Open Source S/R Software - Page 4

Algoritmo divisivo de clustering con determinación automática de componentes.

This project contains the source code associated with the PLoS Computational Biology publication: "Differential Expression Analysis for Pathways". The paper text can be found here: http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002967

Living cells are complex, dynamic, self-regulatory, interactive systems, showing differential states across time and space. Complexity of cellular systems is highlighted with the multi-layered regulatory mechanisms involving the interactions between bio-molecules (such as DNA, RNA, mi-RNA and proteins). These interactions are analyzed in the form of static networks. Likewise, number of experimental techniques like microarray, RNASeq allows quantification of cellular dynamics and aid in discerning differential gene expression across diverse conditions. Computational biology is in need of methods for integration of static networks and gene expression data, since it provides interesting insights into the dynamics of biological systems. DEN is an R/Bioconductor based package designed to assemble different types of human bio-molecular interactions as a complete interactome and contains functions to extract dynamic active networks by integration of gene expression data.

The Dataverse Network is a Java EE5 app that enables researchers to share data on-line. It provides data citation standards, facilitates preservation, distribution and replication of data, and includes statistical analysis. More at: http://thedata.org

Use statistical methods to analyze time-course data (gene expression microarray and RNA-seq data in particular, but not limited to). Apply significance tests to filter out only significant genes or time series. Cluster time series into similar groups. Generate network models, including linear or non-linear models. Variable selection and optimization routines included. Written in Scala and R. The application is a cross-platform desktop app with a simple GUI and is fully functional currently. The app was and is developed at the University of Rochester (http://cbim.urmc.rochester.edu) under the GPL 3.0 license. This is a fork of that project.

DetectiV is an R package for the analysis of pathogen detection microarrays

DEW is a platform that allows users to explore RNA-Seq data. A web-based Graphical User Interface is included. The analysis proceeds as such: gapped alignments are performed and corrected for length, PCR and fragment bias so that a Fragment Per (effective) Kilobase per Million of reads (FPKM) is estimated as well as the simpler Reads Per Kb per Million of reads (RPKM). When provided with multiple isoforms and in the ‘contextual’ mode, corrections include a expectation maximization algorithm estimates effective expression profiles and a corrected alignment is produced. For each gene the user provides, DEW computes coverage descriptive statistics (RPKM, FKMP and total, mean and median corrected counts), expression profiles (normalized as R/FPKM and trimmed mean of fold change). DEW allows users to explore the data by providing interactive graphs.

We have moved, the project is now at http://distancesampling.org

Django-dataplot enables developers using the Django web framework to seamlessly integrate data-driven graphical plots into their web pages.

We developed a systematic algorithmic solution for quantitative drug sensitivity scoring (DSS), based on continuous modeling and integration of multiple dose-response relationships in high-throughput compound testing studies. License: The DSS R-package is made available under the terms of the GNU General Public License, which means that the source code is freely available for use within other software, but if you alter the code and distribute it, you must make the new source code freely available as well. This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY. In case you use the package in your work, we do appreciate a citation to a DSS publication. Citation: Yadav et al. (2014), Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Scientific Reports 4, 5193; DOI:10.1038/srep05193 Please visit the link below for details

DuffyRNAseq is an R package that implements an analysis pipeline for processing RNA-seq data from Illumina NextGen sequencers, to measure gene transcription and differential expression.

A Python library bringing together utilities I've written over the years for work in bioinformatics and biostatistics, which should be generally applicable outside these fields as well. Focuses on string processing, DBI, and math.

