epivizrStandalone Run Epiviz Interactive Genomic Data Visualization App within R
EGAD Extending guilt by association by degree
The package implements a series of highly efficient tools to calculate functional properties of networks based on guilt by association methods.
MMDiff2 Statistical Testing for ChIP-Seq data sets
This 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).
EGSEA Ensemble of Gene Set Enrichment Analyses
This package implements the Ensemble of Gene Set Enrichment Analyses (EGSEA) method for gene set testing.
CHRONOS CHRONOS: A time-varying method for microRNA-mediated sub-pathway enrichment analysis
A 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.
diffloop Differential DNA loop calling from ChIA-PET data
A suite of tools for subsetting, visualizing, annotating, and statistically analyzing the results of one or more ChIA-PET experiments.
epivizrData Data Management API for epiviz interactive visualization app
Serve data from Bioconductor Objects through a WebSocket connection.
epivizrServer WebSocket server infrastructure for epivizr apps and packages
This package provides objects to manage WebSocket connections to epiviz apps. Other epivizr package use this infrastructure.
PureCN Estimating tumor purity, ploidy, LOH, and SNV status using hybrid capture NGS data
This package estimates tumor purity, copy number, loss of heterozygosity (LOH), and status of short nucleotide variants (SNVs). PureCN is designed for hybrid capture next generation sequencing (NGS) data, integrates well with standard somatic variant detection pipelines, and has support for tumor samples without matching normal samples.
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 Data
The 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.
InteractionSet Base Classes for Storing Genomic Interaction Data
Provides the GInteractions, InteractionSet and ContactMatrix objects and associated methods for storing and manipulating genomic interaction data from Hi-C and ChIA-PET experiments.
pbcmc Permutation-Based Confidence for Molecular Classification
The 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.
LymphoSeq Analyze high-throughput sequencing of T and B cell receptors
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.
genbankr Parsing GenBank files into semantically useful objects
Reads Genbank files.
BgeeDB Annotation and gene expression data from Bgee database
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.
oppar Outlier profile and pathway analysis in R
The 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.
BatchQC Batch Effects Quality Control Software
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.
scran Methods for Single-Cell RNA-Seq Data Analysis
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.
Glimma Interactive HTML graphics for RNA-seq data
This package generates interactive visualisations of RNA-sequencing data based on output from limma, edgeR or DESeq2. Interactions are built on top of popular static displays from the limma package, providing users with access to gene IDs and sample information. Plots are generated using d3.js and displayed in HTML pages.
odseq Outlier detection in multiple sequence alignments
Performs 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.
Linnorm Linear model and normality based transformation method (Linnorm)
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.
BadRegionFinder BadRegionFinder: an R/Bioconductor package for identifying regions with bad coverage
BadRegionFinder 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.
EBSEA Exon Based Strategy for Expression Analysis of genes
Calculates differential expression of genes based on exon counts of genes obtained from RNA-seq sequencing data.
CINdex Chromosome Instability Index
The 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.
QUBIC An R package for qualitative biclustering in support of gene co-expression analyses
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).
isomiRs Analyze isomiRs and miRNAs from small RNA-seq
Characterization of miRNAs and isomiRs, clustering and differential expression.
GenoGAM A GAM based framework for analysis of ChIP-Seq data
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.
MultiAssayExperiment Create Classes and Functions for Managing Multiple Assays on Sets of Samples
Develop an integrative environment where multiple assays are managed and preprocessed for genomic data analysis.
sscu Strength of Selected Codon Usage
The 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.
genphen A tool for computing genotype-phenotype associations using statistical learning techniques
Given 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.
recoup An R package for the creation of complex genomic profile plots
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.
AneuFinder Analysis of Copy Number Variation in Single-Cell-Sequencing Data
This package implements functions for CNV calling, plotting, export and analysis from whole-genome single cell sequencing data.
OncoScore A tool to identify potentially oncogenic genes
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.
CountClust Clustering and Visualizing RNA-Seq Expression Data using Grade of Membership Models
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.
ISoLDE Integrative Statistics of alleLe Dependent Expression
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.
GMRP GWAS-based Mendelian Randomization and Path Analyses
Perform 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.
