R-universe search: topic:dimensionreduction

bioc

iSEE:Interactive SummarizedExperiment Explorer

Create an interactive Shiny-based graphical user interface for exploring data stored in SummarizedExperiment objects, including row- and column-level metadata. The interface supports transmission of selections between plots and tables, code tracking, interactive tours, interactive or programmatic initialization, preservation of app state, and extensibility to new panel types via S4 classes. Special attention is given to single-cell data in a SingleCellExperiment object with visualization of dimensionality reduction results.

Maintained by Kevin Rue-Albrecht. Last updated 24 days ago.

cellbasedassays clustering dimensionreduction featureextraction geneexpression gui immunooncology shinyapps singlecell transcription transcriptomics visualization dimension-reduction feature-extraction gene-expression hacktoberfest human-cell-atlas shiny single-cell

225 stars 12.86 score 380 scripts 9 dependents

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BiocSingular:Singular Value Decomposition for Bioconductor Packages

Implements exact and approximate methods for singular value decomposition and principal components analysis, in a framework that allows them to be easily switched within Bioconductor packages or workflows. Where possible, parallelization is achieved using the BiocParallel framework.

Maintained by Aaron Lun. Last updated 5 months ago.

software dimensionreduction principalcomponent bioconductor-package human-cell-atlas singular-value-decomposition cpp

7 stars 12.10 score 1.2k scripts 103 dependents

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scater:Single-Cell Analysis Toolkit for Gene Expression Data in R

A collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control and visualization.

Maintained by Alan OCallaghan. Last updated 23 days ago.

immunooncology singlecell rnaseq qualitycontrol preprocessing normalization visualization dimensionreduction transcriptomics geneexpression sequencing software dataimport datarepresentation infrastructure coverage

11.07 score 12k scripts 43 dependents

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GENESIS:GENetic EStimation and Inference in Structured samples (GENESIS): Statistical methods for analyzing genetic data from samples with population structure and/or relatedness

The GENESIS package provides methodology for estimating, inferring, and accounting for population and pedigree structure in genetic analyses. The current implementation provides functions to perform PC-AiR (Conomos et al., 2015, Gen Epi) and PC-Relate (Conomos et al., 2016, AJHG). PC-AiR performs a Principal Components Analysis on genome-wide SNP data for the detection of population structure in a sample that may contain known or cryptic relatedness. Unlike standard PCA, PC-AiR accounts for relatedness in the sample to provide accurate ancestry inference that is not confounded by family structure. PC-Relate uses ancestry representative principal components to adjust for population structure/ancestry and accurately estimate measures of recent genetic relatedness such as kinship coefficients, IBD sharing probabilities, and inbreeding coefficients. Additionally, functions are provided to perform efficient variance component estimation and mixed model association testing for both quantitative and binary phenotypes.

Maintained by Stephanie M. Gogarten. Last updated 2 months ago.

snp geneticvariability genetics statisticalmethod dimensionreduction principalcomponent genomewideassociation qualitycontrol biocviews

36 stars 10.44 score 342 scripts 1 dependents

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zinbwave:Zero-Inflated Negative Binomial Model for RNA-Seq Data

Implements a general and flexible zero-inflated negative binomial model that can be used to provide a low-dimensional representations of single-cell RNA-seq data. The model accounts for zero inflation (dropouts), over-dispersion, and the count nature of the data. The model also accounts for the difference in library sizes and optionally for batch effects and/or other covariates, avoiding the need for pre-normalize the data.

Maintained by Davide Risso. Last updated 5 months ago.

immunooncology dimensionreduction geneexpression rnaseq software transcriptomics sequencing singlecell

43 stars 10.21 score 190 scripts 6 dependents

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SC3:Single-Cell Consensus Clustering

A tool for unsupervised clustering and analysis of single cell RNA-Seq data.

Maintained by Vladimir Kiselev. Last updated 5 months ago.

immunooncology singlecell software classification clustering dimensionreduction supportvectormachine rnaseq visualization transcriptomics datarepresentation gui differentialexpression transcription bioconductor-package human-cell-atlas single-cell-rna-seq openblas cpp

125 stars 10.10 score 374 scripts 1 dependents

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MOFA2:Multi-Omics Factor Analysis v2

The MOFA2 package contains a collection of tools for training and analysing multi-omic factor analysis (MOFA). MOFA is a probabilistic factor model that aims to identify principal axes of variation from data sets that can comprise multiple omic layers and/or groups of samples. Additional time or space information on the samples can be incorporated using the MEFISTO framework, which is part of MOFA2. Downstream analysis functions to inspect molecular features underlying each factor, vizualisation, imputation etc are available.

Maintained by Ricard Argelaguet. Last updated 5 months ago.

dimensionreduction bayesian visualization factor-analysis mofa multi-omics

326 stars 10.03 score 502 scripts

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pcaExplorer:Interactive Visualization of RNA-seq Data Using a Principal Components Approach

This package provides functionality for interactive visualization of RNA-seq datasets based on Principal Components Analysis. The methods provided allow for quick information extraction and effective data exploration. A Shiny application encapsulates the whole analysis.

Maintained by Federico Marini. Last updated 3 months ago.

immunooncology visualization rnaseq dimensionreduction principalcomponent qualitycontrol gui reportwriting shinyapps bioconductor principal-components reproducible-research rna-seq-analysis rna-seq-data shiny transcriptome user-friendly

56 stars 9.63 score 180 scripts

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Nebulosa:Single-Cell Data Visualisation Using Kernel Gene-Weighted Density Estimation

This package provides a enhanced visualization of single-cell data based on gene-weighted density estimation. Nebulosa recovers the signal from dropped-out features and allows the inspection of the joint expression from multiple features (e.g. genes). Seurat and SingleCellExperiment objects can be used within Nebulosa.

Maintained by Jose Alquicira-Hernandez. Last updated 5 months ago.

software geneexpression singlecell visualization dimensionreduction single-cell single-cell-analysis single-cell-multiomics single-cell-rna-seq

99 stars 9.52 score 494 scripts

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matter:Out-of-core statistical computing and signal processing

Toolbox for larger-than-memory scientific computing and visualization, providing efficient out-of-core data structures using files or shared memory, for dense and sparse vectors, matrices, and arrays, with applications to nonuniformly sampled signals and images.

