paper studies the application of the Internet of things data analysis method in international trade development and economic and industrial growth. Firstly, the Internet of things economic data analysis model (IOT-EET model) based on simulated annealing early warning algorithm is established to store and analyze the data in the whole chain of international trade. Then, combined with the analysis methods of international trade economic data over the years, it is fed back to the IOT-EET model for error calibration. Finally, relevant experiments are designed to analyze the correlation between international trade development and national economic growth. The results show that compared with the traditional method based on module data analysis, this IOT-EET model can realize the correlation matching analysis of the data involved in the development of international trade in combination with the Internet of things technology and analyze the factors affecting international trade transactions. Therefore, it has the advantages of good reliability and strong pertinence.

Grouping concentration response curves by features of their shape to aid rapid and consistent analysis of large data sets in high throughput screens.

With ever increasing compound collection sizes one significant challenge in high throughput screening (HTS) is the rapid and consistent analysis of large numbers of concentration response (CR) curves. Current data analysis methods are well defined in the industry but we describe here a further addition of categorisation of concentration response data. In comparison to single point concentration data from an HTS primary screen the information generated in a concentration response curve contains much more complexity.

Deep Learning for Rapid Identification of Microbes Using Metabolomics Profiles.

Rapid detection of viable microbes remains a challenge in fields such as microbial food safety. We here present the application of deep learning algorithms to the rapid detection of pathogenic and non-pathogenic microbes using metabolomics data. Microbes were incubated for 4 h in a protein-free defined medium, followed by 1D 1H nuclear magnetic resonance (NMR) spectroscopy measurements. NMR spectra were analyzed by spectral binning in an untargeted metabolomics approach. We trained multilayer (“deep”) artificial neural networks (ANN) on the data and used the resulting models to predict spectra of unknown microbes. ANN predicted unknown microbes in this laboratory setting with an average accuracy of 99.2% when using a simple feature selection method. We also describe learning behavior of the employed ANN and the optimization strategies that worked well with these networks for our datasets. Performance was compared to other current data analysis methods, and ANN consistently scored higher than random forest models and support vector machines, highlighting the potential of deep learning in metabolomics data analysis.

Comment on “Berkovich nanoindentation of Zr55Cu30Al10Ni5 bulk metallic glass at a constant loading rate”, J. Non-Cryst. Solids, 561 (2021) 120750

It is argued that the decrease with increasing indenter load in the value of the elastic modulus [1] of a bulk metallic glass determined using a nanoindentation machine combined with the current methods of data analysis is not a manifestation of any damage in the metallic glass. Rather, the cause of the decrease of the elastic modulus is an error in the method of data analysis.

Social Network Big Data Hierarchical High‐Quality Node Mining

Compared with the conventional network data analysis, the data analysis based on social network has a very clear object of analysis, various forms of analysis, and more methods and contents of analysis. If the conventional analysis methods are applied to social network data analysis, we will find that the analysis results do not reach our expected results. The results of the above studies are usually based on statistical methods and machine learning methods, but some systems use other methods, such as self‐organizing self‐learning mechanisms and concept retrieval. With regard to the current data analysis methods, data models, and social network data, this paper conducts a series of researches from data acquisition, data cleaning and processing, data model application and optimization of the model in the process of application, and how the formed data analysis results can be used for managers to make decisions. In this paper, the number of customer evaluations, the time of evaluation, the frequency of evaluation, and the score of evaluation are clustered and analyzed, and finally, the results obtained by the two clustering methods applied in the analysis process are compared to build a customer grading system. The analysis results can be used to maintain the current Amazon purchase customers in a hierarchical manner, and the most valuable customers need to be given key attention, combining social network big data with micro marketing to improve Amazon’s sales performance and influence, developing from the original single shopping mall model to a comprehensive e‐commerce platform, and cultivating their own customer base.