DRIMSeq Differential splicing and sQTL analyses with Dirichlet-multinomial model in RNA-Seq
The 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.
SpidermiR SpidermiR: An R/Bioconductor package for integrative network analysis with miRNA data
The 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).
DNAshapeR High-throughput prediction of DNA shape features
DNAhapeR 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.
SwathXtend SWATH extended library generation and satistical data analysis
It contains utility functions for integrating spectral libraries for SWATH and statistical data analysis for SWATH generated data.
bacon Controlling bias and inflation in association studies using the empirical null distribution
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.
PCAN Phenotype Consensus ANalysis (PCAN)
Phenotypes 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).
psygenet2r psygenet2r - An R package for querying PsyGeNET and to perform comorbidity studies in psychiatric disorders
Package to retrieve data from PsyGeNET database (www.psygenet.org) and to perform comorbidity studies with PsyGeNET's and user's data.
miRNAmeConverter Convert miRNA Names to Different miRBase Versions
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/).
nucleoSim Generate synthetic nucleosome maps
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.
Uniquorn Identification of cancer cell lines based on their weighted mutational/ variational fingerprint
This 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.
biosigner Signature discovery from omics data
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.
MethPed A DNA methylation classifier tool for the identification of pediatric brain tumor subtypes
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).
HDF5Array An array-like container for convenient access and manipulation of HDF5 datasets
This 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.
ExperimentHubData Add resources to ExperimentHub
Functions to add metadata to ExperimentHub db and resource files to AWS S3 buckets.
ExperimentHub Client to access ExperimentHub resources
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.
MBttest Multiple Beta t-Tests
MBttest 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.
dada2 Accurate, high-resolution sample inference from amplicon sequencing data
The 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.
GenRank Candidate gene prioritization based on convergent evidence
Methods for ranking genes based on convergent evidence obtained from multiple independent evidence layers. This package adapts three methods that are popular for meta-analysis.
garfield GWAS Analysis of Regulatory or Functional Information Enrichment with LD correction
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).
cellity Quality Control for Single-Cell RNA-seq Data
A support vector machine approach to identifying and filtering low quality cells from single-cell RNA-seq datasets.
chromPlot Global visualization tool of genomic data
Package designed to visualize genomic data along the chromosomes, where the vertical chromosomes are sorted by number, with sex chromosomes at the end.
RImmPort RImmPort: Enabling Ready-for-analysis Immunology Research Data
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.
ExpressionAtlas Download datasets from EMBL-EBI Expression Atlas
This 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.
scater Single-cell analysis toolkit for gene expression data in R
A collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control.
clustComp Clustering Comparison Package
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.
contiBAIT Improves Early Build Genome Assemblies using Strand-Seq Data
Using 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.
metaCCA Summary Statistics-Based Multivariate Meta-Analysis of Genome-Wide Association Studies Using Canonical Correlation Analysis
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.
Mergeomics Integrative network analysis of omics data
The 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).
SMITE Significance-based Modules Integrating the Transcriptome and Epigenome
This 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.
tximport Import and summarize transcript-level estimates for gene-level analysis
Imports 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.
EWCE Expression Weighted Celltype Enrichment
Used 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.
BasicSTARRseq Basic peak calling on STARR-seq data
Basic 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.
ROTS Reproducibility-Optimized Test Statistic
Calculates the Reproducibility-Optimized Test Statistic (ROTS) for differential testing in omics data.
RGraph2js Convert a Graph into a D3js Script
Generator of web pages which display interactive network/graph visualizations with D3js, jQuery and Raphael.
GenVisR Genomic Visualizations in R
Produce highly customizable publication quality graphics for genomic data primarily at the cohort level.
iCARE A Tool for Individualized Coherent Absolute Risk Estimation (iCARE)
An R package to compute Individualized Coherent Absolute Risk Estimators.
flowAI Automatic and interactive quality control for flow cytometry data
The 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.
EmpiricalBrownsMethod Uses Brown's method to combine p-values from dependent tests
Combining 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.
SC3 Single-Cell Consensus Clustering
Interactive tool for clustering and analysis of single cell RNA-Seq data.
