Statistics for proteomics: a review of tools for analyzing experimental data

Most proteomics experiments make use of 'high throughput' technologies such as 2-DE, MS or protein arrays to measure simultaneously the expression levels of thousands of proteins. Such experiments yield large, high-dimensional data sets which usually reflect not only the biological but also technical and experimental factors. Statistical tools are essential for evaluating these data and preventing false conclusions. Here, an overview is given of some typical statistical tools for proteomics experiments. In particular, we present methods for data preprocessing (e.g. calibration, missing values estimation and outlier detection), comparison of protein expression in different groups (e.g. detection of differentially expressed proteins or classification of new observations) as well as the detection of dependencies between proteins (e.g. protein clusters or networks). We also discuss questions of sample size planning for some of these methods.     

Solid Organ Transplantation in Patients with Inflammatory Bowel Diseases (IBD): Analysis of Transplantation Outcome and IBD Activity in a Large Single Center Cohort

Background

Currently, limited data of the outcome of inflammatory bowel disease (IBD) in patients after solid organ transplantation (SOT) are available. We aimed to analyze effects of SOT on the IBD course in a large IBD patient cohort.

Methods

Clinical data from 1537 IBD patients were analyzed for patients who underwent SOT (n = 31) between July 2002 and May 2014. Sub-analyses included SOT outcome parameters, IBD activity before and after SOT, and efficacy of IBD treatment.

Results

4.74% of patients with ulcerative colitis (UC) and 0.84% of patients with Crohn’s disease (CD) underwent SOT (p = 2.69 x 10⁻⁶, UC vs. CD). 77.4% of patients with SOT underwent liver transplantation (LTx) with tacrolimus-based immunosuppressive therapy after SOT. All LTx were due to primary sclerosing cholangitis (PSC) or PSC overlap syndromes. Six patients (19.4%) required renal transplantation and one patient (3.2%) heart transplantation. A survival rate of 83.9% after a median follow-up period of 103 months was observed. Before SOT, 65.0% of patients were in clinical remission and 5 patients received immunosuppressive therapy (16.1%). After SOT, 61.0% of patients were in remission (p = 1.00 vs. before SOT) and 29.0% required IBD-specific immunosuppressive or anti-TNF therapy (p = 0.54 vs. before SOT). 42.9% of patients with worsening of IBD after SOT were at higher risk of needing steroid therapy for increased IBD activity (p = 0.03; relative risk (RR): 10.29; 95% CI 1.26–84.06). Four patients (13.0%) needed anti-TNF therapy after SOT (response rate 75%).

Conclusions

SOT was more common in UC patients due to the higher prevalence of PSC-related liver cirrhosis in UC. Despite mainly tacrolimus-based immunosuppressive regimens, outcome of SOT and IBD was excellent in this cohort. In this SOT cohort, concomitant immunosuppressive therapy due to IBD was well tolerated.     

Exact distribution of a pattern in a set of random sequences generated by a Markov source: applications to biological data

Background  

In bioinformatics it is common to search for a pattern of interest in a potentially large set of rather short sequences (upstream gene regions, proteins, exons, etc.). Although many methodological approaches allow practitioners to compute the distribution of a pattern count in a random sequence generated by a Markov source, no specific developments have taken into account the counting of occurrences in a set of independent sequences. We aim to address this problem by deriving efficient approaches and algorithms to perform these computations both for low and high complexity patterns in the framework of homogeneous or heterogeneous Markov models.     

Statistics of the mouse bone-marrow micronucleus test: counting, distribution and evaluation of results

The distribution of historical control results from male mice tested in the bone-marrow micronucleus test was used to optimize counting procedures and to develop decision rules for evaluating test results. The ratio of normochromatic to polychromatic erythrocytes followed a normal distribution, while the incidence of micronucleated polychromatic and normochromatic erythrocytes followed a binomial distribution. Recommendations for the number of cells to be scored per animal and for the evaluation of results are based on these distributions and the two-hypothesis multiple-decision approach of Selby and Olsen (1981).     

Sphagnophyta of Europe and Macaronesia: a checklist with distribution data

Abstract

We give the distribution of Sphagnum L. in Europe and Macaronesia, mainly at a country level. The world distribution is also given, though less detailed. We include notes on doubtful and erroneous reports and provide a list of taxa excluded from Europe and Macaronesia. We recognize 52 species from the area and additionally some subspecies and varieties. The country with the largest number of species is Norway while there are no records from nine areas.     

Eight (and a half) deadly sins of spatial analysis

Biogeography is spatial by nature. Over the past 20 years, the literature related to the analysis of spatially structured data has exploded, much of it focused on a perceived problem of spatial autocorrelation and ways to deal with it. However, there are a number of other issues that permeate the biogeographical and macroecological literature that have become entangled in the spatial autocorrelation web. In this piece I discuss some of the assumptions that are often made in the analysis of spatially structured data that can lead to misunderstandings about the nature of spatial data, the methods used to analyse them, and how results can be interpreted.     

Methods for the detection of multiple linked QTL applied to a mixture of full and half sib families

A new multiple trait strategy based on discriminant analysis was studied for efficient detection of linked QTL in outbred sib families, in comparison with a multivariate likelihood technique. The discriminant analysis technique describes the segregation of a linear combination of the traits in a univariate likelihood. This combination is calculated for each pair of positions depending on the inheritance of the pairs of QTL haplotypes in the progeny. The gains in power and accuracy for position estimations of multiple trait methods in grid searches were evaluated in reference to single trait detections of linked QTL. The methods were applied to simulated designs with two correlated traits submitted to various effects from the linked QTL. Multiple trait strategies were generally more powerful and accurate than the single trait technique. Linked QTL were distinguished when they were separated enough to identify informative recombinations: at least two genetic markers and 25 cM between the QTL under the simulated conditions. Except in a particular case, discriminant analysis was at least as powerful as the multivariate technique and its implementation was five times faster. Combining the advantages from both methodologies, we finally propose a complete strategy for rapid and efficient systematic multivariate detections in outbred populations.     

