New Packages: Bioconductor
http://bioconductor.org/
New Bioconductor Packages (devel branch)en-usrubyhttp://bioconductor.org/packages/netprioR/ netprioR A model for network-based prioritisation of genes2016-05-26T12:35:24.575050ZFabian SchmichA model for semi-supervised prioritisation of genes integrating network data, phenotypes and additional prior knowledge about TP and TN gene labels from the literature or experts.
<br/><a href="http://bioconductor.org/packages/netprioR/">link</a>http://bioconductor.org/packages/bioCancer/ bioCancer Interactive Multi-Omics Cancers Data Visualization and Analysis2016-05-20T08:54:13.329116ZKarim Mezhoud [aut, cre]bioCancer is a Shiny App to visualize and analyse interactively Multi-Assays of Cancer Genomic Data.
<br/><a href="http://bioconductor.org/packages/bioCancer/">link</a>http://bioconductor.org/packages/msPurity/ msPurity Performs assessments and predictions of MSMS precursor purity2016-05-20T08:54:13.329116ZThomas N. Lawson, Ralf Weber, Martin Jones, Mark Viant, Warwick DunnPerforms assessments and predictions of MS/MS precursor purity. Works for both LC-MS(/MS) and DI-MS(/MS) data. Also provides simple processing steps for DI-MS data
<br/><a href="http://bioconductor.org/packages/msPurity/">link</a>http://bioconductor.org/packages/covRNA/ covRNA Multivariate Analysis of Transcriptomic Data2016-05-18T22:39:18.758510ZLara Urban <lara.h.urban@ebi.ac.uk>This package provides the analysis methods fourthcorner and RLQ analysis for large-scale transcriptomic data.
<br/><a href="http://bioconductor.org/packages/covRNA/">link</a>http://bioconductor.org/packages/dSimer/ dSimer Integration of Disease Similarity Measures2016-05-18T22:39:18.758510ZPeng Ni <nipeng@csu.edu.cn>, Min Li <limin@mail.csu.edu.cn>dSimer is an R package which provides eight function-based methods for disease similarity calculation. These eight methods take advantage of diverse biological data which calculate disease similarity from different perspectives. The disease similarity matrix obtained from these eight methods can also be visualized by dSimer.
<br/><a href="http://bioconductor.org/packages/dSimer/">link</a>http://bioconductor.org/packages/Director/ Director A dynamic visualization tool of multi-level data2016-05-26T17:30:01.833045ZKatherine Icay [aut, cre]Director is an R package designed to streamline the visualization of molecular effects in regulatory cascades. It utilizes the R package htmltools and a modified Sankey plugin of the JavaScript library D3 to provide a fast and easy, browser-enabled solution to discovering potentially interesting downstream effects of regulatory and/or co-expressed molecules. The diagrams are robust, interactive, and packaged as highly-portable HTML files that eliminate the need for third-party software to view. This enables a straightforward approach for scientists to interpret the data produced, and bioinformatics developers an alternative means to present relevant data.
<br/><a href="http://bioconductor.org/packages/Director/">link</a>http://bioconductor.org/packages/epivizrStandalone/ epivizrStandalone Run Epiviz Interactive Genomic Data Visualization App within R2016-05-15T20:14:22.475999ZHector Corrada Bravo, Jayaram KancherlaThis package imports the epiviz visualization JavaScript app for genomic data interactive visualization. The 'epivizrServer' package is used to provide a web server running completely within R. This standalone version allows to browse arbitrary genomes through genome annotations provided by Bioconductor packages.
<br/><a href="http://bioconductor.org/packages/epivizrStandalone/">link</a>http://bioconductor.org/packages/EGAD/ EGAD Extending guilt by association by degree2016-05-15T20:14:22.475999ZSara Ballouz Developer [aut, cre], Melanie Weber Developer [aut, ctb], Paul Pavlidis Author [aut], Jesse Gillis Author [aut, ctb]The package implements a series of highly efficient tools to calculate functional properties of networks based on guilt by association methods.
<br/><a href="http://bioconductor.org/packages/EGAD/">link</a>http://bioconductor.org/packages/ImmuneSpaceR/ ImmuneSpaceR A Thin Wrapper around the ImmuneSpace Database2016-05-15T20:14:22.475999ZGreg Finak, Renan Sauteraud, Mike Jiang, Gil GudayProvides a convenient API for accessing data sets within ImmuneSpace (www.immunespace.org), the data repository and analysis platform of the Human Immunology Project Consortium (HIPC).
<br/><a href="http://bioconductor.org/packages/ImmuneSpaceR/">link</a>http://bioconductor.org/packages/MMDiff2/ MMDiff2 Statistical Testing for ChIP-Seq data sets2016-05-15T20:14:22.475999ZThis package detects statistically significant differences between read enrichment profiles in different ChIP-Seq samples. To take advantage of shape differences it uses Kernel methods (Maximum Mean Discrepancy, MMD).
<br/><a href="http://bioconductor.org/packages/MMDiff2/">link</a>http://bioconductor.org/packages/EGSEA/ EGSEA Ensemble of Gene Set Enrichment Analyses2016-05-27T06:15:25.008830ZMonther Alhamdoosh, Milica Ng and Matthew RitchieThis package implements the Ensemble of Gene Set Enrichment Analyses (EGSEA) method for gene set testing.
<br/><a href="http://bioconductor.org/packages/EGSEA/">link</a>http://bioconductor.org/packages/CHRONOS/ CHRONOS CHRONOS: A time-varying method for microRNA-mediated sub-pathway enrichment analysis2016-05-15T20:14:22.475999ZAristidis G. Vrahatis, Konstantina Dimitrakopoulou, Panos BalomenosA package used for efficient unraveling of the inherent dynamic properties of pathways. MicroRNA-mediated subpathway topologies are extracted and evaluated by exploiting the temporal transition and the fold change activity of the linked genes/microRNAs.
<br/><a href="http://bioconductor.org/packages/CHRONOS/">link</a>http://bioconductor.org/packages/diffloop/ diffloop Differential DNA loop calling from ChIA-PET data2016-05-15T20:14:22.475999ZCaleb Lareau [aut, cre], Martin Aryee [aut]A suite of tools for subsetting, visualizing, annotating, and statistically analyzing the results of one or more ChIA-PET experiments.
<br/><a href="http://bioconductor.org/packages/diffloop/">link</a>http://bioconductor.org/packages/epivizrData/ epivizrData Data Management API for epiviz interactive visualization app2016-05-15T20:14:22.475999ZHector Corrada Bravo [aut, cre], Florin Chelaru [aut]Serve data from Bioconductor Objects through a WebSocket connection.
<br/><a href="http://bioconductor.org/packages/epivizrData/">link</a>http://bioconductor.org/packages/epivizrServer/ epivizrServer WebSocket server infrastructure for epivizr apps and packages2016-05-15T20:14:22.475999ZHector Corrada Bravo [aut, cre]This package provides objects to manage WebSocket connections to epiviz apps. Other epivizr package use this infrastructure.
