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A bridging R package to facilitate gene set enrichment analysis (GSEA) in the context of single-cell RNA sequencing. Using raw count information, Seurat objects, or SingleCellExperiment format, users can perform and visualize ssGSEA, GSVA, AUCell, and UCell-based enrichment calculations across individual cells. Alternatively, escape supports use of rank-based GSEA, such as the use of differential gene expression via fgsea.

The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve the design and interpretability of multi-dimensional data in 2D data visualizations, layering aesthetics to display multiple variables. The proposed visualization can be applied to spatially-resolved transcriptomics data, but also broadly to data visualized in 2D space, such as embedding visualizations. We provide this open source R package escheR, which is built off of the state-of-the-art ggplot2 visualization framework and can be seamlessly integrated into genomics toolboxes and workflows.

Utility functions for visualization of expressionSet (or SummarizedExperiment) Bioconductor object, including spectral map, tsne and linear discriminant analysis. Static plot via the ggplot2 package or interactive via the ggvis or rbokeh packages are available.

eudysbiome a package that permits to annotate the differential genera as harmful/harmless based on their ability to contribute to host diseases (as indicated in literature) or unknown based on their ambiguous genus classification. Further, the package statistically measures the eubiotic (harmless genera increase or harmful genera decrease) or dysbiotic(harmless genera decrease or harmful genera increase) impact of a given treatment or environmental change on the (gut-intestinal, GI) microbiome in comparison to the microbiome of the reference condition.

Evaluating the reliability of your own metrics and the measurements done on your own datasets by analysing the stability and goodness of the classifications of such metrics.

EventPointer is an R package to identify alternative splicing events that involve either simple (case-control experiment) or complex experimental designs such as time course experiments and studies including paired-samples. The algorithm can be used to analyze data from either junction arrays (Affymetrix Arrays) or sequencing data (RNA-Seq). In the latter, EventPointer can work with annotated splicing events or can build a splicing graph from the RNA-Seq reads and then identify new and specific alternative splicing events. The software returns a data.frame with the detected alternative splicing events: gene name, type of event (cassette, alternative 3',...,etc), genomic position, statistical significance and increment of the percent spliced in (Delta PSI) for all the events. The algorithm can generate a series of files to visualize the detected alternative splicing events in IGV. This eases the interpretation of results and the design of primers for standard PCR validation.

ExCluster flattens Ensembl and GENCODE GTF files into GFF files, which are used to count reads per non-overlapping exon bin from BAM files. This read counting is done using the function featureCounts from the package Rsubread. Library sizes are normalized across all biological replicates, and ExCluster then compares two different conditions to detect signifcantly differentially spliced genes. This process requires at least two independent biological repliates per condition, and ExCluster accepts only exactly two conditions at a time. ExCluster ultimately produces false discovery rates (FDRs) per gene, which are used to detect significance. Exon log2 fold change (log2FC) means and variances may be plotted for each significantly differentially spliced gene, which helps scientists develop hypothesis and target differential splicing events for RT-qPCR validation in the wet lab.

This package contains functions for reading raw data in ImaGene TXT format obtained from Exiqon miRCURY LNA arrays, annotating them with appropriate GAL files, and normalizing them using a spike-in probe-based method. Other platforms and data formats are also supported.

Functions to add metadata to ExperimentHub db and resource files to AWS S3 buckets.

Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for one or more matrix-like assays along with the associated row and column data. Often only a subset of the original data is needed for down-stream analysis. For example, filtering out poor quality samples will require excluding some columns before analysis. The ExperimentSubset object is a container to efficiently manage different subsets of the same data without having to make separate objects for each new subset.

ExpoRiskR provides tools for exposure-aware multi-omics risk modeling in translational and environmental health studies. The package aligns sample identifiers across exposure and multi-omics blocks, performs lightweight preprocessing, and fits exposure-adjusted association models to build interpretable microbe–metabolite networks. It also computes simple exposure perturbation summaries and generates publication-ready visualizations. Workflows support both matrix-based inputs and SummarizedExperiment objects.

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.

This package builds on existing tools and adds some simple but extremely useful capabilities for working wth ChIP-Seq data. The focus is on detecting differential binding windows/regions. One set of functions focusses on set-operations retaining mcols for GRanges objects, whilst another group of functions are to aid visualisation of results. Coercion to tibble objects is also implemented.

