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Bioimage Analysis Tools
Bioimage Analysis Tools Kota Miura, Sébastien Tosi, Christoph Möhl, Chong Zhang, Perrine Paul-Gilloteaux, Ulrike Schulze, Simon Norrelykke, Christian Tischer, Thomas Pengo To cite this version: Kota Miura, Sébastien Tosi, Christoph Möhl, Chong Zhang, Perrine Paul-Gilloteaux, et al.. Bioimage Analysis Tools. Kota Miura. Bioimage Data Analysis, Wiley-VCH, 2016, 978-3-527-80092-6. hal- 02910986 HAL Id: hal-02910986 https://hal.archives-ouvertes.fr/hal-02910986 Submitted on 3 Aug 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. 2 Bioimage Analysis Tools 1 2 3 4 5 6 Kota Miura, Sébastien Tosi, Christoph Möhl, Chong Zhang, Perrine Pau/-Gilloteaux, - Ulrike Schulze,7 Simon F. Nerrelykke,8 Christian Tischer,9 and Thomas Penqo'" 1 European Molecular Biology Laboratory, Meyerhofstraße 1, 69117 Heidelberg, Germany National Institute of Basic Biology, Okazaki, 444-8585, Japan 2/nstitute for Research in Biomedicine ORB Barcelona), Advanced Digital Microscopy, Parc Científic de Barcelona, dBaldiri Reixac 1 O, 08028 Barcelona, Spain 3German Center of Neurodegenerative -
Datascientist Manual
DATASCIENTIST MANUAL . 2 « Approcherait le comportement de la réalité, celui qui aimerait s’epanouir dans l’holistique, l’intégratif et le multiniveaux, l’énactif, l’incarné et le situé, le probabiliste et le non linéaire, pris à la fois dans l’empirique, le théorique, le logique et le philosophique. » 4 (* = not yet mastered) THEORY OF La théorie des probabilités en mathématiques est l'étude des phénomènes caractérisés PROBABILITY par le hasard et l'incertitude. Elle consistue le socle des statistiques appliqué. Rubriques ↓ Back to top ↑_ Notations Formalisme de Kolmogorov Opération sur les ensembles Probabilités conditionnelles Espérences conditionnelles Densités & Fonctions de répartition Variables aleatoires Vecteurs aleatoires Lois de probabilités Convergences et théorèmes limites Divergences et dissimilarités entre les distributions Théorie générale de la mesure & Intégration ------------------------------------------------------------------------------------------------------------------------------------------ 6 Notations [pdf*] Formalisme de Kolmogorov Phé nomé né alé atoiré Expé riéncé alé atoiré L’univérs Ω Ré alisation éléméntairé (ω ∈ Ω) Evé némént Variablé aléatoiré → fonction dé l’univér Ω Opération sur les ensembles : Union / Intersection / Complémentaire …. Loi dé Augustus dé Morgan ? Independance & Probabilités conditionnelles (opération sur des ensembles) Espérences conditionnelles 8 Variables aleatoires (discret vs reel) … Vecteurs aleatoires : Multiplét dé variablés alé atoiré (discret vs reel) … Loi marginalés Loi -
A Graphical User Interface for R in a Rich Client Platform for Ecological Modeling
JSS Journal of Statistical Software June 2012, Volume 49, Issue 4. http://www.jstatsoft.org/ A Graphical User Interface for R in a Rich Client Platform for Ecological Modeling Marcel Austenfeld Wolfram Beyschlag University of Kiel University of Bielefeld Abstract For many ecological analyses powerful statistical tools are required for a profound analysis of spatial and time based data sets. In order to avoid many common errors of analysis and data acquisition a graphical user interface can help to focus on the task of the analysis and minimize the time to fulfill certain tasks in a programming language like R. In this paper we present a graphical user interface for R embedded in the ecological modeling software Bio7 which is based on an Eclipse rich client platform. We demonstrate that within the Bio7 platform R can not only be effectively combined with Java but also with the powerful components of Eclipse. Additionally we present some custom Bio7 components which interact with R and make use of some useful advanced concepts and libraries of this connection. Our overview on the Bio7 R interface also emphasizes a broad applicability for disciplines beyond ecological modelling. Keywords: rich client platform, ecological modeling, Rserve, R, Java. 1. Introduction In recent years the R platform (R Development Core Team 2012) became a very popular tool for statistical analysis in ecology and ecological modeling not only because of the fact that R is open source but also because of the huge possibilities this platform offers to edit, analyze and shape data for analysis (Petzoldt 2003; Ellner and Guckenheimer 2006; Petzoldt and Rinke 2007; Bolker 2008; Soetaert and Herman 2008; Stevens 2009). -