<br/><a href="http://bioconductor.org/packages/epivizrServer/">link</a>http://bioconductor.org/packages/Harman/ Harman The removal of batch effects from datasets using a PCA and constrained optimisation based technique2016-05-15T20:14:22.475999ZJosh Bowden [aut], Jason Ross [aut, cre], Yalchin Oytam [aut]Harman is a PCA and constrained optimisation based technique that maximises the removal of batch effects from datasets, with the constraint that the probability of overcorrection (i.e. removing genuine biological signal along with batch noise) is kept to a fraction which is set by the end-user.
<br/><a href="http://bioconductor.org/packages/Harman/">link</a>http://bioconductor.org/packages/MultiDataSet/ MultiDataSet Implementation of the BRGE's (Bioinformatic Research Group in Epidemiology from Center for Research in Environmental Epidemiology) MultiDataSet and MethylationSet2016-05-15T20:14:22.475999ZImplementation of the BRGE's (Bioinformatic Research Group in Epidemiology from Center for Research in Environmental Epidemiology) MultiDataSet and MethylationSet. MultiDataSet is designed for integrating multi omics data sets and MethylationSet to contain normalized methylation data. These package contains base classes for MEAL and rexposome packages.
<br/><a href="http://bioconductor.org/packages/MultiDataSet/">link</a>http://bioconductor.org/packages/PureCN/ PureCN Estimating tumor purity, ploidy, LOH, and SNV status using hybrid capture sequencing data2016-05-28T16:30:44.535980ZMarkus RiesterThis package estimates tumor purity, copy number, loss of heterozygosity (LOH), and status of short nucleotide variants (SNVs). PureCN is designed for hybrid capture sequencing data, integrates well with standard somatic variant detection pipelines, and has support for tumor samples without matching normal samples.
<br/><a href="http://bioconductor.org/packages/PureCN/">link</a>http://bioconductor.org/packages/ClusterSignificance/ ClusterSignificance Investigates Significance of Clusters by Reducing the Data to One Dimension to be Able to Easy Set a Score for the Separation, and a p-Value is then Calculated from Permutations of the Original Data2016-05-15T20:14:22.475999ZJason T. Serviss and Jesper R. GadinThe ClusterSignificance package provides tools to assess if clusters have a separation different from random or permuted data. ClusterSignificance investigates clusters of two or more groups by first, projecting all points onto a one dimensional line. Cluster separations are then scored and the probability of the seen separation being due to chance is evaluated using a permutation method.
<br/><a href="http://bioconductor.org/packages/ClusterSignificance/">link</a>http://bioconductor.org/packages/InteractionSet/ InteractionSet Base Classes for Storing Genomic Interaction Data2016-05-20T14:01:44.189883ZAaron Lun <alun@wehi.edu.au>, Malcolm Perry <mgperry32@gmail.com>, Liz Ing-Simmons <liz.ing-simmons12@csc.mrc.ac.uk>Provides the GInteractions, InteractionSet and ContactMatrix objects and associated methods for storing and manipulating genomic interaction data from Hi-C and ChIA-PET experiments.
<br/><a href="http://bioconductor.org/packages/InteractionSet/">link</a>http://bioconductor.org/packages/pbcmc/ pbcmc Permutation-Based Confidence for Molecular Classification2016-05-03T21:30:44.790267ZCristobal Fresno, German A. Gonzalez, Andrea S. Llera and Elmer A. FernandezThe pbcmc package characterizes uncertainty assessment on gene expression classifiers, a. k. a. molecular signatures, based on a permutation test. In order to achieve this goal, synthetic simulated subjects are obtained by permutations of gene labels. Then, each synthetic subject is tested against the corresponding subtype classifier to build the null distribution. Thus, classification confidence measurement can be provided for each subject, to assist physician therapy choice. At present, it is only available for PAM50 implementation in genefu package but it can easily be extend to other molecular signatures.
<br/><a href="http://bioconductor.org/packages/pbcmc/">link</a>http://bioconductor.org/packages/LymphoSeq/ LymphoSeq Analyze high-throughput sequencing of T and B cell receptors2016-05-15T20:14:22.475999ZDavid Coffey <dcoffey@fredhutch.org>This R package analyzes high-throughput sequencing of T and B cell receptor complementarity determining region 3 (CDR3) sequences generated by Adaptive Biotechnologies' ImmunoSEQ assay. Its input comes from tab-separated value (.tsv) files exported from the ImmunoSEQ analyzer.
<br/><a href="http://bioconductor.org/packages/LymphoSeq/">link</a>http://bioconductor.org/packages/genbankr/ genbankr Parsing GenBank files into semantically useful objects2016-05-20T16:54:53.258003ZReads Genbank files.
<br/><a href="http://bioconductor.org/packages/genbankr/">link</a>http://bioconductor.org/packages/BgeeDB/ BgeeDB Annotation and gene expression data from Bgee database2016-05-15T20:14:22.475999ZAndrea Komljenovic [aut, cre], Julien Roux [aut, cre]A package for the annotation and gene expression data download from Bgee database, and TopAnat analysis: GO-like enrichment of anatomical terms, mapped to genes by expression patterns.
<br/><a href="http://bioconductor.org/packages/BgeeDB/">link</a>http://bioconductor.org/packages/oppar/ oppar Outlier profile and pathway analysis in R2016-05-15T20:14:22.475999ZThe R implementation of mCOPA package published by Wang et al. (2012). Oppar provides methods for Cancer Outlier profile Analysis. Although initially developed to detect outlier genes in cancer studies, methods presented in oppar can be used for outlier profile analysis in general. In addition, tools are provided for gene set enrichment and pathway analysis.
<br/><a href="http://bioconductor.org/packages/oppar/">link</a>http://bioconductor.org/packages/pcaExplorer/ pcaExplorer Interactive Visualization of RNA-seq Data Using a Principal Components Approach2016-05-25T09:41:20.076104ZFederico Marini [aut, cre]This package provides functionality for interactive visualization of RNA-seq datasets based on Principal Components Analysis. The methods provided allow for quick information extraction and effective data exploration. A Shiny application encapsulates the whole analysis.
<br/><a href="http://bioconductor.org/packages/pcaExplorer/">link</a>http://bioconductor.org/packages/BatchQC/ BatchQC Batch Effects Quality Control Software2016-05-26T20:20:03.000487ZSolaiappan Manimaran <manimaran_1975@hotmail.com>, W. Evan Johnson <wej@bu.edu>, Heather Selby <selbyh@bu.edu>, Claire Ruberman <claireruberman@gmail.com>, Kwame Okrah <kwame.okrah@gmail.com>, Hector Corrada Bravo <hcorrada@gmail.com>Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. BatchQC is a software tool that streamlines batch preprocessing and evaluation by providing interactive diagnostics, visualizations, and statistical analyses to explore the extent to which batch variation impacts the data. BatchQC diagnostics help determine whether batch adjustment needs to be done, and how correction should be applied before proceeding with a downstream analysis. Moreover, BatchQC interactively applies multiple common batch effect approaches to the data, and the user can quickly see the benefits of each method. BatchQC is developed as a Shiny App. The output is organized into multiple tabs, and each tab features an important part of the batch effect analysis and visualization of the data. The BatchQC interface has the following analysis groups: Summary, Differential Expression, Median Correlations, Heatmaps, Circular Dendrogram, PCA Analysis, Shape, ComBat and SVA.
