R-universe search: topic:multiplecomparison

bioc

clusterProfiler:A universal enrichment tool for interpreting omics data

This package supports functional characteristics of both coding and non-coding genomics data for thousands of species with up-to-date gene annotation. It provides a univeral interface for gene functional annotation from a variety of sources and thus can be applied in diverse scenarios. It provides a tidy interface to access, manipulate, and visualize enrichment results to help users achieve efficient data interpretation. Datasets obtained from multiple treatments and time points can be analyzed and compared in a single run, easily revealing functional consensus and differences among distinct conditions.

Maintained by Guangchuang Yu. Last updated 4 months ago.

annotation clustering genesetenrichment go kegg multiplecomparison pathways reactome visualization enrichment-analysis gsea

1.1k stars 17.03 score 11k scripts 48 dependents

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DOSE:Disease Ontology Semantic and Enrichment analysis

This package implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for measuring semantic similarities among DO terms and gene products. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented for discovering disease associations of high-throughput biological data.

Maintained by Guangchuang Yu. Last updated 5 months ago.

annotation visualization multiplecomparison genesetenrichment pathways software disease-ontology enrichment-analysis semantic-similarity

119 stars 14.97 score 2.0k scripts 61 dependents

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phyloseq:Handling and analysis of high-throughput microbiome census data

phyloseq provides a set of classes and tools to facilitate the import, storage, analysis, and graphical display of microbiome census data.

Maintained by Paul J. McMurdie. Last updated 5 months ago.

immunooncology sequencing microbiome metagenomics clustering classification multiplecomparison geneticvariability

597 stars 13.90 score 8.4k scripts 37 dependents

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limma:Linear Models for Microarray and Omics Data

Data analysis, linear models and differential expression for omics data.

Maintained by Gordon Smyth. Last updated 11 days ago.

exonarray geneexpression transcription alternativesplicing differentialexpression differentialsplicing genesetenrichment dataimport bayesian clustering regression timecourse microarray micrornaarray mrnamicroarray onechannel proprietaryplatforms twochannel sequencing rnaseq batcheffect multiplecomparison normalization preprocessing qualitycontrol biomedicalinformatics cellbiology cheminformatics epigenetics functionalgenomics genetics immunooncology metabolomics proteomics systemsbiology transcriptomics

13.81 score 16k scripts 586 dependents

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mixOmics:Omics Data Integration Project

Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. 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. A non exhaustive list of methods include variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares.

Maintained by Eva Hamrud. Last updated 2 days ago.

immunooncology microarray sequencing metabolomics metagenomics proteomics geneprediction multiplecomparison classification regression bioconductor genomics genomics-data genomics-visualization multivariate-analysis multivariate-statistics omics r-pkg r-project

185 stars 13.75 score 1.3k scripts 22 dependents

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edgeR:Empirical Analysis of Digital Gene Expression Data in R

Differential expression analysis of sequence count data. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models, quasi-likelihood, and gene set enrichment. Can perform differential analyses of any type of omics data that produces read counts, including RNA-seq, ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE, CAGE, metabolomics, or proteomics spectral counts. RNA-seq analyses can be conducted at the gene or isoform level, and tests can be conducted for differential exon or transcript usage.

Maintained by Yunshun Chen. Last updated 18 days ago.

alternativesplicing batcheffect bayesian biomedicalinformatics cellbiology chipseq clustering coverage differentialexpression differentialmethylation differentialsplicing dnamethylation epigenetics functionalgenomics geneexpression genesetenrichment genetics immunooncology multiplecomparison normalization pathways proteomics qualitycontrol regression rnaseq sage sequencing singlecell systemsbiology timecourse transcription transcriptomics openblas

13.40 score 17k scripts 255 dependents

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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.

Maintained by Guangchuang Yu. Last updated 5 months ago.

annotation chipseq software visualization multiplecomparison atac-seq chip-seq comparison epigenetics epigenomics

233 stars 13.05 score 1.6k scripts 5 dependents

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ReactomePA:Reactome Pathway Analysis

This package provides functions for pathway analysis based on REACTOME pathway database. It implements enrichment analysis, gene set enrichment analysis and several functions for visualization. This package is not affiliated with the Reactome team.

Maintained by Guangchuang Yu. Last updated 5 months ago.

pathways visualization annotation multiplecomparison genesetenrichment reactome enrichment-analysis reactome-pathway-analysis reactomepa

40 stars 12.25 score 1.5k scripts 7 dependents

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metagenomeSeq:Statistical analysis for sparse high-throughput sequencing

metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations.

Maintained by Joseph N. Paulson. Last updated 3 months ago.

immunooncology classification clustering geneticvariability differentialexpression microbiome metagenomics normalization visualization multiplecomparison sequencing software

69 stars 11.90 score 494 scripts 7 dependents

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bumphunter:Bump Hunter

Tools for finding bumps in genomic data

Maintained by Tamilselvi Guharaj. Last updated 5 months ago.

dnamethylation epigenetics infrastructure multiplecomparison immunooncology

16 stars 11.61 score 210 scripts 43 dependents

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msa:Multiple Sequence Alignment

The 'msa' package provides a unified R/Bioconductor interface to the multiple sequence alignment algorithms ClustalW, ClustalOmega, and Muscle. All three algorithms are integrated in the package, therefore, they do not depend on any external software tools and are available for all major platforms. The multiple sequence alignment algorithms are complemented by a function for pretty-printing multiple sequence alignments using the LaTeX package TeXshade.

Maintained by Ulrich Bodenhofer. Last updated 1 months ago.

multiplesequencealignment alignment multiplecomparison sequencing cpp

17 stars 11.46 score 744 scripts 6 dependents

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miloR:Differential neighbourhood abundance testing on a graph

Milo performs single-cell differential abundance testing. Cell states are modelled as representative neighbourhoods on a nearest neighbour graph. Hypothesis testing is performed using either a negative bionomial generalized linear model or negative binomial generalized linear mixed model.

Maintained by Mike Morgan. Last updated 5 months ago.

singlecell multiplecomparison functionalgenomics software openblas cpp openmp

362 stars 10.49 score 340 scripts 1 dependents

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tradeSeq:trajectory-based differential expression analysis for sequencing data

tradeSeq provides a flexible method for fitting regression models that can be used to find genes that are differentially expressed along one or multiple lineages in a trajectory. Based on the fitted models, it uses a variety of tests suited to answer different questions of interest, e.g. the discovery of genes for which expression is associated with pseudotime, or which are differentially expressed (in a specific region) along the trajectory. It fits a negative binomial generalized additive model (GAM) for each gene, and performs inference on the parameters of the GAM.

Maintained by Hector Roux de Bezieux. Last updated 5 months ago.

clustering regression timecourse differentialexpression geneexpression rnaseq sequencing software singlecell transcriptomics multiplecomparison visualization

251 stars 10.06 score 440 scripts

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sva:Surrogate Variable Analysis

The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The sva package can be used to remove artifacts in three ways: (1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS), (2) directly removing known batch effects using ComBat (Johnson et al. 2007 Biostatistics) and (3) removing batch effects with known control probes (Leek 2014 biorXiv). Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat. Reviews Genetics).

Maintained by Jeffrey T. Leek. Last updated 5 months ago.

immunooncology microarray statisticalmethod preprocessing multiplecomparison sequencing rnaseq batcheffect normalization

10.04 score 3.2k scripts 50 dependents

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MicrobiotaProcess:A comprehensive R package for managing and analyzing microbiome and other ecological data within the tidy framework

MicrobiotaProcess is an R package for analysis, visualization and biomarker discovery of microbial datasets. It introduces MPSE class, this make it more interoperable with the existing computing ecosystem. Moreover, it introduces a tidy microbiome data structure paradigm and analysis grammar. It provides a wide variety of microbiome data analysis procedures under the unified and common framework (tidy-like framework).

Maintained by Shuangbin Xu. Last updated 5 months ago.

visualization microbiome software multiplecomparison featureextraction microbiome-analysis microbiome-data

186 stars 9.70 score 126 scripts 1 dependents

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cytomapper:Visualization of highly multiplexed imaging data in R

Highly multiplexed imaging acquires the single-cell expression of selected proteins in a spatially-resolved fashion. These measurements can be visualised across multiple length-scales. First, pixel-level intensities represent the spatial distributions of feature expression with highest resolution. Second, after segmentation, expression values or cell-level metadata (e.g. cell-type information) can be visualised on segmented cell areas. This package contains functions for the visualisation of multiplexed read-outs and cell-level information obtained by multiplexed imaging technologies. The main functions of this package allow 1. the visualisation of pixel-level information across multiple channels, 2. the display of cell-level information (expression and/or metadata) on segmentation masks and 3. gating and visualisation of single cells.

Maintained by Lasse Meyer. Last updated 5 months ago.

immunooncology software singlecell onechannel twochannel multiplecomparison normalization dataimport bioimaging imaging-mass-cytometry single-cell spatial-analysis

32 stars 9.61 score 354 scripts 5 dependents

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multtest:Resampling-based multiple hypothesis testing

Non-parametric bootstrap and permutation resampling-based multiple testing procedures (including empirical Bayes methods) for controlling the family-wise error rate (FWER), generalized family-wise error rate (gFWER), tail probability of the proportion of false positives (TPPFP), and false discovery rate (FDR). Several choices of bootstrap-based null distribution are implemented (centered, centered and scaled, quantile-transformed). Single-step and step-wise methods are available. Tests based on a variety of t- and F-statistics (including t-statistics based on regression parameters from linear and survival models as well as those based on correlation parameters) are included. When probing hypotheses with t-statistics, users may also select a potentially faster null distribution which is multivariate normal with mean zero and variance covariance matrix derived from the vector influence function. Results are reported in terms of adjusted p-values, confidence regions and test statistic cutoffs. The procedures are directly applicable to identifying differentially expressed genes in DNA microarray experiments.

Maintained by Katherine S. Pollard. Last updated 5 months ago.

microarray differentialexpression multiplecomparison

9.32 score 932 scripts 132 dependents

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IsoformSwitchAnalyzeR:Identify, Annotate and Visualize Isoform Switches with Functional Consequences from both short- and long-read RNA-seq data

Analysis of alternative splicing and isoform switches with predicted functional consequences (e.g. gain/loss of protein domains etc.) from quantification of all types of RNASeq by tools such as Kallisto, Salmon, StringTie, Cufflinks/Cuffdiff etc.

Maintained by Kristoffer Vitting-Seerup. Last updated 5 months ago.

geneexpression transcription alternativesplicing differentialexpression differentialsplicing visualization statisticalmethod transcriptomevariant biomedicalinformatics functionalgenomics systemsbiology transcriptomics rnaseq annotation functionalprediction geneprediction dataimport multiplecomparison batcheffect immunooncology

108 stars 9.26 score 125 scripts

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bambu:Context-Aware Transcript Quantification from Long Read RNA-Seq data

bambu is a R package for multi-sample transcript discovery and quantification using long read RNA-Seq data. You can use bambu after read alignment to obtain expression estimates for known and novel transcripts and genes. The output from bambu can directly be used for visualisation and downstream analysis such as differential gene expression or transcript usage.

Maintained by Ying Chen. Last updated 2 months ago.

alignment coverage differentialexpression featureextraction geneexpression genomeannotation genomeassembly immunooncology longread multiplecomparison normalization rnaseq regression sequencing software transcription transcriptomics bambu bioconductor long-reads nanopore nanopore-sequencing rna-seq rna-seq-analysis transcript-quantification transcript-reconstruction cpp

203 stars 9.04 score 91 scripts 1 dependents

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monocle:Clustering, differential expression, and trajectory analysis for single- cell RNA-Seq

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.

Maintained by Cole Trapnell. Last updated 5 months ago.

immunooncology sequencing rnaseq geneexpression differentialexpression infrastructure dataimport datarepresentation visualization clustering multiplecomparison qualitycontrol cpp

8.71 score 1.6k scripts 2 dependents

bioc

gage:Generally Applicable Gene-set Enrichment for Pathway Analysis

GAGE is a published method for gene set (enrichment or GSEA) or pathway analysis. GAGE is generally applicable independent of microarray or RNA-Seq data attributes including sample sizes, experimental designs, assay platforms, and other types of heterogeneity, and consistently achieves superior performance over other frequently used methods. In gage package, we provide functions for basic GAGE analysis, result processing and presentation. We have also built pipeline routines for of multiple GAGE analyses in a batch, comparison between parallel analyses, and combined analysis of heterogeneous data from different sources/studies. In addition, we provide demo microarray data and commonly used gene set data based on KEGG pathways and GO terms. These funtions and data are also useful for gene set analysis using other methods.

Maintained by Weijun Luo. Last updated 5 months ago.

pathways go differentialexpression microarray onechannel twochannel rnaseq genetics multiplecomparison genesetenrichment geneexpression systemsbiology sequencing

5 stars 8.68 score 784 scripts 1 dependents

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csaw:ChIP-Seq Analysis with Windows

Detection of differentially bound regions in ChIP-seq data with sliding windows, with methods for normalization and proper FDR control.

Maintained by Aaron Lun. Last updated 2 months ago.

multiplecomparison chipseq normalization sequencing coverage genetics annotation differentialpeakcalling curl bzip2 xz-utils zlib cpp

8.32 score 498 scripts 7 dependents

bioc

biobroom:Turn Bioconductor objects into tidy data frames

This package contains methods for converting standard objects constructed by bioinformatics packages, especially those in Bioconductor, and converting them to tidy data. It thus serves as a complement to the broom package, and follows the same the tidy, augment, glance division of tidying methods. Tidying data makes it easy to recombine, reshape and visualize bioinformatics analyses.