Maintained by Kylie A. Bemis. Last updated 4 months ago.

infrastructure datarepresentation dataimport dimensionreduction preprocessing cpp

57 stars 9.52 score 64 scripts 2 dependents

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Banksy:Spatial transcriptomic clustering

Banksy is an R package that incorporates spatial information to cluster cells in a feature space (e.g. gene expression). To incorporate spatial information, BANKSY computes the mean neighborhood expression and azimuthal Gabor filters that capture gene expression gradients. These features are combined with the cell's own expression to embed cells in a neighbor-augmented product space which can then be clustered, allowing for accurate and spatially-aware cell typing and tissue domain segmentation.

Maintained by Joseph Lee. Last updated 26 days ago.

clustering spatial singlecell geneexpression dimensionreduction clustering-algorithm single-cell-omics spatial-omics

90 stars 9.03 score 248 scripts

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schex:Hexbin plots for single cell omics data

Builds hexbin plots for variables and dimension reduction stored in single cell omics data such as SingleCellExperiment. The ideas used in this package are based on the excellent work of Dan Carr, Nicholas Lewin-Koh, Martin Maechler and Thomas Lumley.

Maintained by Saskia Freytag. Last updated 5 months ago.

software sequencing singlecell dimensionreduction visualization immunooncology dataimport

74 stars 8.96 score 102 scripts 2 dependents

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M3Drop:Michaelis-Menten Modelling of Dropouts in single-cell RNASeq

This package fits a model to the pattern of dropouts in single-cell RNASeq data. This model is used as a null to identify significantly variable (i.e. differentially expressed) genes for use in downstream analysis, such as clustering cells. Also includes an method for calculating exact Pearson residuals in UMI-tagged data using a library-size aware negative binomial model.

Maintained by Tallulah Andrews. Last updated 5 months ago.

rnaseq sequencing transcriptomics geneexpression software differentialexpression dimensionreduction featureextraction human-cell-atlas rna-seq single-cell single-cell-rna-seq

29 stars 8.53 score 119 scripts 2 dependents

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POMA:Tools for Omics Data Analysis

The POMA package offers a comprehensive toolkit designed for omics data analysis, streamlining the process from initial visualization to final statistical analysis. Its primary goal is to simplify and unify the various steps involved in omics data processing, making it more accessible and manageable within a single, intuitive R package. Emphasizing on reproducibility and user-friendliness, POMA leverages the standardized SummarizedExperiment class from Bioconductor, ensuring seamless integration and compatibility with a wide array of Bioconductor tools. This approach guarantees maximum flexibility and replicability, making POMA an essential asset for researchers handling omics datasets. See https://github.com/pcastellanoescuder/POMAShiny. Paper: Castellano-Escuder et al. (2021) <doi:10.1371/journal.pcbi.1009148> for more details.

Maintained by Pol Castellano-Escuder. Last updated 4 months ago.

batcheffect classification clustering decisiontree dimensionreduction multidimensionalscaling normalization preprocessing principalcomponent regression rnaseq software statisticalmethod visualization bioconductor bioinformatics data-visualization dimension-reduction exploratory-data-analysis machine-learning omics-data-integration pipeline pre-processing statistical-analysis user-friendly workflow

11 stars 8.16 score 20 scripts 1 dependents

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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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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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lemur:Latent Embedding Multivariate Regression

Fit a latent embedding multivariate regression (LEMUR) model to multi-condition single-cell data. The model provides a parametric description of single-cell data measured with treatment vs. control or more complex experimental designs. The parametric model is used to (1) align conditions, (2) predict log fold changes between conditions for all cells, and (3) identify cell neighborhoods with consistent log fold changes. For those neighborhoods, a pseudobulked differential expression test is conducted to assess which genes are significantly changed.

Maintained by Constantin Ahlmann-Eltze. Last updated 5 months ago.

transcriptomics differentialexpression singlecell dimensionreduction regression openblas cpp

87 stars 7.69 score 81 scripts

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ggsc:Visualizing Single Cell and Spatial Transcriptomics

Useful functions to visualize single cell and spatial data. It supports visualizing 'Seurat', 'SingleCellExperiment' and 'SpatialExperiment' objects through grammar of graphics syntax implemented in 'ggplot2'.

Maintained by Guangchuang Yu. Last updated 5 months ago.

dimensionreduction geneexpression singlecell software spatial transcriptomics visualization openblas cpp openmp

47 stars 7.59 score 18 scripts

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glmSparseNet:Network Centrality Metrics for Elastic-Net Regularized Models

glmSparseNet is an R-package that generalizes sparse regression models when the features (e.g. genes) have a graph structure (e.g. protein-protein interactions), by including network-based regularizers. glmSparseNet uses the glmnet R-package, by including centrality measures of the network as penalty weights in the regularization. The current version implements regularization based on node degree, i.e. the strength and/or number of its associated edges, either by promoting hubs in the solution or orphan genes in the solution. All the glmnet distribution families are supported, namely "gaussian", "poisson", "binomial", "multinomial", "cox", and "mgaussian".

Maintained by André Veríssimo. Last updated 5 months ago.

software statisticalmethod dimensionreduction regression classification survival network graphandnetwork

6 stars 7.42 score 41 scripts 1 dependents

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netSmooth:Network smoothing for scRNAseq

netSmooth is an R package for network smoothing of single cell RNA sequencing data. Using bio networks such as protein-protein interactions as priors for gene co-expression, netsmooth improves cell type identification from noisy, sparse scRNAseq data.

Maintained by Jonathan Ronen. Last updated 5 months ago.

network graphandnetwork singlecell rnaseq geneexpression sequencing transcriptomics normalization preprocessing clustering dimensionreduction bioinformatics genomics single-cell

27 stars 7.41 score 4 scripts

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scry:Small-Count Analysis Methods for High-Dimensional Data

Many modern biological datasets consist of small counts that are not well fit by standard linear-Gaussian methods such as principal component analysis. This package provides implementations of count-based feature selection and dimension reduction algorithms. These methods can be used to facilitate unsupervised analysis of any high-dimensional data such as single-cell RNA-seq.

Maintained by Kelly Street. Last updated 5 months ago.

dimensionreduction geneexpression normalization principalcomponent rnaseq software sequencing singlecell transcriptomics

19 stars 7.34 score 116 scripts

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iSEEu:iSEE Universe

iSEEu (the iSEE universe) contains diverse functionality to extend the usage of the iSEE package, including additional classes for the panels, or modes allowing easy configuration of iSEE applications.