<br/><a href="http://bioconductor.org/packages/BatchQC/">link</a>http://bioconductor.org/packages/scran/ scran Methods for Single-Cell RNA-Seq Data Analysis2016-05-20T17:38:47.907356ZAaron Lun [aut, cre], Karsten Bach [aut], Jong Kyoung Kim [ctb], Antonio Scialdone [ctb]This package implements a variety of low-level analyses of single-cell RNA-seq data. Methods are provided for normalization of cell-specific biases, assignment of cell cycle phase, and detection of highly variable and significantly correlated genes.
<br/><a href="http://bioconductor.org/packages/scran/">link</a>http://bioconductor.org/packages/Glimma/ Glimma Interactive HTML graphics2016-05-26T05:05:15.247336ZShian Su, Matt RitchieThis package generates interactive visualisations for analysis of RNA-sequencing data using output from limma, edgeR or DESeq2 packages in an HTML page. The interactions are built on top of the popular static representations of analysis results in order to provide additional information.
<br/><a href="http://bioconductor.org/packages/Glimma/">link</a>http://bioconductor.org/packages/odseq/ odseq Outlier detection in multiple sequence alignments2016-05-15T20:14:22.475999ZJosé JiménezPerforms outlier detection of sequences in a multiple sequence alignment using bootstrap of predefined distance metrics. Outlier sequences can make downstream analyses unreliable or make the alignments less accurate while they are being constructed. This package implements the OD-seq algorithm proposed by Jehl et al (doi 10.1186/s12859-015-0702-1) for aligned sequences and a variant using string kernels for unaligned sequences.
<br/><a href="http://bioconductor.org/packages/odseq/">link</a>http://bioconductor.org/packages/CONFESS/ CONFESS Cell OrderiNg by FluorEScence Signal2016-05-15T20:14:22.475999ZDiana LOW and Efthimios MOTAKISSingle Cell Fluidigm Spot Detector.
<br/><a href="http://bioconductor.org/packages/CONFESS/">link</a>http://bioconductor.org/packages/Linnorm/ Linnorm Linear model and normality based transformation method (Linnorm)2016-05-15T20:14:22.475999ZShun Hang Yip <shunyip@bu.edu>, Panwen Wang <pwwang@pwwang.com>, Pak Chung Sham <pcsham@hku.hk>, Junwen Wang <junwen@uw.edu>Linnorm is an R package for the analysis of RNA-seq, scRNA-seq, ChIP-seq count data or any large scale count data. Its main function is to normalize and transform these datasets for parametric tests. Examples of parametric tests include using limma for differential expression analysis or differential peak detection, or calculating Pearson correlation coefficient for gene correlation study. Linnorm can work with raw count, CPM, RPKM, FPKM and TPM. Additionally, Linnorm provides the RnaXSim function for the simulation of RNA-seq raw counts for the evaluation of differential expression analysis methods. RnaXSim can simulate RNA-seq dataset in Gamma, Log Normal, Negative Binomial or Poisson distributions.
<br/><a href="http://bioconductor.org/packages/Linnorm/">link</a>http://bioconductor.org/packages/BadRegionFinder/ BadRegionFinder BadRegionFinder: an R/Bioconductor package for identifying regions with bad coverage2016-05-03T21:30:44.790267ZSarah SandmannBadRegionFinder is a package for identifying regions with a bad, acceptable and good coverage in sequence alignment data available as bam files. The whole genome may be considered as well as a set of target regions. Various visual and textual types of output are available.
<br/><a href="http://bioconductor.org/packages/BadRegionFinder/">link</a>http://bioconductor.org/packages/EBSEA/ EBSEA Exon Based Strategy for Expression Analysis of genes2016-05-03T21:30:44.790267ZArfa Mehmood, Asta Laiho, Laura L. EloCalculates differential expression of genes based on exon counts of genes obtained from RNA-seq sequencing data.
<br/><a href="http://bioconductor.org/packages/EBSEA/">link</a>http://bioconductor.org/packages/CINdex/ CINdex Chromosome Instability Index2016-05-15T20:14:22.475999ZLei Song, Krithika Bhuvaneshwar, Yue Wang, Yuanjian Feng, Ie-Ming Shih, Subha Madhavan, Yuriy GusevThe CINdex package addresses important area of high-throughput genomic analysis. It allows the automated processing and analysis of the experimental DNA copy number data generated by Affymetrix SNP 6.0 arrays or similar high throughput technologies. It calculates the chromosome instability (CIN) index that allows to quantitatively characterize genome-wide DNA copy number alterations as a measure of chromosomal instability. This package calculates not only overall genomic instability, but also instability in terms of copy number gains and losses separately at the chromosome and cytoband level.
<br/><a href="http://bioconductor.org/packages/CINdex/">link</a>http://bioconductor.org/packages/QUBIC/ QUBIC An R package for qualitative biclustering in support of gene co-expression analyses2016-05-15T20:14:22.475999ZYu Zhang [aut, cre], Qin Ma [aut]The core function of this R package is to provide the implementation of the well-cited and well-reviewed QUBIC algorithm, aiming to deliver an effective and efficient biclustering capability. This package also includes the following related functions: (i) a qualitative representation of the input gene expression data, through a well-designed discretization way considering the underlying data property, which can be directly used in other biclustering programs; (ii) visualization of identified biclusters using heatmap in support of overall expression pattern analysis; (iii) bicluster-based co-expression network elucidation and visualization, where different correlation coefficient scores between a pair of genes are provided; and (iv) a generalize output format of biclusters and corresponding network can be freely downloaded so that a user can easily do following comprehensive functional enrichment analysis (e.g. DAVID) and advanced network visualization (e.g. Cytoscape).
<br/><a href="http://bioconductor.org/packages/QUBIC/">link</a>http://bioconductor.org/packages/isomiRs/ isomiRs Analyze isomiRs and miRNAs from small RNA-seq2016-05-15T20:56:39.638441ZLorena Pantano, Georgia EscaramisCharacterization of miRNAs and isomiRs, clustering and differential expression.
<br/><a href="http://bioconductor.org/packages/isomiRs/">link</a>http://bioconductor.org/packages/GenoGAM/ GenoGAM A GAM based framework for analysis of ChIP-Seq data2016-05-15T20:14:22.475999ZGeorg Stricker [aut, cre], Alexander Engelhardt [aut], Julien Gagneur [aut]This package allows statistical analysis of genome-wide data with smooth functions using generalized additive models based on the implementation from the R-package 'mgcv'. It provides methods for the statistical analysis of ChIP-Seq data including inference of protein occupancy, and pointwise and region-wise differential analysis. Estimation of dispersion and smoothing parameters is performed by cross-validation. Scaling of generalized additive model fitting to whole chromosomes is achieved by parallelization over overlapping genomic intervals.
<br/><a href="http://bioconductor.org/packages/GenoGAM/">link</a>http://bioconductor.org/packages/MultiAssayExperiment/ MultiAssayExperiment Create Classes and Functions for Managing Multiple Assays on Sets of Samples2016-05-15T20:14:22.475999ZMultiAssay SIGDevelop an integrative environment where multiple assays are managed and preprocessed for genomic data analysis.