JunctionSeq JunctionSeq: A Utility for Detection of Differential Exon and Splice-Junction Usage in RNA-Seq data
A Utility for Detection and Visualization of Differential Exon or Splice-Junction Usage in RNA-Seq data.
ggcyto Visualize Cytometry data with ggplot
With 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.
tofsims Import, process and analysis of Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) imaging data
This 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.
GSALightning Fast Permutation-based Gene Set Analysis
GSALightning 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.
QuaternaryProd Computes the Quaternary Dot Product Scoring Statistic for Signed and Unsigned Causal Graphs
QuaternaryProd 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.
CrispRVariants Tools for counting and visualising mutations in a target location
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.
splineTimeR Time-course differential gene expression data analysis using spline regression models followed by gene association network reconstruction
This 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.
kimod A k-tables approach to integrate multiple Omics-Data
This 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.
lpsymphony Symphony integer linear programming solver in R
This package was derived from Rsymphony_0.1-17 from CRAN. These packages provide an R interface to SYMPHONY, an open-source linear programming solver written in C++. The main difference between this package and Rsymphony is that it includes the solver source code (SYMPHONY version 5.6), while Rsymphony expects to find header and library files on the users' system. Thus the intention of lpsymphony is to provide an easy to install interface to SYMPHONY. For Windows, precompiled DLLs are included in this package.
transcriptR An Integrative Tool for ChIP- And RNA-Seq Based Primary Transcripts Detection and Quantification
The differences in the RNA types being sequenced have an impact on the resulting sequencing profiles. mRNA-seq data is enriched with reads derived from exons, while GRO-, nucRNA- and chrRNA-seq demonstrate a substantial broader coverage of both exonic and intronic regions. The presence of intronic reads in GRO-seq type of data makes it possible to use it to computationally identify and quantify all de novo continuous regions of transcription distributed across the genome. This type of data, however, is more challenging to interpret and less common practice compared to mRNA-seq. One of the challenges for primary transcript detection concerns the simultaneous transcription of closely spaced genes, which needs to be properly divided into individually transcribed units. The R package transcriptR combines RNA-seq data with ChIP-seq data of histone modifications that mark active Transcription Start Sites (TSSs), such as, H3K4me3 or H3K9/14Ac to overcome this challenge. The advantage of this approach over the use of, for example, gene annotations is that this approach is data driven and therefore able to deal also with novel and case specific events. Furthermore, the integration of ChIP- and RNA-seq data allows the identification all known and novel active transcription start sites within a given sample.
profileScoreDist Profile score distributions
Regularization and score distributions for position count matrices.
dcGSA Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles
Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles. In longitudinal studies, the gene expression profiles were collected at each visit from each subject and hence there are multiple measurements of the gene expression profiles for each subject. The dcGSA package could be used to assess the associations between gene sets and clinical outcomes of interest by fully taking advantage of the longitudinal nature of both the gene expression profiles and clinical outcomes.
normalize450K Preprocessing of Illumina Infinium 450K data
Precise measurements are important for epigenome-wide studies investigating DNA methylation in whole blood samples, where effect sizes are expected to be small in magnitude. The 450K platform is often affected by batch effects and proper preprocessing is recommended. This package provides functions to read and normalize 450K '.idat' files. The normalization corrects for dye bias and biases related to signal intensity and methylation of probes using local regression. No adjustment for probe type bias is performed to avoid the trade-off of precision for accuracy of beta-values.
biomformat An interface package for the BIOM file format
This is an R package for interfacing with the BIOM format. This package includes basic tools for reading biom-format files, accessing and subsetting data tables from a biom object (which is more complex than a single table), as well as limited support for writing a biom-object back to a biom-format file. The design of this API is intended to match the python API and other tools included with the biom-format project, but with a decidedly "R flavor" that should be familiar to R users. This includes S4 classes and methods, as well as extensions of common core functions/methods.
BioQC Detect tissue heterogeneity in expression profiles with gene sets
BioQC performs quality control of high-throughput expression data based on tissue gene signatures
iCOBRA Comparison and Visualization of Ranking and Assignment Methods
This package provides functions for calculation and visualization of performance metrics for evaluation of ranking and binary classification (assignment) methods. It also contains a shiny application for interactive exploration of results.