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

immunooncology visualization gui dimensionreduction featureextraction clustering transcription geneexpression transcriptomics singlecell cellbasedassays hacktoberfest

9 stars 7.15 score 35 scripts 1 dependents

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lfa:Logistic Factor Analysis for Categorical Data

Logistic Factor Analysis is a method for a PCA analogue on Binomial data via estimation of latent structure in the natural parameter. The main method estimates genetic population structure from genotype data. There are also methods for estimating individual-specific allele frequencies using the population structure. Lastly, a structured Hardy-Weinberg equilibrium (HWE) test is developed, which quantifies the goodness of fit of the genotype data to the estimated population structure, via the estimated individual-specific allele frequencies (all of which generalizes traditional HWE tests).

Maintained by Alejandro Ochoa. Last updated 5 months ago.

snp dimensionreduction principalcomponent regression openblas

16 stars 7.04 score 57 scripts 1 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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RCM:Fit row-column association models with the negative binomial distribution for the microbiome

Combine ideas of log-linear analysis of contingency table, flexible response function estimation and empirical Bayes dispersion estimation for explorative visualization of microbiome datasets. The package includes unconstrained as well as constrained analysis. In addition, diagnostic plot to detect lack of fit are available.

Maintained by Stijn Hawinkel. Last updated 5 months ago.

metagenomics dimensionreduction microbiome visualization ordination phyloseq rcm

16 stars 6.90 score 25 scripts

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ggspavis:Visualization functions for spatial transcriptomics data

Visualization functions for spatial transcriptomics data. Includes functions to generate several types of plots, including spot plots, feature (molecule) plots, reduced dimension plots, spot-level quality control (QC) plots, and feature-level QC plots, for datasets from the 10x Genomics Visium and other technological platforms. Datasets are assumed to be in either SpatialExperiment or SingleCellExperiment format.

Maintained by Lukas M. Weber. Last updated 5 months ago.

spatial singlecell transcriptomics geneexpression qualitycontrol dimensionreduction

3 stars 6.80 score 264 scripts

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tricycle:tricycle: Transferable Representation and Inference of cell cycle

The package contains functions to infer and visualize cell cycle process using Single Cell RNASeq data. It exploits the idea of transfer learning, projecting new data to the previous learned biologically interpretable space. We provide a pre-learned cell cycle space, which could be used to infer cell cycle time of human and mouse single cell samples. In addition, we also offer functions to visualize cell cycle time on different embeddings and functions to build new reference.

Maintained by Shijie Zheng. Last updated 5 months ago.

singlecell software transcriptomics rnaseq transcription biologicalquestion dimensionreduction immunooncology

25 stars 6.54 score 46 scripts

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APL:Association Plots

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

Maintained by Clemens Kohl. Last updated 5 months ago.

statisticalmethod dimensionreduction singlecell sequencing rnaseq geneexpression

15 stars 6.31 score 15 scripts

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RAIDS:Accurate Inference of Genetic Ancestry from Cancer Sequences

This package implements specialized algorithms that enable genetic ancestry inference from various cancer sequences sources (RNA, Exome and Whole-Genome sequences). This package also implements a simulation algorithm that generates synthetic cancer-derived data. This code and analysis pipeline was designed and developed for the following publication: Belleau, P et al. Genetic Ancestry Inference from Cancer-Derived Molecular Data across Genomic and Transcriptomic Platforms. Cancer Res 1 January 2023; 83 (1): 49–58.

Maintained by Pascal Belleau. Last updated 5 months ago.

genetics software sequencing wholegenome principalcomponent geneticvariability dimensionreduction biocviews ancestry cancer-genomics exome-sequencing genomics inference r-language rna-seq rna-sequencing whole-genome-sequencing

5 stars 6.23 score 19 scripts

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iNETgrate:Integrates DNA methylation data with gene expression in a single gene network

The iNETgrate package provides functions to build a correlation network in which nodes are genes. DNA methylation and gene expression data are integrated to define the connections between genes. This network is used to identify modules (clusters) of genes. The biological information in each of the resulting modules is represented by an eigengene. These biological signatures can be used as features e.g., for classification of patients into risk categories. The resulting biological signatures are very robust and give a holistic view of the underlying molecular changes.

Maintained by Habil Zare. Last updated 5 months ago.

geneexpression rnaseq dnamethylation networkinference network graphandnetwork biomedicalinformatics systemsbiology transcriptomics classification clustering dimensionreduction principalcomponent mrnamicroarray normalization geneprediction kegg survival core-services

74 stars 6.21 score 1 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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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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StabMap:Stabilised mosaic single cell data integration using unshared features

StabMap performs single cell mosaic data integration by first building a mosaic data topology, and for each reference dataset, traverses the topology to project and predict data onto a common embedding. Mosaic data should be provided in a list format, with all relevant features included in the data matrices within each list object. The output of stabMap is a joint low-dimensional embedding taking into account all available relevant features. Expression imputation can also be performed using the StabMap embedding and any of the original data matrices for given reference and query cell lists.

Maintained by Shila Ghazanfar. Last updated 5 months ago.

singlecell dimensionreduction software

5.95 score 60 scripts

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autonomics:Unified Statistical Modeling of Omics Data

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

Maintained by Aditya Bhagwat. Last updated 2 months ago.

software dataimport preprocessing dimensionreduction principalcomponent regression differentialexpression genesetenrichment transcriptomics transcription geneexpression rnaseq microarray proteomics metabolomics massspectrometry

5.95 score 5 scripts

core-bioinformatics

ClustAssess:Tools for Assessing Clustering

A set of tools for evaluating clustering robustness using proportion of ambiguously clustered pairs (Senbabaoglu et al. (2014) <doi:10.1038/srep06207>), as well as similarity across methods and method stability using element-centric clustering comparison (Gates et al. (2019) <doi:10.1038/s41598-019-44892-y>). Additionally, this package enables stability-based parameter assessment for graph-based clustering pipelines typical in single-cell data analysis.

Maintained by Andi Munteanu. Last updated 2 months ago.

software singlecell rnaseq atacseq normalization preprocessing dimensionreduction visualization qualitycontrol clustering classification annotation geneexpression differentialexpression bioinformatics genomics machine-learning parameter-optimization robustness single-cell unsupervised-learning cpp

23 stars 5.70 score 18 scripts

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chevreulProcess:Tools for managing SingleCellExperiment objects as projects

Tools analyzing SingleCellExperiment objects as projects. for input into the Chevreul app downstream. Includes functions for analysis of single cell RNA sequencing data. Supported by NIH grants R01CA137124 and R01EY026661 to David Cobrinik.

