Comparison of the CPU and memory performance of StatPatternRecognition (SPR) and Toolkit for MultiVariate Analysis (TMVA)

High Energy Physics data sets are often characterized by a huge number of events. Therefore, it is extremely important to use statistical packages able to efficiently analyze these unprecedented amounts of data. We compare the performance of the statistical packages StatPatternRecognition (SPR) and Toolkit for MultiVariate Analysis (TMVA). We focus on how CPU time and memory usage of the learning process scale versus data set size. As classifiers, we consider Random Forests, Boosted Decision Trees and Neural Networks. For our tests, we employ a data set widely used in the machine learning community, "Threenorm" data set, as well as data tailored for testing various edge cases. For each data set, we constantly increase its size and check CPU time and memory needed to build the classifiers implemented in SPR and TMVA. We show that SPR is often significantly faster and consumes significantly less memory. For example, the SPR implementation of Random Forest is by an order of magnitude faster and consumes an order of magnitude less memory than TMVA on Threenorm data

clustermq enables efficient parallelization of genomic analyses

Motivation: High performance computing (HPC) clusters play a pivotal role in large-scale bioinformatics analysis and modeling. For the statistical computing language R, packages exist to enable a user to submit their analyses as jobs on HPC schedulers. However, these packages do not scale well to high numbers of tasks, and their processing overhead quickly becomes a prohibitive bottleneck.Results: Here we present clustermq, an R package that can process analyses up to three orders of magnitude faster than previously published alternatives. We show this for investigating genomic associations of drug sensitivity in cancer cell lines, but it can be applied to any kind of parallelizable workflow.</p

Improved testing inference in mixed linear models

Mixed linear models are commonly used in repeated measures studies. They account for the dependence amongst observations obtained from the same experimental unit. Oftentimes, the number of observations is small, and it is thus important to use inference strategies that incorporate small sample corrections. In this paper, we develop modified versions of the likelihood ratio test for fixed effects inference in mixed linear models. In particular, we derive a Bartlett correction to such a test and also to a test obtained from a modified profile likelihood function. Our results generalize those in Zucker et al. (Journal of the Royal Statistical Society B, 2000, 62, 827-838) by allowing the parameter of interest to be vector-valued. Additionally, our Bartlett corrections allow for random effects nonlinear covariance matrix structure. We report numerical evidence which shows that the proposed tests display superior finite sample behavior relative to the standard likelihood ratio test. An application is also presented and discussed.Comment: 17 pages, 1 figur

Genetic variation in the cellular response of Daphnia magna (Crustacea: Cladocera) to its bacterial parasite

Linking measures of immune function with infection, and ultimately, host and parasite fitness is a major goal in the field of ecological immunology. In this study, we tested for the presence and timing of a cellular immune response in the crustacean Daphnia magna following exposure to its sterilizing endoparasite Pasteuria ramosa. We found that D. magna possesses two cell types circulating in the haemolymph: a spherical one, which we call a granulocyte and an irregular-shaped amoeboid cell first described by Metchnikoff over 125 years ago. Daphnia magna mounts a strong cellular response (of the amoeboid cells) just a few hours after parasite exposure. We further tested for, and found, considerable genetic variation for the magnitude of this cellular response. These data fostered a heuristic model of resistance in this naturally coevolving host–parasite interaction. Specifically, the strongest cellular responses were found in the most susceptible hosts, indicating resistance is not always borne from a response that destroys invading parasites, but rather stems from mechanisms that prevent their initial entry. Thus, D. magna may have a two-stage defence—a genetically determined barrier to parasite establishment and a cellular response once establishment has begun

Parallel classification and feature selection in microarray data using SPRINT

The statistical language R is favoured by many biostatisticians for processing microarray data. In recent times, the quantity of data that can be obtained in experiments has risen significantly, making previously fast analyses time consuming or even not possible at all with the existing software infrastructure. High performance computing (HPC) systems offer a solution to these problems but at the expense of increased complexity for the end user. The Simple Parallel R Interface is a library for R that aims to reduce the complexity of using HPC systems by providing biostatisticians with drop‐in parallelised replacements of existing R functions. In this paper we describe parallel implementations of two popular techniques: exploratory clustering analyses using the random forest classifier and feature selection through identification of differentially expressed genes using the rank product method

