netbiov A package for visualizing complex biological network
A package that provides an effective visualization of large biological networks
MultiMed Testing multiple biological mediators simultaneously
Implements permutation method with joint correction for testing multiple mediators
mQTL.NMR Metabolomic Quantitative Trait Locus Mapping for 1H NMR data
mQTL.NMR provides a complete mQTL analysis pipeline for 1H NMR data. Distinctive features include normalisation using most-used approaches, peak alignment using RSPA approach, dimensionality reduction using SRV and binning approaches, and mQTL analysis for animal and human cohorts.
MEIGOR MEIGO - MEtaheuristics for bIoinformatics Global Optimization
GSReg Gene Set Regulation (GS-Reg)
A package for gene set analysis based on the variability of expressions. It implements DIfferential RAnk Conservation (DIRAC) and Gene Set tau Regulation (GS-tau-Reg) methods.
shinyMethyl Interactive visualization for Illumina's 450k methylation arrays
Interactive tool for visualizing Illumina's 450k array data
Pviz Peptide Annotation and Data Visualization using Gviz
Pviz adapts the Gviz package for protein sequences and data.
pRolocGUI Interactive visualisation of organelle (spatial) proteomics data
The package pRolocGUI comprises functions to interactively visualise organelle (spatial) proteomics data on the basis of pRoloc, pRolocdata and shiny.
Polyfit Add-on to DESeq and edgeR to improve p-values and q-values
Polyfit is an add-on to the packages edgeR and DESeq which ensures the p-value distribution is uniform over the interval [0, 1] for data satisfying the null hypothesis of no differential expression, and uses an adpated Storey-Tibshiran method to calculate q-values.
oposSOM Comprehensive analysis of transciptome data
This package translates microarray expression data into metadata of reduced dimension. It provides various sample-centered and group-centered visualizations, sample similarity analyses and functional enrichment analyses. The underlying SOM algorithm combines feature clustering, multidimensional scaling and dimension reduction, along with strong visualization capabilities. It enables extraction and description of functional expression modules inherent in the data.
flowDensity Sequential Flow Cytometry Data Gating
This package provides tools for automated sequential gating analogous to the manual gating strategy based on the density of the data.
DOQTL Genotyping and QTL Mapping in DO Mice
DOQTL is a quantitative trait locus (QTL) mapping pipeline designed for Diversity Outbred mice and other multi-parent outbred populations. The package reads in data from genotyping arrays and perform haplotype reconstruction using a hidden Markov model (HMM). The haplotype probabilities from the HMM are then used to perform linkage mapping. When founder sequences are available, DOQTL can use the haplotype reconstructions to impute the founder sequences onto DO genomes and perform association mapping.
riboSeq Analysis of sequencing data from ribosome profiling experiments.
Plotting functions, frameshift detection and parsing of sequencing data from ribosome profiling experiments.
Pbase Manipulating and exploring protein and proteomics data
A set of classes and functions to investigate and understand protein sequence data in the context of a proteomics experiment.
ssviz A small RNA-seq visualizer and analysis toolkit
Small RNA sequencing viewer
RGSEA Random Gene Set Enrichment Analysis
Combining bootstrap aggregating and Gene set enrichment analysis (GSEA), RGSEA is a classfication algorithm with high robustness and no over-fitting problem. It performs well especially for the data generated from different exprements.
DEGreport Report of DEG analysis
Creation of a HTML report of differential expression analyses of count data. It integrates some of the code mentioned in DESeq2 and edgeR vignettes, and report a ranked list of genes according to the fold changes mean and variability for each selected gene.
GSAR Gene Set Analysis in R
Gene set analysis using specific alternative hypotheses. Tests for differential expression, scale and net correlation structure.
RUVSeq Remove Unwanted Variation from RNA-Seq Data
This package implements the remove unwanted variation (RUV) methods of Risso et al. (2014) for the normalization of RNA-Seq read counts between samples.
