DEqMS is developped on top of Limma. However, Limma assumes same prior variance for all genes. In proteomics, the accuracy of protein abundance estimates varies by the number of peptides/PSMs quantified in both label-free and labelled data. Proteins quantification by multiple peptides or PSMs are more accurate. DEqMS package is able to estimate different prior variances for proteins quantified by different number of PSMs/peptides, therefore acchieving better accuracy. The package can be applied to analyze both label-free and labelled proteomics data.

This package provides functions for creating various visualizations, convenient wrappers, and quality-of-life utilities for single cell experiment objects. It offers a streamlined approach to visualize results and integrates different tools for easy use.

This package builds on existing tools and adds some simple but extremely useful capabilities for working wth ChIP-Seq data. The focus is on detecting differential binding windows/regions. One set of functions focusses on set-operations retaining mcols for GRanges objects, whilst another group of functions are to aid visualisation of results. Coercion to tibble objects is also implemented.

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 E\_coli\_Asv2\_probe\_tab.

epiNEM is an extension of the original Nested Effects Models (NEM). EpiNEM is able to take into account double knockouts and infer more complex network signalling pathways. It is tailored towards large scale double knock-out screens.

The EnrichmentBrowser package implements essential functionality for the enrichment analysis of gene expression data. The analysis combines the advantages of set-based and network-based enrichment analysis in order to derive high-confidence gene sets and biological pathways that are differentially regulated in the expression data under investigation. Besides, the package facilitates the visualization and exploration of such sets and pathways.

eudysbiome a package that permits to annotate the differential genera as harmful/harmless based on their ability to contribute to host diseases (as indicated in literature) or unknown based on their ambiguous genus classification. Further, the package statistically measures the eubiotic (harmless genera increase or harmful genera decrease) or dysbiotic(harmless genera decrease or harmful genera increase) impact of a given treatment or environmental change on the (gut-intestinal, GI) microbiome in comparison to the microbiome of the reference condition.

EpiTxDb facilitates the storage of epitranscriptomic information. More specifically, it can keep track of modification identity, position, the enzyme for introducing it on the RNA, a specifier which determines the position on the RNA to be modified and the literature references each modification is associated with.

This package allows user to quickly access ENCODE project files metadata and give access to helper functions to query the ENCODE rest api, download ENCODE datasets and save the database in SQLite format.

Supporting data for the ELMER package. It includes: - elmer.data.example.promoter: mae.promoter - elmer.data.example: data - EPIC.hg38.manifest - EPIC.hg19.manifest - hm450.hg38.manifest - hm450.hg19.manifest - hocomoco.table - human.TF - LUSC_meth_refined: Meth - LUSC_RNA_refined: GeneExp - Probes.motif.hg19.450K - Probes.motif.hg19.EPIC - Probes.motif.hg38.450K - Probes.motif.hg38.EPIC - TF.family - TF.subfamily - Human_genes__GRCh37_p13 - Human_genes__GRCh38_p12 - Human_genes__GRCh37_p13__tss - Human_genes__GRCh38_p12__tss.

Exposes an annotation databases generated from several sources by exposing these as EpiTxDb object. Generated for Homo sapiens/hg38.

Exposes an annotation databases generated from Ensembl.

This package provides access to eoPred pretrained model hosted on ExperimentHub. Model was trained on placental DNA methylation preeclampsia samples using mixOmics splsda. There are two resources: 1. the model object, and 2. a testing data set used to demonstrate the function.

This package provides reference data for EpipwR. EpipwR is a fast and efficient power analysis for continuous and binary phenotypes of epigenomic-wide association studies. This package is only meant to be used in conjunction with EpipwR.

Combining P-values from multiple statistical tests is common in bioinformatics. However, this procedure is non-trivial for dependent P-values. This package implements an empirical adaptation of Brown’s Method (an extension of Fisher’s Method) for combining dependent P-values which is appropriate for highly correlated data sets found in high-throughput biological experiments.

EDIRquery provides a tool to search for genes of interest within the Exome Database of Interspersed Repeats (EDIR). A gene name is a required input, and users can additionally specify repeat sequence lengths, minimum and maximum distance between sequences, and whether to allow a 1-bp mismatch. Outputs include a summary of results by repeat length, as well as a dataframe of query results. Example data provided includes a subset of the data for the gene GAA (ENSG00000171298). To query the full database requires providing a path to the downloaded database files as a parameter.

Exposes an annotation databases generated from several sources by exposing these as EpiTxDb object. Generated for Mus musculus/mm10.

This package includes gene set collections that are used for the Ensemble of Gene Set Enrichment Analyses (EGSEA) method for gene set testing. It includes Human and Mouse versions of the MSidDB (Subramanian, et al. (2005) PNAS, 102(43):15545-15550) and GeneSetDB (Araki, et al. (2012) FEBS Open Bio, 2:76-82) collections.

Exposes an annotation databases generated from several sources by exposing these as EpiTxDb object. Generated for Saccharomyces cerevisiae/sacCer3.

Affymetrix Affymetrix E_coli_2 Array annotation data (chip ecoli2) assembled using data from public repositories.

EpiCompare is used to compare and analyse epigenetic datasets for quality control and benchmarking purposes. The package outputs an HTML report consisting of three sections: (1. General metrics) Metrics on peaks (percentage of blacklisted and non-standard peaks, and peak widths) and fragments (duplication rate) of samples, (2. Peak overlap) Percentage and statistical significance of overlapping and non-overlapping peaks. Also includes upset plot and (3. Functional annotation) functional annotation (ChromHMM, ChIPseeker and enrichment analysis) of peaks. Also includes peak enrichment around TSS.

We developed EasyCellType which can automatically examine the input marker lists obtained from existing software such as Seurat over the cell markerdatabases. Two quantification approaches to annotate cell types are provided: Gene set enrichment analysis (GSEA) and a modified versio of Fisher's exact test. The function presents annotation recommendations in graphical outcomes: bar plots for each cluster showing candidate cell types, as well as a dot plot summarizing the top 5 significant annotations for each cluster.

To implement disease ontology (DO) enrichment analysis, this package is designed and presents a double weighted model based on the latest annotations of the human genome with DO terms, by integrating the DO graph topology on a global scale. This package exhibits high accuracy that it can identify more specific DO terms, which alleviates the over enriched problem. The package includes various statistical models and visualization schemes for discovering the associations between genes and diseases from biological big data.

Evaluating the reliability of your own metrics and the measurements done on your own datasets by analysing the stability and goodness of the classifications of such metrics.