A general class of zero-or-one inflated beta regression models

This paper proposes a general class of regression models for continuous proportions when the data contain zeros or ones. The proposed class of models assumes that the response variable has a mixed continuous-discrete distribution with probability mass at zero or one. The beta distribution is used to describe the continuous component of the model, since its density has a wide range of different shapes depending on the values of the two parameters that index the distribution. We use a suitable parameterization of the beta law in terms of its mean and a precision parameter. The parameters of the mixture distribution are modeled as functions of regression parameters. We provide inference, diagnostic, and model selection tools for this class of models. A practical application that employs real data is presented.Comment: 21 pages, 3 figures, 5 tables. Computational Statistics and Data Analysis, 17 October 2011, ISSN 0167-9473 (http://www.sciencedirect.com/science/article/pii/S0167947311003628

Multi-Level Characterization of Eggplant Accessions from Greek Islands and the Mainland Contributes to the Enhancement and Conservation of this Germplasm and Reveals a Large Diversity and Signatures of Differentiation between both Origins

[EN] Crop landraces are found in many inhabited islands of Greece. Due to the particularity of environment and isolation from the mainland, Greek islands represent a natural laboratory for comparing the diversity of landraces from the islands with those of the Greek mainland. A collection of 36 Greek eggplant landraces and traditional cultivars from the mainland and the islands has been phenotypically and genetically characterized using 22 morphological descriptors and 5 SSR markers. The mineral composition (K, Mg, Cu, Fe, Mn, Zn) of fruits was also determined. The objectives of this study include the multi-level characterization of eggplant local landraces and the comparison of diversity among accessions from the Greek mainland and the islands. Characterization of eggplant landraces will contribute to the enhancement and prevention of genetic erosion in this local group and will provide a resource for future investigation and breeding. PCA analysis of morphological traits explained 45.4% of the total variance revealing the formation of two clusters, one with most of the island accessions, and another with most of the mainland ones. The SSR markers used exhibited high average values for the number of alleles/locus (4.6), expected heterozygosity (0.60) and PIC (0.55), while the observed heterozygosity was low (0.13). Both STRUCTURE and PCoA analyses based on SSR data revealed two genetic clusters, one made up mainly by the mainland accessions, while the other one was mainly made up by the island accessions. Although there was considerable variation among the landraces for the concentration of minerals studied, only average Mg concentration was significantly different between mainland and island accessions. Based on our data, the Greek eggplant landraces present considerable morphological and genetic diversity with some differentiation signatures between the island and the mainland accessions. Our results have implications for conservation of Greek landraces and suggest that Greece might be considered as part of a secondary center of diversity for eggplant in the Mediterranean basin."PlantUP" (MIS 5002803) which is implemented under the Action "Reinforcement of the Research and Innovation Infrastructure", funded by the Operational Programme "Competitiveness, Entrepreneurship and Innovation" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund). Funding was also received from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No 677379 (G2P-SOL project: Linking genetic resources, genomes and phenotypes of Solanaceous crops), and from Ministerio de Ciencia, Innovacion y Universidades, Agencia Estatal de Investigacion and Fondo Europeo de Desarrollo Regional (grant RTI-2018-094592-B-100 from MCIU/AEI/FEDER, UE). Pietro Gramazio is grateful to Universitat Politecnica de Valencia and to Japan Society for the Promotion of Science for their respective postdoctoral grants (PAID-10-18 and FY2019 JSPS Postdoctoral Fellowship for Research in Japan [Standard]).Gramazio, P.; Chatziefstratiou, E.; Petropoulos, C.; Chioti, V.; Mylona, P.; Kapotis, G.; Vilanova Navarro, S.... (2019). Multi-Level Characterization of Eggplant Accessions from Greek Islands and the Mainland Contributes to the Enhancement and Conservation of this Germplasm and Reveals a Large Diversity and Signatures of Differentiation between both Origins. Agronomy. 9(12):1-20. https://doi.org/10.3390/agronomy9120887S120912Kougioumoutzis, K., Valli, A. T., Georgopoulou, E., Simaiakis, S. M., Triantis, K. A., & Trigas, P. (2016). Network biogeography of a complex island system: the Aegean Archipelago revisited. Journal of Biogeography, 44(3), 651-660. doi:10.1111/jbi.12920GRAHAM, N. R., GRUNER, D. S., LIM, J. Y., & GILLESPIE, R. G. (2017). 