R-universe search: needs:dqrng

satijalab

Seurat:Tools for Single Cell Genomics

A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031>, and Hao, Hao, et al (2020) <doi:10.1101/2020.10.12.335331> for more details.

Maintained by Paul Hoffman. Last updated 1 years ago.

human-cell-atlas single-cell-genomics single-cell-rna-seq cpp

2.4k stars 16.86 score 50k scripts 73 dependents

jlmelville

uwot:The Uniform Manifold Approximation and Projection (UMAP) Method for Dimensionality Reduction

An implementation of the Uniform Manifold Approximation and Projection dimensionality reduction by McInnes et al. (2018) <doi:10.48550/arXiv.1802.03426>. It also provides means to transform new data and to carry out supervised dimensionality reduction. An implementation of the related LargeVis method of Tang et al. (2016) <doi:10.48550/arXiv.1602.00370> is also provided. This is a complete re-implementation in R (and C++, via the 'Rcpp' package): no Python installation is required. See the uwot website (<https://github.com/jlmelville/uwot>) for more documentation and examples.

Maintained by James Melville. Last updated 7 days ago.

dimensionality-reduction umap cpp

329 stars 16.08 score 2.0k scripts 145 dependents

bioc

scran:Methods for Single-Cell RNA-Seq Data Analysis

Implements miscellaneous functions for interpretation of single-cell RNA-seq data. Methods are provided for assignment of cell cycle phase, detection of highly variable and significantly correlated genes, identification of marker genes, and other common tasks in routine single-cell analysis workflows.

Maintained by Aaron Lun. Last updated 5 months ago.

immunooncology normalization sequencing rnaseq software geneexpression transcriptomics singlecell clustering bioconductor-package human-cell-atlas single-cell-rna-seq openblas cpp

41 stars 13.05 score 7.6k scripts 37 dependents

bioc

scDblFinder:scDblFinder

The scDblFinder package gathers various methods for the detection and handling of doublets/multiplets in single-cell sequencing data (i.e. multiple cells captured within the same droplet or reaction volume). It includes methods formerly found in the scran package, the new fast and comprehensive scDblFinder method, and a reimplementation of the Amulet detection method for single-cell ATAC-seq.

Maintained by Pierre-Luc Germain. Last updated 13 days ago.

preprocessing singlecell rnaseq atacseq doublets single-cell

184 stars 12.38 score 888 scripts 1 dependents

bioc

mia:Microbiome analysis

mia implements tools for microbiome analysis based on the SummarizedExperiment, SingleCellExperiment and TreeSummarizedExperiment infrastructure. Data wrangling and analysis in the context of taxonomic data is the main scope. Additional functions for common task are implemented such as community indices calculation and summarization.

Maintained by Tuomas Borman. Last updated 21 hours ago.

microbiome software dataimport analysis bioconductor cpp

51 stars 11.52 score 316 scripts 5 dependents

bioc

PCAtools:PCAtools: Everything Principal Components Analysis

Principal Component Analysis (PCA) is a very powerful technique that has wide applicability in data science, bioinformatics, and further afield. It was initially developed to analyse large volumes of data in order to tease out the differences/relationships between the logical entities being analysed. It extracts the fundamental structure of the data without the need to build any model to represent it. This 'summary' of the data is arrived at through a process of reduction that can transform the large number of variables into a lesser number that are uncorrelated (i.e. the 'principal components'), while at the same time being capable of easy interpretation on the original data. PCAtools provides functions for data exploration via PCA, and allows the user to generate publication-ready figures. PCA is performed via BiocSingular - users can also identify optimal number of principal components via different metrics, such as elbow method and Horn's parallel analysis, which has relevance for data reduction in single-cell RNA-seq (scRNA-seq) and high dimensional mass cytometry data.

Maintained by Kevin Blighe. Last updated 5 months ago.

rnaseq atacseq geneexpression transcription singlecell principalcomponent cpp

348 stars 11.12 score 832 scripts 2 dependents

bioc

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 26 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

bioc

CATALYST:Cytometry dATa anALYSis Tools

CATALYST provides tools for preprocessing of and differential discovery in cytometry data such as FACS, CyTOF, and IMC. Preprocessing includes i) normalization using bead standards, ii) single-cell deconvolution, and iii) bead-based compensation. For differential discovery, the package provides a number of convenient functions for data processing (e.g., clustering, dimension reduction), as well as a suite of visualizations for exploratory data analysis and exploration of results from differential abundance (DA) and state (DS) analysis in order to identify differences in composition and expression profiles at the subpopulation-level, respectively.

Maintained by Helena L. Crowell. Last updated 4 months ago.

clustering dataimport differentialexpression experimentaldesign flowcytometry immunooncology massspectrometry normalization preprocessing singlecell software statisticalmethod visualization

67 stars 10.99 score 362 scripts 2 dependents

bioc

infercnv:Infer Copy Number Variation from Single-Cell RNA-Seq Data

Using single-cell RNA-Seq expression to visualize CNV in cells.

Maintained by Christophe Georgescu. Last updated 5 months ago.

software copynumbervariation variantdetection structuralvariation genomicvariation genetics transcriptomics statisticalmethod bayesian hiddenmarkovmodel singlecell jags cpp

601 stars 10.92 score 674 scripts

welch-lab

rliger:Linked Inference of Genomic Experimental Relationships

Uses an extension of nonnegative matrix factorization to identify shared and dataset-specific factors. See Welch J, Kozareva V, et al (2019) <doi:10.1016/j.cell.2019.05.006>, and Liu J, Gao C, Sodicoff J, et al (2020) <doi:10.1038/s41596-020-0391-8> for more details.

Maintained by Yichen Wang. Last updated 3 months ago.

nonnegative-matrix-factorization single-cell openblas cpp

408 stars 10.77 score 334 scripts 1 dependents

bioc

muscat:Multi-sample multi-group scRNA-seq data analysis tools

`muscat` provides various methods and visualization tools for DS analysis in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data, including cell-level mixed models and methods based on aggregated “pseudobulk” data, as well as a flexible simulation platform that mimics both single and multi-sample scRNA-seq data.

Maintained by Helena L. Crowell. Last updated 5 months ago.

immunooncology differentialexpression sequencing singlecell software statisticalmethod visualization

184 stars 10.74 score 686 scripts 1 dependents

bioc

celda:CEllular Latent Dirichlet Allocation

Celda is a suite of Bayesian hierarchical models for clustering single-cell RNA-sequencing (scRNA-seq) data. It is able to perform "bi-clustering" and simultaneously cluster genes into gene modules and cells into cell subpopulations. It also contains DecontX, a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. A variety of scRNA-seq data visualization functions is also included.

Maintained by Joshua Campbell. Last updated 2 months ago.

singlecell geneexpression clustering sequencing bayesian immunooncology dataimport cpp openmp

147 stars 10.47 score 256 scripts 2 dependents

bioc

singleCellTK:Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

The Single Cell Toolkit (SCTK) in the singleCellTK package provides an interface to popular tools for importing, quality control, analysis, and visualization of single cell RNA-seq data. SCTK allows users to seamlessly integrate tools from various packages at different stages of the analysis workflow. A general "a la carte" workflow gives users the ability access to multiple methods for data importing, calculation of general QC metrics, doublet detection, ambient RNA estimation and removal, filtering, normalization, batch correction or integration, dimensionality reduction, 2-D embedding, clustering, marker detection, differential expression, cell type labeling, pathway analysis, and data exporting. Curated workflows can be used to run Seurat and Celda. Streamlined quality control can be performed on the command line using the SCTK-QC pipeline. Users can analyze their data using commands in the R console or by using an interactive Shiny Graphical User Interface (GUI). Specific analyses or entire workflows can be summarized and shared with comprehensive HTML reports generated by Rmarkdown. Additional documentation and vignettes can be found at camplab.net/sctk.

Maintained by Joshua David Campbell. Last updated 1 months ago.

singlecell geneexpression differentialexpression alignment clustering immunooncology batcheffect normalization qualitycontrol dataimport gui

182 stars 10.17 score 252 scripts

bioc

BASiCS:Bayesian Analysis of Single-Cell Sequencing data

Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments (where the groups of cells of interest are known a priori, e.g. experimental conditions or cell types). BASiCS performs built-in data normalisation (global scaling) and technical noise quantification (based on spike-in genes). BASiCS provides an intuitive detection criterion for highly (or lowly) variable genes within a single group of cells. Additionally, BASiCS can compare gene expression patterns between two or more pre-specified groups of cells. Unlike traditional differential expression tools, BASiCS quantifies changes in expression that lie beyond comparisons of means, also allowing the study of changes in cell-to-cell heterogeneity. The latter can be quantified via a biological over-dispersion parameter that measures the excess of variability that is observed with respect to Poisson sampling noise, after normalisation and technical noise removal. Due to the strong mean/over-dispersion confounding that is typically observed for scRNA-seq datasets, BASiCS also tests for changes in residual over-dispersion, defined by residual values with respect to a global mean/over-dispersion trend.

Maintained by Catalina Vallejos. Last updated 5 months ago.

immunooncology normalization sequencing rnaseq software geneexpression transcriptomics singlecell differentialexpression bayesian cellbiology bioconductor-package gene-expression rcpp rcpparmadillo scrna-seq single-cell openblas cpp openmp

83 stars 10.14 score 368 scripts 1 dependents

constantamateur

SoupX:Single Cell mRNA Soup eXterminator

Quantify, profile and remove ambient mRNA contamination (the "soup") from droplet based single cell RNA-seq experiments. Implements the method described in Young et al. (2018) <doi:10.1101/303727>.

Maintained by Matthew Daniel Young. Last updated 2 years ago.

266 stars 10.08 score 594 scripts 1 dependents

bioc

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

bioc

DropletUtils:Utilities for Handling Single-Cell Droplet Data

Provides a number of utility functions for handling single-cell (RNA-seq) data from droplet technologies such as 10X Genomics. This includes data loading from count matrices or molecule information files, identification of cells from empty droplets, removal of barcode-swapped pseudo-cells, and downsampling of the count matrix.

Maintained by Jonathan Griffiths. Last updated 4 months ago.

immunooncology singlecell sequencing rnaseq geneexpression transcriptomics dataimport coverage zlib cpp

10.01 score 2.7k scripts 9 dependents

bioc

scMerge:scMerge: Merging multiple batches of scRNA-seq data

Like all gene expression data, single-cell data suffers from batch effects and other unwanted variations that makes accurate biological interpretations difficult. The scMerge method leverages factor analysis, stably expressed genes (SEGs) and (pseudo-) replicates to remove unwanted variations and merge multiple single-cell data. This package contains all the necessary functions in the scMerge pipeline, including the identification of SEGs, replication-identification methods, and merging of single-cell data.

Maintained by Yingxin Lin. Last updated 5 months ago.

batcheffect geneexpression normalization rnaseq sequencing singlecell software transcriptomics bioinformatics single-cell

67 stars 9.52 score 137 scripts 1 dependents

stemangiola

tidyseurat:Brings Seurat to the Tidyverse

It creates an invisible layer that allow to see the 'Seurat' object as tibble and interact seamlessly with the tidyverse.

Maintained by Stefano Mangiola. Last updated 8 months ago.

assaydomain infrastructure rnaseq differentialexpression geneexpression normalization clustering qualitycontrol sequencing transcription transcriptomics dplyr ggplot2 pca purrr sct seurat single-cell single-cell-rna-seq tibble tidyr tidyverse transcripts tsne umap

159 stars 9.48 score 398 scripts 1 dependents

bioc

SpatialFeatureExperiment:Integrating SpatialExperiment with Simple Features in sf

A new S4 class integrating Simple Features with the R package sf to bring geospatial data analysis methods based on vector data to spatial transcriptomics. Also implements management of spatial neighborhood graphs and geometric operations. This pakage builds upon SpatialExperiment and SingleCellExperiment, hence methods for these parent classes can still be used.

Maintained by Lambda Moses. Last updated 2 months ago.

datarepresentation transcriptomics spatial

49 stars 9.40 score 322 scripts 1 dependents

tidymodels

embed:Extra Recipes for Encoding Predictors

Predictors can be converted to one or more numeric representations using a variety of methods. Effect encodings using simple generalized linear models <doi:10.48550/arXiv.1611.09477> or nonlinear models <doi:10.48550/arXiv.1604.06737> can be used. There are also functions for dimension reduction and other approaches.

Maintained by Emil Hvitfeldt. Last updated 2 months ago.

142 stars 9.18 score 1.1k scripts

bioc

BatchQC:Batch Effects Quality Control Software

Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. BatchQC is a software tool that streamlines batch preprocessing and evaluation by providing interactive diagnostics, visualizations, and statistical analyses to explore the extent to which batch variation impacts the data. BatchQC diagnostics help determine whether batch adjustment needs to be done, and how correction should be applied before proceeding with a downstream analysis. Moreover, BatchQC interactively applies multiple common batch effect approaches to the data and the user can quickly see the benefits of each method. BatchQC is developed as a Shiny App. The output is organized into multiple tabs and each tab features an important part of the batch effect analysis and visualization of the data. The BatchQC interface has the following analysis groups: Summary, Differential Expression, Median Correlations, Heatmaps, Circular Dendrogram, PCA Analysis, Shape, ComBat and SVA.