Maintained by John D. Storey. Last updated 5 months ago.

multiplecomparison differentialexpression regression geneexpression proteomics dataimport

49 stars 8.22 score 280 scripts 1 dependents

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DEqMS:a tool to perform statistical analysis of differential protein expression for quantitative proteomics data.

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.

Maintained by Yafeng Zhu. Last updated 5 months ago.

immunooncology proteomics massspectrometry preprocessing differentialexpression multiplecomparison normalization bayesian experimenthubsoftware limma quantitative-proteomic-analysis

23 stars 8.18 score 58 scripts 1 dependents

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maaslin3:"Refining and extending generalized multivariate linear models for meta-omic association discovery"

MaAsLin 3 refines and extends generalized multivariate linear models for meta-omicron association discovery. It finds abundance and prevalence associations between microbiome meta-omics features and complex metadata in population-scale epidemiological studies. The software includes multiple analysis methods (including support for multiple covariates, repeated measures, and ordered predictors), filtering, normalization, and transform options to customize analysis for your specific study.

Maintained by William Nickols. Last updated 4 days ago.

metagenomics software microbiome normalization multiplecomparison

33 stars 8.16 score 34 scripts

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scDD:Mixture modeling of single-cell RNA-seq data to identify genes with differential distributions

This package implements a method to analyze single-cell RNA- seq Data utilizing flexible Dirichlet Process mixture models. Genes with differential distributions of expression are classified into several interesting patterns of differences between two conditions. The package also includes functions for simulating data with these patterns from negative binomial distributions.

Maintained by Keegan Korthauer. Last updated 5 months ago.

immunooncology bayesian clustering rnaseq singlecell multiplecomparison visualization differentialexpression

33 stars 7.92 score 50 scripts

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siggenes:Multiple Testing using SAM and Efron's Empirical Bayes Approaches

Identification of differentially expressed genes and estimation of the False Discovery Rate (FDR) using both the Significance Analysis of Microarrays (SAM) and the Empirical Bayes Analyses of Microarrays (EBAM).

Maintained by Holger Schwender. Last updated 5 months ago.

multiplecomparison microarray geneexpression snp exonarray differentialexpression

7.87 score 74 scripts 34 dependents

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EBSeq:An R package for gene and isoform differential expression analysis of RNA-seq data

Differential Expression analysis at both gene and isoform level using RNA-seq data

Maintained by Xiuyu Ma. Last updated 9 days ago.

immunooncology statisticalmethod differentialexpression multiplecomparison rnaseq sequencing cpp

7.86 score 162 scripts 6 dependents

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fishpond:Fishpond: downstream methods and tools for expression data

Fishpond contains methods for differential transcript and gene expression analysis of RNA-seq data using inferential replicates for uncertainty of abundance quantification, as generated by Gibbs sampling or bootstrap sampling. Also the package contains a number of utilities for working with Salmon and Alevin quantification files.

Maintained by Michael Love. Last updated 5 months ago.

sequencing rnaseq geneexpression transcription normalization regression multiplecomparison batcheffect visualization differentialexpression differentialsplicing alternativesplicing singlecell bioconductor gene-expression genomics salmon scrnaseq statistics transcriptomics

28 stars 7.83 score 150 scripts

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PhyloProfile:PhyloProfile

PhyloProfile is a tool for exploring complex phylogenetic profiles. Phylogenetic profiles, presence/absence patterns of genes over a set of species, are commonly used to trace the functional and evolutionary history of genes across species and time. With PhyloProfile we can enrich regular phylogenetic profiles with further data like sequence/structure similarity, to make phylogenetic profiling more meaningful. Besides the interactive visualisation powered by R-Shiny, the package offers a set of further analysis features to gain insights like the gene age estimation or core gene identification.

Maintained by Vinh Tran. Last updated 8 days ago.

software visualization datarepresentation multiplecomparison functionalprediction dimensionreduction bioinformatics heatmap interactive-visualizations orthologs phylogenetic-profile shiny

33 stars 7.79 score 10 scripts

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edge:Extraction of Differential Gene Expression

The edge package implements methods for carrying out differential expression analyses of genome-wide gene expression studies. Significance testing using the optimal discovery procedure and generalized likelihood ratio tests (equivalent to F-tests and t-tests) are implemented for general study designs. Special functions are available to facilitate the analysis of common study designs, including time course experiments. Other packages such as sva and qvalue are integrated in edge to provide a wide range of tools for gene expression analysis.

Maintained by John D. Storey. Last updated 5 months ago.

multiplecomparison differentialexpression timecourse regression geneexpression dataimport

21 stars 7.77 score 62 scripts

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baySeq:Empirical Bayesian analysis of patterns of differential expression in count data

This package identifies differential expression in high-throughput 'count' data, such as that derived from next-generation sequencing machines, calculating estimated posterior likelihoods of differential expression (or more complex hypotheses) via empirical Bayesian methods.

Maintained by Samuel Granjeaud. Last updated 5 months ago.

sequencing differentialexpression multiplecomparison sage bayesian coverage

7.75 score 79 scripts 3 dependents

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pathwayPCA:Integrative Pathway Analysis with Modern PCA Methodology and Gene Selection

pathwayPCA is an integrative analysis tool that implements the principal component analysis (PCA) based pathway analysis approaches described in Chen et al. (2008), Chen et al. (2010), and Chen (2011). pathwayPCA allows users to: (1) Test pathway association with binary, continuous, or survival phenotypes. (2) Extract relevant genes in the pathways using the SuperPCA and AES-PCA approaches. (3) Compute principal components (PCs) based on the selected genes. These estimated latent variables represent pathway activities for individual subjects, which can then be used to perform integrative pathway analysis, such as multi-omics analysis. (4) Extract relevant genes that drive pathway significance as well as data corresponding to these relevant genes for additional in-depth analysis. (5) Perform analyses with enhanced computational efficiency with parallel computing and enhanced data safety with S4-class data objects. (6) Analyze studies with complex experimental designs, with multiple covariates, and with interaction effects, e.g., testing whether pathway association with clinical phenotype is different between male and female subjects. Citations: Chen et al. (2008) <https://doi.org/10.1093/bioinformatics/btn458>; Chen et al. (2010) <https://doi.org/10.1002/gepi.20532>; and Chen (2011) <https://doi.org/10.2202/1544-6115.1697>.

Maintained by Gabriel Odom. Last updated 5 months ago.

copynumbervariation dnamethylation geneexpression snp transcription geneprediction genesetenrichment genesignaling genetarget genomewideassociation genomicvariation cellbiology epigenetics functionalgenomics genetics lipidomics metabolomics proteomics systemsbiology transcriptomics classification dimensionreduction featureextraction principalcomponent regression survival multiplecomparison pathways

11 stars 7.74 score 42 scripts

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EpiCompare:Comparison, Benchmarking & QC of Epigenomic Datasets

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.

Maintained by Hiranyamaya Dash. Last updated 1 months ago.

epigenetics genetics qualitycontrol chipseq multiplecomparison functionalgenomics atacseq dnaseseq benchmark benchmarking bioconductor bioconductor-package comparison html interactive-reporting

15 stars 7.49 score 46 scripts

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metapod:Meta-Analyses on P-Values of Differential Analyses

Implements a variety of methods for combining p-values in differential analyses of genome-scale datasets. Functions can combine p-values across different tests in the same analysis (e.g., genomic windows in ChIP-seq, exons in RNA-seq) or for corresponding tests across separate analyses (e.g., replicated comparisons, effect of different treatment conditions). Support is provided for handling log-transformed input p-values, missing values and weighting where appropriate.

Maintained by Aaron Lun. Last updated 3 months ago.

multiplecomparison differentialpeakcalling cpp

7.45 score 17 scripts 47 dependents

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topconfects:Top Confident Effect Sizes

Rank results by confident effect sizes, while maintaining False Discovery Rate and False Coverage-statement Rate control. Topconfects is an alternative presentation of TREAT results with improved usability, eliminating p-values and instead providing confidence bounds. The main application is differential gene expression analysis, providing genes ranked in order of confident log2 fold change, but it can be applied to any collection of effect sizes with associated standard errors.

Maintained by Paul Harrison. Last updated 4 months ago.

geneexpression differentialexpression transcriptomics rnaseq mrnamicroarray regression multiplecomparison

14 stars 7.38 score 18 scripts 2 dependents

eltebioinformatics

mulea:Enrichment Analysis Using Multiple Ontologies and False Discovery Rate

Background - Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. Results - mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 22 ontology types from 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, mulea's architecture allows for easy integration of user-defined ontologies, or GMT files from external sources (e.g., MSigDB or Enrichr), expanding its applicability across diverse research areas. Conclusions - mulea is distributed as a CRAN R package. It offers researchers a powerful and flexible toolkit for functional enrichment analysis, addressing limitations of traditional tools with its progressive eFDR and by supporting a variety of ontologies. Overall, mulea fosters the exploration of diverse biological questions across various model organisms.

Maintained by Tamas Stirling. Last updated 4 months ago.

annotation differentialexpression geneexpression genesetenrichment go graphandnetwork multiplecomparison pathways reactome software transcription visualization enrichment enrichment-analysis functional-enrichment-analysis gene-set-enrichment ontologies transcriptomics cpp

28 stars 7.36 score 34 scripts

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NormalyzerDE:Evaluation of normalization methods and calculation of differential expression analysis statistics

NormalyzerDE provides screening of normalization methods for LC-MS based expression data. It calculates a range of normalized matrices using both existing approaches and a novel time-segmented approach, calculates performance measures and generates an evaluation report. Furthermore, it provides an easy utility for Limma- or ANOVA- based differential expression analysis.

Maintained by Jakob Willforss. Last updated 5 months ago.

normalization multiplecomparison visualization bayesian proteomics metabolomics differentialexpression bioconductor bioinformatics limma

22 stars 7.30 score 38 scripts 1 dependents

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IHW:Independent Hypothesis Weighting

Independent hypothesis weighting (IHW) is a multiple testing procedure that increases power compared to the method of Benjamini and Hochberg by assigning data-driven weights to each hypothesis. The input to IHW is a two-column table of p-values and covariates. The covariate can be any continuous-valued or categorical variable that is thought to be informative on the statistical properties of each hypothesis test, while it is independent of the p-value under the null hypothesis.

Maintained by Nikos Ignatiadis. Last updated 5 months ago.

immunooncology multiplecomparison rnaseq

7.25 score 264 scripts 2 dependents

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meshes:MeSH Enrichment and Semantic analyses

MeSH (Medical Subject Headings) is the NLM controlled vocabulary used to manually index articles for MEDLINE/PubMed. MeSH terms were associated by Entrez Gene ID by three methods, gendoo, gene2pubmed and RBBH. This association is fundamental for enrichment and semantic analyses. meshes supports enrichment analysis (over-representation and gene set enrichment analysis) of gene list or whole expression profile. The semantic comparisons of MeSH terms provide quantitative ways to compute similarities between genes and gene groups. meshes implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively and supports more than 70 species.

Maintained by Guangchuang Yu. Last updated 5 months ago.

annotation clustering multiplecomparison software enrichment-analysis medical-subject-headings semantic-similarity

12 stars 7.19 score 43 scripts

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DiffBind:Differential Binding Analysis of ChIP-Seq Peak Data

Compute differentially bound sites from multiple ChIP-seq experiments using affinity (quantitative) data. Also enables occupancy (overlap) analysis and plotting functions.

Maintained by Rory Stark. Last updated 2 months ago.

sequencing chipseq atacseq dnaseseq methylseq ripseq differentialpeakcalling differentialmethylation generegulation histonemodification peakdetection biomedicalinformatics cellbiology multiplecomparison normalization reportwriting epigenetics functionalgenomics curl bzip2 xz-utils zlib cpp

7.13 score 512 scripts 2 dependents

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CoGAPS:Coordinated Gene Activity in Pattern Sets

Coordinated Gene Activity in Pattern Sets (CoGAPS) implements a Bayesian MCMC matrix factorization algorithm, GAPS, and links it to gene set statistic methods to infer biological process activity. It can be used to perform sparse matrix factorization on any data, and when this data represents biomolecules, to do gene set analysis.

Maintained by Elana J. Fertig. Last updated 17 days ago.

geneexpression transcription genesetenrichment differentialexpression bayesian clustering timecourse rnaseq microarray multiplecomparison dimensionreduction immunooncology cpp

6.97 score 104 scripts

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satuRn:Scalable Analysis of Differential Transcript Usage for Bulk and Single-Cell RNA-sequencing Applications

satuRn provides a higly performant and scalable framework for performing differential transcript usage analyses. The package consists of three main functions. The first function, fitDTU, fits quasi-binomial generalized linear models that model transcript usage in different groups of interest. The second function, testDTU, tests for differential usage of transcripts between groups of interest. Finally, plotDTU visualizes the usage profiles of transcripts in groups of interest.

Maintained by Jeroen Gilis. Last updated 5 months ago.

regression experimentaldesign differentialexpression geneexpression rnaseq sequencing software singlecell transcriptomics multiplecomparison visualization

21 stars 6.97 score 74 scripts 1 dependents

bioc

psichomics:Graphical Interface for Alternative Splicing Quantification, Analysis and Visualisation

Interactive R package with an intuitive Shiny-based graphical interface for alternative splicing quantification and integrative analyses of alternative splicing and gene expression based on The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression project (GTEx), Sequence Read Archive (SRA) and user-provided data. The tool interactively performs survival, dimensionality reduction and median- and variance-based differential splicing and gene expression analyses that benefit from the incorporation of clinical and molecular sample-associated features (such as tumour stage or survival). Interactive visual access to genomic mapping and functional annotation of selected alternative splicing events is also included.