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Diversity and Relationships of Eggplants from Three Geographically Distant Secondary Centers of Diversity. PLoS ONE, 7(7), e41748. doi:10.1371/journal.pone.0041748Liu, J., Yang, Y., Zhou, X., Bao, S., & Zhuang, Y. (2018). Genetic diversity and population structure of worldwide eggplant (Solanum melongena L.) germplasm using SSR markers. Genetic Resources and Crop Evolution, 65(6), 1663-1670. doi:10.1007/s10722-018-0643-4Rodriguez-Jimenez, J., Amaya-Guerra, C., Baez-Gonzalez, J., Aguilera-Gonzalez, C., Urias-Orona, V., & Nino-Medina, G. (2018). Physicochemical, Functional, and Nutraceutical Properties of Eggplant Flours Obtained by Different Drying Methods. Molecules, 23(12), 3210. doi:10.3390/molecules23123210Raigón, M. D., Prohens, J., Muñoz-Falcón, J. E., & Nuez, F. (2008). Comparison of eggplant landraces and commercial varieties for fruit content of phenolics, minerals, dry matter and protein. Journal of Food Composition and Analysis, 21(5), 370-376. doi:10.1016/j.jfca.2008.03.006Arivalagan, M., Gangopadhyay, K. K., Kumar, G., Bhardwaj, R., Prasad, T. V., Sarkar, S. K., & Roy, A. (2012). Variability in mineral composition of Indian eggplant (Solanum melongena L.) genotypes. Journal of Food Composition and Analysis, 26(1-2), 173-176. doi:10.1016/j.jfca.2012.03.001Ranil, R. H. G., Niran, H. M. L., Plazas, M., Fonseka, R. M., Fonseka, H. H., Vilanova, S., … Prohens, J. (2015). Improving seed germination of the eggplant rootstock Solanum torvum by testing multiple factors using an orthogonal array design. Scientia Horticulturae, 193, 174-181. doi:10.1016/j.scienta.2015.07.030Van der Weerden, G. M., & Barendse, G. W. M. (2007). A WEB-BASED SEARCHABLE DATABASE DEVELOPED FOR THE EGGNET PROJECT AND APPLIED TO THE RADBOUD UNIVERSITY SOLANACEAE DATABASE. Acta Horticulturae, (745), 503-506. doi:10.17660/actahortic.2007.745.37Kaushik, P., Prohens, J., Vilanova, S., Gramazio, P., & Plazas, M. (2016). Phenotyping of Eggplant Wild Relatives and Interspecific Hybrids with Conventional and Phenomics Descriptors Provides Insight for Their Potential Utilization in Breeding. Frontiers in Plant Science, 7. doi:10.3389/fpls.2016.00677Kumar, G., Meena, B. L., Kar, R., Tiwari, S. K., Gangopadhyay, K. K., Bisht, I. S., & Mahajan, R. K. (2008). Morphological diversity in brinjal (Solanum melongena L.) germplasm accessions. Plant Genetic Resources, 6(3), 232-236. doi:10.1017/s1479262108994211Vilanova, S., Manzur, J. P., & Prohens, J. (2011). Development and characterization of genomic simple sequence repeat markers in eggplant and their application to the study of diversity and relationships in a collection of different cultivar types and origins. Molecular Breeding, 30(2), 647-660. doi:10.1007/s11032-011-9650-2Nunome, T., Negoro, S., Kono, I., Kanamori, H., Miyatake, K., Yamaguchi, H., … Fukuoka, H. (2009). Development of SSR markers derived from SSR-enriched genomic library of eggplant (Solanum melongena L.). Theoretical and Applied Genetics, 119(6), 1143-1153. doi:10.1007/s00122-009-1116-0Liu, K., & Muse, S. V. (2005). PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics, 21(9), 2128-2129. doi:10.1093/bioinformatics/bti282Peakall, R., & Smouse, P. E. (2012). GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research--an update. Bioinformatics, 28(19), 2537-2539. doi:10.1093/bioinformatics/bts460Nei, M. (1972). Genetic Distance between Populations. The American Naturalist, 106(949), 283-292. doi:10.1086/282771Wright, S. (1965). THE INTERPRETATION OF POPULATION STRUCTURE BY F-STATISTICS WITH SPECIAL REGARD TO SYSTEMS OF MATING. 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Nature Plants, 4(3), 138-147. doi:10.1038/s41477-018-0108-yMarconi, G., Ferradini, N., Russi, L., Concezzi, L., Veronesi, F., & Albertini, E. (2018). Genetic Characterization of the Apple Germplasm Collection in Central Italy: The Value of Local Varieties. Frontiers in Plant Science, 9. doi:10.3389/fpls.2018.01460Wang, L., Li, J., Zhao, J., & He, C. (2015). Evolutionary developmental genetics of fruit morphological variation within the Solanaceae. Frontiers in Plant Science, 6. doi:10.3389/fpls.2015.00248Prohens, J., Blanca, J. M., & Nuez, F. (2005). Morphological and Molecular Variation in a Collection of Eggplants from a Secondary Center of Diversity: Implications for Conservation and Breeding. Journal of the American Society for Horticultural Science, 130(1), 54-63. doi:10.21273/jashs.130.1.54BOYACI, H. F., TOPCU, V., TEPE, A., YILDIRIM, I. K., OTEN, M., & AKTAS, A. (2015). Morphological and Molecular Characterization and Relationships of Turkish Local Eggplant Heirlooms. 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The Robustness of Pathway Analysis in Identifying Potential Drug Targets in Non-Small Cell Lung Carcinoma