Biclustering by "Factor Analysis for Bicluster Acquisition" (FABIA). FABIA is a model-based technique for biclustering, that is clustering rows and columns simultaneously. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. It captures realistic non-Gaussian data distributions with heavy tails as observed in gene expression measurements. FABIA utilizes well understood model selection techniques like the EM algorithm and variational approaches and is embedded into a Bayesian framework. FABIA ranks biclusters according to their information content and separates spurious biclusters from true biclusters. The code is written in C.

This package provides a set of tools for analyzing data from a factorial designed microarray experiment, or any microarray experiment for which a linear model is appropriate. The functions can be used to evaluate tests of contrast of biological interest and perform single outlier detection.

factR contain tools to process and interact with custom-assembled transcriptomes (GTF). At its core, factR constructs CDS information on custom transcripts and subsequently predicts its functional output. In addition, factR has tools capable of plotting transcripts, correcting chromosome and gene information and shortlisting new transcripts.

The FDA Adverse Event Reporting System (FAERS) is a database used for the spontaneous reporting of adverse events and medication errors related to human drugs and therapeutic biological products. faers pacakge serves as the interface between the FAERS database and R. Furthermore, faers pacakge offers a standardized approach for performing pharmacovigilance analysis.

Framework providing basic pedigree analysis and plotting utilities as well as a variety of methods to evaluate familial aggregation of traits in large pedigrees.

This package extends the function of the LiquidAssociation package for genome-wide application. It integrates a screening method into the LA analysis to reduce the number of triplets to be examined for a high LA value and provides code for use in subsequent significance analyses.

An interactive web application for quality control, filtering and trimming of FASTQ files. This user-friendly tool combines a pipeline for data processing based on Biostrings and ShortRead infrastructure, with a cutting-edge visual environment. Single-Read and Paired-End files can be locally processed. Diagnostic interactive plots (CG content, per-base sequence quality, etc.) are provided for both the input and output files.

fastseg implements a very fast and efficient segmentation algorithm. It has similar functionality as DNACopy (Olshen and Venkatraman 2004), but is considerably faster and more flexible. fastseg can segment data from DNA microarrays and data from next generation sequencing for example to detect copy number segments. Further it can segment data from RNA microarrays like tiling arrays to identify transcripts. Most generally, it can segment data given as a matrix or as a vector. Various data formats can be used as input to fastseg like expression set objects for microarrays or GRanges for sequencing data. The segmentation criterion of fastseg is based on a statistical test in a Bayesian framework, namely the cyber t-test (Baldi 2001). The speed-up arises from the facts, that sampling is not necessary in for fastseg and that a dynamic programming approach is used for calculation of the segments' first and higher order moments.

Computational evaluation of variability across DNA or RNA sequencing datasets is a crucial step in genomics, as it allows both to evaluate reproducibility of replicates, and to compare different datasets to identify potential correlations. fCCAC applies functional Canonical Correlation Analysis to allow the assessment of: (i) reproducibility of biological or technical replicates, analyzing their shared covariance in higher order components; and (ii) the associations between different datasets. fCCAC represents a more sophisticated approach that complements Pearson correlation of genomic coverage.

(f-divergence Cutoff Index), is to find DEGs in the transcriptomic & proteomic data, and identify DEGs by computing the difference between the distribution of fold-changes for the control-control and remaining (non-differential) case-control gene expression ratio data. fCI provides several advantages compared to existing methods.

This package is used to detect combination of genomic coordinates falling within a user defined window size along with user defined overlap between identified neighboring clusters. It can be used for genomic data where the clusters are built on a specific chromosome or specific strand. Clustering can be performed with a "greedy" option allowing thus the presence of additional sites within the allowed window size.

This package contains two main functions. The first is fdr.ma which takes normalized expression data array, experimental design and computes adjusted p-values It returns the fdr adjusted p-values and plots, according to the methods described in (Reiner, Yekutieli and Benjamini 2002). The second, is fdr.gui() which creates a simple graphic user interface to access fdr.ma

Cell clustering is one of the most important and commonly performed tasks in single-cell RNA sequencing (scRNA-seq) data analysis. An important step in cell clustering is to select a subset of genes (referred to as “features”), whose expression patterns will then be used for downstream clustering. A good set of features should include the ones that distinguish different cell types, and the quality of such set could have significant impact on the clustering accuracy. FEAST is an R library for selecting most representative features before performing the core of scRNA-seq clustering. It can be used as a plug-in for the etablished clustering algorithms such as SC3, TSCAN, SHARP, SIMLR, and Seurat. The core of FEAST algorithm includes three steps: 1. consensus clustering; 2. gene-level significance inference; 3. validation of an optimized feature set.

