This package provides the full genome sequences for Bos taurus (UCSC version bosTau8).

This package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. It also contains functions for identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data like gene expression/RNA sequencing/methylation/brain imaging data that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise.

In order to assess the quality of a set of predicted genes for a genome, evidence must first be mapped to that genome. Next, each gene must be categorized based on how strong the evidence is for or against that gene. The AssessORF package provides the functions and class structures necessary for accomplishing those tasks, using proteomics hits and evolutionarily conserved start codons as the forms of evidence.

RnBeads facilitates comprehensive analysis of various types of DNA methylation data at the genome scale.

The data consist of microarrays from 128 different individuals with acute lymphoblastic leukemia (ALL). A number of additional covariates are available. The data have been normalized (using rma) and it is the jointly normalized data that are available here. The data are presented in the form of an exprSet object.

This package contains data from untargeted mass spectrometry (MS) of modifications to oxidized cysteine (Cys) 34 in human serum albumin (HSA).

The package is able to read bead-level data (raw TIFFs and text files) output by BeadScan as well as bead-summary data from BeadStudio. Methods for quality assessment and low-level analysis are provided.

Many modern biological datasets consist of small counts that are not well fit by standard linear-Gaussian methods such as principal component analysis. This package provides implementations of count-based feature selection and dimension reduction algorithms. These methods can be used to facilitate unsupervised analysis of any high-dimensional data such as single-cell RNA-seq.

The fmcsR package introduces an efficient maximum common substructure (MCS) algorithms combined with a novel matching strategy that allows for atom and/or bond mismatches in the substructures shared among two small molecules. The resulting flexible MCSs (FMCSs) are often larger than strict MCSs, resulting in the identification of more common features in their source structures, as well as a higher sensitivity in finding compounds with weak structural similarities. The fmcsR package provides several utilities to use the FMCS algorithm for pairwise compound comparisons, structure similarity searching and clustering.

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

This package provides generic functions for interacting with the AnVIL system. Packages that use either GCP or Azure in AnVIL are built on top of AnVILBase. Extension packages will provide methods for interacting with other cloud providers.

This package awst (Asymmetric Within-Sample Transformation) that regularizes RNA-seq read counts and reduces the effect of noise on the classification of samples. AWST comprises two main steps: standardization and smoothing. These steps transform gene expression data to reduce the noise of the lowly expressed features, which suffer from background effects and low signal-to-noise ratio, and the influence of the highly expressed features, which may be the result of amplification bias and other experimental artifacts.

This package provides tools for the computationally efficient analysis of quantitative trait loci (QTL) data, including eQTL, mQTL, dsQTL, etc. The software in this package aims to support refinements and functional interpretation of members of a collection of association statistics on a family of feature/genome hypotheses.

This is an annotation package for Illumina Infinium DNA methylation probes. It contains the compiled HumanMethylation27 and HumanMethylation450 annotations.

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 a consistent C++ class interface for a variety of commonly used matrix types, including sparse and HDF5-backed matrices.

BBCAnalyzer is a package for visualizing the relative or absolute number of bases, deletions and insertions at defined positions in sequence alignment data available as bam files in comparison to the reference bases. Markers for the relative base frequencies, the mean quality of the detected bases, known mutations or polymorphisms and variants called in the data may additionally be included in the plots.

This package provides functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data. It includes functions for rudimentary data cleaning, construction and summarization of correlation networks, module identification and functions for relating both variables and modules to sample traits. It also includes a number of utility functions for data manipulation and visualization.

RCAS aims to be a standalone RNA-centric annotation system that provides intuitive reports and publication-ready graphics. This package provides the R library implementing most of the pipeline's features.

This package is devoted to analyzing high-throughput data (e.g. gene expression microarray, DNA methylation microarray, RNA-seq) from complex tissues. Current functionalities include

1. detect cell-type specific or cross-cell type differential signals

2. tree-based differential analysis

3. improve variable selection in reference-free deconvolution

4. partial reference-free deconvolution with prior knowledge.

This package defines low-level functions for mass spectrometry data and is independent of any high-level data structures. These functions include mass spectra processing functions (noise estimation, smoothing, binning), quantitative aggregation functions (median polish, robust summarisation, etc.), missing data imputation, data normalisation (quantiles, vsn, etc.) as well as misc helper functions, that are used across high-level data structure within the R for Mass Spectrometry packages.

The package implements a method for normalising microarray intensities, and works for single- and multiple-color arrays. It can also be used for data from other technologies, as long as they have similar format. The method uses a robust variant of the maximum-likelihood estimator for an additive-multiplicative error model and affine calibration. The model incorporates data calibration step (a.k.a. normalization), a model for the dependence of the variance on the mean intensity and a variance stabilizing data transformation. Differences between transformed intensities are analogous to "normalized log-ratios". However, in contrast to the latter, their variance is independent of the mean, and they are usually more sensitive and specific in detecting differential transcription.

Analyze and visualize Mutation Annotation Format (MAF) files from large scale sequencing studies. This package provides various functions to perform most commonly used analyses in cancer genomics and to create feature rich customizable visualzations with minimal effort.