Maintained by Jessica Anderson. Last updated 15 days ago.

batcheffect graphandnetwork microarray normalization principalcomponent sequencing software visualization qualitycontrol rnaseq preprocessing differentialexpression immunooncology

7 stars 9.06 score 54 scripts

bioc

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 29 days ago.

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

90 stars 9.03 score 248 scripts

bioc

scPipe:Pipeline for single cell multi-omic data pre-processing

A preprocessing pipeline for single cell RNA-seq/ATAC-seq data that starts from the fastq files and produces a feature count matrix with associated quality control information. It can process fastq data generated by CEL-seq, MARS-seq, Drop-seq, Chromium 10x and SMART-seq protocols.

Maintained by Shian Su. Last updated 4 months ago.

immunooncology software sequencing rnaseq geneexpression singlecell visualization sequencematching preprocessing qualitycontrol genomeannotation dataimport curl bzip2 xz-utils zlib cpp

68 stars 9.02 score 84 scripts

dexter-psychometrics

dexter:Data Management and Analysis of Tests

A system for the management, assessment, and psychometric analysis of data from educational and psychological tests.

Maintained by Jesse Koops. Last updated 22 days ago.

openblas cpp openmp

8 stars 8.97 score 135 scripts 2 dependents

bioc

Voyager:From geospatial to spatial omics

SpatialFeatureExperiment (SFE) is a new S4 class for working with spatial single-cell genomics data. The voyager package implements basic exploratory spatial data analysis (ESDA) methods for SFE. Univariate methods include univariate global spatial ESDA methods such as Moran's I, permutation testing for Moran's I, and correlograms. Bivariate methods include Lee's L and cross variogram. Multivariate methods include MULTISPATI PCA and multivariate local Geary's C recently developed by Anselin. The Voyager package also implements plotting functions to plot SFE data and ESDA results.

Maintained by Lambda Moses. Last updated 3 months ago.

geneexpression spatial transcriptomics visualization bioconductor eda esda exploratory-data-analysis omics spatial-statistics spatial-transcriptomics

88 stars 8.71 score 173 scripts

bioc

miaViz:Microbiome Analysis Plotting and Visualization

The miaViz package implements functions to visualize TreeSummarizedExperiment objects especially in the context of microbiome analysis. Part of the mia family of R/Bioconductor packages.

Maintained by Tuomas Borman. Last updated 14 days ago.

microbiome software visualization bioconductor microbiome-analysis plotting

10 stars 8.67 score 81 scripts 1 dependents

bioc

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

samuel-marsh

scCustomize:Custom Visualizations & Functions for Streamlined Analyses of Single Cell Sequencing

Collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using 'R'. 'scCustomize' aims to provide 1) Customized visualizations for aid in ease of use and to create more aesthetic and functional visuals. 2) Improve speed/reproducibility of common tasks/pieces of code in scRNA-seq analysis with a single or group of functions. For citation please use: Marsh SE (2021) "Custom Visualizations & Functions for Streamlined Analyses of Single Cell Sequencing" <doi:10.5281/zenodo.5706430> RRID:SCR_024675.

Maintained by Samuel Marsh. Last updated 3 months ago.

customization ggplot2 scrna-seq seurat single-cell single-cell-genomics single-cell-rna-seq visualization

246 stars 8.45 score 1.1k scripts

bioc

lefser:R implementation of the LEfSE method for microbiome biomarker discovery

lefser is the R implementation of the popular microbiome biomarker discovery too, LEfSe. It uses the Kruskal-Wallis test, Wilcoxon-Rank Sum test, and Linear Discriminant Analysis to find biomarkers from two-level classes (and optional sub-classes).

Maintained by Sehyun Oh. Last updated 1 months ago.

software sequencing differentialexpression microbiome statisticalmethod classification bioconductor-package r01ca230551

56 stars 8.44 score 56 scripts

carmonalab

scGate:Marker-Based Cell Type Purification for Single-Cell Sequencing Data

A common bioinformatics task in single-cell data analysis is to purify a cell type or cell population of interest from heterogeneous datasets. 'scGate' automatizes marker-based purification of specific cell populations, without requiring training data or reference gene expression profiles. Briefly, 'scGate' takes as input: i) a gene expression matrix stored in a 'Seurat' object and ii) a “gating model” (GM), consisting of a set of marker genes that define the cell population of interest. The GM can be as simple as a single marker gene, or a combination of positive and negative markers. More complex GMs can be constructed in a hierarchical fashion, akin to gating strategies employed in flow cytometry. 'scGate' evaluates the strength of signature marker expression in each cell using the rank-based method 'UCell', and then performs k-nearest neighbor (kNN) smoothing by calculating the mean 'UCell' score across neighboring cells. kNN-smoothing aims at compensating for the large degree of sparsity in scRNA-seq data. Finally, a universal threshold over kNN-smoothed signature scores is applied in binary decision trees generated from the user-provided gating model, to annotate cells as either “pure” or “impure”, with respect to the cell population of interest. See the related publication Andreatta et al. (2022) <doi:10.1093/bioinformatics/btac141>.

Maintained by Massimo Andreatta. Last updated 2 months ago.

filtering marker-genes scgate signatures single-cell

106 stars 8.38 score 163 scripts

stephenslab

fastTopics:Fast Algorithms for Fitting Topic Models and Non-Negative Matrix Factorizations to Count Data

Implements fast, scalable optimization algorithms for fitting topic models ("grade of membership" models) and non-negative matrix factorizations to count data. The methods exploit the special relationship between the multinomial topic model (also, "probabilistic latent semantic indexing") and Poisson non-negative matrix factorization. The package provides tools to compare, annotate and visualize model fits, including functions to efficiently create "structure plots" and identify key features in topics. The 'fastTopics' package is a successor to the 'CountClust' package. For more information, see <doi:10.48550/arXiv.2105.13440> and <doi:10.1186/s13059-023-03067-9>. Please also see the GitHub repository for additional vignettes not included in the package on CRAN.

Maintained by Peter Carbonetto. Last updated 1 months ago.

openblas cpp

79 stars 8.38 score 678 scripts 1 dependents

willwerscheid

flashier:Empirical Bayes Matrix Factorization

Methods for matrix factorization based on Wang and Stephens (2021) <https://jmlr.org/papers/v22/20-589.html>.

Maintained by Jason Willwerscheid. Last updated 2 months ago.

11 stars 8.32 score 266 scripts

mrc-ide

malariasimulation:An individual based model for malaria

Specifies the latest and greatest malaria model.

Maintained by Giovanni Charles. Last updated 1 months ago.

cpp

17 stars 8.19 score 146 scripts

bioc

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

bioc

FLAMES:FLAMES: Full Length Analysis of Mutations and Splicing in long read RNA-seq data

Semi-supervised isoform detection and annotation from both bulk and single-cell long read RNA-seq data. Flames provides automated pipelines for analysing isoforms, as well as intermediate functions for manual execution.

Maintained by Changqing Wang. Last updated 2 days ago.

rnaseq singlecell transcriptomics dataimport differentialsplicing alternativesplicing geneexpression longread zlib curl bzip2 xz-utils cpp

33 stars 8.04 score 12 scripts

kharchenkolab

pagoda2:Single Cell Analysis and Differential Expression

Analyzing and interactively exploring large-scale single-cell RNA-seq datasets. 'pagoda2' primarily performs normalization and differential gene expression analysis, with an interactive application for exploring single-cell RNA-seq datasets. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. 'pagoda2' was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for conos, <https://github.com/kharchenkolab/conos>. This package interacts with data available through the 'p2data' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/pagoda2>. The size of the 'p2data' package is approximately 6 MB.

Maintained by Evan Biederstedt. Last updated 1 years ago.

scrna-seq single-cell single-cell-rna-seq transcriptomics openblas cpp openmp

223 stars 8.00 score 282 scripts

bioc

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

bioc

BayesSpace:Clustering and Resolution Enhancement of Spatial Transcriptomes

Tools for clustering and enhancing the resolution of spatial gene expression experiments. BayesSpace clusters a low-dimensional representation of the gene expression matrix, incorporating a spatial prior to encourage neighboring spots to cluster together. The method can enhance the resolution of the low-dimensional representation into "sub-spots", for which features such as gene expression or cell type composition can be imputed.

Maintained by Matt Stone. Last updated 5 months ago.

software clustering transcriptomics geneexpression singlecell immunooncology dataimport openblas cpp openmp

126 stars 7.90 score 278 scripts 1 dependents

bioc

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

bioc

MGnifyR:R interface to EBI MGnify metagenomics resource

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

Maintained by Tuomas Borman. Last updated 6 days ago.

infrastructure dataimport metagenomics microbiome microbiomedata

21 stars 7.48 score 32 scripts

bioc

MOSim:Multi-Omics Simulation (MOSim)

MOSim package simulates multi-omic experiments that mimic regulatory mechanisms within the cell, allowing flexible experimental design including time course and multiple groups.

Maintained by Sonia Tarazona. Last updated 2 days ago.

software timecourse experimentaldesign rnaseq cpp

9 stars 7.46 score 11 scripts

bioc

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

kharchenkolab

conos:Clustering on Network of Samples

Wires together large collections of single-cell RNA-seq datasets, which allows for both the identification of recurrent cell clusters and the propagation of information between datasets in multi-sample or atlas-scale collections. 'Conos' focuses on the uniform mapping of homologous cell types across heterogeneous sample collections. For instance, users could investigate a collection of dozens of peripheral blood samples from cancer patients combined with dozens of controls, which perhaps includes samples of a related tissue such as lymph nodes. This package interacts with data available through the 'conosPanel' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/conos>. The size of the 'conosPanel' package is approximately 12 MB.

Maintained by Evan Biederstedt. Last updated 1 years ago.

batch-correction scrna-seq single-cell-rna-seq openblas cpp openmp

205 stars 7.33 score 258 scripts

ropensci

melt:Multiple Empirical Likelihood Tests

Performs multiple empirical likelihood tests. It offers an easy-to-use interface and flexibility in specifying hypotheses and calibration methods, extending the framework to simultaneous inferences. The core computational routines are implemented using the 'Eigen' 'C++' library and 'RcppEigen' interface, with 'OpenMP' for parallel computation. Details of the testing procedures are provided in Kim, MacEachern, and Peruggia (2023) <doi:10.1080/10485252.2023.2206919>. A companion paper by Kim, MacEachern, and Peruggia (2024) <doi:10.18637/jss.v108.i05> is available for further information. This work was supported by the U.S. National Science Foundation under Grants No. SES-1921523 and DMS-2015552.

Maintained by Eunseop Kim. Last updated 11 months ago.

cpp openmp

12 stars 7.24 score 84 scripts

jlmelville

rnndescent:Nearest Neighbor Descent Method for Approximate Nearest Neighbors

The Nearest Neighbor Descent method for finding approximate nearest neighbors by Dong and co-workers (2010) <doi:10.1145/1963405.1963487>. Based on the 'Python' package 'PyNNDescent' <https://github.com/lmcinnes/pynndescent>.

Maintained by James Melville. Last updated 8 months ago.

approximate-nearest-neighbor-search cpp

11 stars 7.16 score 75 scripts

bioc

CuratedAtlasQueryR:Queries the Human Cell Atlas

Provides access to a copy of the Human Cell Atlas, but with harmonised metadata. This allows for uniform querying across numerous datasets within the Atlas using common fields such as cell type, tissue type, and patient ethnicity. Usage involves first querying the metadata table for cells of interest, and then downloading the corresponding cells into a SingleCellExperiment object.

Maintained by Stefano Mangiola. Last updated 5 months ago.

assaydomain infrastructure rnaseq differentialexpression geneexpression normalization clustering qualitycontrol sequencing transcription transcriptomics database duckdb hdf5 human-cell-atlas single-cell singlecellexperiment tidyverse

90 stars 7.04 score 41 scripts

bioc

pipeComp:pipeComp pipeline benchmarking framework

A simple framework to facilitate the comparison of pipelines involving various steps and parameters. The `pipelineDefinition` class represents pipelines as, minimally, a set of functions consecutively executed on the output of the previous one, and optionally accompanied by step-wise evaluation and aggregation functions. Given such an object, a set of alternative parameters/methods, and benchmark datasets, the `runPipeline` function then proceeds through all combinations arguments, avoiding recomputing the same step twice and compiling evaluations on the fly to avoid storing potentially large intermediate data.

Maintained by Pierre-Luc Germain. Last updated 5 months ago.

geneexpression transcriptomics clustering datarepresentation benchmark bioconductor pipeline-benchmarking pipelines single-cell-rna-seq

41 stars 7.02 score 43 scripts

bioc

musicatk:Mutational Signature Comprehensive Analysis Toolkit

Mutational signatures are carcinogenic exposures or aberrant cellular processes that can cause alterations to the genome. We created musicatk (MUtational SIgnature Comprehensive Analysis ToolKit) to address shortcomings in versatility and ease of use in other pre-existing computational tools. Although many different types of mutational data have been generated, current software packages do not have a flexible framework to allow users to mix and match different types of mutations in the mutational signature inference process. Musicatk enables users to count and combine multiple mutation types, including SBS, DBS, and indels. Musicatk calculates replication strand, transcription strand and combinations of these features along with discovery from unique and proprietary genomic feature associated with any mutation type. Musicatk also implements several methods for discovery of new signatures as well as methods to infer exposure given an existing set of signatures. Musicatk provides functions for visualization and downstream exploratory analysis including the ability to compare signatures between cohorts and find matching signatures in COSMIC V2 or COSMIC V3.

