The project is intended to support the use of sequins(synthetic sequencing spike-in controls) owned and made available by the Garvan Institute of Medical Research. The goal is to provide a standard library for quantitative analysis, modelling, and visualization of spike-in controls.

This is the classification package for the automated analysis of Affymetrix arrays.

This is a package for the analysis of alternative splicing and isoform switches with predicted functional consequences (e.g. gain/loss of protein domains etc.) from quantification of all types of RNASeq by tools such as Kallisto, Salmon, StringTie, Cufflinks/Cuffdiff etc.

This is a package for noise-robust soft clustering of gene expression time-series data (including a graphical user interface).

Microarray quality assessment is a major concern of microarray analysts. This package provides some simple approaches to in silico creation of quality problems in CEL-level data to help evaluate performance of quality metrics.

This package can do non-parametric bootstrap and permutation resampling-based multiple testing procedures (including empirical Bayes methods) for controlling the family-wise error rate (FWER), generalized family-wise error rate (gFWER), tail probability of the proportion of false positives (TPPFP), and false discovery rate (FDR). Several choices of bootstrap-based null distribution are implemented (centered, centered and scaled, quantile-transformed). Single-step and step-wise methods are available. Tests based on a variety of T- and F-statistics (including T-statistics based on regression parameters from linear and survival models as well as those based on correlation parameters) are included. When probing hypotheses with T-statistics, users may also select a potentially faster null distribution which is multivariate normal with mean zero and variance covariance matrix derived from the vector influence function. Results are reported in terms of adjusted P-values, confidence regions and test statistic cutoffs. The procedures are directly applicable to identifying differentially expressed genes in DNA microarray experiments.

This package provides classes and other infrastructure to implement filters for manipulating Bioconductor annotation resources. The filters are used by ensembldb, Organism.dplyr, and other packages.

tRNAdbImport imports the entries of the tRNAdb and mtRNAdb as GRanges object.

This package provides tools for Bayesian integrated analysis of Affymetrix GeneChips.

Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential structures. This package provides a highly flexible way to arrange multiple heatmaps and supports self-defined annotation graphics.

This package implements methods to remove unwanted variation (RUV) of Risso et al. (2014) for the normalization of RNA-Seq read counts between samples.

This package is designed to store minor allele frequency data. It retrieves this data from the Genome Aggregation Database (gnomAD version 3.1.2) for the human genome version GRCh38.

This package provides tools for statistical analysis of assembled transcriptomes, including flexible differential expression analysis, visualization of transcript structures, and matching of assembled transcripts to annotation.

This package provides the headers and static library of Protocol buffers for other R packages to compile and link against.

The rpx package implements an interface to proteomics data submitted to the ProteomeXchange consortium.

Genome-wide association studies (GWAS) are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. The R package SNPRelate provides a binary format for single-nucleotide polymorphism (SNP) data in GWAS utilizing CoreArray Genomic Data Structure (GDS) data files. The GDS format offers the efficient operations specifically designed for integers with two bits, since a SNP could occupy only two bits. SNPRelate is also designed to accelerate two key computations on SNP data using parallel computing for multi-core symmetric multiprocessing computer architectures: Principal Component Analysis (PCA) and relatedness analysis using Identity-By-Descent measures. The SNP GDS format is also used by the GWASTools package with the support of S4 classes and generic functions. The extended GDS format is implemented in the SeqArray package to support the storage of single nucleotide variations (SNVs), insertion/deletion polymorphism (indel) and structural variation calls in whole-genome and whole-exome variant data.

This package provides the output of running Salmon on a set of 24 RNA-seq samples from Alasoo, et al. "Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response", published in Nature Genetics, January 2018.

This package implements the GENIE3 algorithm for inferring gene regulatory networks from expression data.

This package provides tools for differential expression analysis at both gene and isoform level using RNA-seq data

This package provides tools for representing and modeling data in the EMBL-EBI GWAS catalog.

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.

This package provides functions for reading array comparative genomic hybridization (aCGH) data from image analysis output files and clone information files, creation of aCGH objects for storing these data. Basic methods are accessing/replacing, subsetting, printing and plotting aCGH objects.

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 offers tools to create DNA barcode sets capable of correcting insertion, deletion, and substitution errors. Existing barcodes can be analyzed regarding their minimal, maximal and average distances between barcodes. Finally, reads that start with a (possibly mutated) barcode can be demultiplexed, i.e. assigned to their original reference barcode.