Infer PAR-CLIP induced transitions and discriminate them from sequencing error, SNPs, contaminants and additional non-experimental causes, using a non-parametric mixture model. wavClusteR resolves cluster boundaries at high resolution and provides robust estimation of cluster statistics. In addition, the package allows to integrate RNA-Seq data to estimate FDR over the entire range of relative substitution frequencies. Furthermore, the package provides post-processing of results and functions to export results for UCSC genome browser visualization and motif search analysis. Key functions support parallel multicore computing. While wavClusteR was designed for PAR-CLIP data analysis, it can be applied to the analysis of other Next-Generation Sequencing data obtained from substitution inducing experimental procedures (e.g. BisSeq)
cosmiq cosmiq - COmbining Single Masses Into Quantities
cosmiq is a tool for the preprocessing of liquid- or gas - chromatography mass spectrometry (LCMS/GCMS) data with a focus on metabolomics or lipidomics applications. To improve the detection of low abundant signals, cosmiq generates master maps of the mZ/RT space from all acquired runs before a peak detection algorithm is applied. The result is a more robust identification and quantification of low-intensity MS signals compared to conventional approaches where peak picking is performed in each LCMS/GCMS file separately. The cosmiq package builds on the xcmsSet object structure and can be therefore integrated well with the package xcms as an alternative preprocessing step.
A quality control tool for flow cytometry data based on compositional data analysis.
msQC An R package for proteomics data quality control
This package creates a HTML format QC report for MS/MS-based proteomics data. The report is intended to allow the user to quickly assess the quality of proteomics data.
MethylAid Visual and interactive quality control of large Illumina 450k data sets
A visual and interactive web application using RStudio's shiny package. Bad quality samples are detected using sample-dependent and sample-independent controls present on the array and user adjustable thresholds. In depth exploration of bad quality samples can be performed using several interactive diagnostic plots of the quality control probes present on the array. Furthermore, the impact of any batch effect provided by the user can be explored.
DupChecker a package for checking high-throughput genomic data redundancy in meta-analysis
Meta-analysis has become a popular approach for high-throughput genomic data analysis because it often can significantly increase power to detect biological signals or patterns in datasets. However, when using public-available databases for meta-analysis, duplication of samples is an often encountered problem, especially for gene expression data. Not removing duplicates would make study results questionable. We developed a Bioconductor package DupChecker that efficiently identifies duplicated samples by generating MD5 fingerprints for raw data.
MoPS MoPS - Model-based Periodicity Screening
Identification and characterization of periodic fluctuations in time-series data.
HDTD Statistical Inference about the Mean Matrix and the Covariance Matrices in High-Dimensional Transposable Data (HDTD)
Characterization of intra-individual variability using physiologically relevant measurements provides important insights into fundamental biological questions ranging from cell type identity to tumor development. For each individual, the data measurements can be written as a matrix with the different subsamples of the individual recorded in the columns and the different phenotypic units recorded in the rows. Datasets of this type are called high-dimensional transposable data. The HDTD package provides functions for conducting statistical inference for the mean relationship between the row and column variables and for the covariance structure within and between the row and column variables.
monocle Analysis tools for single-cell expression experiments.
Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well.
blima Package for analysis of Illumina microarrays bead level data. Package blima includes several bead level algorithms to support bead level and probe level analysis of the data. It utilizes its own method for background correction, performs variance stabilizing method on the bead level and bead level quantile normalization. It also provides some basic methods for differential expression testing.
Analysis of bead level data.
hiAnnotator Functions for annotating GRanges objects.
hiAnnotator contains set of functions which allow users to annotate a GRanges object with custom set of annotations. The basic philosophy of this package is to take two GRanges objects (query & subject) with common set of seqnames (i.e. chromosomes) and return associated annotation per seqnames and rows from the query matching seqnames and rows from the subject (i.e. genes or cpg islands). The package comes with three types of annotation functions which calculates if a position from query is: within a feature, near a feature, or count features in defined window sizes. Moreover, each function is equipped with parallel backend to utilize the foreach package. In addition, the package is equipped with wrapper functions, which finds appropriate columns needed to make a GRanges object from a common data frame.
S4Vectors S4 implementation of vectors and lists
The S4Vectors package defines the Vector and List virtual classes and a set of generic functions that extend the semantic of ordinary vectors and lists in R. Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. In addition, a few low-level concrete subclasses of general interest (e.g. DataFrame, Rle, and Hits) are implemented in the S4Vectors package itself (many more are implemented in the IRanges package and in other Bioconductor infrastructure packages).
fastLiquidAssociation functions for genome-wide application of Liquid Association
This package extends the function of the LiquidAssociation package for genome-wide application. It integrates a screening method into the LA analysis to reduce the number of triplets to be examined for a high LA value and provides code for use in subsequent significance analyses.
