APL is a package developed for computation of Association Plots (AP), a method for visualization and analysis of single cell transcriptomics data. The main focus of APL is the identification of genes characteristic for individual clusters of cells from input data. The package performs correspondence analysis (CA) and allows to identify cluster-specific genes using Association Plots. Additionally, APL computes the cluster-specificity scores for all genes which allows to rank the genes by their specificity for a selected cell cluster of interest.

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was AG\_probe\_tab.

The AWAggregatorData package contains the data associated with the AWAggregator R package. It includes two pre-trained random forest models, one incorporating the average coefficient of variation as a feature, and the other one not including it. It also contains the PSMs in Benchmark Set 1~3 derived from the psm.tsv output files generated by FragPipe, which are used to train the random forest models.

This package unifies access to Statistal Modeling of Omics Data. Across linear modeling engines (lm, lme, lmer, limma, and wilcoxon). Across coding systems (treatment, difference, deviation, etc). Across model formulae (with/without intercept, random effect, interaction or nesting). Across omics platforms (microarray, rnaseq, msproteomics, affinity proteomics, metabolomics). Across projection methods (pca, pls, sma, lda, spls, opls). Across clustering methods (hclust, pam, cmeans). Across survival methods (coxph, survdiff, coin). It provides a fast enrichment analysis implementation.

adverSCarial is an R Package designed for generating and analyzing the vulnerability of scRNA-seq classifiers to adversarial attacks. The package is versatile and provides a format for integrating any type of classifier. It offers functions for studying and generating two types of attacks, single gene attack and max change attack. The single-gene attack involves making a small modification to the input to alter the classification. The max-change attack involves making a large modification to the input without changing its classification. The CGD attack is based on an estimated gradient descent. against adversarial attacks. The package provides a comprehensive solution for evaluating the robustness of scRNA-seq classifiers against adversarial attacks.

Store Google DeepMind AlphaMissense v2023 hg38 pathogenicity scores AnnotationHub Resource Metadata. Provide provenance and citation information for Google DeepMind AlphaMissense v2023 hg38 pathogenicity score AnnotationHub resources. Illustrate in a vignette how to access those resources.

Save MultiAssayExperiments into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

Supplies AnnotationHub with MassBank metabolite/compound annotations bundled in CompDb SQLite databases. CompDb SQLite databases contain general compound annotation as well as fragment spectra representing fragmentation patterns of compounds ions. MassBank data is retrieved from https://massbank.eu/MassBank and processed using helper functions from the CompoundDb Bioconductor package into redistributable SQLite databases.

ADAPT carries out differential abundance analysis for microbiome metagenomics data in phyloseq format. It has two innovations. One is to treat zero counts as left censored and use Tobit models for log count ratios. The other is an innovative way to find non-differentially abundant taxa as reference, then use the reference taxa to find the differentially abundant ones.

Supplies AnnotationHub with some preprocessed sqlite, tibble, and data.table datasets of PubMed. All the datasets are generated by our Snakemake workflow [pubmed-workflow](https://github.com/rikenbit/pubmed-workflow). For the details, see the README.md of pubmed-workflow.

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was ATH1-121501\_probe\_tab.

Assay for Transpose-Accessible Chromatin using sequencing (ATAC-seq) is a technique to assess genome-wide chromatin accessibility by probing open chromatin with hyperactive mutant Tn5 Transposase that inserts sequencing adapters into open regions of the genome. ATACseqTFEA is an improvement of the current computational method that detects differential activity of transcription factors (TFs). ATACseqTFEA not only uses the difference of open region information, but also (or emphasizes) the difference of TFs footprints (cutting sites or insertion sites). ATACseqTFEA provides an easy, rigorous way to broadly assess TF activity changes between two conditions.

This package provides a package containing an environment representing the AG.CDF file.

Studies including both microbiome and metabolomics data are becoming more common. Often, it would be helpful to integrate both datasets in order to see if they corroborate each others patterns. All vs all association is imprecise and likely to yield spurious associations. This package takes a knowledge-based approach to constrain association search space, only considering metabolite-function pairs that have been recorded in a pathway database. This package also provides a framework to assess differential association.

Supplies AnnotationHub with EnsDb Ensembl-based annotation databases for all species. EnsDb SQLite databases are generated separately from Ensembl MySQL databases using functions from the ensembldb package employing the Ensembl Perl API.

ASSIGN is a computational tool to evaluate the pathway deregulation/activation status in individual patient samples. ASSIGN employs a flexible Bayesian factor analysis approach that adapts predetermined pathway signatures derived either from knowledge-based literature or from perturbation experiments to the cell-/tissue-specific pathway signatures. The deregulation/activation level of each context-specific pathway is quantified to a score, which represents the extent to which a patient sample encompasses the pathway deregulation/activation signature.

Data frame containing alternative splicing events. The splicing events were compiled from the annotation files used by the alternative splicing quantification tools MISO, VAST-TOOLS, SUPPA and rMATS.

This package contains pre-built human (GPL96) database of gene expression profiles. The gene expression data was downloaded from NCBI GEO, preprocessed and normalized consistently. The biological context of each sample was recorded and manually verified based on the sample description in GEO.

Supplies AnnotationHub with `MeSHDb` NIH MeSH annotation databases for many species. All the SQLite files and metadata.csv are generated by our Snakemake workflow [mesh-workflow](https://github.com/rikenbit/mesh-workflow).

1D NMR example spectra and additional data for use with the ASICS package. Raw 1D Bruker spectral data files were found in the MetaboLights database (https://www.ebi.ac.uk/metabolights/, study MTBLS1).

Affymetrix Affymetrix ATH1-121501 Array annotation data (chip ath1121501) assembled using data from public repositories.

Umbrella for the alabaster suite, providing a single-line import for all alabaster.* packages. Installing this package ensures that all known alabaster.* packages are also installed, avoiding problems with missing packages when a staging method or loading function is dynamically requested. Obviously, this comes at the cost of needing to install more packages, so advanced users and application developers may prefer to install the required alabaster.* packages individually.

Save BumpyMatrix objects into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

Save Biostrings objects to file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.