Maintained by Nuno Saraiva-Agostinho. Last updated 5 months ago.

sequencing rnaseq alternativesplicing differentialsplicing transcription gui principalcomponent survival biomedicalinformatics transcriptomics immunooncology visualization multiplecomparison geneexpression differentialexpression alternative-splicing bioconductor data-analyses differential-gene-expression differential-splicing-analysis gene-expression gtex recount2 rna-seq-data splicing-quantification sra tcga vast-tools cpp

36 stars 6.95 score 31 scripts

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msqrob2:Robust statistical inference for quantitative LC-MS proteomics

msqrob2 provides a robust linear mixed model framework for assessing differential abundance in MS-based Quantitative proteomics experiments. Our workflows can start from raw peptide intensities or summarised protein expression values. The model parameter estimates can be stabilized by ridge regression, empirical Bayes variance estimation and robust M-estimation. msqrob2's hurde workflow can handle missing data without having to rely on hard-to-verify imputation assumptions, and, outcompetes state-of-the-art methods with and without imputation for both high and low missingness. It builds on QFeature infrastructure for quantitative mass spectrometry data to store the model results together with the raw data and preprocessed data.

Maintained by Lieven Clement. Last updated 1 months ago.

proteomics massspectrometry differentialexpression multiplecomparison regression experimentaldesign software immunooncology normalization timecourse preprocessing

10 stars 6.94 score 83 scripts

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GOstats:Tools for manipulating GO and microarrays

A set of tools for interacting with GO and microarray data. A variety of basic manipulation tools for graphs, hypothesis testing and other simple calculations.

Maintained by Bioconductor Package Maintainer. Last updated 5 months ago.

annotation go multiplecomparison geneexpression microarray pathways genesetenrichment graphandnetwork

6.93 score 528 scripts 12 dependents

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DRIMSeq:Differential transcript usage and tuQTL analyses with Dirichlet-multinomial model in RNA-seq

The package provides two frameworks. One for the differential transcript usage analysis between different conditions and one for the tuQTL analysis. Both are based on modeling the counts of genomic features (i.e., transcripts) with the Dirichlet-multinomial distribution. The package also makes available functions for visualization and exploration of the data and results.

Maintained by Malgorzata Nowicka. Last updated 5 months ago.

immunooncology snp alternativesplicing differentialsplicing genetics rnaseq sequencing workflowstep multiplecomparison geneexpression differentialexpression

6.91 score 136 scripts 2 dependents

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onlineFDR:Online error rate control

This package allows users to control the false discovery rate (FDR) or familywise error rate (FWER) for online multiple hypothesis testing, where hypotheses arrive in a stream. In this framework, a null hypothesis is rejected based on the evidence against it and on the previous rejection decisions.

Maintained by David S. Robertson. Last updated 5 months ago.

multiplecomparison software statisticalmethod error-rate-control fdr fwer hypothesis-testing cpp

14 stars 6.88 score 26 scripts

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GOexpress:Visualise microarray and RNAseq data using gene ontology annotations

The package contains methods to visualise the expression profile of genes from a microarray or RNA-seq experiment, and offers a supervised clustering approach to identify GO terms containing genes with expression levels that best classify two or more predefined groups of samples. Annotations for the genes present in the expression dataset may be obtained from Ensembl through the biomaRt package, if not provided by the user. The default random forest framework is used to evaluate the capacity of each gene to cluster samples according to the factor of interest. Finally, GO terms are scored by averaging the rank (alternatively, score) of their respective gene sets to cluster the samples. P-values may be computed to assess the significance of GO term ranking. Visualisation function include gene expression profile, gene ontology-based heatmaps, and hierarchical clustering of experimental samples using gene expression data.

Maintained by Kevin Rue-Albrecht. Last updated 5 months ago.

software geneexpression transcription differentialexpression genesetenrichment datarepresentation clustering timecourse microarray sequencing rnaseq annotation multiplecomparison pathways go visualization immunooncology bioconductor bioconductor-package bioconductor-stats geneontology geneset-enrichment

9 stars 6.75 score 31 scripts

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SIAMCAT:Statistical Inference of Associations between Microbial Communities And host phenoTypes

Pipeline for Statistical Inference of Associations between Microbial Communities And host phenoTypes (SIAMCAT). A primary goal of analyzing microbiome data is to determine changes in community composition that are associated with environmental factors. In particular, linking human microbiome composition to host phenotypes such as diseases has become an area of intense research. For this, robust statistical modeling and biomarker extraction toolkits are crucially needed. SIAMCAT provides a full pipeline supporting data preprocessing, statistical association testing, statistical modeling (LASSO logistic regression) including tools for evaluation and interpretation of these models (such as cross validation, parameter selection, ROC analysis and diagnostic model plots).

Maintained by Jakob Wirbel. Last updated 5 months ago.

immunooncology metagenomics classification microbiome sequencing preprocessing clustering featureextraction geneticvariability multiplecomparison regression

6.72 score 147 scripts

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categoryCompare:Meta-analysis of high-throughput experiments using feature annotations

Calculates significant annotations (categories) in each of two (or more) feature (i.e. gene) lists, determines the overlap between the annotations, and returns graphical and tabular data about the significant annotations and which combinations of feature lists the annotations were found to be significant. Interactive exploration is facilitated through the use of RCytoscape (heavily suggested).

Maintained by Robert M. Flight. Last updated 5 months ago.

annotation go multiplecomparison pathways geneexpression bioconductor

6 stars 6.68 score

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DiffLogo:DiffLogo: A comparative visualisation of biooligomer motifs

DiffLogo is an easy-to-use tool to visualize motif differences.

Maintained by Hendrik Treutler. Last updated 5 months ago.

software sequencematching multiplecomparison motifannotation visualization alignment

8 stars 6.66 score 27 scripts

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ViSEAGO:ViSEAGO: a Bioconductor package for clustering biological functions using Gene Ontology and semantic similarity

The main objective of ViSEAGO package is to carry out a data mining of biological functions and establish links between genes involved in the study. We developed ViSEAGO in R to facilitate functional Gene Ontology (GO) analysis of complex experimental design with multiple comparisons of interest. It allows to study large-scale datasets together and visualize GO profiles to capture biological knowledge. The acronym stands for three major concepts of the analysis: Visualization, Semantic similarity and Enrichment Analysis of Gene Ontology. It provides access to the last current GO annotations, which are retrieved from one of NCBI EntrezGene, Ensembl or Uniprot databases for several species. Using available R packages and novel developments, ViSEAGO extends classical functional GO analysis to focus on functional coherence by aggregating closely related biological themes while studying multiple datasets at once. It provides both a synthetic and detailed view using interactive functionalities respecting the GO graph structure and ensuring functional coherence supplied by semantic similarity. ViSEAGO has been successfully applied on several datasets from different species with a variety of biological questions. Results can be easily shared between bioinformaticians and biologists, enhancing reporting capabilities while maintaining reproducibility.

Maintained by Aurelien Brionne. Last updated 3 months ago.

software annotation go genesetenrichment multiplecomparison clustering visualization

6.64 score 22 scripts

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condiments:Differential Topology, Progression and Differentiation

This package encapsulate many functions to conduct a differential topology analysis. It focuses on analyzing an 'omic dataset with multiple conditions. While the package is mostly geared toward scRNASeq, it does not place any restriction on the actual input format.

Maintained by Hector Roux de Bezieux. Last updated 4 months ago.

rnaseq sequencing software singlecell transcriptomics multiplecomparison visualization

26 stars 6.52 score 17 scripts

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nipalsMCIA:Multiple Co-Inertia Analysis via the NIPALS Method

Computes Multiple Co-Inertia Analysis (MCIA), a dimensionality reduction (jDR) algorithm, for a multi-block dataset using a modification to the Nonlinear Iterative Partial Least Squares method (NIPALS) proposed in (Hanafi et. al, 2010). Allows multiple options for row- and table-level preprocessing, and speeds up computation of variance explained. Vignettes detail application to bulk- and single cell- multi-omics studies.

Maintained by Maximilian Mattessich. Last updated 1 months ago.

software clustering classification multiplecomparison normalization preprocessing singlecell

6 stars 6.51 score 10 scripts

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GeneOverlap:Test and visualize gene overlaps

Test two sets of gene lists and visualize the results.

Maintained by António Miguel de Jesus Domingues, Max-Planck Institute for Cell Biology and Genetics. Last updated 5 months ago.

multiplecomparison visualization

6.46 score 266 scripts

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artMS:Analytical R tools for Mass Spectrometry

artMS provides a set of tools for the analysis of proteomics label-free datasets. It takes as input the MaxQuant search result output (evidence.txt file) and performs quality control, relative quantification using MSstats, downstream analysis and integration. artMS also provides a set of functions to re-format and make it compatible with other analytical tools, including, SAINTq, SAINTexpress, Phosfate, and PHOTON. Check [http://artms.org](http://artms.org) for details.

Maintained by David Jimenez-Morales. Last updated 5 months ago.

proteomics differentialexpression biomedicalinformatics systemsbiology massspectrometry annotation qualitycontrol genesetenrichment clustering normalization immunooncology multiplecomparison analysis analytical ap-ms bioconductor bioinformatics mass-spectrometry phosphoproteomics post-translational-modification quantitative-analysis

14 stars 6.41 score 13 scripts

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quantro:A test for when to use quantile normalization

A data-driven test for the assumptions of quantile normalization using raw data such as objects that inherit eSets (e.g. ExpressionSet, MethylSet). Group level information about each sample (such as Tumor / Normal status) must also be provided because the test assesses if there are global differences in the distributions between the user-defined groups.

Maintained by Stephanie Hicks. Last updated 5 months ago.

normalization preprocessing multiplecomparison microarray sequencing

6.40 score 69 scripts 2 dependents

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dmrseq:Detection and inference of differentially methylated regions from Whole Genome Bisulfite Sequencing

This package implements an approach for scanning the genome to detect and perform accurate inference on differentially methylated regions from Whole Genome Bisulfite Sequencing data. The method is based on comparing detected regions to a pooled null distribution, that can be implemented even when as few as two samples per population are available. Region-level statistics are obtained by fitting a generalized least squares (GLS) regression model with a nested autoregressive correlated error structure for the effect of interest on transformed methylation proportions.

Maintained by Keegan Korthauer. Last updated 5 months ago.

immunooncology dnamethylation epigenetics multiplecomparison software sequencing differentialmethylation wholegenome regression functionalgenomics

6.39 score 59 scripts 1 dependents

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distinct:distinct: a method for differential analyses via hierarchical permutation tests

distinct is a statistical method to perform differential testing between two or more groups of distributions; differential testing is performed via hierarchical non-parametric permutation tests on the cumulative distribution functions (cdfs) of each sample. While most methods for differential expression target differences in the mean abundance between conditions, distinct, by comparing full cdfs, identifies, both, differential patterns involving changes in the mean, as well as more subtle variations that do not involve the mean (e.g., unimodal vs. bi-modal distributions with the same mean). distinct is a general and flexible tool: due to its fully non-parametric nature, which makes no assumptions on how the data was generated, it can be applied to a variety of datasets. It is particularly suitable to perform differential state analyses on single cell data (i.e., differential analyses within sub-populations of cells), such as single cell RNA sequencing (scRNA-seq) and high-dimensional flow or mass cytometry (HDCyto) data. To use distinct one needs data from two or more groups of samples (i.e., experimental conditions), with at least 2 samples (i.e., biological replicates) per group.

Maintained by Simone Tiberi. Last updated 5 months ago.

genetics rnaseq sequencing differentialexpression geneexpression multiplecomparison software transcription statisticalmethod visualization singlecell flowcytometry genetarget openblas cpp

11 stars 6.35 score 34 scripts 1 dependents

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swfdr:Estimation of the science-wise false discovery rate and the false discovery rate conditional on covariates

This package allows users to estimate the science-wise false discovery rate from Jager and Leek, "Empirical estimates suggest most published medical research is true," 2013, Biostatistics, using an EM approach due to the presence of rounding and censoring. It also allows users to estimate the false discovery rate conditional on covariates, using a regression framework, as per Boca and Leek, "A direct approach to estimating false discovery rates conditional on covariates," 2018, PeerJ.

Maintained by Simina M. Boca. Last updated 5 months ago.

multiplecomparison statisticalmethod software

3 stars 6.25 score 37 scripts

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segmentSeq:Methods for identifying small RNA loci from high-throughput sequencing data

High-throughput sequencing technologies allow the production of large volumes of short sequences, which can be aligned to the genome to create a set of matches to the genome. By looking for regions of the genome which to which there are high densities of matches, we can infer a segmentation of the genome into regions of biological significance. The methods in this package allow the simultaneous segmentation of data from multiple samples, taking into account replicate data, in order to create a consensus segmentation. This has obvious applications in a number of classes of sequencing experiments, particularly in the discovery of small RNA loci and novel mRNA transcriptome discovery.