Raveniola niedermeyeri from Iran: redescription and new data on distribution (Araneae, Nemesiidae)

Raveniola niedermeyeri (Brignoli, 1972), a poorly known species, is rediagnosed and redescribed from the types and from recently collected material from northern and central regions of Iran. This species differs from its congeners in having the male embolus curved distally, as well as in the unique conformation of the spermathecae. New data on the distribution of Raveniola niedermeyeri in Iran are also provided.     

A tool for analyzing information from data storage tags: the continuous wavelet transform (CWT)

Electronic Data Storage Tags (DSTs) have the potential of providing long term, high-resolution observation data of individual fish in the ecosystem. However, traditional time series analysis methods are limited in extracting the required information from DSTs. The continuous wavelet transform (CWT) is a tool for decomposing a time series into instantaneous frequency versus time characteristics. This allows data signals, which are restricted to specific frequencies (localized frequencies), or frequency components, which are restricted to specific time intervals (time localized) to be extracted and displayed in a highly intuitive way. The aim of this short communication is to present the CWT as a tool for extracting information from DST data. In the first case, synthetically generated data is used to demonstrate how the methodology captures different time–frequency dependent patterns in the time series data. The relevance of the methodology to real field data is demonstrated by an analysis of DST depth data from tagged Barents Sea coastal cod. This work has also enjoyed partial funding from the EU-FP5-Q5RS200200813 CODYSSEY project.     

Less than eight (and a half) misconceptions of spatial analysis

Spatial analyses are indispensable analytical tools in biogeography and macroecology. In a recent Guest Editorial, Hawkins (Journal of Biogeography, 2012, 39 , 1–9) raised several issues related to spatial analyses. While we concur with some points, we here clarify those confounding (1) spatial trends and spatial autocorrelation, and (2) spatial autocorrelation in the response variable and in the residuals. We argue that recognizing spatial autocorrelation in statistical modelling is not only a crucial step in model diagnostics, but that disregarding it is essentially wrong.     

Power to detect linkage based on multiple sets of data in the presence of locus heterogeneity: comparative evaluation of model-based linkage methods for affected sib pair data

The development of rigorous methods for evaluating the overall strength of evidence for genetic linkage based on multiple sets of data is becoming increasingly important in connection with genomic screens for complex disorders. We consider here what happens when we attempt to increase power to detect linkage by pooling multiple independently collected sets of families under conditions of variable levels of locus heterogeneity across samples. We show that power can be substantially reduced in pooled samples when compared to the most informative constituent subsamples considered alone, in spite of the increased sample size afforded by pooling. We demonstrate that for affected sib pair data, a simple adaptation of the lod score (which we call the compound lod), which allows for intersample admixture differences can afford appreciably higher power than the ordinary heterogeneity lod; and also, that a statistic we have proposed elsewhere, the posterior probability of linkage, performs at least as well as the compound lod while having considerable computational advantages. The companion paper (this issue, pp 217-225) shows further that in application to multiple data sets, familiar model-free methods are in some sense equivalent to ordinary lod scores based on data pooling, and that they therefore will also suffer dramatic losses in power for pooled data in the presence of locus heterogeneity and other complicating factors.     

Diversity and distribution of Tubificidae, Naididae, and Lumbriculidae (Annelida: Oligochaeta) in the Netherlands: an evaluation of twenty years of monitoring data

Data from 24 water management districts and the rivers Rhine and Meuse in the Netherlands were used to study geographical distribution, relative occurrence, and environmental requirements of 76 aquatic oligochaetes (families Tubificidae, Naididae, and Lumbriculidae) (Annelida, Clitellata). Approximately 50% of the 76 species that occur in the Netherlands are uncommon, rare, or very rare. The other half of the species are common, very common or abundant. The abundant species are: Stylaria lacustris, Ophidonais serpentina, Limnodrilus claparedeianus, Limnodrilus hoffmeisteri, and Lumbriculus variegatus. With the exception of several brackish water species (those restricted in distribution to water management districts close to the sea that are influenced by salt water influx) and specific running water species (restricted mainly to the eastern part of the Netherlands), most of the species occurred throughout the whole Netherlands. The species distribution was related to environmental variables using ordination. In general, species distribution was correlated with either large waters with high chloride and phosphorus concentrations and a high hydrogen ion concentration (as pH), or with small forested (running) waters in more natural (undeveloped) areas that occasionally become intermittent. Vegetation cover was positively correlated with several swimming species in the family Naididae. While the distribution of aquatic oligochaetes in some families occurring in the Netherlands is known to some extent, the occurrence and distribution of rare and small taxa, particularly those that are difficult to identify taxonomically, is virtually unknown. Some of the rare oligochaete taxa, especially those associated with unique habitats, have received only cursory attention. Also in the data studied, the observations of the more rare species were too few to draw conclusions. To improve our knowledge of oligochaete distribution in the Netherlands, additional research should focus on rare species associated with special habitats and water types (natural areas) and those taxa in poorly known families. The standardisation of sampling and processing methodologies, particularly the use of nets and sieves with fine-meshed screening, will ensure the collection of the smaller species of oligochaetes. Subsequently, oligochaetes should be identified to species level by experienced taxonomists trained in oligochaete identification. Finally, many aquatic oligochaete species are identifiable only when sexually mature and therefore the time of year in which samples are collected is critical to the accurate representation of true oligochaete diversity at any given site. For analysing the relation between species and environmental variables the best option is to use composite data from spring and autumn.