<br/><a href="http://bioconductor.org/packages/MultiAssayExperiment/">link</a>http://bioconductor.org/packages/sscu/ sscu Strength of Selected Codon Usage2016-05-15T20:14:22.475999ZYu SunThe package can calculate the selection in codon usage in bacteria species. First and most important, the package can calculate the strength of selected codon usage bias (sscu) based on Paul Sharp's method. The method take into account of background mutation rate, and focus only on codons with universal translational advantages in all bacterial species. Thus the sscu index is comparable among different species. In addition, detainled optimal codons (selected codons) information can be calculated by optimal_codons function, so the users will have a more accurate selective scheme for each codons. Furthermore, we added one more function optimal_index in the package. The function has similar mathematical formula as s index, but focus on the estimates the amount of GC-ending optimal codon for the highly expressed genes in the four and six codon boxes. The function takes into account of background mutation rate, and it is comparable with the s index. However, since the set of GC-ending optimal codons are likely to be different among different species, the index can not be compared among different species.
<br/><a href="http://bioconductor.org/packages/sscu/">link</a>http://bioconductor.org/packages/DEFormats/ DEFormats Differential gene expression data formats converter2016-05-15T20:14:22.475999ZAndrzej OleśCovert between different data formats used by differential gene expression analysis tools.
<br/><a href="http://bioconductor.org/packages/DEFormats/">link</a>http://bioconductor.org/packages/genphen/ genphen A tool for computing genotype-phenotype associations using statistical learning techniques2016-05-03T21:30:44.790267ZSimo KitanovskiGiven a set of genetic polymorphisms in the form of single nucleotide poylmorphisms or single amino acid polymorphisms and a corresponding phenotype data, often we are interested to quantify their association such that we can identify the causal polymorphisms. Using statistical learning techniques such as random forests and support vector machines, this tool provides the means to estimate genotype-phenotype associations. It also provides visualization functions which enable the user to visually inspect the results of such genetic association study and conveniently select the genotypes which have the highest strenght ofassociation with the phenotype.
<br/><a href="http://bioconductor.org/packages/genphen/">link</a>http://bioconductor.org/packages/recoup/ recoup An R package for the creation of complex genomic profile plots2016-05-15T20:14:22.475999ZPanagiotis Moulos <moulos@fleming.gr>recoup calculates and plots signal profiles created from short sequence reads derived from Next Generation Sequencing technologies. The profiles provided are either sumarized curve profiles or heatmap profiles. Currently, recoup supports genomic profile plots for reads derived from ChIP-Seq and RNA-Seq experiments. The package uses ggplot2 and ComplexHeatmap graphics facilities for curve and heatmap coverage profiles respectively.
<br/><a href="http://bioconductor.org/packages/recoup/">link</a>http://bioconductor.org/packages/AneuFinder/ AneuFinder Analysis of Copy Number Variation in Single-Cell-Sequencing Data2016-05-15T20:14:22.475999ZAaron Taudt, Bjorn Bakker, David PorubskyThis package implements functions for CNV calling, plotting, export and analysis from whole-genome single cell sequencing data.
<br/><a href="http://bioconductor.org/packages/AneuFinder/">link</a>http://bioconductor.org/packages/OncoScore/ OncoScore A tool to identify potentially oncogenic genes2016-05-03T21:30:44.790267ZDaniele Ramazzotti [aut, cre], Luca De Sano [aut], Roberta Spinelli [ctb], Carlo Gambacorti Passerini [ctb], Rocco Piazza [ctb]OncoScore is a tool to measure the association of genes to cancer based on citation frequency in biomedical literature. The score is evaluated from PubMed literature by dynamically updatable web queries.
<br/><a href="http://bioconductor.org/packages/OncoScore/">link</a>http://bioconductor.org/packages/CountClust/ CountClust Clustering and Visualizing RNA-Seq Expression Data using Grade of Membership Models2016-05-15T20:14:22.475999ZKushal Dey [aut, cre], Joyce Hsiao [aut], Matthew Stephens [aut]Fits grade of membership models (GoM, also known as admixture models) to cluster RNA-seq gene expression count data, identifies characteristic genes driving cluster memberships, and provides a visual summary of the cluster memberships.
<br/><a href="http://bioconductor.org/packages/CountClust/">link</a>http://bioconductor.org/packages/ISoLDE/ ISoLDE Integrative Statistics of alleLe Dependent Expression2016-05-15T20:14:22.475999ZChristelle Reynès [aut, cre], Marine Rohmer [aut], Guilhem Kister [aut]This package provides ISoLDE a new method for identifying imprinted genes. This method is dedicated to data arising from RNA sequencing technologies. The ISoLDE package implements original statistical methodology described in the publication below.
<br/><a href="http://bioconductor.org/packages/ISoLDE/">link</a>http://bioconductor.org/packages/GMRP/ GMRP GWAS-based Mendelian Randomization and Path Analyses2016-05-03T21:30:44.790267ZYuan-De Tan and Dajiang LiuPerform Mendelian randomization analysis of multiple SNPs to determine risk factors causing disease of study and to exclude confounding variabels and perform path analysis to construct path of risk factors to the disease.
<br/><a href="http://bioconductor.org/packages/GMRP/">link</a>http://bioconductor.org/packages/debrowser/ debrowser debrowser: Interactive Differential Expresion Analysis Browser2016-05-15T20:14:22.475999ZAlper Kucukural <alper.kucukural@umassmed.edu>, Nicholas Merowsky <nicholas.merowsky@umassmed.edu>, Manuel Garber <manuel.garber@umassmed.edu>Bioinformatics platform containing interactive plots and tables for differential gene and region expression studies. Allows visualizing expression data much more deeply in an interactive and faster way. By changing the parameters, user can easily discover different parts of the data that like never have been done before. Manually creating and looking these plots takes time. With this system users can prepare plots without writing any code. Differential expression, PCA and clustering analysis are made on site and the results are shown in various plots such as scatter, bar, box, volcano, ma plots and Heatmaps.
<br/><a href="http://bioconductor.org/packages/debrowser/">link</a>http://bioconductor.org/packages/DRIMSeq/ DRIMSeq Differential splicing and sQTL analyses with Dirichlet-multinomial model in RNA-Seq2016-05-15T20:14:22.475999ZThe package provides two frameworks. One for the differential splicing analysis between different conditions and one for the sQTL analysis. Both are based on modeling the counts of genomic features (i.e., transcripts, exons or exonic bins) with Dirichlet-multinomial distribution. The package also makes available functions for visualization and exploration of the data and results.
<br/><a href="http://bioconductor.org/packages/DRIMSeq/">link</a>http://bioconductor.org/packages/SpidermiR/ SpidermiR SpidermiR: An R/Bioconductor package for integrative network analysis with miRNA data2016-05-18T10:17:50.549285ZClaudia Cava, Antonio Colaprico, Alex Graudenzi, Gloria Bertoli, Tiago C. Silva, Catharina Olsen, Houtan Noushmehr, Gianluca Bontempi, Giancarlo Mauri, Isabella CastiglioniThe aims of SpidermiR are : i) facilitate the network open-access data retrieval from GeneMania data, ii) prepare the data using the appropriate gene nomenclature, iii) integration of miRNA data in a specific network, iv) provide different standard analyses and v) allow the user to visualize the results. In more detail, the package provides multiple methods for query, prepare and download network data (GeneMania), and the integration with validated and predicted miRNA data (mirWalk, miR2Disease,miRTar, miRandola,Pharmaco-miR,DIANA, Miranda, PicTar and TargetScan) and the use of standard analysis (igraph) and visualization methods (networkD3).