Maintained by Samuel Granjeaud. Last updated 5 months ago.

multiplecomparison sequencing alignment differentialexpression qualitycontrol dataimport

6.17 score 42 scripts

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GPA:GPA (Genetic analysis incorporating Pleiotropy and Annotation)

This package provides functions for fitting GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy information and annotation data. In addition, it also includes ShinyGPA, an interactive visualization toolkit to investigate pleiotropic architecture.

Maintained by Dongjun Chung. Last updated 5 months ago.

software statisticalmethod classification genomewideassociation snp genetics clustering multiplecomparison preprocessing geneexpression differentialexpression cpp

14 stars 6.15 score 7 scripts

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made4:Multivariate analysis of microarray data using ADE4

Multivariate data analysis and graphical display of microarray data. Functions include for supervised dimension reduction (between group analysis) and joint dimension reduction of 2 datasets (coinertia analysis). It contains functions that require R package ade4.

Maintained by Aedin Culhane. Last updated 5 months ago.

clustering classification dimensionreduction principalcomponent transcriptomics multiplecomparison geneexpression sequencing microarray

6.11 score 107 scripts 2 dependents

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QUBIC:An R package for qualitative biclustering in support of gene co-expression analyses

The core function of this R package is to provide the implementation of the well-cited and well-reviewed QUBIC algorithm, aiming to deliver an effective and efficient biclustering capability. This package also includes the following related functions: (i) a qualitative representation of the input gene expression data, through a well-designed discretization way considering the underlying data property, which can be directly used in other biclustering programs; (ii) visualization of identified biclusters using heatmap in support of overall expression pattern analysis; (iii) bicluster-based co-expression network elucidation and visualization, where different correlation coefficient scores between a pair of genes are provided; and (iv) a generalize output format of biclusters and corresponding network can be freely downloaded so that a user can easily do following comprehensive functional enrichment analysis (e.g. DAVID) and advanced network visualization (e.g. Cytoscape).

Maintained by Yu Zhang. Last updated 5 months ago.

statisticalmethod microarray differentialexpression multiplecomparison clustering visualization geneexpression network bioconductor-package bioconductor-packages cpp openmp

3 stars 6.10 score 14 scripts 1 dependents

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metaseqR2:An R package for the analysis and result reporting of RNA-Seq data by combining 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.

Maintained by Panagiotis Moulos. Last updated 18 days ago.

software geneexpression differentialexpression workflowstep preprocessing qualitycontrol normalization reportwriting rnaseq transcription sequencing transcriptomics bayesian clustering cellbiology biomedicalinformatics functionalgenomics systemsbiology immunooncology alternativesplicing differentialsplicing multiplecomparison timecourse dataimport atacseq epigenetics regression proprietaryplatforms genesetenrichment batcheffect chipseq

7 stars 6.05 score 3 scripts

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raer:RNA editing tools in R

Toolkit for identification and statistical testing of RNA editing signals from within R. Provides support for identifying sites from bulk-RNA and single cell RNA-seq datasets, and general methods for extraction of allelic read counts from alignment files. Facilitates annotation and exploratory analysis of editing signals using Bioconductor packages and resources.

Maintained by Kent Riemondy. Last updated 5 months ago.

multiplecomparison rnaseq singlecell sequencing coverage epitranscriptomics featureextraction annotation alignment bioconductor-package rna-seq-analysis single-cell-analysis single-cell-rna-seq curl bzip2 xz-utils zlib

8 stars 5.98 score 6 scripts

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dar:Differential Abundance Analysis by Consensus

Differential abundance testing in microbiome data challenges both parametric and non-parametric statistical methods, due to its sparsity, high variability and compositional nature. Microbiome-specific statistical methods often assume classical distribution models or take into account compositional specifics. These produce results that range within the specificity vs sensitivity space in such a way that type I and type II error that are difficult to ascertain in real microbiome data when a single method is used. Recently, a consensus approach based on multiple differential abundance (DA) methods was recently suggested in order to increase robustness. With dar, you can use dplyr-like pipeable sequences of DA methods and then apply different consensus strategies. In this way we can obtain more reliable results in a fast, consistent and reproducible way.

Maintained by Francesc Catala-Moll. Last updated 14 days ago.

software sequencing microbiome metagenomics multiplecomparison normalization bioconductor biomarker-discovery differential-abundance-analysis feature-selection microbiology phyloseq

2 stars 5.98 score 8 scripts

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timeOmics:Time-Course Multi-Omics data integration

timeOmics is a generic data-driven framework to integrate multi-Omics longitudinal data measured on the same biological samples and select key temporal features with strong associations within the same sample group. The main steps of timeOmics are: 1. Plaform and time-specific normalization and filtering steps; 2. Modelling each biological into one time expression profile; 3. Clustering features with the same expression profile over time; 4. Post-hoc validation step.

Maintained by Antoine Bodein. Last updated 5 months ago.

clustering featureextraction timecourse dimensionreduction software sequencing microarray metabolomics metagenomics proteomics classification regression immunooncology geneprediction multiplecomparison cluster integration multi-omics time-series

24 stars 5.98 score 10 scripts

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normr:Normalization and difference calling in ChIP-seq data

Robust normalization and difference calling procedures for ChIP-seq and alike data. Read counts are modeled jointly as a binomial mixture model with a user-specified number of components. A fitted background estimate accounts for the effect of enrichment in certain regions and, therefore, represents an appropriate null hypothesis. This robust background is used to identify significantly enriched or depleted regions.

Maintained by Johannes Helmuth. Last updated 5 months ago.

bayesian differentialpeakcalling classification dataimport chipseq ripseq functionalgenomics genetics multiplecomparison normalization peakdetection preprocessing alignment cpp openmp

11 stars 5.93 score 13 scripts

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MetaNeighbor:Single cell replicability analysis

MetaNeighbor allows users to quantify cell type replicability across datasets using neighbor voting.

Maintained by Stephan Fischer. Last updated 5 months ago.

immunooncology geneexpression go multiplecomparison singlecell transcriptomics

5.89 score 78 scripts

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qsmooth:Smooth quantile normalization

Smooth quantile normalization is a generalization of quantile normalization, which is average of the two types of assumptions about the data generation process: quantile normalization and quantile normalization between groups.

Maintained by Stephanie C. Hicks. Last updated 5 months ago.

normalization preprocessing multiplecomparison microarray sequencing rnaseq batcheffect

5.88 score 84 scripts 1 dependents

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fabia:FABIA: Factor Analysis for Bicluster Acquisition

Biclustering by "Factor Analysis for Bicluster Acquisition" (FABIA). FABIA is a model-based technique for biclustering, that is clustering rows and columns simultaneously. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. It captures realistic non-Gaussian data distributions with heavy tails as observed in gene expression measurements. FABIA utilizes well understood model selection techniques like the EM algorithm and variational approaches and is embedded into a Bayesian framework. FABIA ranks biclusters according to their information content and separates spurious biclusters from true biclusters. The code is written in C.

Maintained by Andreas Mitterecker. Last updated 5 months ago.

statisticalmethod microarray differentialexpression multiplecomparison clustering visualization

5.84 score 32 scripts 6 dependents

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ChromSCape:Analysis of single-cell epigenomics datasets with a Shiny App

ChromSCape - Chromatin landscape profiling for Single Cells - is a ready-to-launch user-friendly Shiny Application for the analysis of single-cell epigenomics datasets (scChIP-seq, scATAC-seq, scCUT&Tag, ...) from aligned data to differential analysis & gene set enrichment analysis. It is highly interactive, enables users to save their analysis and covers a wide range of analytical steps: QC, preprocessing, filtering, batch correction, dimensionality reduction, vizualisation, clustering, differential analysis and gene set analysis.

Maintained by Pacome Prompsy. Last updated 5 months ago.

shinyapps software singlecell chipseq atacseq methylseq classification clustering epigenetics principalcomponent annotation batcheffect multiplecomparison normalization pathways preprocessing qualitycontrol reportwriting visualization genesetenrichment differentialpeakcalling epigenomics shiny single-cell cpp

14 stars 5.83 score 16 scripts

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seqsetvis:Set Based Visualizations for Next-Gen Sequencing Data

seqsetvis enables the visualization and analysis of sets of genomic sites in next gen sequencing data. Although seqsetvis was designed for the comparison of mulitple ChIP-seq samples, this package is domain-agnostic and allows the processing of multiple genomic coordinate files (bed-like files) and signal files (bigwig files pileups from bam file). seqsetvis has multiple functions for fetching data from regions into a tidy format for analysis in data.table or tidyverse and visualization via ggplot2.

Maintained by Joseph R Boyd. Last updated 4 months ago.

software chipseq multiplecomparison sequencing visualization

5.82 score 82 scripts

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EGSEA:Ensemble of Gene Set Enrichment Analyses

This package implements the Ensemble of Gene Set Enrichment Analyses (EGSEA) method for gene set testing. EGSEA algorithm utilizes the analysis results of twelve prominent GSE algorithms in the literature to calculate collective significance scores for each gene set.

Maintained by Monther Alhamdoosh. Last updated 5 months ago.

immunooncology differentialexpression go geneexpression genesetenrichment genetics microarray multiplecomparison onechannel pathways rnaseq sequencing software systemsbiology twochannel metabolomics proteomics kegg graphandnetwork genesignaling genetarget networkenrichment network classification

5.81 score 64 scripts

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bioCancer:Interactive Multi-Omics Cancers Data Visualization and Analysis

This package is a Shiny App to visualize and analyse interactively Multi-Assays of Cancer Genomic Data.

Maintained by Karim Mezhoud. Last updated 5 months ago.

gui datarepresentation network multiplecomparison pathways reactome visualization geneexpression genetarget analysis biocancer-interface cancer cancer-studies rmarkdown

20 stars 5.78 score 7 scripts

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benchdamic:Benchmark of differential abundance methods on microbiome data

Starting from a microbiome dataset (16S or WMS with absolute count values) it is possible to perform several analysis to assess the performances of many differential abundance detection methods. A basic and standardized version of the main differential abundance analysis methods is supplied but the user can also add his method to the benchmark. The analyses focus on 4 main aspects: i) the goodness of fit of each method's distributional assumptions on the observed count data, ii) the ability to control the false discovery rate, iii) the within and between method concordances, iv) the truthfulness of the findings if any apriori knowledge is given. Several graphical functions are available for result visualization.

Maintained by Matteo Calgaro. Last updated 4 months ago.

metagenomics microbiome differentialexpression multiplecomparison normalization preprocessing software benchmark differential-abundance-methods

8 stars 5.78 score 8 scripts

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TVTB:TVTB: The VCF Tool Box

The package provides S4 classes and methods to filter, summarise and visualise genetic variation data stored in VCF files. In particular, the package extends the FilterRules class (S4Vectors package) to define news classes of filter rules applicable to the various slots of VCF objects. Functionalities are integrated and demonstrated in a Shiny web-application, the Shiny Variant Explorer (tSVE).

Maintained by Kevin Rue-Albrecht. Last updated 5 months ago.

software genetics geneticvariability genomicvariation datarepresentation gui dnaseq wholegenome visualization multiplecomparison dataimport variantannotation sequencing coverage alignment sequencematching

2 stars 5.76 score 16 scripts

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BANDITS:BANDITS: Bayesian ANalysis of DIfferenTial Splicing

BANDITS is a Bayesian hierarchical model for detecting differential splicing of genes and transcripts, via differential transcript usage (DTU), between two or more conditions. The method uses a Bayesian hierarchical framework, which allows for sample specific proportions in a Dirichlet-Multinomial model, and samples the allocation of fragments to the transcripts. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques and a DTU test is performed via a multivariate Wald test on the posterior densities for the average relative abundance of transcripts.

Maintained by Simone Tiberi. Last updated 5 months ago.

differentialsplicing alternativesplicing bayesian genetics rnaseq sequencing differentialexpression geneexpression multiplecomparison software transcription statisticalmethod visualization openblas cpp

17 stars 5.75 score 11 scripts 1 dependents

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limpca:An R package for the linear modeling of high-dimensional designed data based on ASCA/APCA family of methods

This package has for objectives to provide a method to make Linear Models for high-dimensional designed data. limpca applies a GLM (General Linear Model) version of ASCA and APCA to analyse multivariate sample profiles generated by an experimental design. ASCA/APCA provide powerful visualization tools for multivariate structures in the space of each effect of the statistical model linked to the experimental design and contrarily to MANOVA, it can deal with mutlivariate datasets having more variables than observations. This method can handle unbalanced design.

Maintained by Manon Martin. Last updated 5 months ago.

statisticalmethod principalcomponent regression visualization experimentaldesign multiplecomparison geneexpression metabolomics

2 stars 5.73 score 2 scripts

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ASSET:An R package for subset-based association analysis of heterogeneous traits and subtypes

An R package for subset-based analysis of heterogeneous traits and disease subtypes. The package allows the user to search through all possible subsets of z-scores to identify the subset of traits giving the best meta-analyzed z-score. Further, it returns a p-value adjusting for the multiple-testing involved in the search. It also allows for searching for the best combination of disease subtypes associated with each variant.

Maintained by Samsiddhi Bhattacharjee. Last updated 5 months ago.

statisticalmethod snp genomewideassociation multiplecomparison

5.71 score 85 scripts 1 dependents

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rexposome:Exposome exploration and outcome data analysis

Package that allows to explore the exposome and to perform association analyses between exposures and health outcomes.