Maintained by Joshua D. Campbell. Last updated 5 months ago.

software biologicalquestion somaticmutation variantannotation

13 stars 6.97 score 20 scripts

bioc

COTAN:COexpression Tables ANalysis

Statistical and computational method to analyze the co-expression of gene pairs at single cell level. It provides the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts' distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can effectively assess the correlated or anti-correlated expression of gene pairs. It provides a numerical index related to the correlation and an approximate p-value for the associated independence test. COTAN can also evaluate whether single genes are differentially expressed, scoring them with a newly defined global differentiation index. Moreover, this approach provides ways to plot and cluster genes according to their co-expression pattern with other genes, effectively helping the study of gene interactions and becoming a new tool to identify cell-identity marker genes.

Maintained by Galfrè Silvia Giulia. Last updated 21 days ago.

systemsbiology transcriptomics geneexpression singlecell

16 stars 6.85 score 96 scripts

bioc

BioTIP:BioTIP: An R package for characterization of Biological Tipping-Point

Adopting tipping-point theory to transcriptome profiles to unravel disease regulatory trajectory.

Maintained by Yuxi (Jennifer) Sun. Last updated 5 months ago.

sequencing rnaseq geneexpression transcription software

18 stars 6.84 score 37 scripts

s3alfisc

fwildclusterboot:Fast Wild Cluster Bootstrap Inference for Linear Models

Implementation of fast algorithms for wild cluster bootstrap inference developed in 'Roodman et al' (2019, 'STATA' Journal, <doi:10.1177/1536867X19830877>) and 'MacKinnon et al' (2022), which makes it feasible to quickly calculate bootstrap test statistics based on a large number of bootstrap draws even for large samples. Multiple bootstrap types as described in 'MacKinnon, Nielsen & Webb' (2022) are supported. Further, 'multiway' clustering, regression weights, bootstrap weights, fixed effects and 'subcluster' bootstrapping are supported. Further, both restricted ('WCR') and unrestricted ('WCU') bootstrap are supported. Methods are provided for a variety of fitted models, including 'lm()', 'feols()' (from package 'fixest') and 'felm()' (from package 'lfe'). Additionally implements a 'heteroskedasticity-robust' ('HC1') wild bootstrap. Last, the package provides an R binding to 'WildBootTests.jl', which provides additional speed gains and functionality, including the 'WRE' bootstrap for instrumental variable models (based on models of type 'ivreg()' from package 'ivreg') and hypotheses with q > 1.

Maintained by Alexander Fischer. Last updated 2 years ago.

clustered-standard-errors linear-regression-models wild-bootstrap wild-cluster-bootstrap openblas cpp openmp

25 stars 6.69 score 109 scripts 2 dependents

bioc

epiregulon:Gene regulatory network inference from single cell epigenomic data

Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and observed phenotypes. Epiregulon infers TF activity in single cells by constructing a gene regulatory network (regulons). This is achieved through integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data. Links between regulatory elements and their target genes are established by computing correlations between chromatin accessibility and gene expressions.

Maintained by Xiaosai Yao. Last updated 23 days ago.

singlecell generegulation networkinference network geneexpression transcription genetarget cpp

14 stars 6.67 score 17 scripts

bioc

scAnnotatR:Pretrained learning models for cell type prediction on single cell RNA-sequencing data

The package comprises a set of pretrained machine learning models to predict basic immune cell types. This enables all users to quickly get a first annotation of the cell types present in their dataset without requiring prior knowledge. scAnnotatR also allows users to train their own models to predict new cell types based on specific research needs.

Maintained by Johannes Griss. Last updated 5 months ago.

singlecell transcriptomics geneexpression supportvectormachine classification software

15 stars 6.61 score 20 scripts

bioc

CiteFuse:CiteFuse: multi-modal analysis of CITE-seq data

CiteFuse pacakage implements a suite of methods and tools for CITE-seq data from pre-processing to integrative analytics, including doublet detection, network-based modality integration, cell type clustering, differential RNA and protein expression analysis, ADT evaluation, ligand-receptor interaction analysis, and interactive web-based visualisation of the analyses.

Maintained by Yingxin Lin. Last updated 5 months ago.

singlecell geneexpression bioinformatics single-cell cpp

27 stars 6.59 score 18 scripts

carmonalab

GeneNMF:Non-Negative Matrix Factorization for Single-Cell Omics

A collection of methods to extract gene programs from single-cell gene expression data using non-negative matrix factorization (NMF). 'GeneNMF' contains functions to directly interact with the 'Seurat' toolkit and derive interpretable gene program signatures.

Maintained by Massimo Andreatta. Last updated 15 days ago.

105 stars 6.58 score 12 scripts

bioc

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

bioc

SpotClean:SpotClean adjusts for spot swapping in spatial transcriptomics data

SpotClean is a computational method to adjust for spot swapping in spatial transcriptomics data. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind mRNA. Ideally, unique molecular identifiers at a spot measure spot-specific expression, but this is often not the case due to bleed from nearby spots, an artifact we refer to as spot swapping. SpotClean is able to estimate the contamination rate in observed data and decontaminate the spot swapping effect, thus increase the sensitivity and precision of downstream analyses.

Maintained by Zijian Ni. Last updated 5 months ago.

dataimport rnaseq sequencing geneexpression spatial singlecell transcriptomics preprocessing rna-seq spatial-transcriptomics

31 stars 6.52 score 36 scripts

bioc

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

bioc

CatsCradle:This package provides methods for analysing spatial transcriptomics data and for discovering gene clusters

This package addresses two broad areas. It allows for in-depth analysis of spatial transcriptomic data by identifying tissue neighbourhoods. These are contiguous regions of tissue surrounding individual cells. 'CatsCradle' allows for the categorisation of neighbourhoods by the cell types contained in them and the genes expressed in them. In particular, it produces Seurat objects whose individual elements are neighbourhoods rather than cells. In addition, it enables the categorisation and annotation of genes by producing Seurat objects whose elements are genes.

Maintained by Michael Shapiro. Last updated 16 days ago.

biologicalquestion statisticalmethod geneexpression singlecell transcriptomics spatial

3 stars 6.52 score

mathewchamberlain

SignacX:Cell Type Identification and Discovery from Single Cell Gene Expression Data

An implementation of neural networks trained with flow-sorted gene expression data to classify cellular phenotypes in single cell RNA-sequencing data. See Chamberlain M et al. (2021) <doi:10.1101/2021.02.01.429207> for more details.

Maintained by Mathew Chamberlain. Last updated 2 years ago.

cellular-phenotypes seurat single-cell-rna-seq

25 stars 6.47 score 34 scripts

kharchenkolab

sccore:Core Utilities for Single-Cell RNA-Seq

Core utilities for single-cell RNA-seq data analysis. Contained within are utility functions for working with differential expression (DE) matrices and count matrices, a collection of functions for manipulating and plotting data via 'ggplot2', and functions to work with cell graphs and cell embeddings. Graph-based methods include embedding kNN cell graphs into a UMAP <doi:10.21105/joss.00861>, collapsing vertices of each cluster in the graph, and propagating graph labels.

Maintained by Evan Biederstedt. Last updated 1 years ago.

cpp

12 stars 6.46 score 36 scripts 9 dependents

qile0317

APackOfTheClones:Visualization of Clonal Expansion for Single Cell Immune Profiles

Visualize clonal expansion via circle-packing. 'APackOfTheClones' extends 'scRepertoire' to produce a publication-ready visualization of clonal expansion at a single cell resolution, by representing expanded clones as differently sized circles. The method was originally implemented by Murray Christian and Ben Murrell in the following immunology study: Ma et al. (2021) <doi:10.1126/sciimmunol.abg6356>.

Maintained by Qile Yang. Last updated 4 months ago.

clonal-analysis immune-repertoire immune-system scrna-seq scrnaseq seurat single-cell single-cell-genomics cpp

15 stars 6.45 score 15 scripts

lhe17

nebula:Negative Binomial Mixed Models Using Large-Sample Approximation for Differential Expression Analysis of ScRNA-Seq Data

A fast negative binomial mixed model for conducting association analysis of multi-subject single-cell data. It can be used for identifying marker genes, differential expression and co-expression analyses. The model includes subject-level random effects to account for the hierarchical structure in multi-subject single-cell data. See He et al. (2021) <doi:10.1038/s42003-021-02146-6>.

Maintained by Liang He. Last updated 7 days ago.

cpp

37 stars 6.43 score 145 scripts

duct317

scDHA:Single-Cell Decomposition using Hierarchical Autoencoder

Provides a fast and accurate pipeline for single-cell analyses. The 'scDHA' software package can perform clustering, dimension reduction and visualization, classification, and time-trajectory inference on single-cell data (Tran et.al. (2021) <DOI:10.1038/s41467-021-21312-2>).

Maintained by Ha Nguyen. Last updated 12 months ago.

cpp

40 stars 6.38 score 20 scripts 2 dependents

bioc

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

bioc

CellMixS:Evaluate Cellspecific Mixing

CellMixS provides metrics and functions to evaluate batch effects, data integration and batch effect correction in single cell trancriptome data with single cell resolution. Results can be visualized and summarised on different levels, e.g. on cell, celltype or dataset level.

Maintained by Almut Lütge. Last updated 5 months ago.

singlecell transcriptomics geneexpression batcheffect

7 stars 6.35 score 64 scripts

bioc

scDataviz:scDataviz: single cell dataviz and downstream analyses

In the single cell World, which includes flow cytometry, mass cytometry, single-cell RNA-seq (scRNA-seq), and others, there is a need to improve data visualisation and to bring analysis capabilities to researchers even from non-technical backgrounds. scDataviz attempts to fit into this space, while also catering for advanced users. Additonally, due to the way that scDataviz is designed, which is based on SingleCellExperiment, it has a 'plug and play' feel, and immediately lends itself as flexibile and compatibile with studies that go beyond scDataviz. Finally, the graphics in scDataviz are generated via the ggplot engine, which means that users can 'add on' features to these with ease.

Maintained by Kevin Blighe. Last updated 5 months ago.

singlecell immunooncology rnaseq geneexpression transcription flowcytometry massspectrometry dataimport

63 stars 6.30 score 16 scripts

bioc

iSEEtree:Interactive visualisation for microbiome data

iSEEtree is an extension of iSEE for the TreeSummarizedExperiment data container. It provides interactive panel designs to explore hierarchical datasets, such as the microbiome and cell lines.

Maintained by Giulio Benedetti. Last updated 14 days ago.

software visualization microbiome gui shinyapps dataimport shiny-apps visualisation

3 stars 6.28 score 5 scripts

bioc

tidyomics:Easily install and load the tidyomics ecosystem

The tidyomics ecosystem is a set of packages for ’omic data analysis that work together in harmony; they share common data representations and API design, consistent with the tidyverse ecosystem. The tidyomics package is designed to make it easy to install and load core packages from the tidyomics ecosystem with a single command.

Maintained by Stefano Mangiola. Last updated 5 months ago.

assaydomain infrastructure rnaseq differentialexpression geneexpression normalization clustering qualitycontrol sequencing transcription transcriptomics cytometry genomics tidyverse

67 stars 6.13 score 5 scripts

feiyoung

DR.SC:Joint Dimension Reduction and Spatial Clustering

Joint dimension reduction and spatial clustering is conducted for Single-cell RNA sequencing and spatial transcriptomics data, and more details can be referred to Wei Liu, Xu Liao, Yi Yang, Huazhen Lin, Joe Yeong, Xiang Zhou, Xingjie Shi and Jin Liu. (2022) <doi:10.1093/nar/gkac219>. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well.

Maintained by Wei Liu. Last updated 1 years ago.

dimension-reduction selfsupervised spatial-clustering spatial-transcriptomics openblas cpp

5 stars 6.12 score 29 scripts 2 dependents

bioc

peco:A Supervised Approach for Predicting cell Cycle Progression using scRNA-seq data

Our approach provides a way to assign continuous cell cycle phase using scRNA-seq data, and consequently, allows to identify cyclic trend of gene expression levels along the cell cycle. This package provides method and training data, which includes scRNA-seq data collected from 6 individual cell lines of induced pluripotent stem cells (iPSCs), and also continuous cell cycle phase derived from FUCCI fluorescence imaging data.