Imagej2: Imagej for the Next Generation of Scientific Image Data
Rueden et al. SOFTWARE ImageJ2: ImageJ for the next generation of scientific image data Curtis T Rueden1, Johannes Schindelin1,2, Mark C Hiner1, Barry E DeZonia1, Alison E Walter1,2, Ellen T Arena1,2 and Kevin W Eliceiri1,2* *Correspondence: [email protected] 1Laboratory for Optical and Abstract Computational Instrumentation, University of Wisconsin at Background: ImageJ is an image analysis program extensively used in the Madison, Madison, Wisconsin, biological sciences and beyond. Due to its ease of use, recordable macro USA 2Morgridge Institute for Research, language, and extensible plug-in architecture, ImageJ enjoys contributions from Madison, Wisconsin, USA non-programmers, amateur programmers, and professional developers alike. Full list of author information is Enabling such a diversity of contributors has resulted in a large community that available at the end of the article spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science. Results: We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called \ImageJ2" in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. -
Open Source Bioimage Informatics Tools for the Analysis of DNA Damage and Associated Biomarkers
Review Article Page 1 of 27 Open source bioimage informatics tools for the analysis of DNA damage and associated biomarkers Jens Schneider1#, Romano Weiss1#, Madeleine Ruhe1, Tobias Jung2, Dirk Roggenbuck1,3, Ralf Stohwasser1, Peter Schierack1, Stefan Rödiger1# 1Institute of Biotechnology, Brandenburg University of Technology Cottbus-Senftenberg, Senftenberg, Germany; 2Department of Molecular Toxicology (MTOX), German Institute of Human Nutrition (DIfE), 14558 Nuthetal, Germany; 3Medipan GmbH, Berlin/Dahlewitz, Germany Contributions: (I) Concept and design: J Schneider, S Rödiger; (II) Administrative support: J Schneider, S Rödiger; (III) Provision of study materials or patients: J Schneider, S Rödiger, R Weiss, R Stohwasser; (IV) Collection and assembly of data: J Schneider, S Rödiger, R Weiss; (V) Data analysis and interpretation: J Schneider, S Rödiger, R Weiss; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. #These authors contributed equally to this work. Correspondence to: Stefan Rödiger. BTU Cottbus-Senftenberg, Senftenberg, Germany. Email: [email protected]. Abstract: DNA double-strand breaks (DSBs) are critical cellular lesions that represent a high risk for genetic instability. DSBs initiate repair mechanisms by recruiting sensor proteins and mediators. DSB biomarkers, like phosphorylated histone 2AX (γH2AX) and p53-binding protein 1 (53BP1), are detectable by immunofluorescence as either foci or distinct patterns in the vicinity of DSBs. The correlation between the number of foci and the number of DSBs makes them a useful tool for quantification of DNA damage in precision medicine, forensics and cancer research. The quantification and characterization of foci ideally uses large cell numbers to achieve statistical validity. This requires software tools that are suitable for the analysis of large complex data sets, even for users with limited experience in digital image processing. -
Biomod2: Ensemble Platform for Species Distribution Modeling