Maintained by Xavier Escribà Montagut. Last updated 5 months ago.

software biologicalquestion infrastructure dataimport datarepresentation biomedicalinformatics experimentaldesign multiplecomparison classification clustering

5.70 score 28 scripts 1 dependents

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CoSIA:An Investigation Across Different Species and Tissues

Cross-Species Investigation and Analysis (CoSIA) is a package that provides researchers with an alternative methodology for comparing across species and tissues using normal wild-type RNA-Seq Gene Expression data from Bgee. Using RNA-Seq Gene Expression data, CoSIA provides multiple visualization tools to explore the transcriptome diversity and variation across genes, tissues, and species. CoSIA uses the Coefficient of Variation and Shannon Entropy and Specificity to calculate transcriptome diversity and variation. CoSIA also provides additional conversion tools and utilities to provide a streamlined methodology for cross-species comparison.

Maintained by Amanda D. Clark. Last updated 5 months ago.

software biologicalquestion geneexpression multiplecomparison thirdpartyclient dataimport gui

5 stars 5.70 score 3 scripts

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cydar:Using Mass Cytometry for Differential Abundance Analyses

Identifies differentially abundant populations between samples and groups in mass cytometry data. Provides methods for counting cells into hyperspheres, controlling the spatial false discovery rate, and visualizing changes in abundance in the high-dimensional marker space.

Maintained by Aaron Lun. Last updated 5 months ago.

immunooncology flowcytometry multiplecomparison proteomics singlecell cpp

5.64 score 48 scripts

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statTarget:Statistical Analysis of Molecular Profiles

A streamlined tool provides a graphical user interface for quality control based signal drift correction (QC-RFSC), integration of data from multi-batch MS-based experiments, and the comprehensive statistical analysis in metabolomics and proteomics.

Maintained by Hemi Luan. Last updated 5 months ago.

immunooncology metabolomics proteomics machine learning lipidomics massspectrometry qualitycontrol normalization qc-rfsc combat differentialexpression batcheffect visualization multiplecomparison preprocessing software

5.64 score 24 scripts

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similaRpeak:Metrics to estimate a level of similarity between two ChIP-Seq profiles

This package calculates metrics which quantify the level of similarity between ChIP-Seq profiles. More specifically, the package implements six pseudometrics specialized in pattern similarity detection in ChIP-Seq profiles.

Maintained by Astrid Deschênes. Last updated 5 months ago.

biologicalquestion chipseq genetics multiplecomparison differentialexpression bioconductor bioconductor-package chip-profiles chip-seq metrics

7 stars 5.62 score 7 scripts

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diffHic:Differential Analysis of Hi-C Data

Detects differential interactions across biological conditions in a Hi-C experiment. Methods are provided for read alignment and data pre-processing into interaction counts. Statistical analysis is based on edgeR and supports normalization and filtering. Several visualization options are also available.

Maintained by Aaron Lun. Last updated 3 months ago.

multiplecomparison preprocessing sequencing coverage alignment normalization clustering hic curl bzip2 xz-utils zlib cpp

5.58 score 38 scripts

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tripr:T-cell Receptor/Immunoglobulin Profiler (TRIP)

TRIP is a software framework that provides analytics services on antigen receptor (B cell receptor immunoglobulin, BcR IG | T cell receptor, TR) gene sequence data. It is a web application written in R Shiny. It takes as input the output files of the IMGT/HighV-Quest tool. Users can select to analyze the data from each of the input samples separately, or the combined data files from all samples and visualize the results accordingly.

Maintained by Nikolaos Pechlivanis. Last updated 5 months ago.

batcheffect multiplecomparison geneexpression immunooncology targetedresequencing bioconductor clonotype

2 stars 5.48 score 4 scripts

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omicade4:Multiple co-inertia analysis of omics datasets

This package performes multiple co-inertia analysis of omics datasets.

Maintained by Chen Meng. Last updated 5 months ago.

software clustering classification multiplecomparison

5.48 score 50 scripts 1 dependents

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MotifPeeker:Benchmarking Epigenomic Profiling Methods Using Motif Enrichment

MotifPeeker is used to compare and analyse datasets from epigenomic profiling methods with motif enrichment as the key benchmark. The package outputs an HTML report consisting of three sections: (1. General Metrics) Overview of peaks-related general metrics for the datasets (FRiP scores, peak widths and motif-summit distances). (2. Known Motif Enrichment Analysis) Statistics for the frequency of user-provided motifs enriched in the datasets. (3. De-Novo Motif Enrichment Analysis) Statistics for the frequency of de-novo discovered motifs enriched in the datasets and compared with known motifs.

Maintained by Hiranyamaya Dash. Last updated 3 months ago.

epigenetics genetics qualitycontrol chipseq multiplecomparison functionalgenomics motifdiscovery sequencematching software alignment bioconductor bioconductor-package chip-seq epigenomics interactive-report motif-enrichment-analysis

2 stars 5.48 score 6 scripts

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goProfiles:goProfiles: an R package for the statistical analysis of functional profiles

The package implements methods to compare lists of genes based on comparing the corresponding 'functional profiles'.

Maintained by Alex Sanchez. Last updated 5 months ago.

annotation go geneexpression genesetenrichment graphandnetwork microarray multiplecomparison pathways software

5.48 score 6 scripts 1 dependents

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metagene2:A package to produce metagene plots

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.

Maintained by Eric Fournier. Last updated 5 months ago.

chipseq genetics multiplecomparison coverage alignment sequencing

4 stars 5.45 score 8 scripts

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PROMISE:PRojection Onto the Most Interesting Statistical Evidence

A general tool to identify genomic features with a specific biologically interesting pattern of associations with multiple endpoint variables as described in Pounds et. al. (2009) Bioinformatics 25: 2013-2019

Maintained by Stan Pounds. Last updated 5 months ago.

microarray onechannel multiplecomparison geneexpression

5.44 score 46 scripts 1 dependents

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R4RNA:An R package for RNA visualization and analysis

A package for RNA basepair analysis, including the visualization of basepairs as arc diagrams for easy comparison and annotation of sequence and structure. Arc diagrams can additionally be projected onto multiple sequence alignments to assess basepair conservation and covariation, with numerical methods for computing statistics for each.

Maintained by Daniel Lai. Last updated 5 months ago.

alignment multiplesequencealignment preprocessing visualization dataimport datarepresentation multiplecomparison

5.36 score 19 scripts 4 dependents

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globalSeq:Global Test for Counts

The method may be conceptualised as a test of overall significance in regression analysis, where the response variable is overdispersed and the number of explanatory variables exceeds the sample size. Useful for testing for association between RNA-Seq and high-dimensional data.

Maintained by Armin Rauschenberger. Last updated 5 months ago.

geneexpression exonarray differentialexpression genomewideassociation transcriptomics dimensionreduction regression sequencing wholegenome rnaseq exomeseq mirna multiplecomparison

1 stars 5.32 score 4 scripts

bioc

MetaboDynamics:Bayesian analysis of longitudinal metabolomics data

MetaboDynamics is an R-package that provides a framework of probabilistic models to analyze longitudinal metabolomics data. It enables robust estimation of mean concentrations despite varying spread between timepoints and reports differences between timepoints as well as metabolite specific dynamics profiles that can be used for identifying "dynamics clusters" of metabolites of similar dynamics. Provides probabilistic over-representation analysis of KEGG functional modules and pathways as well as comparison between clusters of different experimental conditions.

Maintained by Katja Danielzik. Last updated 3 days ago.

software metabolomics bayesian functionalprediction multiplecomparison kegg pathways dynamics functional-analysis longitudinal-analysis metabolomics-data metabolomics-pipeline cpp

5 stars 5.30 score 3 scripts

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DifferentialRegulation:Differentially regulated genes from scRNA-seq data

DifferentialRegulation is a method for detecting differentially regulated genes between two groups of samples (e.g., healthy vs. disease, or treated vs. untreated samples), by targeting differences in the balance of spliced and unspliced mRNA abundances, obtained from single-cell RNA-sequencing (scRNA-seq) data. From a mathematical point of view, DifferentialRegulation accounts for the sample-to-sample variability, and embeds multiple samples in a Bayesian hierarchical model. Furthermore, our method also deals with two major sources of mapping uncertainty: i) 'ambiguous' reads, compatible with both spliced and unspliced versions of a gene, and ii) reads mapping to multiple genes. In particular, ambiguous reads are treated separately from spliced and unsplced reads, while reads that are compatible with multiple genes are allocated to the gene of origin. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques (Metropolis-within-Gibbs).

Maintained by Simone Tiberi. Last updated 5 months ago.

differentialsplicing bayesian genetics rnaseq sequencing differentialexpression geneexpression multiplecomparison software transcription statisticalmethod visualization singlecell genetarget openblas cpp

10 stars 5.30 score 4 scripts

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consensusSeekeR:Detection of consensus regions inside a group of experiences using genomic positions and genomic ranges

This package compares genomic positions and genomic ranges from multiple experiments to extract common regions. The size of the analyzed region is adjustable as well as the number of experiences in which a feature must be present in a potential region to tag this region as a consensus region. In genomic analysis where feature identification generates a position value surrounded by a genomic range, such as ChIP-Seq peaks and nucleosome positions, the replication of an experiment may result in slight differences between predicted values. This package enables the conciliation of the results into consensus regions.

Maintained by Astrid Deschênes. Last updated 5 months ago.

biologicalquestion chipseq genetics multiplecomparison transcription peakdetection sequencing coverage chip-seq-analysis genomic-data-analysis nucleosome-positioning

1 stars 5.26 score 5 scripts 1 dependents

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bacon:Controlling bias and inflation in association studies using the empirical null distribution

Bacon can be used to remove inflation and bias often observed in epigenome- and transcriptome-wide association studies. To this end bacon constructs an empirical null distribution using a Gibbs Sampling algorithm by fitting a three-component normal mixture on z-scores.

Maintained by Maarten van Iterson. Last updated 5 months ago.

immunooncology statisticalmethod bayesian regression genomewideassociation transcriptomics rnaseq methylationarray batcheffect multiplecomparison

5.19 score 97 scripts

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MouseFM:In-silico methods for genetic finemapping in inbred mice

This package provides methods for genetic finemapping in inbred mice by taking advantage of their very high homozygosity rate (>95%).

Maintained by Matthias Munz. Last updated 5 months ago.

genetics snp genetarget variantannotation genomicvariation multiplecomparison systemsbiology mathematicalbiology patternlogic geneprediction biomedicalinformatics functionalgenomics finemap gene-candidates inbred-mice inbred-strains mouse qtl qtl-mapping

5.13 score 5 scripts

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semisup:Semi-Supervised Mixture Model

Implements a parametric semi-supervised mixture model. The permutation test detects markers with main or interactive effects, without distinguishing them. Possible applications include genome-wide association analysis and differential expression analysis.

Maintained by Armin Rauschenberger. Last updated 5 months ago.

snp genomicvariation somaticmutation genetics classification clustering dnaseq microarray multiplecomparison

1 stars 5.08 score 4 scripts

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FindIT2:find influential TF and Target based on multi-omics data

This package implements functions to find influential TF and target based on different input type. It have five module: Multi-peak multi-gene annotaion(mmPeakAnno module), Calculate regulation potential(calcRP module), Find influential Target based on ChIP-Seq and RNA-Seq data(Find influential Target module), Find influential TF based on different input(Find influential TF module), Calculate peak-gene or peak-peak correlation(peakGeneCor module). And there are also some other useful function like integrate different source information, calculate jaccard similarity for your TF.

Maintained by Guandong Shang. Last updated 5 months ago.

software annotation chipseq atacseq generegulation multiplecomparison genetarget

6 stars 5.08 score 7 scripts

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canceR:A Graphical User Interface for accessing and modeling the Cancer Genomics Data of MSKCC

The package is user friendly interface based on the cgdsr and other modeling packages to explore, compare, and analyse all available Cancer Data (Clinical data, Gene Mutation, Gene Methylation, Gene Expression, Protein Phosphorylation, Copy Number Alteration) hosted by the Computational Biology Center at Memorial-Sloan-Kettering Cancer Center (MSKCC).

Maintained by Karim Mezhoud. Last updated 5 months ago.

gui geneexpression clustering go genesetenrichment kegg multiplecomparison cancer cancer-data gene gene-expression gene-methylation gene-mutation gene-sets methylation mskcc mutations tcltk

7 stars 5.08 score 17 scripts

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Harman:The removal of batch effects from datasets using a PCA and constrained optimisation based technique

Harman is a PCA and constrained optimisation based technique that maximises the removal of batch effects from datasets, with the constraint that the probability of overcorrection (i.e. removing genuine biological signal along with batch noise) is kept to a fraction which is set by the end-user.

Maintained by Jason Ross. Last updated 5 months ago.

batcheffect microarray multiplecomparison principalcomponent normalization preprocessing dnamethylation transcription software statisticalmethod cpp

4.97 score 31 scripts 1 dependents

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scShapes:A Statistical Framework for Modeling and Identifying Differential Distributions in Single-cell RNA-sequencing Data

We present a novel statistical framework for identifying differential distributions in single-cell RNA-sequencing (scRNA-seq) data between treatment conditions by modeling gene expression read counts using generalized linear models (GLMs). We model each gene independently under each treatment condition using error distributions Poisson (P), Negative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) with log link function and model based normalization for differences in sequencing depth. Since all four distributions considered in our framework belong to the same family of distributions, we first perform a Kolmogorov-Smirnov (KS) test to select genes belonging to the family of ZINB distributions. Genes passing the KS test will be then modeled using GLMs. Model selection is done by calculating the Bayesian Information Criterion (BIC) and likelihood ratio test (LRT) statistic.