ChIPseeker ChIPseeker for ChIP peak Annotation, Comparison, and Visualization
This package implements functions to retrieve the nearest genes around the peak, annotate genomic region of the peak, statstical methods for estimate the significance of overlap among ChIP peak data sets, and incorporate GEO database for user to compare the own dataset with those deposited in database. The comparison can be used to infer cooperative regulation and thus can be used to generate hypotheses. Several visualization functions are implemented to summarize the coverage of the peak experiment, average profile and heatmap of peaks binding to TSS regions, genomic annotation, distance to TSS, and overlap of peaks or genes.
NetPathMiner NetPathMiner for Biological Network Construction, Path Mining and Visualization
NetPathMiner is a general framework for network path mining using genome-scale networks. It constructs networks from KGML, SBML and BioPAX files, providing three network representations, metabolic, reaction and gene representations. NetPathMiner finds active paths and applies machine learning methods to summarize found paths for easy interpretation. It also provides static and interactive visualizations of networks and paths to aid manual investigation.
meshr Tools for conducting enrichment analysis of MeSH
A set of annotation maps describing the entire MeSH assembled using data from MeSH
MeSHDbi DBI to construct MeSH-related package from sqlite file.
The package is unified implementation of MeSH.db, MeSH.AOR.db, and MeSH.PCR.db and also is interface to construct Gene-MeSH package (org.MeSH.XXX.db). loadMeSHDbiPkg import sqlite file and generate org.MeSH.XXX.db.
GenomicFiles Distributed computing by file or by range
This package provides infrastructure for parallel computations distributed 'by file' or 'by range'. User defined MAPPER and REDUCER functions provide added flexibility for data combination and manipulation.
gaucho Genetic Algorithms for Understanding Clonal Heterogeneity and Ordering
Use genetic algorithms to determine the relationship between clones in heterogenous populations such as cancer sequencing samples
RefNet A queryable collection of molecular interactions, from many sources
Molecular interactions with metadata, some archived, some dynamically obtained
compcodeR RNAseq data simulation, differential expression analysis and performance comparison of differential expression methods
This package provides extensive functionality for comparing results obtained by different methods for differential expression analysis of RNAseq data. It also contains functions for simulating count data and interfaces to several packages for performing the differential expression analysis.
TitanCNA Subclonal copy number and LOH prediction from whole genome sequencing of tumours
Hidden Markov model to segment and predict regions of subclonal copy number alterations (CNA) and loss of heterozygosity (LOH), and estimate cellular prevalenece of clonal clusters in tumour whole genome sequencing data.
npGSEA Permutation approximation methods for gene set enrichment analysis (non-permutation GSEA)
Current gene set enrichment methods rely upon permutations for inference. These approaches are computationally expensive and have minimum achievable p-values based on the number of permutations, not on the actual observed statistics. We have derived three parametric approximations to the permutation distributions of two gene set enrichment test statistics. We are able to reduce the computational burden and granularity issues of permutation testing with our method, which is implemented in this package. npGSEA calculates gene set enrichment statistics and p-values without the computational cost of permutations. It is applicable in settings where one or many gene sets are of interest. There are also built-in plotting functions to help users visualize results.
Sushi Tools for visualizing genomics data
Flexible, quantitative, and integrative genomic visualizations for publication-quality multi-panel figures
DMRforPairs DMRforPairs: identifying Differentially Methylated Regions between unique samples using array based methylation profiles
DMRforPairs (formerly DMR2+) allows researchers to compare n>=2 unique samples with regard to their methylation profile. The (pairwise) comparison of n unique single samples distinguishes DMRforPairs from other existing pipelines as these often compare groups of samples in either single CpG locus or region based analysis. DMRforPairs defines regions of interest as genomic ranges with sufficient probes located in close proximity to each other. Probes in one region are optionally annotated to the same functional class(es). Differential methylation is evaluated by comparing the methylation values within each region between individual samples and (if the difference is sufficiently large), testing this difference formally for statistical significance.
VariantFiltering Filtering of coding and non-coding genetic variants
Filter genetic variants using different criteria such as inheritance model, amino acid change consequence, minimum allele frequencies across human populations, splice site strength, conservation, etc.