Some concerns about the current interpretation and analyses of indentation unloading P – h curves highlighted with Young's modulus studies of single crystals of MgO (100)

Indentation tests using indenter load, P, and indentation penetration, h, curves obtained from instrumented indentation machines have been in use for over 30 years. The unloading part of a P – h curve is frequently used for determining the indentation size at the maximum applied indenter load and for determining the elastic modulus of the test solid. To do this, four fundamental assumptions have been made. We show here experimentally that, unfortunately, none of the assumptions is correct. Therefore, these assumptions lead to incorrect determinations. Examples highlighting the concerns are given from our own work on single crystals of Cu and MgO and from the work of others. It is shown here that for both isotropic and anisotropic solids, the elastic moduli determined using the current methods of data analysis vary very considerably with the applied maximum indenter load. Furthermore, the obtained values do not correlate well with the literature values of the elastic moduli, despite the claim [see abstract of W. C. Oliver and G. M. Pharr (J. Mater. Res. 7, 1564 (1992)] that the agreement is within 5%. To remedy this problem, an alternative approach employing only elastic loading with an elastic sphere is used and the resulting load versus displacement data are analyzed using the Hertzian equation for the normal loading of a sphere on an elastic half-space. This experimental technique yields satisfactory values of the elastic moduli of test surfaces of single crystals of Cu (111), Cu (100), and MgO (100); the elastic moduli thus determined do not vary with the applied indenter load.Indentation tests using indenter load, P, and indentation penetration, h, curves obtained from instrumented indentation machines have been in use for over 30 years. The unloading part of a P – h curve is frequently used for determining the indentation size at the maximum applied indenter load and for determining the elastic modulus of the test solid. To do this, four fundamental assumptions have been made. We show here experimentally that, unfortunately, none of the assumptions is correct. Therefore, these assumptions lead to incorrect determinations. Examples highlighting the concerns are given from our own work on single crystals of Cu and MgO and from the work of others. It is shown here that for both isotropic and anisotropic solids, the elastic moduli determined using the current methods of data analysis vary very considerably with the applied maximum indenter load. Furthermore, the obtained values do not correlate well with the literature values of the elastic moduli, despite the claim [see abstract...

Identification of Cronobacter species by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry with an optimized analysis method

Rapid and precise identification of Cronobacter species is important for foodborne pathogen detection, however, commercial biochemical methods can only identify Cronobacter strains to genus level in most cases. To evaluate the power of mass spectrometry based on matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF MS) for Cronobacter species identification, 51 Cronobacter strains (eight reference and 43 wild strains) were identified by both MALDI-TOF MS and 16S rRNA gene sequencing. Biotyper RTC provided by Bruker identified all eight reference and 43 wild strains as Cronobacter species, which demonstrated the power of MALDI-TOF MS to identify Cronobacter strains to genus level. However, using the Bruker's database (6903 main spectra products) and Biotyper software, the MALDI-TOF MS analysis could not identify the investigated strains to species level. When MALDI-TOF MS analysis was performed using the combined in-house Cronobacter database and Bruker's database, bin setting, and unweighted pair group method with arithmetic mean (UPGMA) clustering, all the 51 strains were clearly identified into six Cronobacter species and the identification accuracy increased from 60% to 100%. We demonstrated that MALDI-TOF MS was reliable and easy-to-use for Cronobacter species identification and highlighted the importance of establishing a reliable database and improving the current data analysis methods by integrating the bin setting and UPGMA clustering.

Identifying biologically relevant putative mechanisms in a given phenotype comparison.

A major challenge in life science research is understanding the mechanism involved in a given phenotype. The ability to identify the correct mechanisms is needed in order to understand fundamental and very important phenomena such as mechanisms of disease, immune systems responses to various challenges, and mechanisms of drug action. The current data analysis methods focus on the identification of the differentially expressed (DE) genes using their fold change and/or p-values. Major shortcomings of this approach are that: i) it does not consider the interactions between genes; ii) its results are sensitive to the selection of the threshold(s) used, and iii) the set of genes produced by this approach is not always conducive to formulating mechanistic hypotheses. Here we present a method that can construct networks of genes that can be considered putative mechanisms. The putative mechanisms constructed by this approach are not limited to the set of DE genes, but also considers all known and relevant gene-gene interactions. We analyzed three real datasets for which both the causes of the phenotype, as well as the true mechanisms were known. We show that the method identified the correct mechanisms when applied on microarray datasets from mouse. We compared the results of our method with the results of the classical approach, showing that our method produces more meaningful biological insights.