Maintained by Malindrie Dharmaratne. Last updated 5 months ago.

rnaseq singlecell multiplecomparison geneexpression

8 stars 4.90 score 6 scripts

bioc

AMARETTO:Regulatory Network Inference and Driver Gene Evaluation using Integrative Multi-Omics Analysis and Penalized Regression

Integrating an increasing number of available multi-omics cancer data remains one of the main challenges to improve our understanding of cancer. One of the main challenges is using multi-omics data for identifying novel cancer driver genes. We have developed an algorithm, called AMARETTO, that integrates copy number, DNA methylation and gene expression data to identify a set of driver genes by analyzing cancer samples and connects them to clusters of co-expressed genes, which we define as modules. We applied AMARETTO in a pancancer setting to identify cancer driver genes and their modules on multiple cancer sites. AMARETTO captures modules enriched in angiogenesis, cell cycle and EMT, and modules that accurately predict survival and molecular subtypes. This allows AMARETTO to identify novel cancer driver genes directing canonical cancer pathways.

Maintained by Olivier Gevaert. Last updated 5 months ago.

statisticalmethod differentialmethylation generegulation geneexpression methylationarray transcription preprocessing batcheffect dataimport mrnamicroarray micrornaarray regression clustering rnaseq copynumbervariation sequencing microarray normalization network bayesian exonarray onechannel twochannel proprietaryplatforms alternativesplicing differentialexpression differentialsplicing genesetenrichment multiplecomparison qualitycontrol timecourse

4.88 score 15 scripts

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ADAPT:Analysis of Microbiome Differential Abundance by Pooling Tobit Models

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.

Maintained by Mukai Wang. Last updated 5 months ago.

differentialexpression microbiome normalization sequencing metagenomics software multiplecomparison openblas cpp

4.81 score 26 scripts

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plotGrouper:Shiny app GUI wrapper for ggplot with built-in statistical analysis

A shiny app-based GUI wrapper for ggplot with built-in statistical analysis. Import data from file and use dropdown menus and checkboxes to specify the plotting variables, graph type, and look of your plots. Once created, plots can be saved independently or stored in a report that can be saved as a pdf. If new data are added to the file, the report can be refreshed to include new data. Statistical tests can be selected and added to the graphs. Analysis of flow cytometry data is especially integrated with plotGrouper. Count data can be transformed to return the absolute number of cells in a sample (this feature requires inclusion of the number of beads per sample and information about any dilution performed).

Maintained by John D. Gagnon. Last updated 5 months ago.

immunooncology flowcytometry graphandnetwork statisticalmethod dataimport gui multiplecomparison bioconductor ggplot2 plotting shiny

6 stars 4.78 score 10 scripts

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Sconify:A toolkit for performing KNN-based statistics for flow and mass cytometry data

This package does k-nearest neighbor based statistics and visualizations with flow and mass cytometery data. This gives tSNE maps"fold change" functionality and provides a data quality metric by assessing manifold overlap between fcs files expected to be the same. Other applications using this package include imputation, marker redundancy, and testing the relative information loss of lower dimension embeddings compared to the original manifold.

Maintained by Tyler J Burns. Last updated 5 months ago.

immunooncology singlecell flowcytometry software multiplecomparison visualization

4.74 score 11 scripts

xinghuq

DA:Discriminant Analysis for Evolutionary Inference

Discriminant Analysis (DA) for evolutionary inference (Qin, X. et al, 2020, <doi:10.22541/au.159256808.83862168>), especially for population genetic structure and community structure inference. This package incorporates the commonly used linear and non-linear, local and global supervised learning approaches (discriminant analysis), including Linear Discriminant Analysis of Kernel Principal Components (LDAKPC), Local (Fisher) Linear Discriminant Analysis (LFDA), Local (Fisher) Discriminant Analysis of Kernel Principal Components (LFDAKPC) and Kernel Local (Fisher) Discriminant Analysis (KLFDA). These discriminant analyses can be used to do ecological and evolutionary inference, including demography inference, species identification, and population/community structure inference.

Maintained by Xinghu Qin. Last updated 4 years ago.

biomedicalinformatics chipseq clustering coverage dnamethylation differentialexpression differentialmethylation software differentialsplicing epigenetics functionalgenomics geneexpression genesetenrichment genetics immunooncology multiplecomparison normalization pathways qualitycontrol rnaseq regression sage sequencing systemsbiology timecourse transcription transcriptomics dapc discriminant-analysis ecological kernel kernel-local kernel-principle-components population-structure-inference principal-components

1 stars 4.70 score 1 scripts

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openPrimeR:Multiplex PCR Primer Design and Analysis

An implementation of methods for designing, evaluating, and comparing primer sets for multiplex PCR. Primers are designed by solving a set cover problem such that the number of covered template sequences is maximized with the smallest possible set of primers. To guarantee that high-quality primers are generated, only primers fulfilling constraints on their physicochemical properties are selected. A Shiny app providing a user interface for the functionalities of this package is provided by the 'openPrimeRui' package.

Maintained by Matthias Döring. Last updated 9 days ago.

software technology coverage multiplecomparison

4.64 score 22 scripts

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limmaGUI:GUI for limma Package With Two Color Microarrays

A Graphical User Interface for differential expression analysis of two-color microarray data using the limma package.

Maintained by Gordon Smyth. Last updated 5 months ago.

gui geneexpression differentialexpression dataimport bayesian regression timecourse microarray mrnamicroarray twochannel batcheffect multiplecomparison normalization preprocessing qualitycontrol

4.60 score 1 scripts

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reconsi:Resampling Collapsed Null Distributions for Simultaneous Inference

Improves simultaneous inference under dependence of tests by estimating a collapsed null distribution through resampling. Accounting for the dependence between tests increases the power while reducing the variability of the false discovery proportion. This dependence is common in genomics applications, e.g. when combining flow cytometry measurements with microbiome sequence counts.

Maintained by Stijn Hawinkel. Last updated 5 months ago.

metagenomics microbiome multiplecomparison flowcytometry

2 stars 4.60 score 2 scripts

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affylmGUI:GUI for limma Package with Affymetrix Microarrays

A Graphical User Interface (GUI) for analysis of Affymetrix microarray gene expression data using the affy and limma packages.

Maintained by Gordon Smyth. Last updated 5 months ago.

gui geneexpression transcription differentialexpression dataimport bayesian regression timecourse microarray mrnamicroarray onechannel proprietaryplatforms batcheffect multiplecomparison normalization preprocessing qualitycontrol

4.60 score 3 scripts

bioc

phenoTest:Tools to test association between gene expression and phenotype in a way that is efficient, structured, fast and scalable. We also provide tools to do GSEA (Gene set enrichment analysis) and copy number variation.

Tools to test correlation between gene expression and phenotype in a way that is efficient, structured, fast and scalable. GSEA is also provided.

Maintained by Evarist Planet. Last updated 5 months ago.

microarray differentialexpression multiplecomparison clustering classification

4.56 score 9 scripts 1 dependents

bioc

ChIPComp:Quantitative comparison of multiple ChIP-seq datasets

ChIPComp detects differentially bound sharp binding sites across multiple conditions considering matching control.

Maintained by Li Chen. Last updated 5 months ago.

chipseq sequencing transcription genetics coverage multiplecomparison dataimport

4.49 score 51 scripts

bioc

ERSSA:Empirical RNA-seq Sample Size Analysis

The ERSSA package takes user supplied RNA-seq differential expression dataset and calculates the number of differentially expressed genes at varying biological replicate levels. This allows the user to determine, without relying on any a priori assumptions, whether sufficient differential detection has been acheived with their RNA-seq dataset.

Maintained by Zixuan Shao. Last updated 5 months ago.

immunooncology geneexpression transcription differentialexpression rnaseq multiplecomparison qualitycontrol

4.48 score 1 scripts

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snm:Supervised Normalization of Microarrays

SNM is a modeling strategy especially designed for normalizing high-throughput genomic data. The underlying premise of our approach is that your data is a function of what we refer to as study-specific variables. These variables are either biological variables that represent the target of the statistical analysis, or adjustment variables that represent factors arising from the experimental or biological setting the data is drawn from. The SNM approach aims to simultaneously model all study-specific variables in order to more accurately characterize the biological or clinical variables of interest.

Maintained by John D. Storey. Last updated 5 months ago.

microarray onechannel twochannel multichannel differentialexpression exonarray geneexpression transcription multiplecomparison preprocessing qualitycontrol

4.41 score 64 scripts

bioc

goSorensen:Statistical inference based on the Sorensen-Dice dissimilarity and the Gene Ontology (GO)

This package implements inferential methods to compare gene lists in terms of their biological meaning as expressed in the GO. The compared gene lists are characterized by cross-tabulation frequency tables of enriched GO items. Dissimilarity between gene lists is evaluated using the Sorensen-Dice index. The fundamental guiding principle is that two gene lists are taken as similar if they share a great proportion of common enriched GO items.

Maintained by Pablo Flores. Last updated 11 days ago.

annotation go genesetenrichment software microarray pathways geneexpression multiplecomparison graphandnetwork reactome clustering kegg

4.38 score 12 scripts

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omicRexposome:Exposome and omic data associatin and integration analysis

omicRexposome systematizes the association evaluation between exposures and omic data, taking advantage of MultiDataSet for coordinated data management, rexposome for exposome data definition and limma for association testing. Also to perform data integration mixing exposome and omic data using multi co-inherent analysis (omicade4) and multi-canonical correlation analysis (PMA).

Maintained by Xavier Escribà Montagut. Last updated 5 months ago.

immunooncology workflowstep multiplecomparison visualization geneexpression differentialexpression differentialmethylation generegulation epigenetics proteomics transcriptomics statisticalmethod regression

4.30 score 5 scripts

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regioneReloaded:RegioneReloaded: Multiple Association for Genomic Region Sets

RegioneReloaded is a package that allows simultaneous analysis of associations between genomic region sets, enabling clustering of data and the creation of ready-to-publish graphs. It takes over and expands on all the features of its predecessor regioneR. It also incorporates a strategy to improve p-value calculations and normalize z-scores coming from multiple analysis to allow for their direct comparison. RegioneReloaded builds upon regioneR by adding new plotting functions for obtaining publication-ready graphs.

Maintained by Roberto Malinverni. Last updated 5 months ago.

genetics chipseq dnaseq methylseq copynumbervariation clustering multiplecomparison

5 stars 4.30 score 2 scripts

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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.

Maintained by Habil Zare. Last updated 5 months ago.

genetics geneticvariability sequencing clustering multiplecomparison bayesian dnaseq exomeseq targetedresequencing immunooncology

4.30 score 3 scripts

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geva:Gene Expression Variation Analysis (GEVA)

Statistic methods to evaluate variations of differential expression (DE) between multiple biological conditions. It takes into account the fold-changes and p-values from previous differential expression (DE) results that use large-scale data (*e.g.*, microarray and RNA-seq) and evaluates which genes would react in response to the distinct experiments. This evaluation involves an unique pipeline of statistical methods, including weighted summarization, quantile detection, cluster analysis, and ANOVA tests, in order to classify a subset of relevant genes whose DE is similar or dependent to certain biological factors.

Maintained by Itamar José Guimarães Nunes. Last updated 5 months ago.

classification differentialexpression geneexpression microarray multiplecomparison rnaseq systemsbiology transcriptomics

2 stars 4.30 score 4 scripts

bioc

OPWeight:Optimal p-value weighting with independent information

This package perform weighted-pvalue based multiple hypothesis test and provides corresponding information such as ranking probability, weight, significant tests, etc . To conduct this testing procedure, the testing method apply a probabilistic relationship between the test rank and the corresponding test effect size.

Maintained by Mohamad Hasan. Last updated 5 months ago.

immunooncology biomedicalinformatics multiplecomparison regression rnaseq snp

2 stars 4.30 score

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microSTASIS:Microbiota STability ASsessment via Iterative cluStering

The toolkit 'µSTASIS', or microSTASIS, has been developed for the stability analysis of microbiota in a temporal framework by leveraging on iterative clustering. Concretely, the core function uses Hartigan-Wong k-means algorithm as many times as possible for stressing out paired samples from the same individuals to test if they remain together for multiple numbers of clusters over a whole data set of individuals. Moreover, the package includes multiple functions to subset samples from paired times, validate the results or visualize the output.

Maintained by Pedro Sánchez-Sánchez. Last updated 5 months ago.

geneticvariability biomedicalinformatics clustering multiplecomparison microbiome

2 stars 4.30 score 1 scripts

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PathNet:An R package for pathway analysis using topological information

PathNet uses topological information present in pathways and differential expression levels of genes (obtained from microarray experiment) to identify pathways that are 1) significantly enriched and 2) associated with each other in the context of differential expression. The algorithm is described in: PathNet: A tool for pathway analysis using topological information. Dutta B, Wallqvist A, and Reifman J. Source Code for Biology and Medicine 2012 Sep 24;7(1):10.

Maintained by Ludwig Geistlinger. Last updated 5 months ago.

pathways differentialexpression multiplecomparison kegg networkenrichment network

4.30 score 5 scripts

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MultiMed:Testing multiple biological mediators simultaneously

Implements methods for testing multiple mediators

Maintained by Simina M. Boca. Last updated 5 months ago.

multiplecomparison statisticalmethod software

4.30 score 8 scripts

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clusterSeq:Clustering of high-throughput sequencing data by identifying co-expression patterns

Identification of clusters of co-expressed genes based on their expression across multiple (replicated) biological samples.