Maintained by Kevin Stachelek. Last updated 2 months ago.

coverage rnaseq sequencing visualization geneexpression transcription singlecell transcriptomics normalization preprocessing qualitycontrol dimensionreduction dataimport

5.38 score 2 scripts 2 dependents

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PLSDAbatch:PLSDA-batch

A novel framework to correct for batch effects prior to any downstream analysis in microbiome data based on Projection to Latent Structures Discriminant Analysis. The main method is named “PLSDA-batch”. It first estimates treatment and batch variation with latent components, then subtracts batch-associated components from the data whilst preserving biological variation of interest. PLSDA-batch is highly suitable for microbiome data as it is non-parametric, multivariate and allows for ordination and data visualisation. Combined with centered log-ratio transformation for addressing uneven library sizes and compositional structure, PLSDA-batch addresses all characteristics of microbiome data that existing correction methods have ignored so far. Two other variants are proposed for 1/ unbalanced batch x treatment designs that are commonly encountered in studies with small sample sizes, and for 2/ selection of discriminative variables amongst treatment groups to avoid overfitting in classification problems. These two variants have widened the scope of applicability of PLSDA-batch to different data settings.

Maintained by Yiwen (Eva) Wang. Last updated 5 months ago.

statisticalmethod dimensionreduction principalcomponent classification microbiome batcheffect normalization visualization

13 stars 5.37 score 18 scripts

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MOSClip:Multi Omics Survival Clip

Topological pathway analysis tool able to integrate multi-omics data. It finds survival-associated modules or significant modules for two-class analysis. This tool have two main methods: pathway tests and module tests. The latter method allows the user to dig inside the pathways itself.

Maintained by Paolo Martini. Last updated 5 months ago.

software statisticalmethod graphandnetwork survival regression dimensionreduction pathways reactome

5.34 score 5 scripts

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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

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CytoMDS:Low Dimensions projection of cytometry samples

This package implements a low dimensional visualization of a set of cytometry samples, in order to visually assess the 'distances' between them. This, in turn, can greatly help the user to identify quality issues like batch effects or outlier samples, and/or check the presence of potential sample clusters that might align with the exeprimental design. The CytoMDS algorithm combines, on the one hand, the concept of Earth Mover's Distance (EMD), a.k.a. Wasserstein metric and, on the other hand, the Multi Dimensional Scaling (MDS) algorithm for the low dimensional projection. Also, the package provides some diagnostic tools for both checking the quality of the MDS projection, as well as tools to help with the interpretation of the axes of the projection.

Maintained by Philippe Hauchamps. Last updated 2 months ago.

flowcytometry qualitycontrol dimensionreduction multidimensionalscaling software visualization

1 stars 5.23 score 2 scripts

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gcatest:Genotype Conditional Association TEST

GCAT is an association test for genome wide association studies that controls for population structure under a general class of trait models. This test conditions on the trait, which makes it immune to confounding by unmodeled environmental factors. Population structure is modeled via logistic factors, which are estimated using the `lfa` package.

Maintained by Alejandro Ochoa. Last updated 5 months ago.

snp dimensionreduction principalcomponent genomewideassociation

5 stars 5.18 score 4 scripts

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SGCP:SGCP: A semi-supervised pipeline for gene clustering using self-training approach in gene co-expression networks

SGC is a semi-supervised pipeline for gene clustering in gene co-expression networks. SGC consists of multiple novel steps that enable the computation of highly enriched modules in an unsupervised manner. But unlike all existing frameworks, it further incorporates a novel step that leverages Gene Ontology information in a semi-supervised clustering method that further improves the quality of the computed modules.

Maintained by Niloofar AghaieAbiane. Last updated 5 months ago.

geneexpression genesetenrichment networkenrichment systemsbiology classification clustering dimensionreduction graphandnetwork neuralnetwork network mrnamicroarray rnaseq visualization bioinformatics genecoexpressionnetwork graphs networkclustering networks self-training semi-supervised-learning unsupervised-learning

2 stars 5.12 score 44 scripts

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retrofit:RETROFIT: Reference-free deconvolution of cell mixtures in spatial transcriptomics

RETROFIT is a Bayesian non-negative matrix factorization framework to decompose cell type mixtures in ST data without using external single-cell expression references. RETROFIT outperforms existing reference-based methods in estimating cell type proportions and reconstructing gene expressions in simulations with varying spot size and sample heterogeneity, irrespective of the quality or availability of the single-cell reference. RETROFIT recapitulates known cell-type localization patterns in a Slide-seq dataset of mouse cerebellum without using any single-cell data.

Maintained by Adam Park. Last updated 5 months ago.

transcriptomics visualization rnaseq bayesian spatial software geneexpression dimensionreduction featureextraction singlecell cpp

3 stars 5.08 score 9 scripts

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DepecheR:Determination of essential phenotypic elements of clusters in high-dimensional entities

The purpose of this package is to identify traits in a dataset that can separate groups. This is done on two levels. First, clustering is performed, using an implementation of sparse K-means. Secondly, the generated clusters are used to predict outcomes of groups of individuals based on their distribution of observations in the different clusters. As certain clusters with separating information will be identified, and these clusters are defined by a sparse number of variables, this method can reduce the complexity of data, to only emphasize the data that actually matters.

Maintained by Jakob Theorell. Last updated 5 months ago.

software cellbasedassays transcription differentialexpression datarepresentation immunooncology transcriptomics classification clustering dimensionreduction featureextraction flowcytometry rnaseq singlecell visualization cpp

5.08 score 15 scripts

bioc

PIUMA:Phenotypes Identification Using Mapper from topological data Analysis

The PIUMA package offers a tidy pipeline of Topological Data Analysis frameworks to identify and characterize communities in high and heterogeneous dimensional data.

Maintained by Mattia Chiesa. Last updated 5 months ago.

clustering graphandnetwork dimensionreduction network classification

4 stars 5.08 score 2 scripts

bioc

chevreulShiny:Tools for managing SingleCellExperiment objects as projects

Tools for managing SingleCellExperiment objects as projects. Includes functions for analysis and visualization of single-cell data. Also included is a shiny app for visualization of pre-processed scRNA data. Supported by NIH grants R01CA137124 and R01EY026661 to David Cobrinik.