<br/><a href="http://bioconductor.org/packages/SpidermiR/">link</a>http://bioconductor.org/packages/DNAshapeR/ DNAshapeR High-throughput prediction of DNA shape features2016-05-15T20:14:22.475999ZTsu-Pei Chiu and Federico ComoglioDNAhapeR is an R/BioConductor package for ultra-fast, high-throughput predictions of DNA shape features. The package allows to predict, visualize and encode DNA shape features for statistical learning.
<br/><a href="http://bioconductor.org/packages/DNAshapeR/">link</a>http://bioconductor.org/packages/SwathXtend/ SwathXtend SWATH extended library generation and satistical data analysis2016-05-03T21:30:44.790267ZJ WU and D PascoviciIt contains utility functions for integrating spectral libraries for SWATH and statistical data analysis for SWATH generated data.
<br/><a href="http://bioconductor.org/packages/SwathXtend/">link</a>http://bioconductor.org/packages/bacon/ bacon Controlling bias and inflation in association studies using the empirical null distribution2016-05-27T07:52:49.457860ZMaarten van Iterson [aut, cre], Erik van Zwet [ctb]Bacon can be used to remove inflation and bias often observed in epigenome- and transcriptome-wide association studies. To this end bacon constructs an empirical null distribution using a Gibbs Sampling algorithm by fitting a three-component normal mixture on z-scores.
<br/><a href="http://bioconductor.org/packages/bacon/">link</a>http://bioconductor.org/packages/PCAN/ PCAN Phenotype Consensus ANalysis (PCAN)2016-05-15T20:14:22.475999ZMatthew Page and Patrice GodardPhenotypes comparison based on a pathway consensus approach. Assess the relationship between candidate genes and a set of phenotypes based on additional genes related to the candidate (e.g. Pathways or network neighbors).
<br/><a href="http://bioconductor.org/packages/PCAN/">link</a>http://bioconductor.org/packages/doppelgangR/ doppelgangR Identify likely duplicate samples from genomic or meta-data2016-05-15T20:14:22.475999ZLevi Waldron, Markus Riester, Marcel RamosThe main function is doppelgangR(), which takes as minimal input a list of ExpressionSet object, and searches all list pairs for duplicated samples. The search is based on the genomic data (exprs(eset)), phenotype/clinical data (pData(eset)), and "smoking guns" - supposedly unique identifiers found in pData(eset).
<br/><a href="http://bioconductor.org/packages/doppelgangR/">link</a>http://bioconductor.org/packages/psygenet2r/ psygenet2r psygenet2r - An R package for querying PsyGeNET and to perform comorbidity studies in psychiatric disorders2016-05-04T09:34:50.832565ZAlba Gutierrez-Sacristan [aut], Carles Hernandez-Ferrer [cre]Package to retrieve data from PsyGeNET database (www.psygenet.org) and to perform comorbidity studies with PsyGeNET's and user's data.
<br/><a href="http://bioconductor.org/packages/psygenet2r/">link</a>http://bioconductor.org/packages/miRNAmeConverter/ miRNAmeConverter Convert miRNA Names to Different miRBase Versions2016-05-15T20:14:22.475999ZStefan Haunsberger [aut, cre]Package containing an S4 class for translating mature miRNA names to different miRBase versions, checking names for validity and detecting miRBase version of a given set of names (data from http://www.mirbase.org/).
<br/><a href="http://bioconductor.org/packages/miRNAmeConverter/">link</a>http://bioconductor.org/packages/nucleoSim/ nucleoSim Generate synthetic nucleosome maps2016-05-15T20:14:22.475999ZRawane Samb [aut], Astrid Deschênes [cre, aut], Pascal Belleau [aut], Arnaud Droit [aut]This package can generate a synthetic map with reads covering the nucleosome regions as well as a synthetic map with forward and reverse reads emulating next-generation sequencing. The user has choice between three different distributions for the read positioning: Normal, Student and Uniform.
<br/><a href="http://bioconductor.org/packages/nucleoSim/">link</a>http://bioconductor.org/packages/Uniquorn/ Uniquorn Identification of cancer cell lines based on their weighted mutational/ variational fingerprint2016-05-27T14:01:30.126091ZRaik OttoThis packages enables users to identify cancer cell lines. Cancer cell line misidentification and cross-contamination reprents a significant challenge for cancer researchers. The identification is vital and in the frame of this package based on the locations/ loci of somatic and germline mutations/ variations. The input format is vcf/ vcf.gz and the files have to contain a single cancer cell line sample (i.e. a single member/genotype/gt column in the vcf file). The implemented method is optimized for the Next-generation whole exome and whole genome DNA-sequencing technology.
<br/><a href="http://bioconductor.org/packages/Uniquorn/">link</a>http://bioconductor.org/packages/biosigner/ biosigner Signature discovery from omics data2016-05-03T21:30:44.790267ZPhilippe Rinaudo <phd.rinaudo@gmail.com>, Etienne Thevenot <etienne.thevenot@cea.fr>Feature selection is critical in omics data analysis to extract restricted and meaningful molecular signatures from complex and high-dimension data, and to build robust classifiers. This package implements a new method to assess the relevance of the variables for the prediction performances of the classifier. The approach can be run in parallel with the PLS-DA, Random Forest, and SVM binary classifiers. The signatures and the corresponding 'restricted' models are returned, enabling future predictions on new datasets. A Galaxy implementation of the package is available within the Workflow4metabolomics.org online infrastructure for computational metabolomics.
<br/><a href="http://bioconductor.org/packages/biosigner/">link</a>http://bioconductor.org/packages/MethPed/ MethPed A DNA methylation classifier tool for the identification of pediatric brain tumor subtypes2016-05-15T20:14:22.475999ZMohammad Tanvir Ahamed [aut, trl], Anna Danielsson [aut], Szilárd Nemes [aut, trl], Helena Carén [aut, cre, cph]Classification of pediatric tumors into biologically defined subtypes is challenging and multifaceted approaches are needed. For this aim, we developed a diagnostic classifier based on DNA methylation profiles. We offer MethPed as an easy-to-use toolbox that allows researchers and clinical diagnosticians to test single samples as well as large cohorts for subclass prediction of pediatric brain tumors. The current version of MethPed can classify the following tumor diagnoses/subgroups: Diffuse Intrinsic Pontine Glioma (DIPG), Ependymoma, Embryonal tumors with multilayered rosettes (ETMR), Glioblastoma (GBM), Medulloblastoma (MB) - Group 3 (MB_Gr3), Group 4 (MB_Gr3), Group WNT (MB_WNT), Group SHH (MB_SHH) and Pilocytic Astrocytoma (PiloAstro).