Prospective motion correction in diffusion-weighted imaging using intermediate pseudo-trace-weighted images

Subject head motion is a major challenge in diffusion-weighted imaging, which requires a precise alignment of images from different time points to allow a reliable quantification of diffusion parameters within each voxel. The technique requires long measurement times, making it highly sensitive to long-term subject motion, even when head restraint is used. Current methods of data analysis rely on retrospective motion correction, but there are potential benefits to using prospective motion correction, in which motion is tracked and compensated for during data acquisition. This technique is regularly used to enhance image quality in blood-oxygen-level dependent (BOLD) imaging, but its application to diffusion-weighted imaging has been limited by the contrast variation between images acquired with different diffusion-gradient directions. This paper describes a novel approach to this topic that exploits the rotational invariance of the trace of the diffusion tensor to reduce the effect of this contrast variation, making it possible to perform a fast image registration using a least-squares cost function. This results in an image-based motion detection algorithm that can be applied in real time during data acquisition to adapt the slice position and orientation in response to subject motion. The motion detection capabilities of the technique were evaluated in a study of ten subjects with b-values up to 3000s/mm². The resulting motion-parameter estimates were in close agreement with reference values provided by interleaved low-b-value images with a correlation coefficient of R=0.9634 for the voxel displacements measured across all subjects and b-values. The technique was also used to perform prospective motion correction on a standard clinical MRI system with b-values up to 2000s/mm². The correction was evaluated in 3 subjects using interleaved low-b-value images, retrospective image registration using the AFNI processing package and mean diffusivity histogram analysis. Compared to acquisitions without motion correction, prospective motion correction based on pseudo-trace-weighted images was found to provide a robust method for substantially reducing the level of misregistration between volumes. In most cases, misregistrations were reduced to less than 0.2mm of translation and 0.2° of rotation for an isotropic voxel size of 2mm, yielding high-quality diffusion parameter maps even in the absence of head restraint and post-acquisition image registration.

Arsenic Levels in Chicken

I commend Nachman et al. (2013) on their careful study of arsenic content in market samples of chicken and am dismayed to learn that arsenic levels in chicken remain high despite our report of arsenic levels in Environmental Health Perspectives in 2004 (Lasky et al. 2004). One possible explanation is the very complicated lines of authority around the regulation of drugs fed to food animals, along with the enforcement of those regulations. Nachman et al. (2013) focused their discussion on the role of the Food and Drug Administration (FDA), but many other agencies participate in the regulation and enforcement of residue safety in food animals. As noted by the Food Safety and Inspection Service (FSIS) in the introduction to their 2012 Residue Sampling Plans (FSIS 2012), The U.S. National Residue Program (NRP) for Meat, Poultry, and Egg Products, administered by the USDA [U.S. Department of Agriculture] FSIS, is an interagency program designed to identify, rank and test for chemical contaminants in meat, poultry, and egg products. They continue, The NRP requires the cooperation and collaboration of several agencies for its successful design and implementation. The USDA FSIS, the EPA [U.S. Environmental Protection Agency], and the Department of Health and Human Services (DHHS) FDA are the primary federal agencies managing this program. The FDA, under the Federal Food, Drug, and Cosmetic Act, establishes tolerances for veterinary drugs, and action levels for food additives and environmental contaminants. The EPA, under the Federal Insecticide, Fungicide, and Rodenticide Act (as modified by the Food Quality Protection Act), establishes tolerance levels for registered pesticides…. Representatives from FSIS, FDA, EPA, the USDA Agricultural Research Service (ARS), the USDA Agricultural Marketing Service (AMS), and the DHHS Centers for Disease Control and Prevention (CDC) collaborate to develop the scheduled sampling program. Setting and enforcing safety levels involves several steps, one of which is the collection of meat and poultry samples, followed by statistical analysis, interpretation, and action. The NRP sampling plan is designed to identify samples with residues above the allowed levels. The data are then analyzed as categorical values (violation, no violation). Current methods of data analysis do not include estimation of mean values that can then be extrapolated to the national food supply. It was by analyzing the data as a continuous variable that my coauthors and I were able to describe the high levels of arsenic in chicken in 2004 (Lasky et al. 2004). Concerns about arsenic levels in chicken are of increasing importance because of the increased per capita consumption of chicken over the past decade (USDA Economic Research Service 2013).