Maintained by Kevin Stachelek. Last updated 26 days ago.

coverage rnaseq sequencing visualization geneexpression transcription singlecell transcriptomics normalization preprocessing qualitycontrol dimensionreduction dataimport

5.08 score

bioc

chevreulPlot:Plots used in the chevreulPlot package

Tools for plotting SingleCellExperiment objects in the chevreulPlot package. Includes functions for analysis and visualization of single-cell data. Supported by NIH grants R01CA137124 and R01EY026661 to David Cobrinik.

Maintained by Kevin Stachelek. Last updated 30 days ago.

coverage rnaseq sequencing visualization geneexpression transcription singlecell transcriptomics normalization preprocessing qualitycontrol dimensionreduction dataimport

5.08 score 2 scripts

bioc

fmrs:Variable Selection in Finite Mixture of AFT Regression and FMR Models

The package obtains parameter estimation, i.e., maximum likelihood estimators (MLE), via the Expectation-Maximization (EM) algorithm for the Finite Mixture of Regression (FMR) models with Normal distribution, and MLE for the Finite Mixture of Accelerated Failure Time Regression (FMAFTR) subject to right censoring with Log-Normal and Weibull distributions via the EM algorithm and the Newton-Raphson algorithm (for Weibull distribution). More importantly, the package obtains the maximum penalized likelihood (MPLE) for both FMR and FMAFTR models (collectively called FMRs). A component-wise tuning parameter selection based on a component-wise BIC is implemented in the package. Furthermore, this package provides Ridge Regression and Elastic Net.

Maintained by Farhad Shokoohi. Last updated 5 months ago.

survival regression dimensionreduction

3 stars 5.00 score 55 scripts 1 dependents

bioc

snifter:R wrapper for the python openTSNE library

Provides an R wrapper for the implementation of FI-tSNE from the python package openTNSE. See Poličar et al. (2019) <doi:10.1101/731877> and the algorithm described by Linderman et al. (2018) <doi:10.1038/s41592-018-0308-4>.

Maintained by Alan OCallaghan. Last updated 5 months ago.

dimensionreduction visualization software singlecell sequencing

3 stars 4.95 score 3 scripts

bioc

densvis:Density-Preserving Data Visualization via Non-Linear Dimensionality Reduction

Implements the density-preserving modification to t-SNE and UMAP described by Narayan et al. (2020) <doi:10.1101/2020.05.12.077776>. The non-linear dimensionality reduction techniques t-SNE and UMAP enable users to summarise complex high-dimensional sequencing data such as single cell RNAseq using lower dimensional representations. These lower dimensional representations enable the visualisation of discrete transcriptional states, as well as continuous trajectory (for example, in early development). However, these methods focus on the local neighbourhood structure of the data. In some cases, this results in misleading visualisations, where the density of cells in the low-dimensional embedding does not represent the transcriptional heterogeneity of data in the original high-dimensional space. den-SNE and densMAP aim to enable more accurate visual interpretation of high-dimensional datasets by producing lower-dimensional embeddings that accurately represent the heterogeneity of the original high-dimensional space, enabling the identification of homogeneous and heterogeneous cell states. This accuracy is accomplished by including in the optimisation process a term which considers the local density of points in the original high-dimensional space. This can help to create visualisations that are more representative of heterogeneity in the original high-dimensional space.

Maintained by Alan OCallaghan. Last updated 5 months ago.

dimensionreduction visualization software singlecell sequencing cpp openmp

2 stars 4.94 score 11 scripts

bioc

iSEEfier:Streamlining the creation of initial states for starting an iSEE instance

iSEEfier provides a set of functionality to quickly and intuitively create, inspect, and combine initial configuration objects. These can be conveniently passed in a straightforward manner to the function call to launch iSEE() with the specified configuration. This package currently works seamlessly with the sets of panels provided by the iSEE and iSEEu packages, but can be extended to accommodate the usage of any custom panel (e.g. from iSEEde, iSEEpathways, or any panel developed independently by the user).

Maintained by Najla Abassi. Last updated 5 months ago.

cellbasedassays clustering dimensionreduction featureextraction gui geneexpression immunooncology shinyapps singlecell software transcription transcriptomics visualization

4.90 score 2 scripts

bioc

RDRToolbox:A package for nonlinear dimension reduction with Isomap and LLE.

A package for nonlinear dimension reduction using the Isomap and LLE algorithm. It also includes a routine for computing the Davis-Bouldin-Index for cluster validation, a plotting tool and a data generator for microarray gene expression data and for the Swiss Roll dataset.

Maintained by Christoph Bartenhagen. Last updated 5 months ago.

dimensionreduction featureextraction visualization clustering microarray

4.88 score 54 scripts

bioc

CelliD:Unbiased Extraction of Single Cell gene signatures using Multiple Correspondence Analysis

CelliD is a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell RNA-seq. CelliD allows unbiased cell identity recognition across different donors, tissues-of-origin, model organisms and single-cell omics protocols. The package can also be used to explore functional pathways enrichment in single cell data.

Maintained by Akira Cortal. Last updated 5 months ago.

rnaseq singlecell dimensionreduction clustering genesetenrichment geneexpression atacseq openblas cpp openmp

4.85 score 70 scripts

bioc

MatrixQCvis:Shiny-based interactive data-quality exploration for omics data

Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualizations include amongst others bar- and violin plots of the (count/intensity) values, mean vs standard deviation plots, MA plots, empirical cumulative distribution function (ECDF) plots, visualizations of the distances between samples, and multiple types of dimension reduction plots. Furthermore, MatrixQCvis allows for differential expression analysis based on the limma (moderated t-tests) and proDA (Wald tests) packages. MatrixQCvis builds upon the popular Bioconductor SummarizedExperiment S4 class and enables thus the facile integration into existing workflows. The package is especially tailored towards metabolomics and proteomics mass spectrometry data, but also allows to assess the data quality of other data types that can be represented in a SummarizedExperiment object.

