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Statistics in physiology and pharmacology: a slow and erratic learning curve.

1. Learning how to apply statistical analyses to the results of experimental or clinical studies may take a lifetime of trial (and sometimes error), as it has done in the author's case. There is no evidence that biomedical investigators of the present generation are on a steeper learning curve. Gross misunderstandings of the purpose and functions of statistical analysis are apparent in applications to research grant-giving bodies and ethics committees, in manuscripts submitted to journals and sometimes in published papers. 2. Although estimation of minimal group (sample) size for a given power is an essential step in planning clinical studies, it seems to be used rarely in laboratory experimental work. This is despite exhortations to restrict the number of animals used to a minimum. 3. Most investigators use hypothesis testing to analyse their results, but their understanding of the meaning of the resultant P-values is slight. 4. A flaw found almost universally in biomedical manuscripts is to make multiple inferences from the results of a single study. The goal of statistical analysis is to maintain the familywise type I error rate (risk of false-positive inference) at a predetermined level (usually 5%). But, when multiple inferences are made from the same experiment, the risk of false-positive error is inflated. There are two solutions to this problem: (i) use a multiple comparison procedure to control the familywise type I error rate; and (ii) test a single, global hypothesis. 5. Biomedical investigators have been quick to acquire computer statistics software and to use it to analyse their experiments. However, they have been slow to recognize the limitations of this software. These include: (i) inadequate documentation of routines, so that neither the user nor the reader of published papers can be sure how the tests have been executed; (ii) flawed algorithms for the execution of statistical procedures; and (iii) failure to recognize that the best software for their purposes is that which takes them just beyond their statistical horizons. 6. The obvious solution to these difficulties is to recruit a biomedical statistician into every research group, at a relatively trivial cost. However, properly qualified biostatisticians are in desperately short supply in Australia. It follows that research groups, national grant-giving agencies and academic institutions must make provision for the proper training and subsequent employment of biostatisticians.

Data Interpretation, Statistical↗
PubMed1999

Mistaken inferences.

Explore the source record for details and available documents.

Data Interpretation, Statistical↗
PubMed2004

Statistical and visual morph movie analysis of crystallographic mutant selection bias in protein mutation resource data.

Structural studies of the effects of non-silent mutations on protein conformational change are an important key in deciphering the language that relates protein amino acid primary structure to tertiary structure. Elsewhere, we presented the Protein Mutant Resource (PMR) database, a set of online tools that systematically identified groups of related mutant structures in the Protein DataBank (PDB), accurately inferred mutant classifications in the Gene Ontology using an innovative, statistically rigorous data-mining algorithm with more general applicability, and illustrated the relationship of these mutant structures via an intuitive user interface. Here, we perform a comprehensive statistical analysis of the effect of PMR mutations on protein tertiary structure. We find that, although the PMR does contain spectacular examples of conformational change, in general there is a counter-intuitive inverse relationship between conformational change (measured as C-alpha displacement or RMS of the core structure) and the number of mutations in a structure. That is, point mutations by structural biologists present in the PDB contrast naturally evolved mutations. We compare the frequency of mutations in the PMR/PDB datasets against the accepted PAM250 natural amino acid mutation frequency to confirm these observations. We generated morph movies from PMR structure pairs using technology previously developed for the Macromolecular Motions Database (http://molmovdb.org), allowing bioinformaticians, geneticists, protein engineers, and rational drug designers to analyze visually the mechanisms of protein conformational change and distinguish between conformational change due to motions (e.g., ligand binding) and mutations. The PMR morph movies and statistics can be freely viewed from the PMR website, http://pmr.sdsc.edu.

Amino Acid Sequence↗
PubMed2004

Pediatric sleep disorders: exploratory modulation of their relationships.