CoverageView Coverage visualization package for R
This package provides a framework for the visualization of genome coverage profiles. It can be used for ChIP-seq experiments, but it can be also used for genome-wide nucleosome positioning experiments or other experiment types where it is important to have a framework in order to inspect how the coverage distributed across the genome
metaMS MS-based metabolomics annotation pipeline
MS-based metabolomics data processing and compound annotation pipeline.
flowCL Semantic labelling of flow cytometric cell populations
Semantic labelling of flow cytometric cell populations.
BiocCheck Bioconductor-specific package checks
Bioconductor-specific package checks
Rariant Identification and Assessment of Single Nucleotide Variants through Shifts in Non-Consensus Base Call Frequencies
The 'Rariant' package identifies single nucleotide variants from sequencing data based on the difference of binomially distributed mismatch rates between matched samples.
FRGEpistasis Epistasis Analysis for Quantitative Traits by Functional Regression Model
A Tool for Epistasis Analysis Based on Functional Regression Model
flowCyBar Analyze flow cytometric data using gate information
A package to analyze flow cytometric data using gate information to follow population/community dynamics
CompGO An R pipeline for .bed file annotation, comparing GO term enrichment between gene sets and data visualisation
This package contains functions to accomplish several tasks. It is able to download full genome databases from UCSC, import .bed files easily, annotate these .bed file regions with genes (plus distance) from aforementioned database dumps, interface with DAVID to create functional annotation and gene ontology enrichment charts based on gene lists (such as those generated from input .bed files) and finally visualise and compare these enrichments using either directed acyclic graphs or scatterplots.
ChIPQC Quality metrics for ChIPseq data
Quality metrics for ChIPseq data
ABSSeq ABSSeq: a new RNA-Seq analysis method based on absolute expression differences and generalized Poisson model
Inferring differential expression genes by absolute expression differences between two groups, utilizing generalized Poisson model to account for over-dispersion across samples and heterogeneity of differential expression across genes.
ASSIGN Adaptive Signature Selection and InteGratioN (ASSIGN)
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 literatures 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.
nondetects Non-detects in qPCR data
Methods to model and impute non-detects in the results of qPCR experiments.
messina Single-gene classifiers and outlier-resistant detection of differential expression for two-group and survival problems.
Messina is a collection of algorithms for constructing optimally robust single-gene classifiers, and for identifying differential expression in the presence of outliers or unknown sample subgroups. The methods have application in identifying lead features to develop into clinical tests (both diagnostic and prognostic), and in identifying differential expression when a fraction of samples show unusual patterns of expression.
viper Virtual Inference of Protein-activity by Enriched Regulon analysis
Inference of protein activity from gene expression data, including the VIPER and msVIPER algorithms
UNDO Unsupervised Deconvolution of Tumor-Stromal Mixed Expressions
UNDO is an R package for unsupervised deconvolution of tumor and stromal mixed expression data. It detects marker genes and deconvolutes the mixing expression data without any prior knowledge.
sapFinder A package for variant peptides detection and visualization in shotgun proteomics.
sapFinder is developed to automate (1) variation-associated database construction, (2) database searching, (3) post-processing, (4) HTML-based report generation in shotgun proteomics.
Rcpi Toolkit for Compound-Protein Interaction in Drug Discovery
The Rcpi package offers an R/Bioconductor package emphasizing the comprehensive integration of bioinformatics and chemoinformatics into a molecular informatics platform for drug discovery.
QDNAseq Quantitative DNA sequencing for chromosomal aberrations
Quantitative DNA sequencing for chromosomal aberrations.
rpx R Interface to the ProteomeXchange Repository
This package implements an interface to proteomics data submitted to the ProteomeXchange consortium.
MLSeq Machine learning interface for RNA-Seq data
This package applies several machine learning methods, including SVM, bagSVM, Random Forest and CART, to RNA-Seq data.
metaseqR metaseqR: an R package for the analysis and result reporting of RNA-Seq gene expression data using multiple statistical algorithms.
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.
massiR massiR: MicroArray Sample Sex Identifier
Predicts the sex of samples in gene expression microarray datasets
INPower An R package for computing the number of susceptibility SNPs
An R package for computing the number of susceptibility SNPs and power of future studies
COPDSexualDimorphism Sexual dimorphic and COPD differential analysis for gene expression and methylation.