Maintained by Chiaowen Joyce Hsiao. Last updated 5 months ago.

sequencing rnaseq geneexpression transcriptomics singlecell software statisticalmethod classification visualization cell-cycle single-cell-rna-seq

12 stars 6.09 score 34 scripts

bioc

CRISPRball:Shiny Application for Interactive CRISPR Screen Visualization, Exploration, Comparison, and Filtering

A Shiny application for visualization, exploration, comparison, and filtering of CRISPR screens analyzed with MAGeCK RRA or MLE. Features include interactive plots with on-click labeling, full customization of plot aesthetics, data upload and/or download, and much more. Quickly and easily explore your CRISPR screen results and generate publication-quality figures in seconds.

Maintained by Jared Andrews. Last updated 3 months ago.

software shinyapps crispr qualitycontrol visualization gui crispr-screen data-visualization interactive-visualizations mageck plotly screening shiny

9 stars 6.03 score 24 scripts

bioc

Dino:Normalization of Single-Cell mRNA Sequencing Data

Dino normalizes single-cell, mRNA sequencing data to correct for technical variation, particularly sequencing depth, prior to downstream analysis. The approach produces a matrix of corrected expression for which the dependency between sequencing depth and the full distribution of normalized expression; many existing methods aim to remove only the dependency between sequencing depth and the mean of the normalized expression. This is particuarly useful in the context of highly sparse datasets such as those produced by 10X genomics and other uninque molecular identifier (UMI) based microfluidics protocols for which the depth-dependent proportion of zeros in the raw expression data can otherwise present a challenge.

Maintained by Jared Brown. Last updated 5 months ago.

software normalization rnaseq singlecell sequencing geneexpression transcriptomics regression cellbasedassays

9 stars 6.02 score 13 scripts

exaexa

EmbedSOM:Fast Embedding Guided by Self-Organizing Map

Provides a smooth mapping of multidimensional points into low-dimensional space defined by a self-organizing map. Designed to work with 'FlowSOM' and flow-cytometry use-cases. See Kratochvil et al. (2019) <doi:10.12688/f1000research.21642.1>.

Maintained by Mirek Kratochvil. Last updated 2 months ago.

cytometry som visualization cpp

26 stars 6.02 score 8 scripts

bioc

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 18 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

bioc

SingleCellSignalR:Cell Signalling Using Single Cell RNAseq Data Analysis

Allows single cell RNA seq data analysis, clustering, creates internal network and infers cell-cell interactions.

Maintained by Jacques Colinge Developer. Last updated 5 months ago.

singlecell network clustering rnaseq classification

5.87 score 35 scripts 1 dependents

feiyoung

ProFAST:Probabilistic Factor Analysis for Spatially-Aware Dimension Reduction

Probabilistic factor analysis for spatially-aware dimension reduction across multi-section spatial transcriptomics data with millions of spatial locations. More details can be referred to Wei Liu, et al. (2023) <doi:10.1101/2023.07.11.548486>.

Maintained by Wei Liu. Last updated 2 months ago.

openblas cpp

2 stars 5.86 score 12 scripts 1 dependents

bioc

SpaNorm:Spatially-aware normalisation for spatial transcriptomics data

This package implements the spatially aware library size normalisation algorithm, SpaNorm. SpaNorm normalises out library size effects while retaining biology through the modelling of smooth functions for each effect. Normalisation is performed in a gene- and cell-/spot- specific manner, yielding library size adjusted data.

Maintained by Dharmesh D. Bhuva. Last updated 5 months ago.

software geneexpression transcriptomics spatial cellbiology

9 stars 5.86 score 3 scripts

bioc

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

bioc

scBubbletree:Quantitative visual exploration of scRNA-seq data

scBubbletree is a quantitative method for the visual exploration of scRNA-seq data, preserving key biological properties such as local and global cell distances and cell density distributions across samples. It effectively resolves overplotting and enables the visualization of diverse cell attributes from multiomic single-cell experiments. Additionally, scBubbletree is user-friendly and integrates seamlessly with popular scRNA-seq analysis tools, facilitating comprehensive and intuitive data interpretation.

Maintained by Simo Kitanovski. Last updated 5 months ago.

visualization clustering singlecell transcriptomics rnaseq big-data bigdata scrna-seq scrna-seq-analysis visual visual-exploration

6 stars 5.82 score 8 scripts

bioc

SpatialExperimentIO:Read in Xenium, CosMx, MERSCOPE or STARmapPLUS data as SpatialExperiment object

Read in imaging-based spatial transcriptomics technology data. Current available modules are for Xenium by 10X Genomics, CosMx by Nanostring, MERSCOPE by Vizgen, or STARmapPLUS from Broad Institute. You can choose to read the data in as a SpatialExperiment or a SingleCellExperiment object.

Maintained by Yixing E. Dong. Last updated 2 months ago.

datarepresentation dataimport infrastructure transcriptomics singlecell spatial geneexpression

9 stars 5.81 score 16 scripts

bioc

dandelionR:Single-cell Immune Repertoire Trajectory Analysis in R

dandelionR is an R package for performing single-cell immune repertoire trajectory analysis, based on the original python implementation. It provides the necessary functions to interface with scRepertoire and a custom implementation of an absorbing Markov chain for pseudotime inference, inspired by the Palantir Python package.

Maintained by Kelvin Tuong. Last updated 30 days ago.

software immunooncology singlecell

8 stars 5.81 score 7 scripts

bioc

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

bioc

scRNAseqApp:A single-cell RNAseq Shiny app-package

The scRNAseqApp is a Shiny app package designed for interactive visualization of single-cell data. It is an enhanced version derived from the ShinyCell, repackaged to accommodate multiple datasets. The app enables users to visualize data containing various types of information simultaneously, facilitating comprehensive analysis. Additionally, it includes a user management system to regulate database accessibility for different users.

Maintained by Jianhong Ou. Last updated 20 days ago.

visualization singlecell rnaseq interactive-visualizations multiple-users shiny-apps single-cell-rna-seq

4 stars 5.76 score 3 scripts

ahudde

greeks:Sensitivities of Prices of Financial Options and Implied Volatilities

Methods to calculate sensitivities of financial option prices for European, geometric and arithmetic Asian, and American options, with various payoff functions in the Black Scholes model, and in more general jump diffusion models. A shiny app to interactively plot the results is included. Furthermore, methods to compute implied volatilities are provided for a wide range of option types and custom payoff functions. Classical formulas are implemented for European options in the Black Scholes Model, as is presented in Hull, J. C. (2017), Options, Futures, and Other Derivatives. In the case of Asian options, Malliavin Monte Carlo Greeks are implemented, see Hudde, A. & Rüschendorf, L. (2023). European and Asian Greeks for exponential Lévy processes. <doi:10.1007/s11009-023-10014-5>. For American options, the Binomial Tree Method is implemented, as is presented in Hull, J. C. (2017).

Maintained by Anselm Hudde. Last updated 19 days ago.

asian-option greeks implied-volatility option cpp

7 stars 5.75 score 6 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

bioc

scFeatures:scFeatures: Multi-view representations of single-cell and spatial data for disease outcome prediction

scFeatures constructs multi-view representations of single-cell and spatial data. scFeatures is a tool that generates multi-view representations of single-cell and spatial data through the construction of a total of 17 feature types. These features can then be used for a variety of analyses using other software in Biocondutor.

Maintained by Yue Cao. Last updated 5 months ago.

cellbasedassays singlecell spatial software transcriptomics

11 stars 5.69 score 15 scripts

xiaozhangryy

CAESAR.Suite:CAESAR: a Cross-Technology and Cross-Resolution Framework for Spatial Omics Annotation

Biotechnology in spatial omics has advanced rapidly over the past few years, enhancing both throughput and resolution. However, existing annotation pipelines in spatial omics predominantly rely on clustering methods, lacking the flexibility to integrate extensive annotated information from single-cell RNA sequencing (scRNA-seq) due to discrepancies in spatial resolutions, species, or modalities. Here we introduce the CAESAR suite, an open-source software package that provides image-based spatial co-embedding of locations and genomic features. It uniquely transfers labels from scRNA-seq reference, enabling the annotation of spatial omics datasets across different technologies, resolutions, species, and modalities, based on the conserved relationship between signature genes and cells/locations at an appropriate level of granularity. Notably, CAESAR enriches location-level pathways, allowing for the detection of gradual biological pathway activation within spatially defined domain types. More details on the methods related to our paper currently under submission. A full reference to the paper will be provided in future versions once the paper is published.

Maintained by Xiao Zhang. Last updated 8 days ago.

openblas cpp

1 stars 5.67 score 2 scripts

bioc

lute:Framework for cell size scale factor normalized bulk transcriptomics deconvolution experiments

Provides a framework for adjustment on cell type size when performing bulk transcripomics deconvolution. The main framework function provides a means of reference normalization using cell size scale factors. It allows for marker selection and deconvolution using non-negative least squares (NNLS) by default. The framework is extensible for other marker selection and deconvolution algorithms, and users may reuse the generics, methods, and classes for these when developing new algorithms.

Maintained by Sean K Maden. Last updated 5 months ago.

rnaseq sequencing singlecell coverage transcriptomics normalization

3 stars 5.65 score 3 scripts

kharchenkolab

leidenAlg:Implements the Leiden Algorithm via an R Interface

An R interface to the Leiden algorithm, an iterative community detection algorithm on networks. The algorithm is designed to converge to a partition in which all subsets of all communities are locally optimally assigned, yielding communities guaranteed to be connected. The implementation proves to be fast, scales well, and can be run on graphs of millions of nodes (as long as they can fit in memory). The original implementation was constructed as a python interface "leidenalg" found here: <https://github.com/vtraag/leidenalg>. The algorithm was originally described in Traag, V.A., Waltman, L. & van Eck, N.J. "From Louvain to Leiden: guaranteeing well-connected communities". Sci Rep 9, 5233 (2019) <doi:10.1038/s41598-019-41695-z>.

Maintained by Evan Biederstedt. Last updated 6 months ago.

fortran cpp

9 stars 5.61 score 28 scripts 5 dependents

rezakj

iCellR:Analyzing High-Throughput Single Cell Sequencing Data

A toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST). Single (i) Cell R package ('iCellR') provides unprecedented flexibility at every step of the analysis pipeline, including normalization, clustering, dimensionality reduction, imputation, visualization, and so on. Users can design both unsupervised and supervised models to best suit their research. In addition, the toolkit provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells, genes and clusters, data merging, normalizing for dropouts, data imputation methods, correcting for batch differences, pathway analysis, tools to find marker genes for clusters and conditions, predict cell types and pseudotime analysis. See Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.05.05.078550> and Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.03.31.019109> for more details.

Maintained by Alireza Khodadadi-Jamayran. Last updated 9 months ago.

10xgenomics 3d batch-normalization cell-type-classification cite-seq clustering clustering-algorithm diffusion-maps dropout icellr imputation intractive-graph normalization pseudotime scrna-seq scvdj-seq singel-cell-sequencing umap cpp

121 stars 5.56 score 7 scripts 1 dependents

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 1 months ago.

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

5.56 score 2 scripts 1 dependents

bioc

scDotPlot:Cluster a Single-cell RNA-seq Dot Plot

Dot plots of single-cell RNA-seq data allow for an examination of the relationships between cell groupings (e.g. clusters) and marker gene expression. The scDotPlot package offers a unified approach to perform a hierarchical clustering analysis and add annotations to the columns and/or rows of a scRNA-seq dot plot. It works with SingleCellExperiment and Seurat objects as well as data frames.

Maintained by Benjamin I Laufer. Last updated 13 days ago.

software visualization differentialexpression geneexpression transcription rnaseq singlecell sequencing clustering

7 stars 5.45 score 2 scripts

bioc

speckle:Statistical methods for analysing single cell RNA-seq data

The speckle package contains functions for the analysis of single cell RNA-seq data. The speckle package currently contains functions to analyse differences in cell type proportions. There are also functions to estimate the parameters of the Beta distribution based on a given counts matrix, and a function to normalise a counts matrix to the median library size. There are plotting functions to visualise cell type proportions and the mean-variance relationship in cell type proportions and counts. As our research into specialised analyses of single cell data continues we anticipate that the package will be updated with new functions.

Maintained by Belinda Phipson. Last updated 5 months ago.

singlecell rnaseq regression geneexpression

5.41 score 258 scripts

bioc

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

teazrq

RLT:Reinforcement Learning Trees

Random forest with a variety of additional features for regression, classification, survival analysis and graphical model. New features include parallel computing with OpenMP, reproduciblity with random seeds, variance and confidence band estimations, embedded model for selecting splitting varibles and constructing linear combination splits, observaton and variable weights, setting and tracking subjects used in each tree, etc.

Maintained by Ruoqing Zhu. Last updated 5 months ago.

openblas cpp openmp

11 stars 5.36 score 21 scripts

bioc

visiumStitched:Enable downstream analysis of Visium capture areas stitched together with Fiji

This package provides helper functions for working with multiple Visium capture areas that overlap each other. This package was developed along with the companion example use case data available from https://github.com/LieberInstitute/visiumStitched_brain. visiumStitched prepares SpaceRanger (10x Genomics) output files so you can stitch the images from groups of capture areas together with Fiji. Then visiumStitched builds a SpatialExperiment object with the stitched data and makes an artificial hexogonal grid enabling the seamless use of spatial clustering methods that rely on such grid to identify neighboring spots, such as PRECAST and BayesSpace. The SpatialExperiment objects created by visiumStitched are compatible with spatialLIBD, which can be used to build interactive websites for stitched SpatialExperiment objects. visiumStitched also enables casting SpatialExperiment objects as Seurat objects.