The identification of genes responsible for causing cancers from gene expression data has had varied success. Often the genes identified depend on the methods used for detecting expression patterns, or on the ways that the data had been normalized and filtered. The use of gene set enrichment analysis is one way to introduce biological information in order to improve the detection of differentially expressed genes and pathways. In this paper we show that the use of network models while still subject to the problems of normalization is a more robust method for detecting pathways that are differentially overrepresented in lung cancer data. Such differences may provide opportunities for novel therapeutics. In addition, we present evidence that non-small cell lung carcinoma is not a series of homogeneous diseases; rather that there is a heterogeny within the genotype which defies phenotype classification. This diversity helps to explain the lack of progress in developing therapies against non-small cell carcinoma and suggests that drug development may consider multiple pathways as treatment targets

Protective socks for people with diabetes: a systematic review and narrative analysis

Padded socks to protect the at-risk diabetic foot have been available for a number of years. However, the evidence base to support their use is not well known. We aimed to undertake a systematic review of padded socks for people with diabetes. Additionally, a narrative analysis of knitted stitch structures, yarn and fibres used together with the proposed benefits fibre properties may add to the sock. Assessment of the methodological quality was undertaken using a quality tool to assess non-randomised trials. From the 81 articles identified only seven met the inclusion criteria. The evidence to support to use of padded socks is limited. There is a suggestion these simple-to-use interventions could be of value, particularly in terms of plantar pressure reduction. However, the range of methods used and limited methodological quality limits direct comparison between studies. The socks were generally of a sophisticated design with complex use of knit patterns and yarn content. This systematic review provides limited support for the use of padded socks in the diabetic population to protect vulnerable feet. More high quality studies are needed; including qualitative components of sock wear and sock design, prospective randomized controlled trials and analysis of the cost-effectiveness of protective socks as a non-surgical intervention