Sexual dimoprhic and COPD differential (SDCD) analysis contrasts regression coefficients from two stratified analysis. Stratification can be done in two ways: by COPD status or by sex. For COPD-stratified analysis, SDCD analysis contrasts sexual dimorphism between cases and controls, while sex-stratified SDCD analsysis contrasts COPD differential expression pattern between males and females. The package is meant to be used in conjunction with the package limma.
CAFE Chromosmal Aberrations Finder in Expression data
Detection and visualizations of gross chromosomal aberrations using Affymetrix expression microarrays as input
Basic4Cseq Basic4Cseq: an R/Bioconductor package for analyzing 4C-seq data
Basic4Cseq is an R/Bioconductor package for basic filtering, analysis and subsequent visualization of 4C-seq data. Virtual fragment libraries can be created for any BSGenome package, and filter functions for both reads and fragments and basic quality controls are included. Fragment data in the vicinity of the experiment's viewpoint can be visualized as a coverage plot based on a running median approach and a multi-scale contact profile.
alsace ALS for the Automatic Chemical Exploration of mixtures
Alternating Least Squares (or Multivariate Curve Resolution) for analytical chemical data, in particular hyphenated data where the first direction is a retention time axis, and the second a spectral axis. Package builds on the basic als function from the ALS package and adds functionality for high-throughput analysis, including definition of time windows, clustering of profiles, retention time correction, etcetera.
dualKS Dual KS Discriminant Analysis and Classification
This package implements a Kolmogorov Smirnov rank-sum based algorithm for training (i.e. discriminant analysis--identification of genes that discriminate between classes) and classification of gene expression data sets. One of the chief strengths of this approach is that it is amenable to the "multiclass" problem. That is, it can discriminate between more than 2 classes.
Clomial Infers clonal composition of a tumor
Clomial fits binomial distributions to counts obtained from Next Gen Sequencing data of multiple samples of the same tumor. The trained parameters can be interpreted to infer the clonal structure of the tumor.
scsR SiRNA correction for seed mediated off-target effect
Corrects genome-wide siRNA screens for seed mediated off-target effect. Suitable functions to identify the effective seeds/miRNAs and to visualize their effect are also provided in the package.
EDDA Experimental Design in Differential Abundance analysis
EDDA can aid in the design of a range of common experiments such as RNA-seq, Nanostring assays, RIP-seq and Metagenomic sequencing, and enables researchers to comprehensively investigate the impact of experimental decisions on the ability to detect differential abundance.
DMRcate Illumina 450K methylation array spatial analysis methods
De novo identification and extraction of differentially methylated regions (DMRs) in the human genome using Illumina Infinium HumanMethylation450 BeadChip array data. Provides functionality for filtering probes possibly confounded by SNPs and cross-hybridisation. Includes bedGraph and plotting functions.
unifiedWMWqPCR Unified Wilcoxon-Mann Whitney Test for testing differential expression in qPCR data
This packages implements the unified Wilcoxon-Mann-Whitney Test for qPCR data. This modified test allows for testing differential expression in qPCR data.
sangerseqR Tools for Sanger Sequencing Data in R
This package contains several tools for analyzing Sanger Sequencing data files in R, including reading .scf and .ab1 files, making basecalls and plotting chromatograms.
CopyNumber450k R package for calling CNV from Illumina 450k methylation microarrays
This package contains a set of functions that allow CNV calling from Illumina 450k methylation microarrays.
GSCA GSCA: Gene Set Context Analysis
GSCA takes as input several lists of activated and repressed genes. GSCA then searches through a compendium of publicly available gene expression profiles for biological contexts that are enriched with a specified pattern of gene expression. GSCA provides both traditional R functions and interactive, user-friendly user interface.
COMPASS Combinatorial Polyfunctionality Analysis of Single Cells
COMPASS is a statistical framework that enables unbiased analysis of antigen-specific T-cell subsets. COMPASS uses a Bayesian hierarchical framework to model all observed cell-subsets and select the most likely to be antigen-specific while regularizing the small cell counts that often arise in multi-parameter space. The model provides a posterior probability of specificity for each cell subset and each sample, which can be used to profile a subject's immune response to external stimuli such as infection or vaccination.