Maintained by Nicholas J. Eagles. Last updated 4 months ago.

software spatial transcriptomics transcription geneexpression visualization dataimport 10xgenomics bioconductor spatial-transcriptomics spatialexperiment spatiallibd visium

1 stars 5.36 score 4 scripts

bioc

scCB2:CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data

scCB2 is an R package implementing CB2 for distinguishing real cells from empty droplets in droplet-based single cell RNA-seq experiments (especially for 10x Chromium). It is based on clustering similar barcodes and calculating Monte-Carlo p-value for each cluster to test against background distribution. This cluster-level test outperforms single-barcode-level tests in dealing with low count barcodes and homogeneous sequencing library, while keeping FDR well controlled.

Maintained by Zijian Ni. Last updated 5 months ago.

dataimport rnaseq singlecell sequencing geneexpression transcriptomics preprocessing clustering

10 stars 5.30 score 5 scripts

zhiyuan-hu-lab

CIDER:Meta-Clustering for scRNA-Seq Integration and Evaluation

A workflow of (a) meta-clustering based on inter-group similarity measures and (b) a ground-truth-free test metric to assess the biological correctness of integration in real datasets. See Hu Z, Ahmed A, Yau C (2021) <doi:10.1101/2021.03.29.437525> for more details.

Maintained by Zhiyuan Hu. Last updated 2 months ago.

5.30 score

bioc

scviR:experimental inferface from R to scvi-tools

This package defines interfaces from R to scvi-tools. A vignette works through the totalVI tutorial for analyzing CITE-seq data. Another vignette compares outputs of Chapter 12 of the OSCA book with analogous outputs based on totalVI quantifications. Future work will address other components of scvi-tools, with a focus on building understanding of probabilistic methods based on variational autoencoders.

Maintained by Vincent Carey. Last updated 5 months ago.

infrastructure singlecell dataimport bioconductor cite-seq scverse

6 stars 5.30 score 11 scripts

bioc

DelayedRandomArray:Delayed Arrays of Random Values

Implements a DelayedArray of random values where the realization of the sampled values is delayed until they are needed. Reproducible sampling within any subarray is achieved by chunking where each chunk is initialized with a different random seed and stream. The usual distributions in the stats package are supported, along with scalar, vector and arrays for the parameters.

Maintained by Aaron Lun. Last updated 3 months ago.

datarepresentation cpp

5.26 score 6 scripts 1 dependents

jiang-junyao

CACIMAR:cross-species analysis of cell identities, markers and regulations

A toolkit to perform cross-species analysis based on scRNA-seq data. CACIMAR contains 5 main features. (1) identify Markers in each cluster. (2) Cell type annotaion (3) identify conserved markers. (4) identify conserved cell types. (5) identify conserved modules of regulatory networks.

Maintained by Junyao Jiang. Last updated 4 months ago.

cross-species-analysis scrna-seq

12 stars 5.26 score 6 scripts

bioc

msImpute:Imputation of label-free mass spectrometry peptides

MsImpute is a package for imputation of peptide intensity in proteomics experiments. It additionally contains tools for MAR/MNAR diagnosis and assessment of distortions to the probability distribution of the data post imputation. The missing values are imputed by low-rank approximation of the underlying data matrix if they are MAR (method = "v2"), by Barycenter approach if missingness is MNAR ("v2-mnar"), or by Peptide Identity Propagation (PIP).

Maintained by Soroor Hediyeh-zadeh. Last updated 5 months ago.

massspectrometry proteomics software imputation-algorithm label-free-proteomics low-rank-approximation

14 stars 5.15 score 7 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 30 days ago.

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

5.08 score

bioc

CDI:Clustering Deviation Index (CDI)

Single-cell RNA-sequencing (scRNA-seq) is widely used to explore cellular variation. The analysis of scRNA-seq data often starts from clustering cells into subpopulations. This initial step has a high impact on downstream analyses, and hence it is important to be accurate. However, there have not been unsupervised metric designed for scRNA-seq to evaluate clustering performance. Hence, we propose clustering deviation index (CDI), an unsupervised metric based on the modeling of scRNA-seq UMI counts to evaluate clustering of cells.

Maintained by Jiyuan Fang. Last updated 5 months ago.

singlecell software clustering visualization sequencing rnaseq cellbasedassays

5 stars 5.00 score 4 scripts

bioc

epiregulon.extra:Companion package to epiregulon with additional plotting, differential and graph functions

Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and observed phenotypes. Epiregulon infers TF activity in single cells by constructing a gene regulatory network (regulons). This is achieved through integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data. Links between regulatory elements and their target genes are established by computing correlations between chromatin accessibility and gene expressions.

Maintained by Xiaosai Yao. Last updated 14 days ago.

generegulation network geneexpression transcription chiponchip differentialexpression genetarget normalization graphandnetwork

4.95 score 10 scripts

bioc

decontX:Decontamination of single cell genomics data

This package contains implementation of DecontX (Yang et al. 2020), a decontamination algorithm for single-cell RNA-seq, and DecontPro (Yin et al. 2023), a decontamination algorithm for single cell protein expression data. DecontX is a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. DecontPro is a Bayesian method that estimates the level of contamination from ambient and background sources in CITE-seq ADT dataset and decontaminate the dataset.

Maintained by Joshua Campbell. Last updated 1 months ago.

singlecell bayesian cpp

4.94 score 29 scripts

jgasmits

AnanseSeurat:Construct ANANSE GRN-Analysis Seurat

Enables gene regulatory network (GRN) analysis on single cell clusters, using the GRN analysis software 'ANANSE', Xu et al.(2021) <doi:10.1093/nar/gkab598>. Export data from 'Seurat' objects, for GRN analysis by 'ANANSE' implemented in 'snakemake'. Finally, incorporate results for visualization and interpretation.

Maintained by Jos Smits. Last updated 1 years ago.

grn-analysis seurat-objects single-cell single-cell-atac-seq single-cell-rna-seq

8 stars 4.90 score 4 scripts

diegommcc

SpatialDDLS:Deconvolution of Spatial Transcriptomics Data Based on Neural Networks

Deconvolution of spatial transcriptomics data based on neural networks and single-cell RNA-seq data. SpatialDDLS implements a workflow to create neural network models able to make accurate estimates of cell composition of spots from spatial transcriptomics data using deep learning and the meaningful information provided by single-cell RNA-seq data. See Torroja and Sanchez-Cabo (2019) <doi:10.3389/fgene.2019.00978> and Mañanes et al. (2024) <doi:10.1093/bioinformatics/btae072> to get an overview of the method and see some examples of its performance.

Maintained by Diego Mañanes. Last updated 5 months ago.

deconvolution deep-learning neural-network spatial-transcriptomics

5 stars 4.88 score 1 scripts

yuelyu21

SCIntRuler:Guiding the Integration of Multiple Single-Cell RNA-Seq Datasets

The accumulation of single-cell RNA-seq (scRNA-seq) studies highlights the potential benefits of integrating multiple datasets. By augmenting sample sizes and enhancing analytical robustness, integration can lead to more insightful biological conclusions. However, challenges arise due to the inherent diversity and batch discrepancies within and across studies. SCIntRuler, a novel R package, addresses these challenges by guiding the integration of multiple scRNA-seq datasets.

Maintained by Yue Lyu. Last updated 6 months ago.

sequencing geneticvariability singlecell cpp

2 stars 4.85 score 3 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

papatheodorou-group

scGOclust:Measuring Cell Type Similarity with Gene Ontology in Single-Cell RNA-Seq

Traditional methods for analyzing single cell RNA-seq datasets focus solely on gene expression, but this package introduces a novel approach that goes beyond this limitation. Using Gene Ontology terms as features, the package allows for the functional profile of cell populations, and comparison within and between datasets from the same or different species. Our approach enables the discovery of previously unrecognized functional similarities and differences between cell types and has demonstrated success in identifying cell types' functional correspondence even between evolutionarily distant species.

Maintained by Yuyao Song. Last updated 12 days ago.

9 stars 4.80 score 14 scripts

bioc

MEB:A normalization-invariant minimum enclosing ball method to detect differentially expressed genes for RNA-seq and scRNA-seq data

This package provides a method to identify differential expression genes in the same or different species. Given that non-DE genes have some similarities in features, a scaling-free minimum enclosing ball (SFMEB) model is built to cover those non-DE genes in feature space, then those DE genes, which are enormously different from non-DE genes, being regarded as outliers and rejected outside the ball. The method on this package is described in the article 'A minimum enclosing ball method to detect differential expression genes for RNA-seq data'. The SFMEB method is extended to the scMEB method that considering two or more potential types of cells or unknown labels scRNA-seq dataset DEGs identification.

Maintained by Jiadi Zhu. Last updated 5 months ago.

differentialexpression geneexpression normalization classification sequencing

4.78 score 1 scripts

bioc

airpart:Differential cell-type-specific allelic imbalance

Airpart identifies sets of genes displaying differential cell-type-specific allelic imbalance across cell types or states, utilizing single-cell allelic counts. It makes use of a generalized fused lasso with binomial observations of allelic counts to partition cell types by their allelic imbalance. Alternatively, a nonparametric method for partitioning cell types is offered. The package includes a number of visualizations and quality control functions for examining single cell allelic imbalance datasets.

Maintained by Wancen Mu. Last updated 5 months ago.

singlecell rnaseq atacseq chipseq sequencing generegulation geneexpression transcription transcriptomevariant cellbiology functionalgenomics differentialexpression graphandnetwork regression clustering qualitycontrol

2 stars 4.78 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

stJoincount:stJoincount - Join count statistic for quantifying spatial correlation between clusters

stJoincount facilitates the application of join count analysis to spatial transcriptomic data generated from the 10x Genomics Visium platform. This tool first converts a labeled spatial tissue map into a raster object, in which each spatial feature is represented by a pixel coded by label assignment. This process includes automatic calculation of optimal raster resolution and extent for the sample. A neighbors list is then created from the rasterized sample, in which adjacent and diagonal neighbors for each pixel are identified. After adding binary spatial weights to the neighbors list, a multi-categorical join count analysis is performed to tabulate "joins" between all possible combinations of label pairs. The function returns the observed join counts, the expected count under conditions of spatial randomness, and the variance calculated under non-free sampling. The z-score is then calculated as the difference between observed and expected counts, divided by the square root of the variance.

Maintained by Jiarong Song. Last updated 5 months ago.

transcriptomics clustering spatial biocviews software

4 stars 4.60 score 3 scripts

colemanrharris

mxnorm:Apply Normalization Methods to Multiplexed Images

Implements methods to normalize multiplexed imaging data, including statistical metrics and visualizations to quantify technical variation in this data type. Reference for methods listed here: Harris, C., Wrobel, J., & Vandekar, S. (2022). mxnorm: An R Package to Normalize Multiplexed Imaging Data. Journal of Open Source Software, 7(71), 4180, <doi:10.21105/joss.04180>.

Maintained by Coleman Harris. Last updated 2 years ago.

7 stars 4.54 score 7 scripts

bioc

MAPFX:MAssively Parallel Flow cytometry Xplorer (MAPFX): A Toolbox for Analysing Data from the Massively-Parallel Cytometry Experiments

MAPFX is an end-to-end toolbox that pre-processes the raw data from MPC experiments (e.g., BioLegend's LEGENDScreen and BD Lyoplates assays), and further imputes the ‘missing’ infinity markers in the wells without those measurements. The pipeline starts by performing background correction on raw intensities to remove the noise from electronic baseline restoration and fluorescence compensation by adapting a normal-exponential convolution model. Unwanted technical variation, from sources such as well effects, is then removed using a log-normal model with plate, column, and row factors, after which infinity markers are imputed using the informative backbone markers as predictors. The completed dataset can then be used for clustering and other statistical analyses. Additionally, MAPFX can be used to normalise data from FFC assays as well.

Maintained by Hsiao-Chi Liao. Last updated 5 months ago.

software flowcytometry cellbasedassays singlecell proteomics clustering

1 stars 4.54 score

raysinensis

colorrepel:Repel Visually Similar Colors for Colorblind Users in Various Plots

Iterate and repel visually similar colors away in various 'ggplot2' plots. When many groups are plotted at the same time on multiple axes, for instance stacked bars or scatter plots, effectively ordering colors becomes difficult. This tool iterates through color combinations to find the best solution to maximize visual distinctness of nearby groups, so plots are more friendly toward colorblind users. This is achieved by two distance measurements, distance between groups within the plot, and CIELAB color space distances between colors as described in Carter et al., (2018) <doi:10.25039/TR.015.2018>.

Maintained by Rui Fu. Last updated 2 months ago.

10 stars 4.48 score 1 scripts

dosorio

rPanglaoDB:Download and Merge Single-Cell RNA-Seq Data from the PanglaoDB Database

Download and merge labeled single-cell RNA-seq data from the PanglaoDB <https://panglaodb.se/> into a Seurat object.