Chicago CHiCAGO: Capture Hi-C Analysis of Genomic Organization
A pipeline for analysing Capture Hi-C data.
multiClust A collection of gene feature selection and clustering analysis algorithms
Whole transcriptomic profiles are useful for studying the expression levels of thousands of genes across samples. Clustering algorithms are used to identify patterns in these profiles to determine clinically relevant subgroups. Feature selection is a critical integral part of the process. Currently, there are many feature selection and clustering methods to identify the relevant genes and perform clustering of samples. However, choosing the appropriate methods is difficult as recent work demonstrates that no method is the clear winner. Hence, we present an R-package called `multiClust` that allows researchers to experiment with the choice of combination of methods for gene selection and clustering with ease. In addition, using multiClust, we present the merit of gene selection and clustering methods in the context of clinical relevance of clustering, specifically clinical outcome. Our integrative R- package contains: 1. A function to read in gene expression data and format appropriately for analysis in R. 2. Four different ways to select the number of genes a. Fixed b. Percent c. Poly d. GMM 3. Four gene ranking options that order genes based on different statistical criteria a. CV_Rank b. CV_Guided c. SD_Rank d. Poly 4. Two ways to determine the cluster number a. Fixed b. Gap Statistic 5. Two clustering algorithms a. Hierarchical clustering b. K-means clustering 6. A function to calculate average gene expression in each sample cluster 7. A function to correlate sample clusters with clinical outcome Order of Function use: 1. input_file, a function to read-in the gene expression file and assign gene probe names as the rownames. 2. number_probes, a function to determine the number of probes to select for in the gene feature selection process. 3. probe_ranking, a function to select for gene probes using one of the available gene probe ranking options. 4. number_clusters, a function to determine the number of clusters to be used to cluster genes and samples. 5. cluster_analysis, a function to perform Kmeans or Hierarchical clustering analysis of the selected gene expression data. 6. avg_probe_exp, a function to produce a matrix containing the average expression of each gene probe within each sample cluster. 7. surv_analysis, a function to produce Kaplan-Meier Survival Plots of selected gene expression data.
consensusSeekeR Detection of consensus regions inside a group of experiences using genomic positions and genomic ranges
This package compares genomic positions and genomic ranges from multiple experiments to extract common regions. The size of the analyzed region is adjustable as well as the number of experiences in which a feature must be present in a potential region to tag this region as a consensus region.
globalSeq Testing for association between RNA-Seq and high-dimensional data
The method may be conceptualised as a test of overall significance in regression analysis, where the response variable is overdispersed and the number of explanatory variables exceeds the sample size.
scde Single Cell Differential Expression
The scde package implements a set of statistical methods for analyzing single-cell RNA-seq data. scde fits individual error models for single-cell RNA-seq measurements. These models can then be used for assessment of differential expression between groups of cells, as well as other types of analysis. The scde package also contains the pagoda framework which applies pathway and gene set overdispersion analysis to identify and characterize putative cell subpopulations based on transcriptional signatures. The overall approach to the differential expression analysis is detailed in the following publication: "Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi: 10.1038/nmeth.2967). The overall approach to subpopulation identification and characterization is detailed in the following pre-print: "Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis" (Fan J, Salathia N, Liu R, Kaeser G, Yung Y, Herman J, Kaper F, Fan JB, Zhang K, Chun J, and Kharchenko PV, Nature Methods, doi:10.1038/nmeth.3734).
R4RNA An R package for RNA visualization and analysis
A package for RNA basepair analysis, including the visualization of basepairs as arc diagrams for easy comparison and annotation of sequence and structure. Arc diagrams can additionally be projected onto multiple sequence alignments to assess basepair conservation and covariation, with numerical methods for computing statistics for each.
CNPBayes Bayesian mixture models for copy number polymorphisms
Bayesian hierarchical mixture models for batch effects and copy number.
subSeq Subsampling of high-throughput sequencing count data
Subsampling of high throughput sequencing count data for use in experiment design and analysis.
biobroom Turn Bioconductor objects into tidy data frames
This package contains methods for converting standard objects constructed by bioinformatics packages, especially those in Bioconductor, and converting them to tidy data. It thus serves as a complement to the broom package, and follows the same the tidy, augment, glance division of tidying methods. Tidying data makes it easy to recombine, reshape and visualize bioinformatics analyses.
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