<br/><a href="http://bioconductor.org/packages/MethPed/">link</a>http://bioconductor.org/packages/HDF5Array/ HDF5Array An array-like container for convenient access and manipulation of HDF5 datasets2016-05-27T00:28:52.948642ZHervé PagèsThis package implements the HDF5Array class for convenient access and manipulation of HDF5 datasets. In order to reduce memory usage and optimize performance, operations on an HDF5Array object are either delayed or executed using a block processing mechanism. The delaying and block processing mechanisms are independent of the on-disk backend and implemented via the DelayedArray class. They even work on ordinary arrays where they can sometimes improve performance.
<br/><a href="http://bioconductor.org/packages/HDF5Array/">link</a>http://bioconductor.org/packages/ExperimentHubData/ ExperimentHubData Add resources to ExperimentHub2016-05-26T21:35:02.774983ZFunctions to add metadata to ExperimentHub db and resource files to AWS S3 buckets.
<br/><a href="http://bioconductor.org/packages/ExperimentHubData/">link</a>http://bioconductor.org/packages/ExperimentHub/ ExperimentHub Client to access ExperimentHub resources2016-05-15T20:14:22.475999ZBioconductor Package Maintainer <maintainer@bioconductor.org>This package provides a client for the Bioconductor ExperimentHub web resource. ExperimentHub provides a central location where curated data from experiments, publications or training courses can be accessed. Each resource has associated metadata, tags and date of modification. The client creates and manages a local cache of files retrieved enabling quick and reproducible access.
<br/><a href="http://bioconductor.org/packages/ExperimentHub/">link</a>http://bioconductor.org/packages/MBttest/ MBttest Multiple Beta t-Tests2016-05-03T21:30:44.790267ZYuan-De TanMBttest method was developed from beta t-test method of Baggerly et al(2003). Compared to baySeq (Hard castle and Kelly 2010), DESeq (Anders and Huber 2010) and exact test (Robinson and Smyth 2007, 2008) and the GLM of McCarthy et al(2012), MBttest is of high work efficiency,that is, it has high power, high conservativeness of FDR estimation and high stability. MBttest is suit- able to transcriptomic data, tag data, SAGE data (count data) from small samples or a few replicate libraries. It can be used to identify genes, mRNA isoforms or tags differentially expressed between two conditions.
<br/><a href="http://bioconductor.org/packages/MBttest/">link</a>http://bioconductor.org/packages/dada2/ dada2 Accurate, high-resolution sample inference from amplicon sequencing data2016-05-20T20:49:21.449279ZBenjamin Callahan <benjamin.j.callahan@gmail.com>, Paul McMurdie, Susan HolmesThe dada2 package provides "OTU picking" functionality, but instead of picking OTUs the DADA2 algorithm exactly infers samples sequences. The dada2 pipeline starts from demultiplexed fastq files, and outputs inferred sample sequences and associated abundances after removing substitution and chimeric errors. Taxonomic classification is also available via a native implementation of the RDP classifier method.
<br/><a href="http://bioconductor.org/packages/dada2/">link</a>http://bioconductor.org/packages/GenRank/ GenRank Candidate gene prioritization based on convergent evidence2016-05-15T20:14:22.475999ZChakravarthi KanduriMethods for ranking genes based on convergent evidence obtained from multiple independent evidence layers. This package adapts three methods that are popular for meta-analysis.
<br/><a href="http://bioconductor.org/packages/GenRank/">link</a>http://bioconductor.org/packages/garfield/ garfield GWAS Analysis of Regulatory or Functional Information Enrichment with LD correction2016-05-15T20:14:22.475999ZSandro Morganella <sm22@sanger.ac.uk>GARFIELD is a non-parametric functional enrichment analysis approach described in the paper GARFIELD: GWAS analysis of regulatory or functional information enrichment with LD correction. Briefly, it is a method that leverages GWAS findings with regulatory or functional annotations (primarily from ENCODE and Roadmap epigenomics data) to find features relevant to a phenotype of interest. It performs greedy pruning of GWAS SNPs (LD r2 > 0.1) and then annotates them based on functional information overlap. Next, it quantifies Fold Enrichment (FE) at various GWAS significance cutoffs and assesses them by permutation testing, while matching for minor allele frequency, distance to nearest transcription start site and number of LD proxies (r2 > 0.8).
<br/><a href="http://bioconductor.org/packages/garfield/">link</a>http://bioconductor.org/packages/cellity/ cellity Quality Control for Single-Cell RNA-seq Data2016-05-15T20:14:22.475999ZTomislav Illicic, Davis McCarthyA support vector machine approach to identifying and filtering low quality cells from single-cell RNA-seq datasets.
<br/><a href="http://bioconductor.org/packages/cellity/">link</a>http://bioconductor.org/packages/chromPlot/ chromPlot Global visualization tool of genomic data2016-05-03T21:30:44.790267ZRicardo A. Verdugo and Karen Y. OrosticaPackage designed to visualize genomic data along the chromosomes, where the vertical chromosomes are sorted by number, with sex chromosomes at the end.
<br/><a href="http://bioconductor.org/packages/chromPlot/">link</a>http://bioconductor.org/packages/RImmPort/ RImmPort RImmPort: Enabling Ready-for-analysis Immunology Research Data2016-05-15T20:14:22.475999ZRavi Shankar <rshankar@stanford.edu>The RImmPort package simplifies access to ImmPort data for analysis in the R environment. It provides a standards-based interface to the ImmPort study data that is in a proprietary format.
<br/><a href="http://bioconductor.org/packages/RImmPort/">link</a>http://bioconductor.org/packages/ExpressionAtlas/ ExpressionAtlas Download datasets from EMBL-EBI Expression Atlas2016-05-15T20:14:22.475999ZMaria KeaysThis package is for searching for datasets in EMBL-EBI Expression Atlas, and downloading them into R for further analysis. Each Expression Atlas dataset is represented as a SimpleList object with one element per platform. Sequencing data is contained in a SummarizedExperiment object, while microarray data is contained in an ExpressionSet or MAList object.
<br/><a href="http://bioconductor.org/packages/ExpressionAtlas/">link</a>http://bioconductor.org/packages/scater/ scater Single-cell analysis toolkit for gene expression data in R2016-05-19T17:49:39.017638ZDavis McCarthyA collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control.
<br/><a href="http://bioconductor.org/packages/scater/">link</a>http://bioconductor.org/packages/clustComp/ clustComp Clustering Comparison Package2016-05-03T21:30:44.790267ZAurora Torrente and Alvis Brazma.clustComp is a package that implements several techniques for the comparison and visualisation of relationships between different clustering results, either flat versus flat or hierarchical versus flat. These relationships among clusters are displayed using a weighted bi-graph, in which the nodes represent the clusters and the edges connect pairs of nodes with non-empty intersection; the weight of each edge is the number of elements in that intersection and is displayed through the edge thickness. The best layout of the bi-graph is provided by the barycentre algorithm, which minimises the weighted number of crossings. In the case of comparing a hierarchical and a non-hierarchical clustering, the dendrogram is pruned at different heights, selected by exploring the tree by depth-first search, starting at the root. Branches are decided to be split according to the value of a scoring function, that can be based either on the aesthetics of the bi-graph or on the mutual information between the hierarchical and the flat clusterings. A mapping between groups of clusters from each side is constructed with a greedy algorithm, and can be additionally visualised.