Maintained by Thomas Naake. Last updated 5 months ago.

visualization shinyapps gui qualitycontrol dimensionreduction metabolomics proteomics transcriptomics

4.74 score 4 scripts

bioc

weitrix:Tools for matrices with precision weights, test and explore weighted or sparse data

Data type and tools for working with matrices having precision weights and missing data. This package provides a common representation and tools that can be used with many types of high-throughput data. The meaning of the weights is compatible with usage in the base R function "lm" and the package "limma". Calibrate weights to account for known predictors of precision. Find rows with excess variability. Perform differential testing and find rows with the largest confident differences. Find PCA-like components of variation even with many missing values, rotated so that individual components may be meaningfully interpreted. DelayedArray matrices and BiocParallel are supported.

Maintained by Paul Harrison. Last updated 5 months ago.

software datarepresentation dimensionreduction geneexpression transcriptomics rnaseq singlecell regression

4.70 score 8 scripts

bioc

ILoReg:ILoReg: a tool for high-resolution cell population identification from scRNA-Seq data

ILoReg is a tool for identification of cell populations from scRNA-seq data. In particular, ILoReg is useful for finding cell populations with subtle transcriptomic differences. The method utilizes a self-supervised learning method, called Iteratitive Clustering Projection (ICP), to find cluster probabilities, which are used in noise reduction prior to PCA and the subsequent hierarchical clustering and t-SNE steps. Additionally, functions for differential expression analysis to find gene markers for the populations and gene expression visualization are provided.

Maintained by Johannes Smolander. Last updated 5 months ago.

singlecell software clustering dimensionreduction rnaseq visualization transcriptomics datarepresentation differentialexpression transcription geneexpression

5 stars 4.70 score 2 scripts

bioc

DelayedTensor:R package for sparse and out-of-core arithmetic and decomposition of Tensor

DelayedTensor operates Tensor arithmetic directly on DelayedArray object. DelayedTensor provides some generic function related to Tensor arithmetic/decompotision and dispatches it on the DelayedArray class. DelayedTensor also suppors Tensor contraction by einsum function, which is inspired by numpy einsum.

Maintained by Koki Tsuyuzaki. Last updated 5 months ago.

software infrastructure datarepresentation dimensionreduction

4 stars 4.68 score 3 scripts

bioc

corral:Correspondence Analysis for Single Cell Data

Correspondence analysis (CA) is a matrix factorization method, and is similar to principal components analysis (PCA). Whereas PCA is designed for application to continuous, approximately normally distributed data, CA is appropriate for non-negative, count-based data that are in the same additive scale. The corral package implements CA for dimensionality reduction of a single matrix of single-cell data, as well as a multi-table adaptation of CA that leverages data-optimized scaling to align data generated from different sequencing platforms by projecting into a shared latent space. corral utilizes sparse matrices and a fast implementation of SVD, and can be called directly on Bioconductor objects (e.g., SingleCellExperiment) for easy pipeline integration. The package also includes additional options, including variations of CA to address overdispersion in count data (e.g., Freeman-Tukey chi-squared residual), as well as the option to apply CA-style processing to continuous data (e.g., proteomic TOF intensities) with the Hellinger distance adaptation of CA.

Maintained by Lauren Hsu. Last updated 5 months ago.

batcheffect dimensionreduction geneexpression preprocessing principalcomponent sequencing singlecell software visualization

4.64 score 22 scripts

bioc

Pigengene:Infers biological signatures from gene expression data

Pigengene package provides an efficient way to infer biological signatures from gene expression profiles. The signatures are independent from the underlying platform, e.g., the input can be microarray or RNA Seq data. It can even infer the signatures using data from one platform, and evaluate them on the other. Pigengene identifies the modules (clusters) of highly coexpressed genes using coexpression network analysis, summarizes the biological information of each module in an eigengene, learns a Bayesian network that models the probabilistic dependencies between modules, and builds a decision tree based on the expression of eigengenes.

Maintained by Habil Zare. Last updated 5 months ago.

geneexpression rnaseq networkinference network graphandnetwork biomedicalinformatics systemsbiology transcriptomics classification clustering decisiontree dimensionreduction principalcomponent microarray normalization immunooncology

4.56 score 10 scripts 1 dependents

bioc

seqArchR:Identify Different Architectures of Sequence Elements

seqArchR enables unsupervised discovery of _de novo_ clusters with characteristic sequence architectures characterized by position-specific motifs or composition of stretches of nucleotides, e.g., CG-richness. seqArchR does _not_ require any specifications w.r.t. the number of clusters, the length of any individual motifs, or the distance between motifs if and when they occur in pairs/groups; it directly detects them from the data. seqArchR uses non-negative matrix factorization (NMF) as its backbone, and employs a chunking-based iterative procedure that enables processing of large sequence collections efficiently. Wrapper functions are provided for visualizing cluster architectures as sequence logos.

Maintained by Sarvesh Nikumbh. Last updated 5 months ago.

motifdiscovery generegulation mathematicalbiology systemsbiology transcriptomics genetics clustering dimensionreduction featureextraction dnaseq nmf nonnegative-matrix-factorization promoter-sequence-architectures scikit-learn sequence-analysis sequence-architectures unsupervised-machine-learning

1 stars 4.48 score 9 scripts 1 dependents

bioc

combi:Compositional omics model based visual integration

This explorative ordination method combines quasi-likelihood estimation, compositional regression models and latent variable models for integrative visualization of several omics datasets. Both unconstrained and constrained integration are available. The results are shown as interpretable, compositional multiplots.

Maintained by Stijn Hawinkel. Last updated 5 months ago.

metagenomics dimensionreduction microbiome visualization metabolomics

1 stars 4.48 score 7 scripts

bioc

gep2pep:Creation and Analysis of Pathway Expression Profiles (PEPs)

Pathway Expression Profiles (PEPs) are based on the expression of pathways (defined as sets of genes) as opposed to individual genes. This package converts gene expression profiles to PEPs and performs enrichment analysis of both pathways and experimental conditions, such as "drug set enrichment analysis" and "gene2drug" drug discovery analysis respectively.

Maintained by Francesco Napolitano. Last updated 5 months ago.

geneexpression differentialexpression genesetenrichment dimensionreduction pathways go

4.48 score 4 scripts

bioc

RCSL:Rank Constrained Similarity Learning for single cell RNA sequencing data

A novel clustering algorithm and toolkit RCSL (Rank Constrained Similarity Learning) to accurately identify various cell types using scRNA-seq data from a complex tissue. RCSL considers both lo-cal similarity and global similarity among the cells to discern the subtle differences among cells of the same type as well as larger differences among cells of different types. RCSL uses Spearman’s rank correlations of a cell’s expression vector with those of other cells to measure its global similar-ity, and adaptively learns neighbour representation of a cell as its local similarity. The overall similar-ity of a cell to other cells is a linear combination of its global similarity and local similarity.