Package ‘biomod2’ June 11, 2021 Type Package Title Ensemble Platform for Species Distribution Modeling Version 3.5.1 Date 2021-06-11 Author Wilfried Thuiller [aut, cre], Damien Georges [aut, cre], Maya Gueguen [aut], Robin Engler [aut], Frank Breiner [aut] Maintainer Damien Georges <[email protected]> Contact Wilfried Thuiller <[email protected]>, Damien Georges <[email protected]>, Maya Gueguen <[email protected]> BugReports https://r-forge.r-project.org/forum/forum.php?eforum_id=995&group_id=302 Description Functions for species distribution modeling, calibration and evaluation, ensemble of models, ensemble forecasting and visualization. The package permits to run consistently up to 10 single models on a presence/absences (resp presences/pseudo-absences) dataset and to combine them in ensemble models and ensemble projections. Some bench of other evaluation and visualisation tools are also available within the package. Depends R (>= 3.4.0) Imports stats, utils, lattice, sp, raster, parallel, reshape, ggplot2, abind, rasterVis, pROC (>= 1.15.0), nnet, gbm (>= 2.1.3), mda, randomForest, rpart, MASS, methods, PresenceAbsence, dismo, caret, ENMeval, doParallel, foreach, earth, dplyr, tidyr, checkmate, maxnet, purrr, rlang Suggests ade4, foreign, Hmisc, mgcv, gam, testthat, ecospat License GPL-3 RoxygenNote 7.1.1 1 2 R topics documented: Collate 'BIOMOD_ConvertOldRun.R' 'BIOMOD_EnsembleForecasting.R' 'BIOMOD_EnsembleModeling.R' 'BIOMOD_FormatingData.R' 'BIOMOD_LoadModels.R' 'BIOMOD_Modeling.R' -
An Introduction to Scientific Image Data Analysis Using R
An Introduction to Scientific Image Data Analysis using R M. Austenfeld • Introduction • First steps with the R environment and Language • Statistics and Plots in R • Data Transfer and Analysis with ImageJ and R • Useful Packages and Resources •R is a language and environment for statistical computing and graphics •OpenSource (GPL, GPL2) •R is an implementation of the S programming language combined with lexical scoping semantics inspired by Scheme •First binary 1993 (source avail. 1995) by Ross Ihaka and Robert Gentleman •First production release 2000 (1.0) •Current Release 2.15.2 Why Use R? •Open Source and available for all popular OS environments •Huge community •Well documented (Many specialized books) •Statistical routines not yet available in other packages •Can be extended with over 4000 Packages (at the moment)! •Packages can be written in different languages (C, C++, Java, Fortran) •Can be called from Java (libraries available) •Many different free GUI-Applications available •Many Plot types available Installation Windows: “base” installation MacOSX: pkg installation Linux: sudo yum install R-core.i386 or R-core.x86_64 (Fedora) sudo apt-get install r-base (Ubuntu) Start Linux: e.g. /usr/sbin/R (Shell) or: /usr/sbin/R -g TK & (with Window) Packages Linux: e.g.: /usr/lib/R/site-library Data Editor Matrices and Dataframes: edit(object) R Workspace The so-called workspace is the current R working environment. It contains all defined objects. This workspace can be saved or reloaded with the following commands: Save Workspace: save.image(„yourFile.RData") Save objects to a file: save(object list,file="myfile.RData") Load Workspace: load(„yourFile.RData") Basic Data Structures Vectors Values have the same data type (Character or numeric. -
NIH Image to Imagej: 25 Years of Image Analysis Caroline a Schneider, Wayne S Rasband & Kevin W Eliceiri
FOCUS ON BIOIMAGE INFORMATICS HISTORICAL COMMENTARY NIH Image to ImageJ: 25 years of image analysis Caroline A Schneider, Wayne S Rasband & Kevin W Eliceiri For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects. The last 50 years have seen tremendous Given the great success and impact Maryland graduate program that would technological advances, few greater of ImageJ, one would expect that this allow him to pursue his master’s degree than in the area of scientific computing. application was a software initiative with in computer science and thus leave the One of the fields in which scientific official backing and formal planning by a service early. One day in 1970, in the computing has made particular inroads central funding body. Despite its original commons at the University of Maryland, has been the area of biological imaging. name, NIH Image, and its home at the US College Park, he saw a notice for a part- The modern computer coupled to National Institutes of Health (NIH) for time programming position at the NIH in advances in microscopy technology is over 30 years in some form, ImageJ is a Bethesda, Maryland, USA, to work on the enabling previously inaccessible realms product of need, user-driven development laboratory instrument computer (LINC) in biology to be visualized. Although the and collaboration—rather than a specific created at the Massachusetts Institute roles of optical technologies and methods plan by the NIH to create it at the onset. -