STUDY OBJECTIVES: This study explores relationships between clinically encountered sleep problems in children. DESIGN: The Sleep Disturbance Scale for Children (SDSC) (Bruni et al 1996), which screens 26 sleep problems and results in 6 sleep disorder-subscales, was used as core in a larger health-behavior questionnaire (HBQ). PARTICIPANTS: Caregivers of 3045 6- to 12-year-olds filled out the HBQ. INTERVENTIONS: N/A. MEASUREMENTS AND RESULTS: 21 SDSC-items plus 15 new sleep items were selected. Via structural equation modelling we explored (1) relations within and between disorder-subscales, (2) relations between the disorder-subscales with 3 indexes added (i.e., sleep efficiency index, sleep environment index, and sleep enuresis) and (3) relations of the disorder-subscales to their categorization: dyssomnia and parasomnia, without and with the indexes. CONCLUSIONS: The final model had a satisfying fit and approximates the Association of Sleep Disorders Centers' classification. Each disorder-subscale can be applied individually. However, some items are statistically of low value. For that reason, the use as the inference of relations between such items should be cautious. Adding the 3 indexes improved the fit of the model. The complexity of 'sleep enuresis' was especially revealed. For the practicing care provider, as for researchers, the statistical model proposed could be a valuable directive in the diagnostic classification process of pediatric sleep problems.

Adolescent↗
PubMed2026

transfactor: transcription factor activity estimation via probabilistic gene expression deconvolution.

Gene expression is a primary modality being studied to differentiate between biological cells. Contemporary single-cell studies simultaneously measure genome-wide transcription levels for thousands of individual cells in a single experiment. While the characterization of cell population differences has often occurred through differential gene expression analysis, tiny effect sizes become statistically significant when thousands of cells are available for each population, compromising biological interpretation. Moreover, these large studies have spurred the development of methods to infer gene regulatory networks (GRNs) directly from the data, and GRN databases are becoming more comprehensive. In this work, we propose a statistical model for gene expression measures and an inference method that leverage GRNs to deconvolve transcription factor (TF) activity from gene expression, by probabilistically assigning mRNA molecules to TFs. This shifts the paradigm from investigating gene expression differences to regulatory differences at the level of TF activity, aiding interpretation and allowing prioritization of a limited number of TFs responsible for significant contributions to the observed gene expression differences. The inferred TF activities result in intuitive prioritization of TFs in terms of the (difference in) estimated number of molecules they produce, in contrast to other widely used methods relying on arbitrary enrichment scores. Our model allows the incorporation of prior information on the regulatory potential between each TF and target gene and is able to deal with both repressing and activating interactions. We compare our approach to other TF activity estimation methods using two simulation experiments and two case studies. Single-cell RNA-sequencing; TF activity; bioinformatics; GRN.

Transcription Factors↗
PubMed2006

A novel structure-based encoding for machine-learning applied to the inference of SH3 domain specificity.

MOTIVATION: Unravelling the rules underlying protein-protein and protein-ligand interactions is a crucial step in understanding cell machinery. Peptide recognition modules (PRMs) are globular protein domains which focus their binding targets on short protein sequences and play a key role in the frame of protein-protein interactions. High-throughput techniques permit the whole proteome scanning of each domain, but they are characterized by a high incidence of false positives. In this context, there is a pressing need for the development of in silico experiments to validate experimental results and of computational tools for the inference of domain-peptide interactions. RESULTS: We focused on the SH3 domain family and developed a machine-learning approach for inferring interaction specificity. SH3 domains are well-studied PRMs which typically bind proline-rich short sequences characterized by the PxxP consensus. The binding information is known to be held in the conformation of the domain surface and in the short sequence of the peptide. Our method relies on interaction data from high-throughput techniques and benefits from the integration of sequence and structure data of the interacting partners. Here, we propose a novel encoding technique aimed at representing binding information on the basis of the domain-peptide contact residues in complexes of known structure. Remarkably, the new encoding requires few variables to represent an interaction, thus avoiding the 'curse of dimension'. Our results display an accuracy >90% in detecting new binders of known SH3 domains, thus outperforming neural models on standard binary encodings, profile methods and recent statistical predictors. The method, moreover, shows a generalization capability, inferring specificity of unknown SH3 domains displaying some degree of similarity with the known data.

Algorithms↗
PubMed2003

Statistics in medical research.

The role of statistics in medical research starts at the planning stage of a clinical trial or laboratory experiment to establish the design and size of an experiment that will ensure a good prospect of detecting effects of clinical or scientific interest. Statistics is again used during the analysis of data (sample data) to make inferences valid in a wider population. In simple situations computation of simple quantities such as P-values, confidence intervals, standard deviations, standard errors or application of some standard parametric or nonparametric tests may suffice. Despite their wide use even these simple notions are sometimes misunderstood or misinterpreted by research workers in other disciplines who have only a limited knowledge of statistics. More sophisticated research projects often need advanced statistical methods including the formulation and testing of mathematical models to make relevant inferences from observed data. Such advanced methods should only be applied with a clear understanding both of their purposes and the implication of any conclusions based upon their use. Close collaboration between statisticians, whether professionals in that field or medical research workers with a sound statistical background, and other members of a research team is needed to ensure a seamless integration of the statistical elements into the reporting and discussion of research outcomes. Some suggestions are made as to how that collaboration is best achieved.