Maintained by Daniel Osorio. Last updated 2 years ago.

data-integration data-mining rna-seq single-cell single-cell-rna-seq

26 stars 4.41 score 20 scripts

bioc

PRONE:The PROteomics Normalization Evaluator

High-throughput omics data are often affected by systematic biases introduced throughout all the steps of a clinical study, from sample collection to quantification. Normalization methods aim to adjust for these biases to make the actual biological signal more prominent. However, selecting an appropriate normalization method is challenging due to the wide range of available approaches. Therefore, a comparative evaluation of unnormalized and normalized data is essential in identifying an appropriate normalization strategy for a specific data set. This R package provides different functions for preprocessing, normalizing, and evaluating different normalization approaches. Furthermore, normalization methods can be evaluated on downstream steps, such as differential expression analysis and statistical enrichment analysis. Spike-in data sets with known ground truth and real-world data sets of biological experiments acquired by either tandem mass tag (TMT) or label-free quantification (LFQ) can be analyzed.

Maintained by Lis Arend. Last updated 12 days ago.

proteomics preprocessing normalization differentialexpression visualization data-analysis evaluation

2 stars 4.41 score 9 scripts

bioc

mumosa:Multi-Modal Single-Cell Analysis Methods

Assorted utilities for multi-modal analyses of single-cell datasets. Includes functions to combine multiple modalities for downstream analysis, perform MNN-based batch correction across multiple modalities, and to compute correlations between assay values for different modalities.

Maintained by Aaron Lun. Last updated 5 months ago.

immunooncology singlecell rnaseq

4.41 score 13 scripts

niaid

HDStIM:High Dimensional Stimulation Immune Mapping ('HDStIM')

A method for identifying responses to experimental stimulation in mass or flow cytometry that uses high dimensional analysis of measured parameters and can be performed with an end-to-end unsupervised approach. In the context of in vitro stimulation assays where high-parameter cytometry was used to monitor intracellular response markers, using cell populations annotated either through automated clustering or manual gating for a combined set of stimulated and unstimulated samples, 'HDStIM' labels cells as responding or non-responding. The package also provides auxiliary functions to rank intracellular markers based on their contribution to identifying responses and generating diagnostic plots.

Maintained by Rohit Farmer. Last updated 1 years ago.

complexheatmap assay cytof cytometry cytometry-analysis-pipeline flowcytometry stimulation

3 stars 4.41 score 17 scripts

joemsong

FunChisq:Model-Free Functional Chi-Squared and Exact Tests

Statistical hypothesis testing methods for inferring model-free functional dependency using asymptotic chi-squared or exact distributions. Functional test statistics are asymmetric and functionally optimal, unique from other related statistics. Tests in this package reveal evidence for causality based on the causality-by- functionality principle. They include asymptotic functional chi-squared tests (Zhang & Song 2013) <doi:10.48550/arXiv.1311.2707>, an adapted functional chi-squared test (Kumar & Song 2022) <doi:10.1093/bioinformatics/btac206>, and an exact functional test (Zhong & Song 2019) <doi:10.1109/TCBB.2018.2809743> (Nguyen et al. 2020) <doi:10.24963/ijcai.2020/372>. The normalized functional chi-squared test was used by Best Performer 'NMSUSongLab' in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges (Hill et al. 2016) <doi:10.1038/nmeth.3773>. A function index (Zhong & Song 2019) <doi:10.1186/s12920-019-0565-9> (Kumar et al. 2018) <doi:10.1109/BIBM.2018.8621502> derived from the functional test statistic offers a new effect size measure for the strength of functional dependency, a better alternative to conditional entropy in many aspects. For continuous data, these tests offer an advantage over regression analysis when a parametric functional form cannot be assumed; for categorical data, they provide a novel means to assess directional dependency not possible with symmetrical Pearson's chi-squared or Fisher's exact tests.

Maintained by Joe Song. Last updated 11 months ago.

cpp

4.37 score 29 scripts

bioc

ClusterFoldSimilarity:Calculate similarity of clusters from different single cell samples using foldchanges

This package calculates a similarity coefficient using the fold changes of shared features (e.g. genes) among clusters of different samples/batches/datasets. The similarity coefficient is calculated using the dot-product (Hadamard product) of every pairwise combination of Fold Changes between a source cluster i of sample/dataset n and all the target clusters j in sample/dataset m

Maintained by Oscar Gonzalez-Velasco. Last updated 5 months ago.

singlecell clustering featureextraction graphandnetwork genetarget rnaseq

4.34 score 11 scripts

bioc

Spaniel:Spatial Transcriptomics Analysis

Spaniel includes a series of tools to aid the quality control and analysis of Spatial Transcriptomics data. Spaniel can import data from either the original Spatial Transcriptomics system or 10X Visium technology. The package contains functions to create a SingleCellExperiment Seurat object and provides a method of loading a histologial image into R. The spanielPlot function allows visualisation of metrics contained within the S4 object overlaid onto the image of the tissue.

Maintained by Rachel Queen. Last updated 5 months ago.

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

4.34 score 22 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

cytofQC:Labels normalized cells for CyTOF data and assigns probabilities for each label

cytofQC is a package for initial cleaning of CyTOF data. It uses a semi-supervised approach for labeling cells with their most likely data type (bead, doublet, debris, dead) and the probability that they belong to each label type. This package does not remove data from the dataset, but provides labels and information to aid the data user in cleaning their data. Our algorithm is able to distinguish between doublets and large cells.

Maintained by Jill Lundell. Last updated 5 months ago.

software singlecell annotation

2 stars 4.30 score 3 scripts

yanpd01

ggsector:Draw Sectors

Some useful functions that can use 'grid' and 'ggplot2' to plot sectors and interact with 'Seurat' to plot gene expression percentages. Also, there are some examples of how to draw sectors in 'ComplexHeatmap'.

Maintained by Pengdong Yan. Last updated 5 months ago.

4 stars 4.30 score 5 scripts

bioc

RegionalST:Investigating regions of interest and performing regional cell type-specific analysis with spatial transcriptomics data

This package analyze spatial transcriptomics data through cross-regional cell type-specific analysis. It selects regions of interest (ROIs) and identifys cross-regional cell type-specific differential signals. The ROIs can be selected using automatic algorithm or through manual selection. It facilitates manual selection of ROIs using a shiny application.

Maintained by Ziyi Li. Last updated 4 months ago.

spatial transcriptomics reactome kegg

4.30 score 8 scripts

bioc

spillR:Spillover Compensation in Mass Cytometry Data

Channel interference in mass cytometry can cause spillover and may result in miscounting of protein markers. We develop a nonparametric finite mixture model and use the mixture components to estimate the probability of spillover. We implement our method using expectation-maximization to fit the mixture model.

Maintained by Marco Guazzini. Last updated 5 months ago.

flowcytometry immunooncology massspectrometry preprocessing singlecell software statisticalmethod visualization regression

4.30 score 3 scripts

bioc

regionalpcs:Summarizing Regional Methylation with Regional Principal Components Analysis

Functions to summarize DNA methylation data using regional principal components. Regional principal components are computed using principal components analysis within genomic regions to summarize the variability in methylation levels across CpGs. The number of principal components is chosen using either the Marcenko-Pasteur or Gavish-Donoho method to identify relevant signal in the data.

Maintained by Tiffany Eulalio. Last updated 5 months ago.

dnamethylation differentialmethylation statisticalmethod software methylationarray

2 stars 4.30 score 4 scripts

cyrillagger

scDiffCom:Differential Analysis of Intercellular Communication from scRNA-Seq Data

Analysis tools to investigate changes in intercellular communication from scRNA-seq data. Using a Seurat object as input, the package infers which cell-cell interactions are present in the dataset and how these interactions change between two conditions of interest (e.g. young vs old). It relies on an internal database of ligand-receptor interactions (available for human, mouse and rat) that have been gathered from several published studies. Detection and differential analyses rely on permutation tests. The package also contains several tools to perform over-representation analysis and visualize the results. See Lagger, C. et al. (2023) <doi:10.1038/s43587-023-00514-x> for a full description of the methodology.

Maintained by Cyril Lagger. Last updated 1 years ago.

21 stars 4.25 score 17 scripts

bioc

MPAC:Multi-omic Pathway Analysis of Cells

Multi-omic Pathway Analysis of Cells (MPAC), integrates multi-omic data for understanding cellular mechanisms. It predicts novel patient groups with distinct pathway profiles as well as identifying key pathway proteins with potential clinical associations. From CNA and RNA-seq data, it determines genes’ DNA and RNA states (i.e., repressed, normal, or activated), which serve as the input for PARADIGM to calculate Inferred Pathway Levels (IPLs). It also permutes DNA and RNA states to create a background distribution to filter IPLs as a way to remove events observed by chance. It provides multiple methods for downstream analysis and visualization.

Maintained by Peng Liu. Last updated 19 days ago.

software technology sequencing rnaseq survival clustering immunooncology

4.18 score 1 scripts

bioc

partCNV:Infer locally aneuploid cells using single cell RNA-seq data

This package uses a statistical framework for rapid and accurate detection of aneuploid cells with local copy number deletion or amplification. Our method uses an EM algorithm with mixtures of Poisson distributions while incorporating cytogenetics information (e.g., regional deletion or amplification) to guide the classification (partCNV). When applicable, we further improve the accuracy by integrating a Hidden Markov Model for feature selection (partCNVH).

Maintained by Ziyi Li. Last updated 5 months ago.

software copynumbervariation hiddenmarkovmodel singlecell classification

4.18 score 4 scripts

rikenbit

WormTensor:A Clustering Method for Time-Series Whole-Brain Activity Data of 'C. elegans'

A toolkit to detect clusters from distance matrices. The distance matrices are assumed to be calculated between the cells of multiple animals ('Caenorhabditis elegans') from input time-series matrices. Some functions for generating distance matrices, performing clustering, evaluating the clustering, and visualizing the results of clustering and evaluation are available. We're also providing the download function to retrieve the calculated distance matrices from 'figshare' <https://figshare.com>.

Maintained by Kentaro Yamamoto. Last updated 8 months ago.

1 stars 4.18 score 3 scripts

khodosevichlab

CRMetrics:Cell Ranger Output Filtering and Metrics Visualization

Sample and cell filtering as well as visualisation of output metrics from 'Cell Ranger' by Grace X.Y. Zheng et al. (2017) <doi:10.1038/ncomms14049>. 'CRMetrics' allows for easy plotting of output metrics across multiple samples as well as comparative plots including statistical assessments of these. 'CRMetrics' allows for easy removal of ambient RNA using 'SoupX' by Matthew D Young and Sam Behjati (2020) <doi:10.1093/gigascience/giaa151> or 'CellBender' by Stephen J Fleming et al. (2022) <doi:10.1101/791699>. Furthermore, it is possible to preprocess data using 'Pagoda2' by Nikolas Barkas et al. (2021) <https://github.com/kharchenkolab/pagoda2> or 'Seurat' by Yuhan Hao et al. (2021) <doi:10.1016/j.cell.2021.04.048> followed by embedding of cells using 'Conos' by Nikolas Barkas et al. (2019) <doi:10.1038/s41592-019-0466-z>. Finally, doublets can be detected using 'scrublet' by Samuel L. Wolock et al. (2019) <doi:10.1016/j.cels.2018.11.005> or 'DoubletDetection' by Gayoso et al. (2020) <doi:10.5281/zenodo.2678041>. In the end, cells are filtered based on user input for use in downstream applications.

Maintained by Rasmus Rydbirk. Last updated 5 months ago.

5 stars 4.18 score 8 scripts

ruzhangzhao

mixhvg:Mixture of Multiple Highly Variable Feature Selection Methods

Highly variable gene selection methods, including popular public available methods, and also the mixture of multiple highly variable gene selection methods, <https://github.com/RuzhangZhao/mixhvg>. Reference: <doi:10.1101/2024.08.25.608519>.

Maintained by Ruzhang Zhao. Last updated 1 months ago.

rna-seq-analysis rna-seq-pipeline single-cell single-cell-rna-seq variable-selection

5 stars 4.18 score 6 scripts

kzst

nda:Generalized Network-Based Dimensionality Reduction and Analysis

Non-parametric dimensionality reduction function. Reduction with and without feature selection. Plot functions. Automated feature selections. Kosztyan et. al. (2024) <doi:10.1016/j.eswa.2023.121779>.

Maintained by Zsolt T. Kosztyan. Last updated 2 months ago.

2 stars 4.08 score 1 scripts

bioc

VAExprs:Generating Samples of Gene Expression Data with Variational Autoencoders

A fundamental problem in biomedical research is the low number of observations, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. By augmenting a few real observations with artificially generated samples, their analysis could lead to more robust and higher reproducible. One possible solution to the problem is the use of generative models, which are statistical models of data that attempt to capture the entire probability distribution from the observations. Using the variational autoencoder (VAE), a well-known deep generative model, this package is aimed to generate samples with gene expression data, especially for single-cell RNA-seq data. Furthermore, the VAE can use conditioning to produce specific cell types or subpopulations. The conditional VAE (CVAE) allows us to create targeted samples rather than completely random ones.