Combining Species Distribution Modelling and Environmental Perceptions to Support Sustainable Strategies for Amazon-Nut (Bertholletia Excelsa Bonpl.) Planting and Conservation
University of São Paulo “Luiz de Queiroz” College of Agriculture Center of Nuclear Energy in Agriculture Combining species distribution modelling and environmental perceptions to support sustainable strategies for Amazon-nut (Bertholletia excelsa Bonpl.) planting and conservation Daiana Carolina Monteiro Tourne Thesis presented to obtain the degree of Doctor in Science. Area: Applied Ecology Piracicaba 2018 Daiana Carolina Monteiro Tourne Forest engineer Combining species distribution modelling and environmental perceptions to support sustainable strategies for Amazon-nut (Bertholletia excelsa Bonpl.) planting and conservation versão revisada de acordo com a resolução CoPGr 6018 de 2011 Advisor: Profa. Dra. MARIA VICTORIA RAMOS BALLESTER Thesis presented to obtain the degree of Doctor in Science. Area: Applied Ecology Piracicaba 2018 2 Dados Internacionais de Catalogação na Publicação DIVISÃO DE BIBLIOTECA – DIBD/ESALQ/USP Tourne, Daiana Carolina Monteiro Combining species distribution modelling and environmental perceptions to support sustainable strategies for Amazon-nut (Bertholletia excelsa Bonpl.) planting and conservation / Daiana Carolina Monteiro Tourne. -- versão revisada de acordo com a resolução CoPGr 6018 de 2011. - - Piracicaba, 2018. 102p. Tese (Doutorado) - - USP / Escola Superior de Agricultura “Luiz de Queiroz”/ Centro de Energia Nuclear na Agricultura. 1. Modelagem de habitat 2. Percepção ambiental 3. Espécie ameaçada 4. Conservação. 5. Amazônia I. Título 3 ACKNOWLEDGMENTS I would like to thanks my advisor Dr. Maria Victoria Ramos Ballester for her supporting and encouraging me to develop this research. I also thank my supervisor in the internship abroad, Dr. Patrick M. A. James for has sharing his knowledge and opportunity of work together. I am grateful to orientation committee’s members for their comments and suggestions for this research. -
A Methodology for Flexible Species Distribution Modelling Within an Open Source Framework
A methodology for flexible species distribution modelling within an Open Source framework Duncan Golicher∗and Luis Cayuela† August 29, 2007 Technical report presented to the third International workshop on Species Distri- bution Modelling financed by the MNP 20-24 August 2007: San Crist´obal de Las Casas, Chiapas, Mexico. Contents 1 Introduction 3 2 Installing the tools 4 2.1 Installing Linux . 4 2.2 Installing GRASS under Linux . 5 2.3 Installing R . 6 2.4 Installing R-packages . 7 2.5 Installing maxent and Google Earth . 7 3 Downloading and importing the climate data. 7 3.1 Importing Worldclim data . 7 3.2 Importing VCF data . 13 4 Species distribution modelling 14 4.1 Moving climate data from GRASS and visualizing the data . 14 4.2 Data reduction using PCA: Producing derived climate layers with R . 18 4.3 Exporting data to ASCII grid and running maxent . 25 4.4 Comparing maxent results with GAM and CART (rpart) in R . 30 4.5 Investigating the model: Relative importance of the predictor variables . 32 4.6 Combining species distribution modelling with multivariate analysis of species association with cli- mate: The CASP approach . 37 ∗El Colegio de La Frontera Sur, San Cristobal de Las Casas, Chiapas Mexico †Universidad de Alcala Henares, Madrid Spain 1 5 Modelling climate change scenarios 45 5.1 Extracting WMO GRIB formatted data . 45 5.2 Installing CDO . 46 5.3 Obtaining the data . 46 5.4 Extracting fifty year monthly means . 47 5.5 Exporting to GRASS and visualising the data . 49 5.6 Downscaling the data .