This package provides an R wrapper of the popular bowtie2 sequencing reads aligner and AdapterRemoval, a convenient tool for rapid adapter trimming, identification, and read merging.

This package provides tools to import transcript-level abundance, estimated counts and transcript lengths, and to summarize them 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.

This package provides the lengths of mRNA transcripts for a number of genomes and gene ID formats, largely based on the UCSC table browser.

Milo performs single-cell differential abundance testing. Cell states are modelled as representative neighbourhoods on a nearest neighbour graph. Hypothesis testing is performed using a negative bionomial generalized linear model.

This is a package for multivariate data analysis and graphical display of microarray data. Functions are included for supervised dimension reduction (between group analysis) and joint dimension reduction of two datasets (coinertia analysis).

XBSeq is a novel algorithm for testing RNA-seq differential expression (DE), where a statistical model was established based on the assumption that observed signals are the convolution of true expression signals and sequencing noises. The mapped reads in non-exonic regions are considered as sequencing noises, which follows a Poisson distribution. Given measurable observed signal and background noise from RNA-seq data, true expression signals, assuming governed by the negative binomial distribution, can be delineated and thus the accurate detection of differential expressed genes.

This package provides an annotation database of Homo sapiens genome data. It is derived from the UCSC hg38 genome and based on the "knownGene" track. The database is exposed as a TxDb object.

This package provides per-exon and per-gene read counts computed for selected genes from RNA-seq data that were presented in the article 'Conservation of an RNA regulatory map between Drosophila and mammals' by Brooks et al., Genome Research 2011.

This package provides methods for microarray analysis that take basic data types such as matrices and lists of vectors. These methods can be used standalone, be utilized in other packages, or be wrapped up in higher-level classes.

The objective of AGDEX is to evaluate whether the results of a pair of two-group differential expression analysis comparisons show a level of agreement that is greater than expected if the group labels for each two-group comparison are randomly assigned. The agreement is evaluated for the entire transcriptome and (optionally) for a collection of pre-defined gene-sets. Additionally, the procedure performs permutation-based differential expression and meta analysis at both gene and gene-set levels of the data from each experiment.

This package provides functions and routines useful in the analysis of somatic signatures (cf. L. Alexandrov et al., Nature 2013). In particular, functions to perform a signature analysis with known signatures and a signature analysis on stratified mutational catalogue (SMC) are provided.

This package contains reads from an RNA-seq experiment between two lung cancer cell lines: H1993 (met) and H2073 (primary). The reads are stored as Fastq files and are meant for use with the TP53Genome object in the gmapR package.

This package can do differential expression analysis of RNA-seq expression profiles with biological replication. It implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests. It be applied to differential signal analysis of other types of genomic data that produce counts, including ChIP-seq, SAGE and CAGE.

This package implements clustering of microarray gene expression profiles according to functional annotations. For each term genes are annotated to, splits into two subclasses are computed and a significance of the supporting gene set is determined.

This package aggregateBioVar contains tools to summarize single cell gene expression profiles at the level of subject for single cell RNA-seq data collected from more than one subject (e.g. biological sample or technical replicates). A SingleCellExperiment object is taken as input and converted to a list of SummarizedExperiment objects, where each list element corresponds to an assigned cell type. The SummarizedExperiment objects contain aggregate gene-by-subject count matrices and inter-subject column metadata for individual subjects that can be processed using downstream bulk RNA-seq tools.

This package provides full genome sequences for Mus musculus (Mouse) as provided by UCSC (mm10, December 2011) and stored in Biostrings objects.

This package provides functionality for the compression and decompression of raw bead-level data from the Illumina BeadArray platform.

Linnorm is an R package for the analysis of RNA-seq, scRNA-seq, ChIP-seq count data or any large scale count data. It transforms such datasets for parametric tests. In addition to the transformtion function (Linnorm), the following pipelines are implemented:

1. Library size/batch effect normalization (Linnorm.Norm)

2. Cell subpopluation analysis and visualization using t-SNE or PCA K-means clustering or hierarchical clustering (Linnorm.tSNE, Linnorm.PCA, Linnorm.HClust)

3. Differential expression analysis or differential peak detection using limma (Linnorm.limma)

4. Highly variable gene discovery and visualization (Linnorm.HVar)

5. Gene correlation network analysis and visualization (Linnorm.Cor)

6. Stable gene selection for scRNA-seq data; for users without or who do not want to rely on spike-in genes (Linnorm.SGenes)

7. Data imputation (Linnorm.DataImput).

Linnorm can work with raw count, CPM, RPKM, FPKM and TPM. Additionally, the RnaXSim function is included for simulating RNA-seq data for the evaluation of DEG analysis methods.

This package aims to bring the power and flexibility of AnnData to the R ecosystem, allowing you to effortlessly manipulate and analyze your single-cell data. This package lets you work with backed h5ad and zarr files, directly access various slots (e.g. X, obs, var), or convert the data into SingleCellExperiment and Seurat objects.

This package provides a universal, user friendly, single-cell and bulk RNA sequencing visualization toolkit that allows highly customizable creation of color blindness friendly, publication-quality figures. dittoSeq accepts both SingleCellExperiment (SCE) and Seurat objects, as well as the import and usage, via conversion to an SCE, of SummarizedExperiment or DGEList bulk data. Visualizations include dimensionality reduction plots, heatmaps, scatterplots, percent composition or expression across groups, and more. Customizations range from size and title adjustments to automatic generation of annotations for heatmaps, overlay of trajectory analysis onto any dimensionality reduciton plot, hidden data overlay upon cursor hovering via ggplotly conversion, and many more. All with simple, discrete inputs. Color blindness friendliness is powered by legend adjustments (enlarged keys), and by allowing the use of shapes or letter-overlay in addition to the carefully selected codedittoColors().

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.

The epigenomics road map describes locations of epigenetic marks in DNA from a variety of cell types. Of interest are locations of histone modifications, sites of DNA methylation, and regions of accessible chromatin. This package presents a selection of elements of the road map including metadata and outputs of the ChromImpute procedure applied to ENCODE cell lines by Ernst and Kellis.

This package provides functions to detect and correct for batch effects in DNA methylation data. The core function is based on latent factor models and can also be used to predict missing values in any other matrix containing real numbers.

This package provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files, which are portable across platforms with hierarchical structure to store multiple scalable array-oriented data sets with metadata information. It is suited for large-scale datasets, especially for data which are much larger than the available random-access memory. The gdsfmt package offers efficient operations specifically designed for integers of less than 8 bits, since a diploid genotype, like single-nucleotide polymorphism (SNP), usually occupies fewer bits than a byte. Data compression and decompression are available with relatively efficient random access. It is also allowed to read a GDS file in parallel with multiple R processes supported by the package parallel.