Enrichment of metabolomics data using KEGG entries. Given a set of affected compounds, FELLA suggests affected reactions, enzymes, modules and pathways using label propagation in a knowledge model network. The resulting subnetwork can be visualised and exported.

Identify low-quality data using metrics developed for expression data derived from Formalin-Fixed, Paraffin-Embedded (FFPE) data. Also a function for making Concordance at the Top plots (CAT-plots).

Package that implements the FGGA algorithm. This package provides a hierarchical ensemble method based ob factor graphs for the consistent cross-ontology annotation of protein coding genes. FGGA embodies elements of predicate logic, communication theory, supervised learning and inference in graphical models.

Build and visualize functional gene and term networks from clustering of enrichment analyses in multiple annotation spaces. The package includes a graphical user interface (GUI) and functions to perform the functional enrichment analysis through DAVID, GeneTerm Linker, gage (GSEA) and topGO.

The package implements an algorithm for fast gene set enrichment analysis. Using the fast algorithm allows to make more permutations and get more fine grained p-values, which allows to use accurate stantard approaches to multiple hypothesis correction.

This package finds and filters artificial chimeric reads specifically generated in next-generation sequencing (NGS) process of formalin-fixed paraffin-embedded (FFPE) tissues. These artificial chimeric reads can lead to a large number of false positive structural variation (SV) calls. The required input is an indexed BAM file of a FFPE sample.

FISHalyseR provides functionality to process and analyse digital cell culture images, in particular to quantify FISH probes within nuclei. Furthermore, it extract the spatial location of each nucleus as well as each probe enabling spatial co-localisation analysis.

Fishpond contains methods for differential transcript and gene expression analysis of RNA-seq data using inferential replicates for uncertainty of abundance quantification, as generated by Gibbs sampling or bootstrap sampling. Also the package contains a number of utilities for working with Salmon and Alevin quantification files.

Fit-Hi-C is a tool for assigning statistical confidence estimates to intra-chromosomal contact maps produced by genome-wide genome architecture assays such as Hi-C.

Fragment-level analysis of gas chromatography-massspectrometry metabolomics data.

Semi-supervised isoform detection and annotation from both bulk and single-cell long read RNA-seq data. Flames provides automated pipelines for analysing isoforms, as well as intermediate functions for manual execution.

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.

This package extends flowCore to provide functionality specific to bead data. One of the goals of this package is to automate analysis of bead data for the purpose of normalisation.

Software to combine flow cytometry data that has been multiplexed into multiple tubes with common markers between them, by establishing common bins across tubes in terms of the common markers, then determining expression within each tube for each bin in terms of the tube-specific markers.

A package to analyze flow cytometric data of complex microbial communities based on histogram images

A quality control tool for flow cytometry data based on compositional data analysis.

Robust model-based clustering using a t-mixture model with Box-Cox transformation. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'.

Provides S4 data structures and basic functions to deal with flow cytometry data.

Common techinical complications such as clogging can result in spurious events and fluorescence intensity shifting, flowCut is designed to detect and remove technical artifacts from your data by removing segments that show statistical differences from other segments.

A package to analyze flow cytometric data using gate information to follow population/community dynamics

This package provides tools for automated sequential gating analogous to the manual gating strategy based on the density of the data.

Fingerprint generation of flow cytometry data, used to facilitate the application of machine learning and datamining tools for flow cytometry.

flowGate adds an interactive Shiny app to allow manual GUI-based gating of flow cytometry data in R. Using flowGate, you can draw 1D and 2D span/rectangle gates, quadrant gates, and polygon gates on flow cytometry data by interactively drawing the gates on a plot of your data, rather than by specifying gate coordinates. This package is especially geared toward wet-lab cytometerists looking to take advantage of R for cytometry analysis, without necessarily having a lot of R experience.

Matching cell populations and building meta-clusters and templates from a collection of FC samples.