Clustering is carried out to identify patterns in transcriptomics profiles to determine clinically relevant subgroups of patients. Feature (gene) selection is a critical and an integral part of the process. Currently, there are many feature selection and clustering methods to identify the relevant genes and perform clustering of samples. However, choosing an appropriate methodology is difficult. In addition, extensive feature selection methods have not been supported by the available packages. Hence, we developed an integrative R-package called multiClust that allows researchers to experiment with the choice of combination of methods for gene selection and clustering with ease. Using multiClust, we identified the best performing clustering methodology in the context of clinical outcome. Our observations demonstrate that simple methods such as variance-based ranking perform well on the majority of data sets, provided that the appropriate number of genes is selected. However, different gene ranking and selection methods remain relevant as no methodology works for all studies.

This package produces metagene plots to compare coverages of sequencing experiments at selected groups of genomic regions. It can be used for such analyses as assessing the binding of DNA-interacting proteins at promoter regions or surveying antisense transcription over the length of a gene. The metagene2 package can manage all aspects of the analysis, from normalization of coverages to plot facetting according to experimental metadata. Bootstraping analysis is used to provide confidence intervals of per-sample mean coverages.

MetaPhOR was developed to enable users to assess metabolic dysregulation using transcriptomic-level data (RNA-sequencing and Microarray data) and produce publication-quality figures. A list of differentially expressed genes (DEGs), which includes fold change and p value, from DESeq2 or limma, can be used as input, with sample size for MetaPhOR, and will produce a data frame of scores for each KEGG pathway. These scores represent the magnitude and direction of transcriptional change within the pathway, along with estimated p-values.MetaPhOR then uses these scores to visualize metabolic profiles within and between samples through a variety of mechanisms, including: bubble plots, heatmaps, and pathway models.

Agilent Chips that use Agilent design number 026655 annotation data (chip MmAgilentDesign026655) assembled using data from public repositories.

This package provides a package for the detection of de novo copy number deletions in targeted sequencing of trios with high sensitivity and positive predictive value.

methylscaper is an R package for processing and visualizing data jointly profiling methylation and chromatin accessibility (MAPit, NOMe-seq, scNMT-seq, nanoNOMe, etc.). The package supports both single-cell and single-molecule data, and a common interface for jointly visualizing both data types through the generation of ordered representational methylation-state matrices. The Shiny app allows for an interactive seriation process of refinement and re-weighting that optimally orders the cells or DNA molecules to discover methylation patterns and nucleosome positioning.

Utility package to facilitate integration and analysis of EBI MGnify data in R. The package can be used to import microbial data for instance into TreeSummarizedExperiment (TreeSE). In TreeSE format, the data is directly compatible with miaverse framework.

gene target tabale of miRNA for human/mouse used for MiRaGE package.

An increasing number of microbiome datasets have been generated and analyzed with the help of rapidly developing sequencing technologies. At present, analysis of taxonomic profiling data is mainly conducted using composition-based methods, which ignores interactions between community members. Besides this, a lack of efficient ways to compare microbial interaction networks limited the study of community dynamics. To better understand how community diversity is affected by complex interactions between its members, we developed a framework (Microbial community dIversity and Network Analysis, mina), a comprehensive framework for microbial community diversity analysis and network comparison. By defining and integrating network-derived community features, we greatly reduce noise-to-signal ratio for diversity analyses. A bootstrap and permutation-based method was implemented to assess community network dissimilarities and extract discriminative features in a statistically principled way.

Collection of functions to calculate a nucleotide sequence surrounding for splice donors sites to either activate or repress donor usage. The proposed alternative nucleotide sequence encodes the same amino acid and could be applied e.g. in reporter systems to silence or activate cryptic splice donor sites.

Mixture Nested Effects Models (mnem) is an extension of Nested Effects Models and allows for the analysis of single cell perturbation data provided by methods like Perturb-Seq (Dixit et al., 2016) or Crop-Seq (Datlinger et al., 2017). In those experiments each of many cells is perturbed by a knock-down of a specific gene, i.e. several cells are perturbed by a knock-down of gene A, several by a knock-down of gene B, ... and so forth. The observed read-out has to be multi-trait and in the case of the Perturb-/Crop-Seq gene are expression profiles for each cell. mnem uses a mixture model to simultaneously cluster the cell population into k clusters and and infer k networks causally linking the perturbed genes for each cluster. The mixture components are inferred via an expectation maximization algorithm.

Data from human (HG18) 4plex NimbleGen array. It has 24k genes with 3 60mer probes per gene.

This package aligns LC-HRMS metabolomics datasets acquired from biologically similar specimens analyzed under similar, but not necessarily identical, conditions. Peak-picked and simply aligned metabolomics feature tables (consisting of m/z, rt, and per-sample abundance measurements, plus optional identifiers & adduct annotations) are accepted as input. The package outputs a combined table of feature pair alignments, organized into groups of similar m/z, and ranked by a similarity score. Input tables are assumed to be acquired using similar (but not necessarily identical) analytical methods.

This package provides an interface to several normalization and statistical testing packages for RNA-Seq gene expression data. Additionally, it creates several diagnostic plots, performs meta-analysis by combinining the results of several statistical tests and reports the results in an interactive way.

This package contains tools and methods for preprocessing microbiome data. Functionality includes library generation, demultiplexing, alignment, and microbe identification. It is in part an R translation of the PathoScope 2.0 pipeline.

MapScape integrates clonal prevalence, clonal hierarchy, anatomic and mutational information to provide interactive visualization of spatial clonal evolution. There are four inputs to MapScape: (i) the clonal phylogeny, (ii) clonal prevalences, (iii) an image reference, which may be a medical image or drawing and (iv) pixel locations for each sample on the referenced image. Optionally, MapScape can accept a data table of mutations for each clone and their variant allele frequencies in each sample. The output of MapScape consists of a cropped anatomical image surrounded by two representations of each tumour sample. The first, a cellular aggregate, visually displays the prevalence of each clone. The second shows a skeleton of the clonal phylogeny while highlighting only those clones present in the sample. Together, these representations enable the analyst to visualize the distribution of clones throughout anatomic space.

Affymetrix mogene20 annotation data (chip mogene20stprobeset) assembled using data from public repositories.

Affymetrix mogene10 annotation data (chip mogene10sttranscriptcluster) assembled using data from public repositories.

MetaboSignal is an R package that allows merging, analyzing and customizing metabolic and signaling KEGG pathways. It is a network-based approach designed to explore the topological relationship between genes (signaling- or enzymatic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape and regulatory networks of metabolic phenotypes.

Affymetrix Affymetrix MG_U74A Array annotation data (chip mgu74a) assembled using data from public repositories.

Affymetrix moex10 annotation data (chip moex10sttranscriptcluster) assembled using data from public repositories.

This package provides a set of tools for network analysis using mass spectrometry-based proteomics data and network databases. The package takes as input the output of MSstats differential abundance analysis and provides functions to perform enrichment analysis and visualization in the context of prior knowledge from past literature. Notably, this package integrates with INDRA, which is a database of biological networks extracted from the literature using text mining techniques.

maSigPro is a regression based approach to find genes for which there are significant gene expression profile differences between experimental groups in time course microarray and RNA-Seq experiments.

Affymetrix Affymetrix Mu11KsubA Array annotation data (chip mu11ksuba) assembled using data from public repositories.