Maintained by Samuel Granjeaud. Last updated 5 months ago.

sequencing differentialexpression multiplecomparison clustering geneexpression

4.26 score 2 scripts

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gaga:GaGa hierarchical model for high-throughput data analysis

Implements the GaGa model for high-throughput data analysis, including differential expression analysis, supervised gene clustering and classification. Additionally, it performs sequential sample size calculations using the GaGa and LNNGV models (the latter from EBarrays package).

Maintained by David Rossell. Last updated 1 months ago.

immunooncology onechannel massspectrometry multiplecomparison differentialexpression classification

4.26 score 9 scripts 1 dependents

bioc

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.

Maintained by Joris Meys. Last updated 5 months ago.

differentialexpression geneexpression microtitreplateassay multiplecomparison qualitycontrol software visualization qpcr

4.18 score

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LBE:Estimation of the false discovery rate

LBE is an efficient procedure for estimating the proportion of true null hypotheses, the false discovery rate (and so the q-values) in the framework of estimating procedures based on the marginal distribution of the p-values without assumption for the alternative hypothesis.

Maintained by Cyril Dalmasso. Last updated 5 months ago.

multiplecomparison

4.18 score 1 scripts

bioc

betaHMM:A Hidden Markov Model Approach for Identifying Differentially Methylated Sites and Regions for Beta-Valued DNA Methylation Data

A novel approach utilizing a homogeneous hidden Markov model. And effectively model untransformed beta values. To identify DMCs while considering the spatial. Correlation of the adjacent CpG sites.

Maintained by Koyel Majumdar. Last updated 3 months ago.

dnamethylation differentialmethylation immunooncology biomedicalinformatics methylationarray software multiplecomparison sequencing spatial coverage genetarget hiddenmarkovmodel microarray

4.18 score

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OCplus:Operating characteristics plus sample size and local fdr for microarray experiments

This package allows to characterize the operating characteristics of a microarray experiment, i.e. the trade-off between false discovery rate and the power to detect truly regulated genes. The package includes tools both for planned experiments (for sample size assessment) and for already collected data (identification of differentially expressed genes).

Maintained by Alexander Ploner. Last updated 5 months ago.

microarray differentialexpression multiplecomparison

4.08 score 2 scripts

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nearBynding:Discern RNA structure proximal to protein binding

Provides a pipeline to discern RNA structure at and proximal to the site of protein binding within regions of the transcriptome defined by the user. CLIP protein-binding data can be input as either aligned BAM or peak-called bedGraph files. RNA structure can either be predicted internally from sequence or users have the option to input their own RNA structure data. RNA structure binding profiles can be visually and quantitatively compared across multiple formats.

Maintained by Veronica Busa. Last updated 5 months ago.

visualization motifdiscovery datarepresentation structuralprediction clustering multiplecomparison

4.08 score 12 scripts

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parody:Parametric And Resistant Outlier DYtection

Provide routines for univariate and multivariate outlier detection with a focus on parametric methods, but support for some methods based on resistant statistics.

Maintained by Vince Carey. Last updated 2 months ago.

multiplecomparison

4.08 score 12 scripts

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microbiomeExplorer:Microbiome Exploration App

The MicrobiomeExplorer R package is designed to facilitate the analysis and visualization of marker-gene survey feature data. It allows a user to perform and visualize typical microbiome analytical workflows either through the command line or an interactive Shiny application included with the package. In addition to applying common analytical workflows the application enables automated analysis report generation.

Maintained by Janina Reeder. Last updated 5 months ago.

classification clustering geneticvariability differentialexpression microbiome metagenomics normalization visualization multiplecomparison sequencing software immunooncology

4.00 score 8 scripts

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LinkHD:LinkHD: a versatile framework to explore and integrate heterogeneous data

Here we present Link-HD, an approach to integrate heterogeneous datasets, as a generalization of STATIS-ACT (“Structuration des Tableaux A Trois Indices de la Statistique–Analyse Conjointe de Tableaux”), a family of methods to join and compare information from multiple subspaces. However, STATIS-ACT has some drawbacks since it only allows continuous data and it is unable to establish relationships between samples and features. In order to tackle these constraints, we incorporate multiple distance options and a linear regression based Biplot model in order to stablish relationships between observations and variable and perform variable selection.

Maintained by "Laura M Zingaretti". Last updated 5 months ago.

classification multiplecomparison regression software

4.00 score 2 scripts

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consensusDE:RNA-seq analysis using multiple algorithms

This package allows users to perform DE analysis using multiple algorithms. It seeks consensus from multiple methods. Currently it supports "Voom", "EdgeR" and "DESeq". It uses RUV-seq (optional) to remove unwanted sources of variation.

Maintained by Ashley J. Waardenberg. Last updated 5 months ago.

transcriptomics multiplecomparison clustering sequencing software

4.00 score 10 scripts

bioc

OVESEG:OVESEG-test to detect tissue/cell-specific markers

An R package for multiple-group comparison to detect tissue/cell-specific marker genes among subtypes. It provides functions to compute OVESEG-test statistics, derive component weights in the mixture null distribution model and estimate p-values from weightedly aggregated permutations. Obtained posterior probabilities of component null hypotheses can also portrait all kinds of upregulation patterns among subtypes.

Maintained by Lulu Chen. Last updated 5 months ago.

software multiplecomparison cellbiology geneexpression cpp

1 stars 4.00 score 2 scripts

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OMICsPCA:An R package for quantitative integration and analysis of multiple omics assays from heterogeneous samples

OMICsPCA is an analysis pipeline designed to integrate multi OMICs experiments done on various subjects (e.g. Cell lines, individuals), treatments (e.g. disease/control) or time points and to analyse such integrated data from various various angles and perspectives. In it's core OMICsPCA uses Principal Component Analysis (PCA) to integrate multiomics experiments from various sources and thus has ability to over data insufficiency issues by using the ingegrated data as representatives. OMICsPCA can be used in various application including analysis of overall distribution of OMICs assays across various samples /individuals /time points; grouping assays by user-defined conditions; identification of source of variation, similarity/dissimilarity between assays, variables or individuals.

Maintained by Subhadeep Das. Last updated 5 months ago.

immunooncology multiplecomparison principalcomponent datarepresentation workflow visualization dimensionreduction clustering biologicalquestion epigeneticsworkflow transcription geneticvariability gui biomedicalinformatics epigenetics functionalgenomics singlecell

4.00 score 1 scripts

bioc

sights:Statistics and dIagnostic Graphs for HTS

SIGHTS is a suite of normalization methods, statistical tests, and diagnostic graphical tools for high throughput screening (HTS) assays. HTS assays use microtitre plates to screen large libraries of compounds for their biological, chemical, or biochemical activity.

Maintained by Elika Garg. Last updated 5 months ago.

immunooncology cellbasedassays microtitreplateassay normalization multiplecomparison preprocessing qualitycontrol batcheffect visualization

4.00 score 9 scripts

bioc

CellScore:Tool for Evaluation of Cell Identity from Transcription Profiles

The CellScore package contains functions to evaluate the cell identity of a test sample, given a cell transition defined with a starting (donor) cell type and a desired target cell type. The evaluation is based upon a scoring system, which uses a set of standard samples of known cell types, as the reference set. The functions have been carried out on a large set of microarray data from one platform (Affymetrix Human Genome U133 Plus 2.0). In principle, the method could be applied to any expression dataset, provided that there are a sufficient number of standard samples and that the data are normalized.

Maintained by Nancy Mah. Last updated 5 months ago.

geneexpression transcription microarray multiplecomparison reportwriting dataimport visualization

4.00 score 5 scripts

bioc

compSPOT:compSPOT: Tool for identifying and comparing significantly mutated genomic hotspots

Clonal cell groups share common mutations within cancer, precancer, and even clinically normal appearing tissues. The frequency and location of these mutations may predict prognosis and cancer risk. It has also been well established that certain genomic regions have increased sensitivity to acquiring mutations. Mutation-sensitive genomic regions may therefore serve as markers for predicting cancer risk. This package contains multiple functions to establish significantly mutated hotspots, compare hotspot mutation burden between samples, and perform exploratory data analysis of the correlation between hotspot mutation burden and personal risk factors for cancer, such as age, gender, and history of carcinogen exposure. This package allows users to identify robust genomic markers to help establish cancer risk.

Maintained by Sydney Grant. Last updated 5 months ago.

software technology sequencing dnaseq wholegenome classification singlecell survival multiplecomparison

4.00 score 3 scripts

bioc

TTMap:Two-Tier Mapper: a clustering tool based on topological data analysis

TTMap is a clustering method that groups together samples with the same deviation in comparison to a control group. It is specially useful when the data is small. It is parameter free.

Maintained by Rachel Jeitziner. Last updated 5 months ago.

software microarray differentialexpression multiplecomparison clustering classification

4.00 score

bioc

MBttest:Multiple Beta t-Tests

MBttest method was developed from beta t-test method of Baggerly et al(2003). Compared to baySeq (Hard castle and Kelly 2010), DESeq (Anders and Huber 2010) and exact test (Robinson and Smyth 2007, 2008) and the GLM of McCarthy et al(2012), MBttest is of high work efficiency,that is, it has high power, high conservativeness of FDR estimation and high stability. MBttest is suit- able to transcriptomic data, tag data, SAGE data (count data) from small samples or a few replicate libraries. It can be used to identify genes, mRNA isoforms or tags differentially expressed between two conditions.

Maintained by Yuan-De Tan. Last updated 5 months ago.

sequencing differentialexpression multiplecomparison sage geneexpression transcription alternativesplicing coverage differentialsplicing

4.00 score 3 scripts

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surfaltr:Rapid Comparison of Surface Protein Isoform Membrane Topologies Through surfaltr

Cell surface proteins form a major fraction of the druggable proteome and can be used for tissue-specific delivery of oligonucleotide/cell-based therapeutics. Alternatively spliced surface protein isoforms have been shown to differ in their subcellular localization and/or their transmembrane (TM) topology. Surface proteins are hydrophobic and remain difficult to study thereby necessitating the use of TM topology prediction methods such as TMHMM and Phobius. However, there exists a need for bioinformatic approaches to streamline batch processing of isoforms for comparing and visualizing topologies. To address this gap, we have developed an R package, surfaltr. It pairs inputted isoforms, either known alternatively spliced or novel, with their APPRIS annotated principal counterparts, predicts their TM topologies using TMHMM or Phobius, and generates a customizable graphical output. Further, surfaltr facilitates the prioritization of biologically diverse isoform pairs through the incorporation of three different ranking metrics and through protein alignment functions. Citations for programs mentioned here can be found in the vignette.

Maintained by Pooja Gangras. Last updated 5 months ago.

software visualization datarepresentation splicedalignment alignment multiplesequencealignment multiplecomparison

4.00 score 2 scripts

bioc

GGPA:graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture

Genome-wide association studies (GWAS) is a widely used tool for identification of genetic variants associated with phenotypes and diseases, though complex diseases featuring many genetic variants with small effects present difficulties for traditional these studies. By leveraging pleiotropy, the statistical power of a single GWAS can be increased. This package provides functions for fitting graph-GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy. 'GGPA' package provides user-friendly interface to fit graph-GPA models, implement association mapping, and generate a phenotype graph.

Maintained by Dongjun Chung. Last updated 5 months ago.

software statisticalmethod classification genomewideassociation snp genetics clustering multiplecomparison preprocessing geneexpression differentialexpression openblas cpp

1 stars 4.00 score 2 scripts

bioc

abseqR:Reporting and data analysis functionalities for Rep-Seq datasets of antibody libraries

AbSeq is a comprehensive bioinformatic pipeline for the analysis of sequencing datasets generated from antibody libraries and abseqR is one of its packages. abseqR empowers the users of abseqPy (https://github.com/malhamdoosh/abseqPy) with plotting and reporting capabilities and allows them to generate interactive HTML reports for the convenience of viewing and sharing with other researchers. Additionally, abseqR extends abseqPy to compare multiple repertoire analyses and perform further downstream analysis on its output.

Maintained by JiaHong Fong. Last updated 5 months ago.

sequencing visualization reportwriting qualitycontrol multiplecomparison

4.00 score 3 scripts

bioc

erccdashboard:Assess Differential Gene Expression Experiments with ERCC Controls

Technical performance metrics for differential gene expression experiments using External RNA Controls Consortium (ERCC) spike-in ratio mixtures.

Maintained by Sarah Munro. Last updated 5 months ago.

immunooncology geneexpression transcription alternativesplicing differentialexpression differentialsplicing genetics microarray mrnamicroarray rnaseq batcheffect multiplecomparison qualitycontrol

3.95 score 4 scripts

bioc

CGEN:An R package for analysis of case-control studies in genetic epidemiology

This is a package for analysis of case-control data in genetic epidemiology. It provides a set of statistical methods for evaluating gene-environment (or gene-genes) interactions under multiplicative and additive risk models, with or without assuming gene-environment (or gene-gene) independence in the underlying population.

Maintained by Justin Lee. Last updated 5 months ago.

snp multiplecomparison clustering fortran

3.90 score 10 scripts

bioc

SpeCond:Condition specific detection from expression data

This package performs a gene expression data analysis to detect condition-specific genes. Such genes are significantly up- or down-regulated in a small number of conditions. It does so by fitting a mixture of normal distributions to the expression values. Conditions can be environmental conditions, different tissues, organs or any other sources that you wish to compare in terms of gene expression.