QUANTIFYING ALTERNATIVE SPLICING FROM PAIRED-END RNA-SEQUENCING DATA.

RNA-sequencing has revolutionized biomedical research and, in particular, our ability to study gene alternative splicing. The problem has important implications for human health, as alternative splicing may be involved in malfunctions at the cellular level and multiple diseases. However, the high-dimensional nature of the data and the existence of experimental biases pose serious data analysis challenges. We find that the standard data summaries used to study alternative splicing are severely limited, as they ignore a substantial amount of valuable information. Current data analysis methods are based on such summaries and are hence sub-optimal. Further, they have limited flexibility in accounting for technical biases. We propose novel data summaries and a Bayesian modeling framework that overcome these limitations and determine biases in a non-parametric, highly flexible manner. These summaries adapt naturally to the rapid improvements in sequencing technology. We provide efficient point estimates and uncertainty assessments. The approach allows to study alternative splicing patterns for individual samples and can also be the basis for downstream analyses. We found a several fold improvement in estimation mean square error compared popular approaches in simulations, and substantially higher consistency between replicates in experimental data. Our findings indicate the need for adjusting the routine summarization and analysis of alternative splicing RNA-seq studies. We provide a software implementation in the R package casper.

Making cancer transcriptome sequencing assays practical for the research and clinical scientist

Next generation DNA sequencing (NGS) technologies are increasing in their appeal for studying cancer genomics. High-throughput data and a growing repertoire of applications that quantitatively measure gene expression, splicing, noncoding RNAs, and genomic variation are revealing that cancer is a more complex and heterogeneous disease than previously imagined. Fully characterizing the ~10,000 types and subtypes of cancer that exist to develop biomarkers that can be used to clinically define tumors and target specific treatments requires large studies that examine specific tumors in thousands of patients. This goal will fail without significantly reducing both data production and analysis costs, so that most cancer biologists and clinicians can conduct NGS assays and analyze their data in routine ways. Currently, most cancer biology NGS papers are published either by genome centers or through collaborations with instrument vendors. However, this is going to change rapidly with efforts like the Cancer Genome Anatomy project. In any case, large teams of bioinformaticians are involved in analyzing data through labor-intensive processes. With refinements offered by the Illumina HiSeq 2000, or Life Technologies SOLiD 4, the cost of collecting data for transcriptome analysis and mate-pair genome sequencing is sufficiently inexpensive for small groups and individuals, beyond genome centers, to conduct the required studies. However, current data analysis methods need to be automated with established tools in scalable and adaptable systems that provide standard reports to make results available to enable interactive exploration by biologists and clinicians. In our presentation, we will examine the time and costs required to analyze data that will be collected in future cancer studies. Using data from existing matched tumor and normal transcriptome studies from random oral cancer samples, and samples grouped by drinking and smoking behavior (as a tool to define data analysis requirements), we will compare the costs of conducting large studies using current data analysis approaches with those using integrated software systems to demonstrate how automation reduces costs, while providing comparable results for identifying transcript isoforms, mutations and novel translocations. Geospiza's GeneSifter distributed cloud- based software architecture, including open source tools, like BioHDF, will be described to share insights into high performance computing requirements for scalable data processing.

Standards for physiological data transmission and archiving for the support of the service of critical care

Physiological data is monitored and displayed on medical devices around the world every day, and the volume of this data is steadily increasing and newer monitoring devices enter the clinical setting. However, the vast majority of this data is lost since it is most often displayed once as it is recorded, perhaps replayed one or more times while it exists in the device's volatile memory. What little data that is permanently recorded is most commonly saved through hand written annotations, in paper records and in some limited samples stored on hospital clinical information systems. Meanwhile, current methods of data analysis provide opportunities to utilize this data for improved care of these same critical care patients. A major inhibitor to this becoming reality is the lack of standards for the representation, transmission and storage of physiological data. HL7, for example, does not include definitions for time series data. Research into the use of these data will soon be reaching the clinical setting and the need for such standards to be defined is becoming urgent.