Maintained by Dongmin Jung. Last updated 5 months ago.

software geneexpression singlecell openjdk

4.00 score 4 scripts

bioc

scTreeViz:R/Bioconductor package to interactively explore and visualize single cell RNA-seq datasets with hierarhical annotations

scTreeViz provides classes to support interactive data aggregation and visualization of single cell RNA-seq datasets with hierarchies for e.g. cell clusters at different resolutions. The `TreeIndex` class provides methods to manage hierarchy and split the tree at a given resolution or across resolutions. The `TreeViz` class extends `SummarizedExperiment` and can performs quick aggregations on the count matrix defined by clusters.

Maintained by Jayaram Kancherla. Last updated 5 months ago.

visualization infrastructure gui singlecell

4.00 score 3 scripts

fentouxungui

SeuratExplorer:An 'Shiny' App for Exploring scRNA-seq Data Processed in 'Seurat'

A simple, one-command package which runs an interactive dashboard capable of common visualizations for single cell RNA-seq. 'SeuratExplorer' requires a processed 'Seurat' object, which is saved as 'rds' or 'qs2' file.

Maintained by Yongchao Zhang. Last updated 1 days ago.

3.98 score

joycekang

symphony:Efficient and Precise Single-Cell Reference Atlas Mapping

Implements the Symphony single-cell reference building and query mapping algorithms and additional functions described in Kang et al <https://www.nature.com/articles/s41467-021-25957-x>.

Maintained by Joyce Kang. Last updated 2 years ago.

openblas cpp

3.83 score 134 scripts

bioc

BASiCStan:Stan implementation of BASiCS

Provides an interface to infer the parameters of BASiCS using the variational inference (ADVI), Markov chain Monte Carlo (NUTS), and maximum a posteriori (BFGS) inference engines in the Stan programming language. BASiCS is a Bayesian hierarchical model that uses an adaptive Metropolis within Gibbs sampling scheme. Alternative inference methods provided by Stan may be preferable in some situations, for example for particularly large data or posterior distributions with difficult geometries.

Maintained by Alan OCallaghan. Last updated 5 months ago.

immunooncology normalization sequencing rnaseq software geneexpression transcriptomics singlecell differentialexpression bayesian cellbiology single-cell-rna-seq cpp

3.78 score 1 scripts

cran

Platypus:Single-Cell Immune Repertoire and Gene Expression Analysis

We present 'Platypus', an open-source software platform providing a user-friendly interface to investigate B-cell receptor and T-cell receptor repertoires from scSeq experiments. 'Platypus' provides a framework to automate and ease the analysis of single-cell immune repertoires while also incorporating transcriptional information involving unsupervised clustering, gene expression and gene ontology. This R version of 'Platypus' is part of the 'ePlatypus' ecosystem for computational analysis of immunogenomics data: Yermanos et al. (2021) <doi:10.1093/nargab/lqab023>, Cotet et al. (2023) <doi:10.1093/bioinformatics/btad553>.

Maintained by Alexander Yermanos. Last updated 6 months ago.

3.70 score

sistia01

DWLS:Gene Expression Deconvolution Using Dampened Weighted Least Squares

The rapid development of single-cell transcriptomic technologies has helped uncover the cellular heterogeneity within cell populations. However, bulk RNA-seq continues to be the main workhorse for quantifying gene expression levels due to technical simplicity and low cost. To most effectively extract information from bulk data given the new knowledge gained from single-cell methods, we have developed a novel algorithm to estimate the cell-type composition of bulk data from a single-cell RNA-seq-derived cell-type signature. Comparison with existing methods using various real RNA-seq data sets indicates that our new approach is more accurate and comprehensive than previous methods, especially for the estimation of rare cell types. More importantly,our method can detect cell-type composition changes in response to external perturbations, thereby providing a valuable, cost-effective method for dissecting the cell-type-specific effects of drug treatments or condition changes. As such, our method is applicable to a wide range of biological and clinical investigations. Dampened weighted least squares ('DWLS') is an estimation method for gene expression deconvolution, in which the cell-type composition of a bulk RNA-seq data set is computationally inferred. This method corrects common biases towards cell types that are characterized by highly expressed genes and/or are highly prevalent, to provide accurate detection across diverse cell types. See: <https://www.nature.com/articles/s41467-019-10802-z.pdf> for more information about the development of 'DWLS' and the methods behind our functions.

Maintained by Adriana Sistig. Last updated 3 years ago.

2 stars 3.62 score 42 scripts

bioc

infinityFlow:Augmenting Massively Parallel Cytometry Experiments Using Multivariate Non-Linear Regressions

Pipeline to analyze and merge data files produced by BioLegend's LEGENDScreen or BD Human Cell Surface Marker Screening Panel (BD Lyoplates).

Maintained by Etienne Becht. Last updated 5 months ago.

software flowcytometry cellbasedassays singlecell proteomics

3.60 score 4 scripts

liuy12

SCdeconR:Deconvolution of Bulk RNA-Seq Data using Single-Cell RNA-Seq Data as Reference

Streamlined workflow from deconvolution of bulk RNA-seq data to downstream differential expression and gene-set enrichment analysis. Provide various visualization functions.

Maintained by Yuanhang Liu. Last updated 10 months ago.

bulk-rna-seq-deconvolution deconvolution differential-expression ffpe geneset-enrichment-analysis scdeconr single-cell

4 stars 3.60 score 4 scripts

s3alfisc

wildrwolf:Fast Computation of Romano-Wolf Corrected p-Values for Linear Regression Models

Fast Routines to Compute Romano-Wolf corrected p-Values (Romano and Wolf (2005a) <DOI:10.1198/016214504000000539>, Romano and Wolf (2005b) <DOI:10.1111/j.1468-0262.2005.00615.x>) for objects of type 'fixest' and 'fixest_multi' from the 'fixest' package via a wild (cluster) bootstrap.

Maintained by Alexander Fischer. Last updated 1 years ago.

fixest multiple-comparisons romano-wolf wild-bootstrap wild-cluster-bootstrap

7 stars 3.59 score 37 scripts 1 dependents

erhard-lab

HetSeq:Identifying Modulators of Cellular Responses Leveraging Intercellular Heterogeneity

Cellular responses to perturbations are highly heterogeneous and depend largely on the initial state of cells. Connecting post-perturbation cells via cellular trajectories to untreated cells (e.g. by leveraging metabolic labeling information) enables exploitation of intercellular heterogeneity as a combined knock-down and overexpression screen to identify pathway modulators, termed Heterogeneity-seq (see 'Berg et al' <doi:10.1101/2024.10.28.620481>). This package contains functions to generate cellular trajectories based on scSLAM-seq (single-cell, thiol-(SH)-linked alkylation of RNA for metabolic labelling sequencing) time courses, functions to identify pathway modulators and to visualize the results.

Maintained by Kevin Berg. Last updated 2 months ago.

3.54 score

bioc

SCArray.sat:Large-scale single-cell RNA-seq data analysis using GDS files and Seurat

Extends the Seurat classes and functions to support Genomic Data Structure (GDS) files as a DelayedArray backend for data representation. It relies on the implementation of GDS-based DelayedMatrix in the SCArray package to represent single cell RNA-seq data. The common optimized algorithms leveraging GDS-based and single cell-specific DelayedMatrix (SC_GDSMatrix) are implemented in the SCArray package. SCArray.sat introduces a new SCArrayAssay class (derived from the Seurat Assay), which wraps raw counts, normalized expressions and scaled data matrix based on GDS-specific DelayedMatrix. It is designed to integrate seamlessly with the Seurat package to provide common data analysis in the SeuratObject-based workflow. Compared with Seurat, SCArray.sat significantly reduces the memory usage without downsampling and can be applied to very large datasets.

Maintained by Xiuwen Zheng. Last updated 9 days ago.

datarepresentation dataimport singlecell rnaseq

1 stars 3.48 score 3 scripts

psyen0824

HDMAADMM:ADMM for High-Dimensional Mediation Models

We use the Alternating Direction Method of Multipliers (ADMM) for parameter estimation in high-dimensional, single-modality mediation models. To improve the sensitivity and specificity of estimated mediation effects, we offer the sure independence screening (SIS) function for dimension reduction. The available penalty options include Lasso, Elastic Net, Pathway Lasso, and Network-constrained Penalty. The methods employed in the package are based on Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011). <doi:10.1561/2200000016>, Fan, J., & Lv, J. (2008) <doi:10.1111/j.1467-9868.2008.00674.x>, Li, C., & Li, H. (2008) <doi:10.1093/bioinformatics/btn081>, Tibshirani, R. (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>, Zhao, Y., & Luo, X. (2022) <doi:10.4310/21-sii673>, and Zou, H., & Hastie, T. (2005) <doi:10.1111/j.1467-9868.2005.00503.x>.

Maintained by Pei-Shan Yen. Last updated 7 months ago.

cpp

3.48 score

thecailab

SCRIP:An Accurate Simulator for Single-Cell RNA Sequencing Data

We provide a comprehensive scheme that is capable of simulating Single Cell RNA Sequencing data for various parameters of Biological Coefficient of Variation, busting kinetics, differential expression (DE), cell or sample groups, cell trajectory, batch effect and other experimental designs. 'SCRIP' proposed and compared two frameworks with Gamma-Poisson and Beta-Gamma-Poisson models for simulating Single Cell RNA Sequencing data. Other reference is available in Zappia et al. (2017) <https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1305-0>.

Maintained by Fei Qin. Last updated 2 years ago.

2 stars 3.41 score 13 scripts

dsokolo

scMappR:Single Cell Mapper

The single cell mapper (scMappR) R package contains a suite of bioinformatic tools that provide experimentally relevant cell-type specific information to a list of differentially expressed genes (DEG). The function "scMappR_and_pathway_analysis" reranks DEGs to generate cell-type specificity scores called cell-weighted fold-changes. Users input a list of DEGs, normalized counts, and a signature matrix into this function. scMappR then re-weights bulk DEGs by cell-type specific expression from the signature matrix, cell-type proportions from RNA-seq deconvolution and the ratio of cell-type proportions between the two conditions to account for changes in cell-type proportion. With cwFold-changes calculated, scMappR uses two approaches to utilize cwFold-changes to complete cell-type specific pathway analysis. The "process_dgTMatrix_lists" function in the scMappR package contains an automated scRNA-seq processing pipeline where users input scRNA-seq count data, which is made compatible for scMappR and other R packages that analyze scRNA-seq data. We further used this to store hundreds up regularly updating signature matrices. The functions "tissue_by_celltype_enrichment", "tissue_scMappR_internal", and "tissue_scMappR_custom" combine these consistently processed scRNAseq count data with gene-set enrichment tools to allow for cell-type marker enrichment of a generic gene list (e.g. GWAS hits). Reference: Sokolowski,D.J., Faykoo-Martinez,M., Erdman,L., Hou,H., Chan,C., Zhu,H., Holmes,M.M., Goldenberg,A. and Wilson,M.D. (2021) Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes. NAR Genomics and Bioinformatics. 3(1). Iqab011. <doi:10.1093/nargab/lqab011>.

Maintained by Dustin Sokolowski. Last updated 2 years ago.

4 stars 3.30 score 9 scripts

shufeyangyi2015310117

SC.MEB:Spatial Clustering with Hidden Markov Random Field using Empirical Bayes

Spatial clustering with hidden markov random field fitted via EM algorithm, details of which can be found in Yi Yang (2021) <doi:10.1101/2021.06.05.447181>. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well.

Maintained by Yi Yang. Last updated 3 years ago.

openblas cpp

3.04 score 11 scripts

ellisdoro

jrSiCKLSNMF:Clustering Single-Cell Multimodal Omics Data with Joint Graph Regularized Single-Cell Kullback-Leibler Sparse Non-Negative Matrix Factorization

Methods to perform Joint graph Regularized Single-Cell Kullback-Leibler Sparse Non-negative Matrix Factorization (jrSiCKLSNMF, pronounced "junior sickles NMF") on quality controlled multi-assay single-cell omics count data, specifically dual-assay scRNA-seq and scATAC-seq data. 'jrSiCKLSNMF' extracts meaningful latent factors that are shared across omics views. These factors enable accurate cell-type clustering, and facilitate visualizations. Also includes methods for mini- batch updates and other adaptations for larger datasets.

Maintained by Dorothy Ellis. Last updated 10 months ago.

openblas cpp openmp

3.00 score 6 scripts

timobechger

dexterMST:CML and Bayesian Calibration of Multistage Tests

Conditional Maximum Likelihood Calibration and data management of multistage tests. Supports polytomous items and incomplete designs with linear as well as multistage tests. Extended Nominal Response and Interaction models, DIF and profile analysis. See Robert J. Zwitser and Gunter Maris (2015)<doi:10.1007/s11336-013-9369-6>.

Maintained by Timo Bechger. Last updated 1 years ago.

cpp openmp

2.93 score 17 scripts

obenno

scSpotlight:A Single Cell Analysis Shiny App

A single cell analysis (viewer) app based on Seurat.