<br/><a href="http://bioconductor.org/packages/clustComp/">link</a>http://bioconductor.org/packages/contiBAIT/ contiBAIT Improves Early Build Genome Assemblies using Strand-Seq Data2016-05-03T21:30:44.790267ZKieran O'Neill, Mark Hills, Mike GottliebUsing strand inheritance data from multiple single cells from the organism whose genome is to be assembled, contiBAIT can cluster unbridged contigs together into putative chromosomes, and order the contigs within those chromosomes.
<br/><a href="http://bioconductor.org/packages/contiBAIT/">link</a>http://bioconductor.org/packages/metaCCA/ metaCCA Summary Statistics-Based Multivariate Meta-Analysis of Genome-Wide Association Studies Using Canonical Correlation Analysis2016-05-15T20:14:22.475999ZAnna Cichonska <anna.cichonska@helsinki.fi>metaCCA performs multivariate analysis of a single or multiple GWAS based on univariate regression coefficients. It allows multivariate representation of both phenotype and genotype. metaCCA extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness.
<br/><a href="http://bioconductor.org/packages/metaCCA/">link</a>http://bioconductor.org/packages/Mergeomics/ Mergeomics Integrative network analysis of omics data2016-05-24T04:25:59.270346ZVille-Petteri Makinen, Le Shu, Yuqi Zhao, Zeyneb Kurt, Bin Zhang, Xia YangThe Mergeomics pipeline serves as a flexible framework for integrating multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It includes two main parts, 1) Marker set enrichment analysis (MSEA); 2) Weighted Key Driver Analysis (wKDA).
<br/><a href="http://bioconductor.org/packages/Mergeomics/">link</a>http://bioconductor.org/packages/SMITE/ SMITE Significance-based Modules Integrating the Transcriptome and Epigenome2016-05-15T20:14:22.475999ZNeil Ari Wijetunga, Andrew Damon Johnston, John Murray GreallyThis package builds on the Epimods framework which facilitates finding weighted subnetworks ("modules") on Illumina Infinium 27k arrays using the SpinGlass algorithm, as implemented in the iGraph package. We have created a class of gene centric annotations associated with p-values and effect sizes and scores from any researchers prior statistical results to find functional modules.
<br/><a href="http://bioconductor.org/packages/SMITE/">link</a>http://bioconductor.org/packages/tximport/ tximport Import and summarize transcript-level estimates for gene-level analysis2016-05-15T20:14:22.475999ZMichael Love, Charlotte Soneson, Mark RobinsonImports transcript-level abundance, estimated counts and transcript lengths, and summarizes into matrices for use with downstream gene-level analysis packages. Average transcript length, weighted by sample-specific transcript abundance estimates, is provided as a matrix which can be used as an offset for different expression of gene-level counts.
<br/><a href="http://bioconductor.org/packages/tximport/">link</a>http://bioconductor.org/packages/EWCE/ EWCE Expression Weighted Celltype Enrichment2016-05-15T20:14:22.475999ZDr Nathan SkeneUsed to determine which cell types are enriched within gene lists. The package provides tools for testing enrichments within simple gene lists (such as human disease associated genes) and those resulting from differential expression studies. The package does not depend upon any particular Single Cell Transcriptome dataset and user defined datasets can be loaded in and used in the analyses.
<br/><a href="http://bioconductor.org/packages/EWCE/">link</a>http://bioconductor.org/packages/BasicSTARRseq/ BasicSTARRseq Basic peak calling on STARR-seq data2016-05-15T20:14:22.475999ZAnnika BuergerBasic peak calling on STARR-seq data based on a method introduced in "Genome-Wide Quantitative Enhancer Activity Maps Identified by STARR-seq" Arnold et al. Science. 2013 Mar 1;339(6123):1074-7. doi: 10.1126/science. 1232542. Epub 2013 Jan 17.
<br/><a href="http://bioconductor.org/packages/BasicSTARRseq/">link</a>http://bioconductor.org/packages/ROTS/ ROTS Reproducibility-Optimized Test Statistic2016-05-17T13:34:43.421894ZFatemeh Seyednasrollah, Tomi Suomi, Laura L. EloCalculates the Reproducibility-Optimized Test Statistic (ROTS) for differential testing in omics data.
<br/><a href="http://bioconductor.org/packages/ROTS/">link</a>http://bioconductor.org/packages/RGraph2js/ RGraph2js Convert a Graph into a D3js Script2016-05-09T12:10:49.210720ZStephane Cano [aut, cre], Sylvain Gubian [aut], Florian Martin [aut]Generator of web pages which display interactive network/graph visualizations with D3js, jQuery and Raphael.
<br/><a href="http://bioconductor.org/packages/RGraph2js/">link</a>http://bioconductor.org/packages/IHW/ IHW Independent Hypothesis Weighting2016-05-15T20:14:22.475999ZIndependent hypothesis weighting (IHW) is a multiple testing procedure that increases power compared to the method of Benjamini and Hochberg by assigning data-driven weights to each hypothesis. The input to IHW is a two-column table of p-values and covariates. The covariate can be any continuous-valued or categorical variable that is thought to be informative on the statistical properties of each hypothesis test, while it is independent of the p-value under the null hypothesis.
<br/><a href="http://bioconductor.org/packages/IHW/">link</a>http://bioconductor.org/packages/GenVisR/ GenVisR Genomic Visualizations in R2016-05-20T16:10:42.046576ZZachary Skidmore [aut, cre], Alex Wagner [aut], Robert Lesurf [aut], Katie Campbell [aut], Jason Kunisaki [aut], Obi Griffith [aut], Malachi Griffith [aut]Produce highly customizable publication quality graphics for genomic data primarily at the cohort level.
<br/><a href="http://bioconductor.org/packages/GenVisR/">link</a>http://bioconductor.org/packages/iCARE/ iCARE A Tool for Individualized Coherent Absolute Risk Estimation (iCARE)2016-05-03T21:30:44.790267ZPaige Maas, Nilanjan Chatterjee and William WheelerAn R package to compute Individualized Coherent Absolute Risk Estimators.
<br/><a href="http://bioconductor.org/packages/iCARE/">link</a>http://bioconductor.org/packages/flowAI/ flowAI Automatic and interactive quality control for flow cytometry data2016-05-19T17:09:58.497151ZGianni Monaco, Chen HaoThe package is able to perform an automatic or interactive quality control on FCS data acquired using flow cytometry instruments. By evaluating three different properties: 1) flow rate, 2) signal acquisition, 3) dynamic range, the quality control enables the detection and removal of anomalies.
<br/><a href="http://bioconductor.org/packages/flowAI/">link</a>http://bioconductor.org/packages/EmpiricalBrownsMethod/ EmpiricalBrownsMethod Uses Brown's method to combine p-values from dependent tests2016-05-15T20:14:22.475999ZWilliam PooleCombining P-values from multiple statistical tests is common in bioinformatics. However, this procedure is non-trivial for dependent P-values. This package implements an empirical adaptation of Brown’s Method (an extension of Fisher’s Method) for combining dependent P-values which is appropriate for highly correlated data sets found in high-throughput biological experiments.