Nanoindentation Techniques: A Critical Assessment of the Current Methods of Data Analysis

Although instrumented indentation machines have existed since the 1950s, it is only during the past 20 years or so that there has been a very rapid rise in their use and popularity for determining mechanical properties of bulk solids, thin films and biological materials. Nowadays, machines are available with which indentation experiments are conducted automatically and the data obtained are also analyzed automatically using software, which is based on certain hypotheses and simplifying assumptions. By carrying out several series of comprehensive experiments, using rigid and elastic indenters and elastic substrates, it is shown that the current methods of nanoindentation data analysis are fundamentally flawed, which can lead to incorrect and misleading results. A new experimental approach has been suggested here, which is free from such flaws.

Abundances in the High-redshift Intergalactic Medium

The enrichment of the intergalactic medium (IGM) with heavy elements provides us with a record of past star formation and with an opportunity to study the interactions between galaxies and their environments. We summarize current data analysis methods and observational constraints on abundances in the diffuse, high-redshift ( z >2) IGM. This review is targeted at interested outsiders and attempts to answer the following questions: Why should you care? What do we want to measure? How do we do it? What do we know? What are the common misconceptions?

RealSpot: software validating results from DNA microarray data analysis with spot images

The spot images from DNA microarray highly affect the discovery of biological knowledge from gene expression data. However, results from quality analysis, normalization, differential expression, and cluster analysis are rarely validated with spot images in current data analysis methods or software packages. We designed RealSpot, a software package, to validate the results by directly associating spot quality and data with spot images in a spreadsheet table. RealSpot splits hybridization images into individual spots stored in a spreadsheet table. It subsequently associates microarray data with spot images and performs data validation through the standard table operation such as sorting, searching, and editing. RealSpot has several built-in functions to facilitate data validation, including spot quality analysis, data organization, one-way ANOVA, gene ontology association, verification, import, and export. We used RealSpot to evaluate 77 slides (30,000 features each) from real hybridization experiments and to validate results from each step of data analysis. It took approximately 10 min to validate results of spot quality after initial evaluation and correct approximately 0.3% of falsely assigned qualities of 10,000 spots. We validated 1,641 of 2,110 differentially expressed genes identified by SAM analysis in approximately 1/2 h by comparing each gene with its respective spot image. Furthermore, we found that 6 of 48 genes in one cluster from k-mean clustering method showed inconsistent trends of spot images. RealSpot is efficient for validating microarray results and thus helpful for improving the reliability of the whole microarray experiment for experimentalists.

Percent relative cumulative frequency analysis in indirect calorimetry: application to studies of transgenic mice

Indirect calorimetry is commonly used in research and clinical settings to assess characteristics of energy expenditure. Respiration chambers in indirect calorimetry allow measurements over long periods of time (e.g., hours to days) and thus the collection of large sets of data. Current methods of data analysis usually involve the extraction of only a selected small proportion of data, most commonly the data that reflects resting metabolic rate. Here, we describe a simple quantitative approach for the analysis of large data sets that is capable of detecting small differences in energy metabolism. We refer to it as the percent relative cumulative frequency (PRCF) approach and have applied it to the study of uncoupling protein-1 (UCP1) deficient and control mice. The approach involves sorting data in ascending order, calculating their cumulative frequency, and expressing the frequencies in the form of percentile curves. Results demonstrate the sensitivity of the PRCF approach for analyses of oxygen consumption (.VO2) as well as respiratory exchange ratio data. Statistical comparisons of PRCF curves are based on the 50th percentile values and curve slopes (H values). The application of the PRCF approach revealed that energy expenditure in UCP1-deficient mice housed and studied at room temperature (24 degrees C) is on average 10% lower (p < 0.0001) than in littermate controls. The gradual acclimation of mice to 12 degrees C caused a near-doubling of .VO2 in both UCP1-deficient and control mice. At this lower environmental temperature, there were no differences in .VO2 between groups. The latter is likely due to augmented shivering thermogenesis in UCP1-deficient mice compared with controls. With the increased availability of murine models of metabolic disease, indirect calorimetry is increasingly used, and the PRCF approach provides a novel and powerful means for data analysis.