Biomedical Research↗
PubMed1989

Dinoflagellates in evolution. A molecular phylogenetic analysis of large subunit ribosomal RNA.

The sequence of the large subunit ribosomal RNA (LsuRNA) gene of the dinoflagellate Prorocentrum micans has been determined. The inferred rRNA sequence [3408 nucleotides (nt)] is presented in its most probable secondary structure based on compensatory mutations, energy, and conservation criteria. No introns have been found but a hidden break is present in the second variable domain, 690 nt from the 5' end, as judged by agarose gel electrophoresis and primer extension experiments. Prorocentrum micans LsuRNA length and G+C content are close to those of ciliates and yeast. The conserved portions of the molecule (1900 nt) have been aligned with corresponding sequences from various eukaryotes, including five protista, one metaphyta, and three metazoa. An extensive phylogenetic study was performed, comparing two phenetic methods (neighbor joining on difference matrix, and Fitch and Margoliash on Knuc values matrix) and one cladistic (parsimony). The three methods led to similar tree topologies, except for the emergence of yeast that groups with ciliates and dinoflagellates when phenetic methods are used, but emerges later in the most parsimonious tree. This discrepancy was checked by statistical analyses on reduced trees (limited to four species) inferred using parsimony and evolutionary parsimony methods. The data support the phenetic tree topologies and a close relationship between dinoflagellates, ciliates, and yeast.

Animals↗
PubMed1994

Regression analysis of censored and truncated data: estimating reporting-delay distributions and AIDS incidence from surveillance data.

AIDS surveillance provides a vital source of information for health departments to assess the AIDS epidemic and to plan for future health-care needs. However, the use of surveillance data requires proper adjustments for the underreporting of AIDS cases caused by the delay in reporting diagnosed AIDS cases to the surveillance system. The statistical problem of adjusting for this underreporting concerns making inferences about an unobservable random sample of which only a portion is observed in a chronologic time interval defined by the analysis. Most regression methods for making inferences using right-truncated data employ a reverse-time hazard function, which requires that the observed data be transformed so that methods for left-truncated data can be applied. In this paper, we discuss fitting regression models to data that can be truncated and even censored in arbitrary intervals. The proposed methodology was applied to the national AIDS surveillance data provided by the Centers for Disease Control to analyze the trend of delays over chronologic time and variation among different geographic regions as well as across risk groups.

Acquired Immunodeficiency Syndrome↗
PubMed2006

Approximate likelihood-ratio test for branches: A fast, accurate, and powerful alternative.

We revisit statistical tests for branches of evolutionary trees reconstructed upon molecular data. A new, fast, approximate likelihood-ratio test (aLRT) for branches is presented here as a competitive alternative to nonparametric bootstrap and Bayesian estimation of branch support. The aLRT is based on the idea of the conventional LRT, with the null hypothesis corresponding to the assumption that the inferred branch has length 0. We show that the LRT statistic is asymptotically distributed as a maximum of three random variables drawn from the chi(0)2 + chi(1)2 distribution. The new aLRT of interior branch uses this distribution for significance testing, but the test statistic is approximated in a slightly conservative but practical way as 2(l1- l2), i.e., double the difference between the maximum log-likelihood values corresponding to the best tree and the second best topological arrangement around the branch of interest. Such a test is fast because the log-likelihood value l2 is computed by optimizing only over the branch of interest and the four adjacent branches, whereas other parameters are fixed at their optimal values corresponding to the best ML tree. The performance of the new test was studied on simulated 4-, 12-, and 100-taxon data sets with sequences of different lengths. The aLRT is shown to be accurate, powerful, and robust to certain violations of model assumptions. The aLRT is implemented within the algorithm used by the recent fast maximum likelihood tree estimation program PHYML (Guindon and Gascuel, 2003).

Classification↗
PubMed2026

Differential cell signaling testing for cell-cell communication inference from single-cell data by dominoSignal.