Maintained by Qinglin Mei. Last updated 5 months ago.

singlecell software clustering dimensionreduction rnaseq visualization sequencing

2 stars 4.48 score 10 scripts

bioc

ccImpute:ccImpute: an accurate and scalable consensus clustering based approach to impute dropout events in the single-cell RNA-seq data (https://doi.org/10.1186/s12859-022-04814-8)

Dropout events make the lowly expressed genes indistinguishable from true zero expression and different than the low expression present in cells of the same type. This issue makes any subsequent downstream analysis difficult. ccImpute is an imputation algorithm that uses cell similarity established by consensus clustering to impute the most probable dropout events in the scRNA-seq datasets. ccImpute demonstrated performance which exceeds the performance of existing imputation approaches while introducing the least amount of new noise as measured by clustering performance characteristics on datasets with known cell identities.

Maintained by Marcin Malec. Last updated 5 months ago.

singlecell sequencing principalcomponent dimensionreduction clustering rnaseq transcriptomics openblas cpp openmp

2 stars 4.48 score 2 scripts

bioc

rnaEditr:Statistical analysis of RNA editing sites and hyper-editing regions

RNAeditr analyzes site-specific RNA editing events, as well as hyper-editing regions. The editing frequencies can be tested against binary, continuous or survival outcomes. Multiple covariate variables as well as interaction effects can also be incorporated in the statistical models.

Maintained by Lanyu Zhang. Last updated 5 months ago.

genetarget epigenetics dimensionreduction featureextraction regression survival rnaseq

3 stars 4.48 score 9 scripts

bioc

BiocSklearn:interface to python sklearn via Rstudio reticulate

This package provides interfaces to selected sklearn elements, and demonstrates fault tolerant use of python modules requiring extensive iteration.

Maintained by Vince Carey. Last updated 5 months ago.

statisticalmethod dimensionreduction infrastructure

4.34 score 11 scripts

bioc

tpSVG:Thin plate models to detect spatially variable genes

The goal of `tpSVG` is to detect and visualize spatial variation in the gene expression for spatially resolved transcriptomics data analysis. Specifically, `tpSVG` introduces a family of count-based models, with generalizable parametric assumptions such as Poisson distribution or negative binomial distribution. In addition, comparing to currently available count-based model for spatially resolved data analysis, the `tpSVG` models improves computational time, and hence greatly improves the applicability of count-based models in SRT data analysis.

Maintained by Boyi Guo. Last updated 5 months ago.

spatial transcriptomics geneexpression software statisticalmethod dimensionreduction regression preprocessing spatially-resolve spatially-variable-genes

2 stars 4.30 score 2 scripts

bioc

scBFA:A dimensionality reduction tool using gene detection pattern to mitigate noisy expression profile of scRNA-seq

This package is designed to model gene detection pattern of scRNA-seq through a binary factor analysis model. This model allows user to pass into a cell level covariate matrix X and gene level covariate matrix Q to account for nuisance variance(e.g batch effect), and it will output a low dimensional embedding matrix for downstream analysis.

Maintained by Ruoxin Li. Last updated 5 months ago.

singlecell transcriptomics dimensionreduction geneexpression atacseq batcheffect kegg qualitycontrol

4.30 score 4 scripts

bioc

qmtools:Quantitative Metabolomics Data Processing Tools

The qmtools (quantitative metabolomics tools) package provides basic tools for processing quantitative metabolomics data with the standard SummarizedExperiment class. This includes functions for imputation, normalization, feature filtering, feature clustering, dimension-reduction, and visualization to help users prepare data for statistical analysis. This package also offers a convenient way to compute empirical Bayes statistics for which metabolic features are different between two sets of study samples. Several functions in this package could also be used in other types of omics data.

Maintained by Jaehyun Joo. Last updated 5 months ago.

metabolomics preprocessing normalization dimensionreduction massspectrometry

1 stars 4.30 score 5 scripts

bioc

netboost:Network Analysis Supported by Boosting

Boosting supported network analysis for high-dimensional omics applications. This package comes bundled with the MC-UPGMA clustering package by Yaniv Loewenstein.

Maintained by Pascal Schlosser. Last updated 5 months ago.

software statisticalmethod graphandnetwork network clustering dimensionreduction biomedicalinformatics epigenetics metabolomics transcriptomics cpp

4.18 score 1 scripts

bioc

tenXplore:ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics

Perform ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics.

Maintained by VJ Carey. Last updated 5 months ago.

immunooncology dimensionreduction principalcomponent transcriptomics singlecell

4.18 score 7 scripts

bioc

STATegRa:Classes and methods for multi-omics data integration

Classes and tools for multi-omics data integration.

Maintained by David Gomez-Cabrero. Last updated 5 months ago.

software statisticalmethod clustering dimensionreduction principalcomponent

4.15 score 3 scripts

bioc

ReducedExperiment:Containers and tools for dimensionally-reduced -omics representations

Provides SummarizedExperiment-like containers for storing and manipulating dimensionally-reduced assay data. The ReducedExperiment classes allow users to simultaneously manipulate their original dataset and their decomposed data, in addition to other method-specific outputs like feature loadings. Implements utilities and specialised classes for the application of stabilised independent component analysis (sICA) and weighted gene correlation network analysis (WGCNA).

Maintained by Jack Gisby. Last updated 2 months ago.

geneexpression infrastructure datarepresentation software dimensionreduction network bioconductor-package bioinformatics dimensionality-reduction

3 stars 4.13 score 8 scripts

bioc

slalom:Factorial Latent Variable Modeling of Single-Cell RNA-Seq Data

slalom is a scalable modelling framework for single-cell RNA-seq data that uses gene set annotations to dissect single-cell transcriptome heterogeneity, thereby allowing to identify biological drivers of cell-to-cell variability and model confounding factors. The method uses Bayesian factor analysis with a latent variable model to identify active pathways (selected by the user, e.g. KEGG pathways) that explain variation in a single-cell RNA-seq dataset. This an R/C++ implementation of the f-scLVM Python package. See the publication describing the method at https://doi.org/10.1186/s13059-017-1334-8.

