This package provides a new clustering algorithm, binary cut, for clustering similarity matrices of functional terms is implemented in this package. It also provides functionalities for visualizing, summarizing and comparing the clusterings.

Saves the delayed operations of a DelayedArray to a HDF5 file. This enables efficient recovery of the DelayedArray's contents in other languages and analysis frameworks.

This package corrects GC and mappability biases for readcounts (i.e. coverage) in non-overlapping windows of fixed length for single whole genome samples, yielding a rough estimate of copy number for further analysis. It was designed for rapid correction of high coverage whole genome tumor and normal samples.

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 provides a collection of functions for left-censored missing data imputation. Left-censoring is a special case of missing not at random (MNAR) mechanism that generates non-responses in proteomics experiments. The package also contains functions to artificially generate peptide/protein expression data (log-transformed) as random draws from a multivariate Gaussian distribution as well as a function to generate missing data (both randomly and non-randomly). For comparison reasons, the package also contains several wrapper functions for the imputation of non-responses that are missing at random.

This package provides an expressionSet containing gene expression data from 60 bone marrow samples of patients with one of the four main types of leukemia (ALL, AML, CLL, CML) or non-leukemia.

This package provides tools to acquire, annotate, convert and store data for use in Bioconductor’s AnnotationHub.

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.

This package contains functions and classes that are needed by arrayCGH packages.

The dada2 package infers exact amplicon sequence variants (ASVs) from high-throughput amplicon sequencing data, replacing the coarser and less accurate OTU clustering approach. The dada2 pipeline takes as input demultiplexed fastq files, and outputs the sequence variants and their sample-wise abundances after removing substitution and chimera errors. Taxonomic classification is available via a native implementation of the RDP naive Bayesian classifier, and species-level assignment to 16S rRNA gene fragments by exact matching.

GOfuncR performs a gene ontology enrichment analysis based on the ontology enrichment software FUNC. GO-annotations are obtained from OrganismDb or OrgDb packages (Homo.sapiens by default); the GO-graph is included in the package and updated regularly. GOfuncR provides the standard candidate vs background enrichment analysis using the hypergeometric test, as well as three additional tests:

1. the Wilcoxon rank-sum test that is used when genes are ranked,

2. a binomial test that is used when genes are associated with two counts, and

3. a Chi-square or Fisher's exact test that is used in cases when genes are associated with four counts.

To correct for multiple testing and interdependency of the tests, family-wise error rates are computed based on random permutations of the gene-associated variables. GOfuncR also provides tools for exploring the ontology graph and the annotations, and options to take gene-length or spatial clustering of genes into account. It is also possible to provide custom gene coordinates, annotations and ontologies.

This package aims to make NMR spectroscopy data analysis as easy as possible. It only requires a small set of functions to perform an entire analysis. Speaq offers the possibility of raw spectra alignment and quantitation but also an analysis based on features whereby the spectra are converted to peaks which are then grouped and turned into features. These features can be processed with any number of statistical tools either included in speaq or available elsewhere on CRAN.

This package provides tools for identifying preferential usage of APA sites, comparing two biological conditions, starting from known alternative sites and alignments obtained from standard RNA-seq experiments.

This package provides tools to analyze and visualize high-throughput metabolomics data acquired using chromatography-mass spectrometry. These tools preprocess data in a way that enables reliable and powerful differential analysis.

This package performs hybrid multiple testing that incorporates method selection and assumption evaluations into the analysis using EBP estimates obtained by Grenander density estimation. For instance, for 3-group comparison analysis, Hybrid Multiple testing considers EBPs as weighted EBPs between F-test and H-test with EBPs from Shapiro Wilk test of normality as weight. Instead of just using EBPs from F-test only or using H-test only, this methodology combines both types of EBPs through EBPs from Shapiro Wilk test of normality. This methodology uses then the law of total EBPs.

This package is designed for visualization of RNA-related genomic features with respect to the landmarks of RNA transcripts, i.e., transcription starting site, start codon, stop codon and transcription ending site.

SGSeq is a package for analyzing splice events from RNA-seq data. Input data are RNA-seq reads mapped to a reference genome in BAM format. Genes are represented as a splice graph, which can be obtained from existing annotation or predicted from the mapped sequence reads. Splice events are identified from the graph and are quantified locally using structurally compatible reads at the start or end of each splice variant. The software includes functions for splice event prediction, quantification, visualization and interpretation.

This is a package for saving SingleCellExperiment into file artifacts, and loading them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

This package contains example data for Illumina microarray output files, for testing purposes.

Wrapping an array-like object (typically an on-disk object) in a DelayedArray object allows one to perform common array operations on it without loading the object in memory. In order to reduce memory usage and optimize performance, operations on the object are either delayed or executed using a block processing mechanism. Note that this also works on in-memory array-like objects like DataFrame objects (typically with Rle columns), Matrix objects, and ordinary arrays and data frames.

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.

This package extends beachmat to support initialization of tatami matrices from HDF5-backed arrays. This allows C++ code in downstream packages to directly call the HDF5 C/C++ library to access array data, without the need for block processing via DelayedArray. Some utilities are also provided for direct creation of an in-memory tatami matrix from a HDF5 file.

mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data.

This R package enables the user to read pfam predictions into R. Most human protein domains exist as multiple distinct variants termed domain isotypes. This R package enables the identification and classification of such domain isotypes from pfam data.