MOTIVATION: Algorithms for ligand-receptor network inference have emerged as commonly used tools to estimate cell-cell communication from reference single-cell data. Many studies employ these algorithms to compare signaling between conditions and lack methods to statistically identify signals that are significantly different. We previously developed the cell communication inference algorithm Domino, which considers ligand and receptor gene expression in association with downstream transcription factor activity scoring. We developed the dominoSignal software to innovate upon Domino and extend its functionality to test statistically differential cellular signaling. RESULTS: This new functionality includes the compilation of active signals as linkages from multiple subjects in a single-cell data set and testing condition-dependent signaling linkage. The software is applicable for analysis of single-cell data sets with multiple subjects as biological replicates as well as with bootstrapped replicates from data sets with few or pooled subjects. We use simulation studies to benchmark the number of subjects in compared groups and cells within an annotated cell type sufficient to accurately identify differential linkages. We demonstrate the application of the Differential Cell Signaling Test (DCST) in the dominoSignal software to investigate consequences of cancer cell phenotypes and immunotherapy on cell-cell communication in tumor microenvironments. These applications in cancer studies demonstrate the ability of differential cell signaling analysis to infer changes to cell communication networks from therapeutic or experimental perturbations, which is broadly applicable across biological systems. AVAILABILITY: dominoSignal is available through Bioconductor at https://www.bioconductor.org/packages/release/bioc/html/dominoSignal.html.

Cell Communication↗
PubMed1997

Advances in statistical methods to map quantitative trait loci in outbred populations.

Statistical methods to map quantitative trait loci (QTL) in outbred populations are reviewed, extensions and applications to human and plant genetic data are indicated, and areas for further research are identified. Simple and computationally inexpensive methods include (multiple) linear regression of phenotype on marker genotypes and regression of squared phenotypic differences among relative pairs on estimated proportions of identity-by-descent at a locus. These methods are less suited for genetic parameter estimation in outbred populations but allow the determination of test statistic distributions via simulation or data permutation; however, further inferences including confidence intervals of QTL location require the use of Monte Carlo or bootstrap sampling techniques. A method which is intermediate in computational requirements is residual maximum likelihood (REML) with a covariance matrix of random QTL effects conditional on information from multiple linked markers. Testing for the number of QTLs on a chromosome is difficult in a classical framework. The computationally most demanding methods are maximum likelihood and Bayesian analysis, which take account of the distribution of multilocus marker-QTL genotypes on a pedigree and permit investigators to fit different models of variation at the QTL. The Bayesian analysis includes the number of QTLs on a chromosome as an unknown.

Bayes Theorem↗
PubMed2006

Using local gene expression similarities to discover regulatory binding site modules.

BACKGROUND: We present an approach designed to identify gene regulation patterns using sequence and expression data collected for Saccharomyces cerevisae. Our main goal is to relate the combinations of transcription factor binding sites (also referred to as binding site modules) identified in gene promoters to the expression of these genes. The novel aspects include local expression similarity clustering and an exact IF-THEN rule inference algorithm. We also provide a method of rule generalization to include genes with unknown expression profiles. RESULTS: We have implemented the proposed framework and tested it on publicly available datasets from yeast S. cerevisae. The testing procedure consists of thorough statistical analyses of the groups of genes matching the rules we infer from expression data against known sets of co-regulated genes. For this purpose we have used published ChIP-Chip data and Gene Ontology annotations. In order to make these tests more objective we compare our results with recently published similar studies. CONCLUSION: Results we obtain show that local expression similarity clustering greatly enhances overall quality of the derived rules, both in terms of enrichment of Gene Ontology functional annotation and coherence with ChIP-Chip binding data. Our approach thus provides reliable hypotheses on co-regulation that can be experimentally verified. An important feature of the method is its reliance only on widely accessible sequence and expression data. The same procedure can be easily applied to other microbial organisms.

Binding Sites↗
PubMed2006

Dependence among sites in RNA evolution.