<br/><a href="http://bioconductor.org/packages/EmpiricalBrownsMethod/">link</a>http://bioconductor.org/packages/PanVizGenerator/ PanVizGenerator Generate PanViz visualisations from your pangenome2016-05-15T20:14:22.475999ZThomas Lin PedersenPanViz is a JavaScript based visualisation tool for functionaly annotated pangenomes. PanVizGenerator is a companion for PanViz that facilitates the necessary data preprocessing step necessary to create a working PanViz visualization. The output is fully self-contained so the recipient of the visualization does not need R or PanVizGenerator installed.
<br/><a href="http://bioconductor.org/packages/PanVizGenerator/">link</a>http://bioconductor.org/packages/SC3/ SC3 Single-Cell Consensus Clustering2016-05-15T20:14:22.475999ZVladimir KiselevInteractive tool for clustering and analysis of single cell RNA-Seq data.
<br/><a href="http://bioconductor.org/packages/SC3/">link</a>http://bioconductor.org/packages/JunctionSeq/ JunctionSeq JunctionSeq: A Utility for Detection of Differential Exon and Splice-Junction Usage in RNA-Seq data2016-05-23T17:31:32.960248ZA Utility for Detection and Visualization of Differential Exon or Splice-Junction Usage in RNA-Seq data.
<br/><a href="http://bioconductor.org/packages/JunctionSeq/">link</a>http://bioconductor.org/packages/cellTree/ cellTree Inference and visualisation of Single-Cell RNA-seq data as a hierarchical tree structure2016-05-15T20:14:22.475999ZDavid duVerle [aut, cre], Koji Tsuda [aut]This packages computes a Latent Dirichlet Allocation (LDA) model of single-cell RNA-seq data and builds a compact tree modelling the relationship between individual cells over time or space.
<br/><a href="http://bioconductor.org/packages/cellTree/">link</a>http://bioconductor.org/packages/ggcyto/ ggcyto Visualize Cytometry data with ggplot2016-05-20T18:27:49.214374ZMike JiangWith the dedicated fority method implemented for flowSet, ncdfFlowSet and GatingSet classes, both raw and gated flow cytometry data can be plotted directly with ggplot. ggcyto wrapper and some customed layers also make it easy to add gates and population statistics to the plot.
<br/><a href="http://bioconductor.org/packages/ggcyto/">link</a>http://bioconductor.org/packages/tofsims/ tofsims Import, process and analysis of Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) imaging data2016-05-15T20:14:22.475999ZLorenz Gerber, Viet Mai HoangThis packages offers a pipeline for import, processing and analysis of ToF-SIMS 2D image data. Import of Iontof and Ulvac-Phi raw or preprocessed data is supported. For rawdata, mass calibration, peak picking and peak integration exist. General funcionality includes data binning, scaling, image subsetting and visualization. A range of multivariate tools common in the ToF-SIMS community are implemented (PCA, MCR, MAF, MNF). An interface to the bioconductor image processing package EBImage offers image segmentation functionality.
<br/><a href="http://bioconductor.org/packages/tofsims/">link</a>http://bioconductor.org/packages/GSALightning/ GSALightning Fast Permutation-based Gene Set Analysis2016-05-15T20:14:22.475999ZBilly Heung Wing ChangGSALightning provides a fast implementation of permutation-based gene set analysis for two-sample problem. This package is particularly useful when testing simultaneously a large number of gene sets, or when a large number of permutations is necessary for more accurate p-values estimation.
<br/><a href="http://bioconductor.org/packages/GSALightning/">link</a>http://bioconductor.org/packages/QuaternaryProd/ QuaternaryProd Computes the Quaternary Dot Product Scoring Statistic for Signed and Unsigned Causal Graphs2016-05-15T20:14:22.475999ZQuaternaryProd is an R package that performs causal reasoning on biological networks, including publicly available networks such as String-db. QuaternaryProd is a free alternative to commercial products such as Quiagen and Inginuity pathway analysis. For a given a set of differentially expressed genes, QuaternaryProd computes the significance of upstream regulators in the network by performing causal reasoning using the Quaternary Dot Product Scoring Statistic (Quaternary Statistic), Ternary Dot product Scoring Statistic (Ternary Statistic) and Fisher's exact test. The Quaternary Statistic handles signed, unsigned and ambiguous edges in the network. Ambiguity arises when the direction of causality is unknown, or when the source node (e.g., a protein) has edges with conflicting signs for the same target gene. On the other hand, the Ternary Statistic provides causal reasoning using the signed and unambiguous edges only. The Vignette provides more details on the Quaternary Statistic and illustrates an example of how to perform causal reasoning using String-db.
<br/><a href="http://bioconductor.org/packages/QuaternaryProd/">link</a>http://bioconductor.org/packages/CrispRVariants/ CrispRVariants Tools for counting and visualising mutations in a target location2016-05-15T20:14:22.475999ZHelen Lindsay [aut, cre]CrispRVariants provides tools for analysing the results of a CRISPR-Cas9 mutagenesis sequencing experiment, or other sequencing experiments where variants within a given region are of interest. These tools allow users to localize variant allele combinations with respect to any genomic location (e.g. the Cas9 cut site), plot allele combinations and calculate mutation rates with flexible filtering of unrelated variants.
<br/><a href="http://bioconductor.org/packages/CrispRVariants/">link</a>http://bioconductor.org/packages/splineTimeR/ splineTimeR Time-course differential gene expression data analysis using spline regression models followed by gene association network reconstruction2016-05-15T20:14:22.475999ZAgata MichnaThis package provides functions for differential gene expression analysis of gene expression time-course data. Natural cubic spline regression models are used. Identified genes may further be used for pathway enrichment analysis and/or the reconstruction of time dependent gene regulatory association networks.
<br/><a href="http://bioconductor.org/packages/splineTimeR/">link</a>http://bioconductor.org/packages/kimod/ kimod A k-tables approach to integrate multiple Omics-Data2016-05-03T21:30:44.790267ZMaria Laura Zingaretti, Johanna Altair Demey-Zambrano, Jose Luis Vicente-Villardon, Jhonny Rafael DemeyThis package allows to work with mixed omics data (transcriptomics, proteomics, microarray-chips, rna-seq data), introducing the following improvements: distance options (for numeric and/or categorical variables) for each of the tables, bootstrap resampling techniques on the residuals matrices for all methods, that enable perform confidence ellipses for the projection of individuals, variables and biplot methodology to project variables (gene expression) on the compromise. Since the main purpose of the package is to use these techniques to omic data analysis, it includes an example data from four different microarray platforms (i.e.,Agilent, Affymetrix HGU 95, Affymetrix HGU 133 and Affymetrix HGU 133plus 2.0) on the NCI-60 cell lines.NCI60_4arrays is a list containing the NCI-60 microarray data with only few hundreds of genes randomly selected in each platform to keep the size of the package small. The data are the same that the package omicade4 used to implement the co-inertia analysis. The references in packages follow the style of the APA-6th norm.
<br/><a href="http://bioconductor.org/packages/kimod/">link</a>