CNEr CNE detection and visualization.
Large-scale identification and advanced visualization of sets of conserved noncoding elements.
MIMOSA Mixture Models for Single-Cell Assays
Modeling count data using Dirichlet-multinomial and beta-binomial mixtures with applications to single-cell assays.
Detect synergistic miRNA regulatory modules by overlapping neighbourhood expansion.
SomaticSignatures Somatic Signatures
The SomaticSignatures package identifies mutational signatures of single nucleotide variants (SNVs).
trackViewer light package to plot elegant track layers
plot ChIP-seq, RNA-seq, miRNA-seq, DNA-seq and etc NGS sequence data, especially for big files.
iClusterPlus Integrative clustering of multi-type genomic data
Integrative clustering of multiple genomic data using a joint latent variable model
AtlasRDF Gene Expression Atlas query and gene set enrichment package.
Query the Gene Expression Atlas RDF data at the European Bioinformatics Institute using genes, experimental factors (such as disease, cell type, compound treatments), pathways and proteins. Also contains a function to perform an enrichment of your gene list across Experimental Factor Ontology (EFO) using the Atlas background set.
GeneOverlap Test and visualize gene overlaps
Test two sets of gene lists and visualize the results.
COHCAP City of Hope CpG Island Analysis Pipeline
This package provides a pipeline to analyze single-nucleotide resolution methylation data (Illumina 450k methylation array, targeted BS-Seq, etc.). It provides QC metrics, differential methylation for CpG Sites, differential methylation for CpG Islands, integration with gene expression data, and visualization of methylation values.
asmn All sample mean normalization.
Performs all sample mean normalization using raw data output from BeadStudio and MethyLumiM data.
geneRxCluster gRx Differential Clustering
Detect Differential Clustering of Genomic Sites such as gene therapy integrations. The package provides some functions for exploring genomic insertion sites originating from two different sources. Possibly, the two sources are two different gene therapy vectors. Vectors are preferred that target sensitive regions less frequently, motivating the search for localized clusters of insertions and comparison of the clusters formed by integration of different vectors. Scan statistics allow the discovery of spatial differences in clustering and calculation of False Discovery Rates (FDRs) providing statistical methods for comparing retroviral vectors. A scan statistic for comparing two vectors using multiple window widths to detect clustering differentials and compute FDRs is implemented here.
flowBin Combining multitube flow cytometry data by binning
Software to combine flow cytometry data that has been multiplexed into multiple tubes with common markers between them, by establishing common bins across tubes in terms of the common markers, then determining expression within each tube for each bin in terms of the tube-specific markers.
roar Identify differential APA usage from RNA-seq alignments
Identify preferential usage of APA sites, comparing two biological conditions, starting from known alternative sites and alignments obtained from standard RNA-seq experiments.
PhenStat Statistical analysis of phenotypic data
Package contains methods for statistical analysis of phenotypic data such as Mixed Models and Fisher Exact Test.
The CCREPE (Compositionality Corrected by REnormalizaion and PErmutation) package is designed to assess the significance of general similarity measures in compositional datasets. In microbial abundance data, for example, the total abundances of all microbes sum to one; CCREPE is designed to take this constraint into account when assigning p-values to similarity measures between the microbes. The package has two functions: ccrepe: Calculates similarity measures, p-values and q-values for relative abundances of bugs in one or two body sites using bootstrap and permutation matrices of the data. nc.score: Calculates species-level co-variation and co-exclusion patterns based on an extension of the checkerboard score to ordinal data.
flowMatch Matching and meta-clustering in flow cytometry
Matching cell populations and building meta-clusters and templates from a collection of FC samples.
PECA Probe-level Expression Change Averaging
Calculates Probe-level Expression Change Averages (PECA) to identify differential expression in Affymetrix gene expression microarray studies or in proteomic studies using peptide-level mesurements respectively.
mmnet A metagenomic pipeline for systems biology
This package gives the implementations microbiome metabolic network constructing and analyzing. It introduces a unique metagenomic systems biology approach, mapping metagenomic data to the KEGG global metabolic pathway and constructing a systems-level network. The system-level network and the next topological analysis will be of great help to analysis the various functional properties, including regulation and metabolic functionality of the metagenome.
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