Maintained by Davis McCarthy. Last updated 5 months ago.

immunooncology singlecell rnaseq normalization visualization dimensionreduction transcriptomics geneexpression sequencing software reactome kegg openblas cpp

4.08 score 12 scripts

csoneson

dreval:Evaluate Reduced Dimension Representations

Evaluate and compare multiple reduced dimension representations, based on how well they retain structure from the original data set.

Maintained by Charlotte Soneson. Last updated 3 months ago.

dimensionreduction principalcomponent visualization

7 stars 4.02 score 2 scripts

bioc

scTGIF:Cell type annotation for unannotated single-cell RNA-Seq data

scTGIF connects the cells and the related gene functions without cell type label.

Maintained by Koki Tsuyuzaki. Last updated 5 months ago.

dimensionreduction qualitycontrol singlecell software geneexpression

4.00 score 2 scripts

bioc

AffiXcan:A Functional Approach To Impute Genetically Regulated Expression

Impute a GReX (Genetically Regulated Expression) for a set of genes in a sample of individuals, using a method based on the Total Binding Affinity (TBA). Statistical models to impute GReX can be trained with a training dataset where the real total expression values are known.

Maintained by Alessandro Lussana. Last updated 5 months ago.

geneexpression transcription generegulation dimensionreduction regression principalcomponent

4.00 score

bioc

CellTrails:Reconstruction, visualization and analysis of branching trajectories

CellTrails is an unsupervised algorithm for the de novo chronological ordering, visualization and analysis of single-cell expression data. CellTrails makes use of a geometrically motivated concept of lower-dimensional manifold learning, which exhibits a multitude of virtues that counteract intrinsic noise of single cell data caused by drop-outs, technical variance, and redundancy of predictive variables. CellTrails enables the reconstruction of branching trajectories and provides an intuitive graphical representation of expression patterns along all branches simultaneously. It allows the user to define and infer the expression dynamics of individual and multiple pathways towards distinct phenotypes.

Maintained by Daniel Ellwanger. Last updated 5 months ago.

immunooncology clustering datarepresentation differentialexpression dimensionreduction geneexpression sequencing singlecell software timecourse

4.00 score 7 scripts

bioc

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

cellity:Quality Control for Single-Cell RNA-seq Data

A support vector machine approach to identifying and filtering low quality cells from single-cell RNA-seq datasets.

Maintained by Tomislav Ilicic. Last updated 5 months ago.

immunooncology rnaseq qualitycontrol preprocessing normalization visualization dimensionreduction transcriptomics geneexpression sequencing software supportvectormachine

4.00 score 9 scripts

bioc

ssPATHS:ssPATHS: Single Sample PATHway Score

This package generates pathway scores from expression data for single samples after training on a reference cohort. The score is generated by taking the expression of a gene set (pathway) from a reference cohort and performing linear discriminant analysis to distinguish samples in the cohort that have the pathway augmented and not. The separating hyperplane is then used to score new samples.

Maintained by Natalie R. Davidson. Last updated 5 months ago.

software geneexpression biomedicalinformatics rnaseq pathways transcriptomics dimensionreduction classification

4.00 score 1 scripts

bioc

scTensor:Detection of cell-cell interaction from single-cell RNA-seq dataset by tensor decomposition

The algorithm is based on the non-negative tucker decomposition (NTD2) of nnTensor.

Maintained by Koki Tsuyuzaki. Last updated 5 months ago.

dimensionreduction singlecell software geneexpression

4.00 score 2 scripts

bioc

veloviz:VeloViz: RNA-velocity informed 2D embeddings for visualizing cell state trajectories

VeloViz uses each cell’s current observed and predicted future transcriptional states inferred from RNA velocity analysis to build a nearest neighbor graph between cells in the population. Edges are then pruned based on a cosine correlation threshold and/or a distance threshold and the resulting graph is visualized using a force-directed graph layout algorithm. VeloViz can help ensure that relationships between cell states are reflected in the 2D embedding, allowing for more reliable representation of underlying cellular trajectories.

Maintained by Lyla Atta. Last updated 5 months ago.

transcriptomics visualization geneexpression sequencing rnaseq dimensionreduction cpp

4.00 score 6 scripts

bioc

ClustAll:ClustAll: Data driven strategy to robustly identify stratification of patients within complex diseases

Data driven strategy to find hidden groups of patients with complex diseases using clinical data. ClustAll facilitates the unsupervised identification of multiple robust stratifications. ClustAll, is able to overcome the most common limitations found when dealing with clinical data (missing values, correlated data, mixed data types).

Maintained by Asier Ortega-Legarreta. Last updated 5 months ago.

software statisticalmethod clustering dimensionreduction principalcomponent

3.70 score 1 scripts

bioc

esetVis:Visualizations of expressionSet Bioconductor object

Utility functions for visualization of expressionSet (or SummarizedExperiment) Bioconductor object, including spectral map, tsne and linear discriminant analysis. Static plot via the ggplot2 package or interactive via the ggvis or rbokeh packages are available.

Maintained by Laure Cougnaud. Last updated 5 months ago.

visualization datarepresentation dimensionreduction principalcomponent pathways

3.30 score 6 scripts

bioc

missRows:Handling Missing Individuals in Multi-Omics Data Integration

The missRows package implements the MI-MFA method to deal with missing individuals ('biological units') in multi-omics data integration. The MI-MFA method generates multiple imputed datasets from a Multiple Factor Analysis model, then the yield results are combined in a single consensus solution. The package provides functions for estimating coordinates of individuals and variables, imputing missing individuals, and various diagnostic plots to inspect the pattern of missingness and visualize the uncertainty due to missing values.

Maintained by Gonzalez Ignacio. Last updated 5 months ago.

software statisticalmethod dimensionreduction principalcomponent mathematicalbiology visualization

3.30 score 3 scripts

cran

binomialRF:Binomial Random Forest Feature Selection

The 'binomialRF' is a new feature selection technique for decision trees that aims at providing an alternative approach to identify significant feature subsets using binomial distributional assumptions (Rachid Zaim, S., et al. (2019)) <doi:10.1101/681973>. Treating each splitting variable selection as a set of exchangeable correlated Bernoulli trials, 'binomialRF' then tests whether a feature is selected more often than by random chance.

Maintained by Samir Rachid Zaim. Last updated 5 years ago.

software geneprediction statisticalmethod decisiontree dimensionreduction experimentaldesign

2.70 score