Maintained by Florence Cavalli. Last updated 5 months ago.

microarray differentialexpression multiplecomparison clustering reportwriting

3.89 score 13 scripts

bioc

rain:Rhythmicity Analysis Incorporating Non-parametric Methods

This package uses non-parametric methods to detect rhythms in time series. It deals with outliers, missing values and is optimized for time series comprising 10-100 measurements. As it does not assume expect any distinct waveform it is optimal or detecting oscillating behavior (e.g. circadian or cell cycle) in e.g. genome- or proteome-wide biological measurements such as: micro arrays, proteome mass spectrometry, or metabolome measurements.

Maintained by Paul F. Thaben. Last updated 5 months ago.

timecourse genetics systemsbiology proteomics microarray multiplecomparison

3.88 score 19 scripts

bioc

iGC:An integrated analysis package of Gene expression and Copy number alteration

This package is intended to identify differentially expressed genes driven by Copy Number Alterations from samples with both gene expression and CNA data.

Maintained by Liang-Bo Wang. Last updated 5 months ago.

software biological question differentialexpression genomicvariation assaydomain copynumbervariation geneexpression researchfield genetics technology microarray sequencing workflowstep multiplecomparison

1 stars 3.78 score 1 scripts

arcolombo

imcExperiment:Mass Cytometry S4 Class Structure Pipeline for Images

Containerizes cytometry data and allows for S4 class structure to extend slots related to cell morphology, spatial coordinates, phenotype network information, and unique cellular labeling.

Maintained by Anthony Colombo. Last updated 4 years ago.

software workflowstep multiplecomparison imc

3.70 score 5 scripts

bioc

GOpro:Find the most characteristic gene ontology terms for groups of human genes

Find the most characteristic gene ontology terms for groups of human genes. This package was created as a part of the thesis which was developed under the auspices of MI^2 Group (http://mi2.mini.pw.edu.pl/, https://github.com/geneticsMiNIng).

Maintained by Lidia Chrabaszcz. Last updated 5 months ago.

annotation clustering go geneexpression genesetenrichment multiplecomparison cpp

2 stars 3.60 score 4 scripts

bioc

ssviz:A small RNA-seq visualizer and analysis toolkit

Small RNA sequencing viewer

Maintained by Diana Low. Last updated 5 months ago.

immunooncology sequencing rnaseq visualization multiplecomparison genetics

3.60 score 2 scripts

bioc

twilight:Estimation of local false discovery rate

In a typical microarray setting with gene expression data observed under two conditions, the local false discovery rate describes the probability that a gene is not differentially expressed between the two conditions given its corrresponding observed score or p-value level. The resulting curve of p-values versus local false discovery rate offers an insight into the twilight zone between clear differential and clear non-differential gene expression. Package 'twilight' contains two main functions: Function twilight.pval performs a two-condition test on differences in means for a given input matrix or expression set and computes permutation based p-values. Function twilight performs a stochastic downhill search to estimate local false discovery rates and effect size distributions. The package further provides means to filter for permutations that describe the null distribution correctly. Using filtered permutations, the influence of hidden confounders could be diminished.

Maintained by Stefanie Senger. Last updated 1 months ago.

microarray differentialexpression multiplecomparison

3.40 score 14 scripts 1 dependents

bioc

mCSEA:Methylated CpGs Set Enrichment Analysis

Identification of diferentially methylated regions (DMRs) in predefined regions (promoters, CpG islands...) from the human genome using Illumina's 450K or EPIC microarray data. Provides methods to rank CpG probes based on linear models and includes plotting functions.

Maintained by Jordi Martorell-Marugán. Last updated 4 months ago.

immunooncology differentialmethylation dnamethylation epigenetics genetics genomeannotation methylationarray microarray multiplecomparison twochannel

3.38 score 15 scripts

bioc

dks:The double Kolmogorov-Smirnov package for evaluating multiple testing procedures.

The dks package consists of a set of diagnostic functions for multiple testing methods. The functions can be used to determine if the p-values produced by a multiple testing procedure are correct. These functions are designed to be applied to simulated data. The functions require the entire set of p-values from multiple simulated studies, so that the joint distribution can be evaluated.

Maintained by Jeffrey T. Leek. Last updated 5 months ago.

multiplecomparison qualitycontrol

3.30 score 1 scripts

bioc

clippda:A package for the clinical proteomic profiling data analysis

Methods for the nalysis of data from clinical proteomic profiling studies. The focus is on the studies of human subjects, which are often observational case-control by design and have technical replicates. A method for sample size determination for planning these studies is proposed. It incorporates routines for adjusting for the expected heterogeneities and imbalances in the data and the within-sample replicate correlations.

Maintained by Stephen Nyangoma. Last updated 5 months ago.

proteomics onechannel preprocessing differentialexpression multiplecomparison

3.30 score 2 scripts

bioc

GEWIST:Gene Environment Wide Interaction Search Threshold

This 'GEWIST' package provides statistical tools to efficiently optimize SNP prioritization for gene-gene and gene-environment interactions.

Maintained by Wei Q. Deng. Last updated 5 months ago.

multiplecomparison genetics

3.30 score 3 scripts

bioc

SimFFPE:NGS Read Simulator for FFPE Tissue

The NGS (Next-Generation Sequencing) reads from FFPE (Formalin-Fixed Paraffin-Embedded) samples contain numerous artifact chimeric reads (ACRS), which can lead to false positive structural variant calls. These ACRs are derived from the combination of two single-stranded DNA (ss-DNA) fragments with short reverse complementary regions (SRCRs). This package simulates these artifact chimeric reads as well as normal reads for FFPE samples on the whole genome / several chromosomes / large regions.

Maintained by Lanying Wei. Last updated 5 months ago.

sequencing alignment multiplecomparison sequencematching dataimport

3.30 score 1 scripts

bioc

calm:Covariate Assisted Large-scale Multiple testing

Statistical methods for multiple testing with covariate information. Traditional multiple testing methods only consider a list of test statistics, such as p-values. Our methods incorporate the auxiliary information, such as the lengths of gene coding regions or the minor allele frequencies of SNPs, to improve power.

Maintained by Kun Liang. Last updated 5 months ago.

bayesian differentialexpression geneexpression regression microarray sequencing rnaseq multiplecomparison genetics immunooncology metabolomics proteomics transcriptomics

3.30 score 2 scripts

bioc

OrderedList:Similarities of Ordered Gene Lists

Detection of similarities between ordered lists of genes. Thereby, either simple lists can be compared or gene expression data can be used to deduce the lists. Significance of similarities is evaluated by shuffling lists or by resampling in microarray data, respectively.

Maintained by Claudio Lottaz. Last updated 5 months ago.

microarray differentialexpression multiplecomparison

3.30 score 9 scripts

bioc

fdrame:FDR adjustments of Microarray Experiments (FDR-AME)

This package contains two main functions. The first is fdr.ma which takes normalized expression data array, experimental design and computes adjusted p-values It returns the fdr adjusted p-values and plots, according to the methods described in (Reiner, Yekutieli and Benjamini 2002). The second, is fdr.gui() which creates a simple graphic user interface to access fdr.ma

Maintained by Effi Kenigsberg. Last updated 5 months ago.

microarray differentialexpression multiplecomparison

3.30 score

bioc

IWTomics:Interval-Wise Testing for Omics Data

Implementation of the Interval-Wise Testing (IWT) for omics data. This inferential procedure tests for differences in "Omics" data between two groups of genomic regions (or between a group of genomic regions and a reference center of symmetry), and does not require fixing location and scale at the outset.

Maintained by Marzia A Cremona. Last updated 5 months ago.

statisticalmethod multiplecomparison differentialexpression differentialmethylation differentialpeakcalling genomeannotation dataimport

3.30 score 5 scripts

bioc

cnvGSA:Gene Set Analysis of (Rare) Copy Number Variants

This package is intended to facilitate gene-set association with rare CNVs in case-control studies.

Maintained by Joseph Lugo. Last updated 5 months ago.

multiplecomparison

3.30 score 3 scripts

bioc

matchBox:Utilities to compute, compare, and plot the agreement between ordered vectors of features (ie. distinct genomic experiments). The package includes Correspondence-At-the-TOP (CAT) analysis.

The matchBox package enables comparing ranked vectors of features, merging multiple datasets, removing redundant features, using CAT-plots and Venn diagrams, and computing statistical significance.

Maintained by Luigi Marchionni. Last updated 5 months ago.

software annotation microarray multiplecomparison visualization

3.30 score 1 scripts

bioc

gCrisprTools:Suite of Functions for Pooled Crispr Screen QC and Analysis

Set of tools for evaluating pooled high-throughput screening experiments, typically employing CRISPR/Cas9 or shRNA expression cassettes. Contains methods for interrogating library and cassette behavior within an experiment, identifying differentially abundant cassettes, aggregating signals to identify candidate targets for empirical validation, hypothesis testing, and comprehensive reporting. Version 2.0 extends these applications to include a variety of tools for contextualizing and integrating signals across many experiments, incorporates extended signal enrichment methodologies via the "sparrow" package, and streamlines many formal requirements to aid in interpretablity.

Maintained by Russell Bainer. Last updated 5 months ago.

immunooncology crispr pooledscreens experimentaldesign biomedicalinformatics cellbiology functionalgenomics pharmacogenomics pharmacogenetics systemsbiology differentialexpression genesetenrichment genetics multiplecomparison normalization preprocessing qualitycontrol rnaseq regression software visualization

3.30 score 8 scripts

bioc

Mulcom:Calculates Mulcom test

Identification of differentially expressed genes and false discovery rate (FDR) calculation by Multiple Comparison test.

Maintained by Claudio Isella. Last updated 5 months ago.

statisticalmethod multiplecomparison microarray differentialexpression geneexpression cpp

3.00 score

csoneson

ConfoundingExplorer:Confounding Explorer

This package provides a simple interactive application for investigating the effect of confounding between a signal of interest and a batch effect. It uses simulated data with user-specified effect sizes for both batch and condition effects. The user can also specify the number of samples in each condition and batch, and thereby the degree of confounding.

Maintained by Charlotte Soneson. Last updated 3 months ago.

regression experimentaldesign multiplecomparison batcheffect

2 stars 2.60 score 3 scripts

bioc

TransView:Read density map construction and accession. Visualization of ChIPSeq and RNASeq data sets

This package provides efficient tools to generate, access and display read densities of sequencing based data sets such as from RNA-Seq and ChIP-Seq.

Maintained by Julius Muller. Last updated 2 months ago.

immunooncology dnamethylation geneexpression transcription microarray sequencing chipseq rnaseq methylseq dataimport visualization clustering multiplecomparison curl bzip2 xz-utils zlib

2.60 score

bioc

ISoLDE:Integrative Statistics of alleLe Dependent Expression

This package provides ISoLDE a new method for identifying imprinted genes. This method is dedicated to data arising from RNA sequencing technologies. The ISoLDE package implements original statistical methodology described in the publication below.

Maintained by Christelle Reynès. Last updated 5 months ago.

immunooncology geneexpression transcription genesetenrichment genetics sequencing rnaseq multiplecomparison snp geneticvariability epigenetics mathematicalbiology generegulation openmp

2.30 score 2 scripts

moseleybioinformaticslab

categoryCompare2:Meta-Analysis of High-Throughput Experiments Using Feature Annotations

Facilitates comparison of significant annotations (categories) generated on one or more feature lists. Interactive exploration is facilitated through the use of RCytoscape (heavily suggested).

Maintained by Robert M Flight. Last updated 5 months ago.

annotation go multiplecomparison pathways geneexpression bioconductor bioinformatics gene-annotation gene-expression gene-sets

1 stars 2.30 score 9 scripts

bioc

roastgsa:Rotation based gene set analysis

This package implements a variety of functions useful for gene set analysis using rotations to approximate the null distribution. It contributes with the implementation of seven test statistic scores that can be used with different goals and interpretations. Several functions are available to complement the statistical results with graphical representations.

Maintained by Adria Caballe. Last updated 5 months ago.

microarray preprocessing normalization geneexpression survival transcription sequencing transcriptomics bayesian clustering regression rnaseq micrornaarray mrnamicroarray functionalgenomics systemsbiology immunooncology differentialexpression genesetenrichment batcheffect multiplecomparison qualitycontrol timecourse metabolomics proteomics epigenetics cheminformatics exonarray onechannel twochannel proprietaryplatforms cellbiology biomedicalinformatics alternativesplicing differentialsplicing dataimport pathways

2.30 score

empiricalbayes

LFDREmpiricalBayes:Estimating Local False Discovery Rates Using Empirical Bayes Methods

New empirical Bayes methods aiming at analyzing the association of single nucleotide polymorphisms (SNPs) to some particular disease are implemented in this package. The package uses local false discovery rate (LFDR) estimates of SNPs within a sample population defined as a "reference class" and discovers if SNPs are associated with the corresponding disease. Although SNPs are used throughout this document, other biological data such as protein data and other gene data can be used. Karimnezhad, Ali and Bickel, D. R. (2016) <http://hdl.handle.net/10393/34889>.

Maintained by Ali Karimnezhad. Last updated 8 years ago.

bayesian mathematicalbiology multiplecomparison

2.00 score 5 scripts

empiricalbayes

LFDR.MME:Estimating Local False Discovery Rates Using the Method of Moments

Estimation of the local false discovery rate using the method of moments.

Maintained by Ali Karimnezhad. Last updated 4 years ago.

bayesian mathematicalbiology multiplecomparison

1.00 score