Enrichment analysis for customized organisms

EDDA is a tool for systematic assessment of the impact of experimental design and the statistical test used on the ability to detect differential abundance. EDDA can aid in the design of a range of common experiments such as RNA-seq, ChIP-seq, Nanostring assays, RIP-seq and Metagenomic sequencing, and enables researchers to comprehensively investigate the impact of experimental decisions on the ability to detect differential abundance. More details of EDDA can be found at Luo, Huaien et al. “The Importance of Study Design for Detecting Differentially Abundant Features in High-Throughput Experiments.” Genome Biology 2014;15(12):527 (http://www.ncbi.nlm.nih.gov/pubmed/25517037/). An accompanying web server (http://edda.gis.a-star.edu.sg/) is available for easy access to some functionality of EDDA. Additionally a Bioconductor package (http://www.bioconductor.org/packages/release/bioc/html/EDDA.html) is available for easy installation of EDDA R package.

My goal is a library for R (www.r-project.org) which encodes concepts relevant to statistical analysis in ecology in relatively easy scalable functions. Includes hierarchical scaling of predictors, density calculations, lme4 model sets, etc...

EigenMS is a normalization method implemented in R (and older version in Matlab) available as a set of two functions that should be used in a sequence. Please download EigenMS.zip file (latest version). Latest version uploaded in October 2017 has a bugfix for single treatment group normalization. Rescaling has been omitted from 2015. EigenMS utilizes SVD to detect bias trends in the data and eliminates them. EigenMS eliminates effects from known and unknown factors and can be utilized for any -omic platform. We have shown its utility in LC-MS/MS and metabolomics in the following two papers: 1) PMID: 19602524. "Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition". Karpievitch YV, Taverner T, Adkins JN, Callister SJ, Anderson GA, Smith RD, Dabney AR. Bioinformatics 2009 2) "Metabolomics data normalization with EigenMS" Karpievitch YV, Nikolic SB, Wilson R, Sharman JE, Edwards LM. PLoS One 2014

Set of tools for basic analysis of electrophysiological data. The Python classes show how to call OpenElectrophy functions and save data. The R library applies circular statistics to spike phase data and saves the best von Mises fit and the Rayleigh statistics on the disk. The wavelet coherence analysis is done in R by the package "sowas". Check the module R_coherence to see how we solved that problem. This packages may be useful for people who start using OpenElectrophy and circular statistics in R. If you find errors, please report them. The project is still in development, so in the future you'll get updates.

ElicitN uses R and a tcltk gui interface for eliciting a number from an appropriate expert. The code has been modified from it's original version designed specifically for eliciting the number of species on coral reefs from expert taxonomists. For more information please refer to: http://dx.doi.org/10.1016/j.envsoft.2011.11.011 Please also refer to GitHub for a even simpler more recently maintained version of the project: https://github.com/beckyfisher/ElicitN

EnvStats is an R package for environmental statistics. It is the open-source successor to the commercial module for S-Plus© called "EnvironmentalStats for S-Plus", which was first released in April, 1997. The EnvStats package, along with the R software environment, provides comprehensive and powerful software for environmental data analysis. EnvStats brings the major environmental statistical methods found in the literature and regulatory guidance documents into one statistical package, along with an extensive hypertext help system that explains what these methods do, how to use these methods, and where to find them in the environmental statistics literature. Also included are numerous built-in data sets from regulatory guidance documents and the environmental statistics literature. EnvStats combined with other R packages (e.g., for spatial analysis) provides the environmental scientist, statistician, researcher, and technician with tools to “get the job done!”

Ericscript is a computational framework to detect gene fusions from paired end RNA-seq data with high sensitity and specificity.

Excelsi-R is an Excel add-in that allows Excel spreadsheets to harness the power of the R statistical language by connecting to a running R process. R does not need to be installed on the computer running Excel, but it does require access to an R instance running Rserve (which can be on a remote computer). It supports incorporating R plots and graphs in an Excel workbook.

Open, extensible web-based collaborative platform for microarray gene expression, sequence and PPI data analysis, exposing distinct chainable components for clustering, pattern discovery, statistics (thru R), machine-learning algorithms and visualization

An R package that performs forward-backward fragment assembling segmentation. Reference: BMC Bioinformatics. 2007 May 3;8:145.

A library of R packages for use in fisheries modelling, including the evaluation of management strategies and stock assessment.