This package is a collection of gene expression data from a breast cancer study published in Wang et al. 2005 and Minn et al 2007.

This package contains the basic methods needed to generate interactive Shiny-based display methods for Bioconductor objects.

This package provides functions for bipartite network rewiring through N consecutive switching steps and for the computation of the minimal number of switching steps to be performed in order to maximise the dissimilarity with respect to the original network. It includes functions for the analysis of the introduced randomness across the switching steps and several other routines to analyse the resulting networks and their natural projections.

This package provides a method to purify a cell type or cell population of interest from heterogeneous datasets. scGate package automatizes marker-based purification of specific cell populations, without requiring training data or reference gene expression profiles. scGate takes as input a gene expression matrix stored in a Seurat object and a GM, consisting of a set of marker genes that define the cell population of interest. It evaluates the strength of signature marker expression in each cell using the rank-based method UCell, and then performs kNN smoothing by calculating the mean UCell score across neighboring cells. kNN-smoothing aims at compensating for the large degree of sparsity in scRNAseq data. Finally, a universal threshold over kNN-smoothed signature scores is applied in binary decision trees generated from the user-provided gating model, to annotate cells as either “pure” or “impure”, with respect to the cell population of interest.

Logistic Factor Analysis (LFA) is a method for a PCA analogue on Binomial data via estimation of latent structure in the natural parameter.

This package comprises a set of pretrained machine learning models to predict basic immune cell types. This enables to quickly get a first annotation of the cell types present in the dataset without requiring prior knowledge. The package also lets you train using own models to predict new cell types based on specific research needs.

This is a package for parsing Affymetrix files (CDF, CEL, CHP, BPMAP, BAR). It provides methods for fast and memory efficient parsing of Affymetrix files using the Affymetrix' Fusion SDK. Both ASCII- and binary-based files are supported. Currently, there are methods for reading chip definition file (CDF) and a cell intensity file (CEL). These files can be read either in full or in part. For example, probe signals from a few probesets can be extracted very quickly from a set of CEL files into a convenient list structure.

The package implements an algorithm for fast gene set enrichment analysis. Using the fast algorithm makes more permutations and gets more fine grained p-values, which allows using accurate standard approaches to multiple hypothesis correction.

This package extracts tandem mass spectrometry (MS/MS) ID data from mzIdentML (leveraging the mzID package) or text files. After collating the search results from multiple datasets it assesses their identification quality and optimize filtering criteria to achieve the maximum number of identifications while not exceeding a specified false discovery rate. It also contains a number of utilities to explore the MS/MS results and assess missed and irregular enzymatic cleavages, mass measurement accuracy, etc.

Exploratory data analysis to assess the quality of a set of LC-MS/MS experiments, and visualize de influence of the involved factors.

This package provides supporting data for the TCGAbiolinksGUI package.

This package provides a number of utility functions for handling single-cell RNA-seq data from droplet technologies such as 10X Genomics. This includes data loading from count matrices or molecule information files, identification of cells from empty droplets, removal of barcode-swapped pseudo-cells, and downsampling of the count matrix.

This package provides an R wrapper around the popular bowtie short read aligner and around SpliceMap, a de novo splice junction discovery and alignment tool.

The biodb package provides access to standard remote chemical and biological databases (ChEBI, KEGG, HMDB, ...), as well as to in-house local database files (CSV, SQLite), with easy retrieval of entries, access to web services, search of compounds by mass and/or name, and mass spectra matching for LCMS and MSMS. Its architecture as a development framework facilitates the development of new database connectors for local projects or inside separate published packages.

This package provides Escherichia coli full genomes for several strains as provided by NCBI on 2008/08/05 and stored in Biostrings objects.

This package provides raw data objects to be used for blood cell proportion estimation in minfi and similar packages. The FlowSorted.Blood.EPIC object is based in samples assayed by Brock Christensen and colleagues; for details see Salas et al. 2018. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE110554.

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 a variety of low-level analyses of single-cell RNA-seq data. Methods are provided for normalization of cell-specific biases, assignment of cell cycle phase, and detection of highly variable and significantly correlated genes.

This package allows biologists to judge in the first place whether the sequence surrounding the polymorphism is a good match, and in the second place how much information is gained or lost in one allele of the polymorphism relative to another. This package gives a choice of algorithms for interrogation of genomes with motifs from public sources:

1. a weighted-sum probability matrix;

2. log-probabilities;

3. weighted by relative entropy.

This package can predict effects for novel or previously described variants in public databases, making it suitable for tasks beyond the scope of its original design. Lastly, it can be used to interrogate any genome curated within Bioconductor.

Dirichlet-multinomial mixture models can be used to describe variability in microbial metagenomic data. This package is an interface to code originally made available by Holmes, Harris, and Quince, 2012, PLoS ONE 7(2): 1-15.

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 package provides classes for storing very large GWAS data sets and annotation, and functions for GWAS data cleaning and analysis.

This is an R package to make it easier to import and store phylogenetic trees with associated data; and to link external data from different sources to phylogeny. It also supports exporting phylogenetic trees with heterogeneous associated data to a single tree file and can be served as a platform for merging tree with associated data and converting file formats.

The method implemented in this package performs bottom-up hierarchical clustering, using a Dirichlet Process (infinite mixture) to model uncertainty in the data and Bayesian model selection to decide at each step which clusters to merge. This avoids several limitations of traditional methods, for example how many clusters there should be and how to choose a principled distance metric. This implementation accepts multinomial (i.e. discrete, with 2+ categories) or time-series data. This version also includes a randomised algorithm which is more efficient for larger data sets.