Latent variable modeling with Principal Component Analysis (PCA) and Partial Least Squares (PLS) are powerful methods for visualization, regression, classification, and feature selection of omics data where the number of variables exceeds the number of samples and with multicollinearity among variables. Orthogonal Partial Least Squares (OPLS) enables to separately model the variation correlated (predictive) to the factor of interest and the uncorrelated (orthogonal) variation. While performing similarly to PLS, OPLS facilitates interpretation.

This package provides imlementations of PCA, PLS, and OPLS for multivariate analysis and feature selection of omics data. In addition to scores, loadings and weights plots, the package provides metrics and graphics to determine the optimal number of components (e.g. with the R2 and Q2 coefficients), check the validity of the model by permutation testing, detect outliers, and perform feature selection (e.g. with Variable Importance in Projection or regression coefficients).

This package provides tools for analysis of ChIP-seq and other functional sequencing data.

iClusterPlus is developed for integrative clustering analysis of multi-type genomic data and is an enhanced version of iCluster proposed and developed by Shen, Olshen and Ladanyi (2009). Multi-type genomic data arise from the experiments where biological samples (e.g. tumor samples) are analyzed by multiple techniques, for instance, array comparative genomic hybridization (aCGH), gene expression microarray, RNA-seq and DNA-seq, and so on. In the iClusterPlus model, binary observations such as somatic mutation are modeled as Binomial processes; categorical observations such as copy number states are realizations of Multinomial random variables; counts are modeled as Poisson random processes; and continuous measures are modeled by Gaussian distributions.

Phyloseq provides a set of classes and tools to facilitate the import, storage, analysis, and graphical display of microbiome census data.

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 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.

This package provides a Poisson mixture model is implemented to cluster genes from high-throughput transcriptome sequencing (RNA-seq) data. Parameter estimation is performed using either the EM or CEM algorithm, and the slope heuristics are used for model selection (i.e., to choose the number of clusters).

This package provides dataset samples (Affymetrix: Expression, Gene, Exon, SNP; NimbleGen: Expression, Tiling) to be used with the oligo package.

Genomic data analyses requires integrated visualization of known genomic information and new experimental data. Gviz uses the biomaRt and the rtracklayer packages to perform live annotation queries to Ensembl and UCSC and translates this to e.g. gene/transcript structures in viewports of the grid graphics package. This results in genomic information plotted together with your data.

This package provides tools to parse Illumina Sequence Analysis Viewer (SAV) files, access data, and generate QC plots.

This package provides a function that performs gene-permuting of a gene-set enrichment analysis (GSEA) calculation with or without the absolute filtering. Without filtering, users can perform (original) two-tailed or one-tailed absolute GSEA.

Store minor allele frequency data from the Phase 1 of the 1000 Genomes Project for the human genome version hs37d5.

This package provides a database of PolyPhen predictions for Homo sapiens dbSNP build 131.

This is a package providing tools to quantify and interpret multiple sources of biological and technical variation in gene expression experiments. It uses a linear mixed model to quantify variation in gene expression attributable to individual, tissue, time point, or technical variables. The package includes dream differential expression analysis for repeated measures.

The biovizBase package is designed to provide a set of utilities, color schemes and conventions for genomic data. It serves as the base for various high-level packages for biological data visualization. This saves development effort and encourages consistency.

Representing nucleotide modifications in a nucleotide sequence is usually done via special characters from a number of sources. This represents a challenge to work with in R and the Biostrings package. The Modstrings package implements this functionality for RNA and DNA sequences containing modified nucleotides by translating the character internally in order to work with the infrastructure of the Biostrings package. For this the ModRNAString and ModDNAString classes and derivates and functions to construct and modify these objects despite the encoding issues are implemenented. In addition the conversion from sequences to list like location information (and the reverse operation) is implemented as well.

BAnOCC is a package designed for compositional data, where each sample sums to one. It infers the approximate covariance of the unconstrained data using a Bayesian model coded with rstan. It provides as output the stanfit object as well as posterior median and credible interval estimates for each correlation element.

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

This package provides a simple interface to and data from the Human Protein Atlas project.

This package provides full genome sequences for Caenorhabditis elegans (Worm) as provided by UCSC (ce10, Oct 2010) and stored in Biostrings objects.

This package provides a flexible representation of copy number, mutation, and other data that fit into the ragged array schema for genomic location data. The basic representation of such data provides a rectangular flat table interface to the user with range information in the rows and samples/specimen in the columns. The RaggedExperiment class derives from a GRangesList representation and provides a semblance of a rectangular dataset.

Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (dropout imputation). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. The ADImpute package proposes two methods to address this issue:

1. a gene regulatory network-based approach using gene-gene relationships learnt from external data;

2. a baseline approach corresponding to a sample-wide average.

ADImpute implements these novel methods and also combines them with existing imputation methods like DrImpute and SAVER. ADImpute can learn the best performing method per gene and combine the results from different methods into an ensemble.

This package provides code for generating Annotation packages and their databases. Packages produced are intended to be used with AnnotationDbi.

TrackViewer offers multi-omics analysis with web based tracks and lollipops. Visualize mapped reads along with annotation as track layers for NGS datasets such as ChIP-seq, RNA-seq, miRNA-seq, DNA-seq, SNPs and methylation data.