Although probabilistic models of genotype (e.g., DNA sequence) evolution have been greatly elaborated, less attention has been paid to the effect of phenotype on the evolution of the genotype. Here we propose an evolutionary model and a Bayesian inference procedure that are aimed at filling this gap. In the model, RNA secondary structure links genotype and phenotype by treating the approximate free energy of a sequence folded into a secondary structure as a surrogate for fitness. The underlying idea is that a nucleotide substitution resulting in a more stable secondary structure should have a higher rate than a substitution that yields a less stable secondary structure. This free energy approach incorporates evolutionary dependencies among sequence positions beyond those that are reflected simply by jointly modeling change at paired positions in an RNA helix. Although there is not a formal requirement with this approach that secondary structure be known and nearly invariant over evolutionary time, computational considerations make these assumptions attractive and they have been adopted in a software program that permits statistical analysis of multiple homologous sequences that are related via a known phylogenetic tree topology. Analyses of 5S ribosomal RNA sequences are presented to illustrate and quantify the strong impact that RNA secondary structure has on substitution rates. Analyses on simulated sequences show that the new inference procedure has reasonable statistical properties. Potential applications of this procedure, including improved ancestral sequence inference and location of functionally interesting sites, are discussed.

Animals↗
PubMed2006

Statistical evaluation of biomarkers as surrogate endpoints: a literature review.

A valid surrogate endpoint allows correct inference to be drawn regarding the effect of an intervention on the unobserved true clinical endpoint of interest. The perceived practical and ethical advantages of substituting a surrogate endpoint for a clinical endpoint have led to a considerable number of statistical methods being proposed for the evaluation of a biomarker as a surrogate endpoint. We review the main statistical schools of thought which have developed and consider how the validation process might be arranged within the regulatory and practical constraints of the drug development process. We conclude by assessing which of the candidate statistical methods offer the best approach for surrogate endpoint evaluation.

Biomarkers↗
PubMed1995

[Use and presentation of statistics methods in original articles published in MEDICINA CLINICA in 1993].

BACKGROUND: In the last few years a marked increase has been observed in the use of statistical techniques in biomedical publications. Some characteristics of the statistical quality of the first 100 articles consecutively published in 1993 in the section of originals and surveys of the journal MEDICINA CLINICA are presented in this study. METHODS: In each original one reviewer identified errors and/or criticisms in statistical methodology. An adaptation of the protocol of statistical revision made by the journal The Lancet since november 1990 was used in addition to its own classification. Likewise, an error and/or criticism involving minimum statistical quality necessary was considered as major and that which only had a decrease in optimum statistical level was considered as minor. Statistical analysis consisted in descriptive tables of the number of originals in which each of the classified errors and/or criticisms were observed. RESULTS: Sixty-seven percent of the originals (CI 95%: 64;70) presented major errors and/or criticisms in design, analysis and inference. The most frequent were found in power and sample size (design), need for better analysis (analysis) and lack of confidence intervals (inference). Only 15% (CI 95%: 13;17) had major errors in statistical presentation and 82% (CI 95%: 80;84) minor errors among which the so-called orphaned p was of note. CONCLUSIONS: The need for accompanying the p values by the confidence intervals or taking the calculation of the required sample size into account are of note. Furthermore, the diffusion of explicit recommendations concerning the carrying out and presentation of statistical analysis is necessary.

Periodicals as Topic↗
PubMed2006

Computer inference of network of ecological interactions from sampling data.

Both direct and indirect ecological interactions may occur in an ecosystem with large numbers of taxa. Traditional food web technique is a popular tool to measure the quality and health of the environment. Much of works must be done before constructing a food web for an ecosystem especially with many taxa. This food web is generally specific for some ecological interactions and fixed for a set of given species. It is therefore not an effective method for dynamic and prompt assessment of environment. Ecological interactions and their interactive intensity may be detected by sampling biological taxa in the field and by detecting various between-taxa distances or similarities. Network may clearly exhibit the complex interactions among biological taxa. Statistic tests on various distance or similarity measures and computer designs are required to infer the {network. We develop an algorithm and software to infer the network of direct or indirect ecological interactions in ecosystem. It is a prompt and effective tool in monitoring and assessment of the environment. A redundant network may be inferred and drawn by computer based on the statistic tests on sampling data or the pathway information given in HTML file. Dominant taxa may be found in the network. In total of 16 distance and similarity measures, including Euclidean distance, Manhattan distance, Pearson correlation, partial correlation, point correlation, linkage coefficients, Jaccard coefficient etc., are provided to detect taxa pairs with significant parametric or nonparametric similarities, based on randomization tests and ordinary statistic tests. Criteria to use distance and similarity measures are discussed.

Algorithms↗