Maintained by Zhixia Xiao. Last updated 7 months ago.

seurat shiny-apps single-cell

2 stars 2.78 score

sridhara-omics

scPipeline:A Wrapper for 'Seurat' and Related R Packages for End-to-End Single Cell Analysis

Reports markers list, differentially expressed genes, associated pathways, cell-type annotations, does batch correction and other related single cell analyses all wrapped within 'Seurat'.

Maintained by Viswanadham Sridhara. Last updated 26 days ago.

2.70 score

jessekps

dextergui:A Graphical User Interface for Dexter

Classical Test and Item analysis, Item Response analysis and data management for educational and psychological tests.

Maintained by Jesse Koops. Last updated 3 years ago.

2.70 score 6 scripts

strancsus

scCAN:Single-Cell Clustering using Autoencoder and Network Fusion

A single-cell Clustering method using 'Autoencoder' and Network fusion ('scCAN') Bang Tran (2022) <doi:10.1038/s41598-022-14218-6> for segregating the cells from the high-dimensional 'scRNA-Seq' data. The software automatically determines the optimal number of clusters and then partitions the cells in a way such that the results are robust to noise and dropouts. 'scCAN' is fast and it supports Windows, Linux, and Mac OS.

Maintained by Bang Tran. Last updated 10 months ago.

2.70 score

yuepan027

scpoisson:Single Cell Poisson Probability Paradigm

Useful to visualize the Poissoneity (an independent Poisson statistical framework, where each RNA measurement for each cell comes from its own independent Poisson distribution) of Unique Molecular Identifier (UMI) based single cell RNA sequencing (scRNA-seq) data, and explore cell clustering based on model departure as a novel data representation.

Maintained by Yue Pan. Last updated 3 years ago.

2.70 score 4 scripts

mohmedsoudy

ScRNAIMM:Performing Single-Cell RNA-Seq Imputation by Using Mean/Median Imputation

Performing single-cell imputation in a way that preserves the biological variations in the data. The package clusters the input data to do imputation for each cluster, and do a distribution check using the Anderson-Darling normality test to impute dropouts using mean or median (Yazici, B., & Yolacan, S. (2007) <DOI:10.1080/10629360600678310>).

Maintained by Mohamed Soudy. Last updated 1 years ago.

2.70 score

joemsong

GridOnClusters:Cluster-Preserving Multivariate Joint Grid Discretization

Discretize multivariate continuous data using a grid that captures the joint distribution via preserving clusters in the original data (Wang et al. 2020) <doi:10.1145/3388440.3412415>. Joint grid discretization is applicable as a data transformation step to prepare data for model-free inference of association, function, or causality.

Maintained by Joe Song. Last updated 11 months ago.

cpp

2.70 score 3 scripts

schiebout

CAMML:Cell-Typing using Variance Adjusted Mahalanobis Distances with Multi-Labeling

Creates multi-label cell-types for single-cell RNA-sequencing data based on weighted VAM scoring of cell-type specific gene sets. Schiebout, Frost (2022) <https://psb.stanford.edu/psb-online/proceedings/psb22/schiebout.pdf>.

Maintained by Courtney Schiebout. Last updated 1 years ago.

2.60 score

cran

BioM2:Biologically Explainable Machine Learning Framework

Biologically Explainable Machine Learning Framework for Phenotype Prediction using omics data described in Chen and Schwarz (2017) <doi:10.48550/arXiv.1712.00336>.Identifying reproducible and interpretable biological patterns from high-dimensional omics data is a critical factor in understanding the risk mechanism of complex disease. As such, explainable machine learning can offer biological insight in addition to personalized risk scoring.In this process, a feature space of biological pathways will be generated, and the feature space can also be subsequently analyzed using WGCNA (Described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559> ) methods.

Maintained by Shunjie Zhang. Last updated 1 months ago.

2.54 score

igordot

scooter:Streamlined scRNA-Seq Analysis Pipeline

Streamlined scRNA-Seq analysis pipeline.

Maintained by Igor Dolgalev. Last updated 1 years ago.

4 stars 2.51 score 16 scripts

cran

SPECK:Receptor Abundance Estimation using Reduced Rank Reconstruction and Clustered Thresholding

Surface Protein abundance Estimation using CKmeans-based clustered thresholding ('SPECK') is an unsupervised learning-based method that performs receptor abundance estimation for single cell RNA-sequencing data based on reduced rank reconstruction (RRR) and a clustered thresholding mechanism. Seurat's normalization method is described in: Hao et al., (2021) <doi:10.1016/j.cell.2021.04.048>, Stuart et al., (2019) <doi:10.1016/j.cell.2019.05.031>, Butler et al., (2018) <doi:10.1038/nbt.4096> and Satija et al., (2015) <doi:10.1038/nbt.3192>. Method for the RRR is further detailed in: Erichson et al., (2019) <doi:10.18637/jss.v089.i11> and Halko et al., (2009) <arXiv:0909.4061>. Clustering method is outlined in: Song et al., (2020) <doi:10.1093/bioinformatics/btaa613> and Wang et al., (2011) <doi:10.32614/RJ-2011-015>.

Maintained by Azka Javaid. Last updated 1 years ago.

2.48 score 1 dependents

cran

scaper:Single Cell Transcriptomics-Level Cytokine Activity Prediction and Estimation

Generates cell-level cytokine activity estimates using relevant information from gene sets constructed with the 'CytoSig' and the 'Reactome' databases and scored using the modified 'Variance-adjusted Mahalanobis (VAM)' framework for single-cell RNA-sequencing (scRNA-seq) data. 'CytoSig' database is described in: Jiang at al., (2021) <doi:10.1038/s41592-021-01274-5>. 'Reactome' database is described in: Gillespie et al., (2021) <doi:10.1093/nar/gkab1028>. The 'VAM' method is outlined in: Frost (2020) <doi:10.1093/nar/gkaa582>.

Maintained by Azka Javaid. Last updated 1 years ago.

2.30 score

igordot

phenomenalist:Analysis Toolkit for PhenoCycler (CODEX) Data in R

A collection of tools for cleaning, clustering, and plotting PhenoCycler (CODEX) data.

Maintained by Igor Dolgalev. Last updated 1 years ago.

codex phenocycler

3 stars 2.18 score 1 scripts

cran

STREAK:Receptor Abundance Estimation using Feature Selection and Gene Set Scoring

Performs receptor abundance estimation for single cell RNA-sequencing data using a supervised feature selection mechanism and a thresholded gene set scoring procedure. Seurat's normalization method is described in: Hao et al., (2021) <doi:10.1016/j.cell.2021.04.048>, Stuart et al., (2019) <doi:10.1016/j.cell.2019.05.031>, Butler et al., (2018) <doi:10.1038/nbt.4096> and Satija et al., (2015) <doi:10.1038/nbt.3192>. Method for reduced rank reconstruction and rank-k selection is detailed in: Javaid et al., (2022) <doi:10.1101/2022.10.08.511197>. Gene set scoring procedure is described in: Frost et al., (2020) <doi:10.1093/nar/gkaa582>. Clustering method is outlined in: Song et al., (2020) <doi:10.1093/bioinformatics/btaa613> and Wang et al., (2011) <doi:10.32614/RJ-2011-015>.

Maintained by Azka Javaid. Last updated 1 years ago.

2.00 score 2 scripts

welch-lab

SiNMFiD:Supervised iNMF informed Deconvolution

A package for completing cell type deconvolution on bulk spatial transcriptomic data utilizing multiple reference scRNA-seq datasets.

Maintained by Joshua Sodicoff. Last updated 1 years ago.

2.00 score 1 scripts

ubcxzhang

scAnnotate:An Automated Cell Type Annotation Tool for Single-Cell RNA-Sequencing Data

An entirely data-driven cell type annotation tools, which requires training data to learn the classifier, but not biological knowledge to make subjective decisions. It consists of three steps: preprocessing training and test data, model fitting on training data, and cell classification on test data. See Xiangling Ji,Danielle Tsao, Kailun Bai, Min Tsao, Li Xing, Xuekui Zhang.(2022)<doi:10.1101/2022.02.19.481159> for more details.

Maintained by Xuekui Zhang. Last updated 1 years ago.

2.00 score 4 scripts

cran

PoweREST:A Bootstrap-Based Power Estimation Tool for Spatial Transcriptomics

Power estimation and sample size calculation for 10X Visium Spatial Transcriptomics data to detect differential expressed genes between two conditions based on bootstrap resampling. See Shui et al. (2024) <doi:10.1101/2024.08.30.610564> for method details.

Maintained by Lan Shui. Last updated 7 months ago.

2.00 score

j-mitchel

scITD:Single-Cell Interpretable Tensor Decomposition

Single-cell Interpretable Tensor Decomposition (scITD) employs the Tucker tensor decomposition to extract multicell-type gene expression patterns that vary across donors/individuals. This tool is geared for use with single-cell RNA-sequencing datasets consisting of many source donors. The method has a wide range of potential applications, including the study of inter-individual variation at the population-level, patient sub-grouping/stratification, and the analysis of sample-level batch effects. Each "multicellular process" that is extracted consists of (A) a multi cell type gene loadings matrix and (B) a corresponding donor scores vector indicating the level at which the corresponding loadings matrix is expressed in each donor. Additional methods are implemented to aid in selecting an appropriate number of factors and to evaluate stability of the decomposition. Additional tools are provided for downstream analysis, including integration of gene set enrichment analysis and ligand-receptor analysis. Tucker, L.R. (1966) <doi:10.1007/BF02289464>. Unkel, S., Hannachi, A., Trendafilov, N. T., & Jolliffe, I. T. (2011) <doi:10.1007/s13253-011-0055-9>. Zhou, G., & Cichocki, A. (2012) <doi:10.2478/v10175-012-0051-4>.

Maintained by Jonathan Mitchel. Last updated 2 years ago.

cpp

1.98 score 19 scripts

s3alfisc

wildwyoung:Westfall-Young adjusted p-values for objects linear models via a wild bootstrap

Implements Westfall-Young corrected p-values for objects of type 'fixest' and 'fixest_multi' via a wild (cluster) bootstrap.

Maintained by Alexander Fischer. Last updated 2 years ago.

1.70 score 5 scripts

dzhang777

SlideCNA:Calls Copy Number Alterations from Slide-Seq Data

This takes spatial single-cell-type RNA-seq data (specifically designed for Slide-seq v2) that calls copy number alterations (CNAs) using pseudo-spatial binning, clusters cellular units (e.g. beads) based on CNA profile, and visualizes spatial CNA patterns. Documentation about 'SlideCNA' is included in the the pre-print by Zhang et al. (2022, <doi:10.1101/2022.11.25.517982>). The package 'enrichR' (>= 3.0), conditionally used to annotate SlideCNA-determined clusters with gene ontology terms, can be installed at <https://github.com/wjawaid/enrichR> or with install_github("wjawaid/enrichR").

Maintained by Diane Zhang. Last updated 2 months ago.

1.70 score 3 scripts

lbosshard

scROSHI:Robust Supervised Hierarchical Identification of Single Cells

Identifying cell types based on expression profiles is a pillar of single cell analysis. 'scROSHI' identifies cell types based on expression profiles of single cell analysis by utilizing previously obtained cell type specific gene sets. It takes into account the hierarchical nature of cell type relationship and does not require training or annotated data. A detailed description of the method can be found at: Prummer, Bertolini, Bosshard, Barkmann, Yates, Boeva, The Tumor Profiler Consortium, Stekhoven, and Singer (2022) <doi:10.1101/2022.04.05.487176>.

Maintained by Lars Bosshard. Last updated 2 years ago.

1.70 score 9 scripts

blaserlab

blaseRdata:Supporting Data for the blaseRtools Package

What the package does (one paragraph).

Maintained by Brad Blaser. Last updated 1 years ago.

1.70 score 6 scripts

cran

SpaCCI:Spatially Aware Cell-Cell Interaction Analysis

Provides tools for analyzing spatial cell-cell interactions based on ligand-receptor pairs, including functions for local, regional, and global analysis using spatial transcriptomics data. Integrates with databases like 'CellChat' <http://www.cellchat.org/>, 'CellPhoneDB' <https://www.cellphonedb.org/>, 'Cellinker' <https://www.rna-society.org/cellinker/>, 'ICELLNET' <https://github.com/soumelis-lab/ICELLNET>, and 'ConnectomeDB' <https://humanconnectome.org/software/connectomedb/> to identify ligand-receptor pairs, visualize interactions through heatmaps, chord diagrams, and infer interactions on different spatial scales.

Maintained by Li-Ting Ku. Last updated 2 months ago.

openblas cpp

1.48 score

pigian

tetragon:Automatic Sequence Prediction by Expansion of the Distance Matrix

Each sequence is predicted by expanding the distance matrix. The compact set of hyper-parameters is tuned through random search.

Maintained by Giancarlo Vercellino. Last updated 3 years ago.

1.00 score

stefanpeidli

scperturbR:E-Statistics for Seurat Objects

R version of 'scperturb' tool for single-cell perturbation analysis. Contains wrappers for performing E-statistics for Seurat objects. More details on the method can be found in Peidli et al. (2023) <doi:10.1101/2022.08.20.504663> and in Székely and Rizzo (2004).

Maintained by Stefan Peidli. Last updated 2 years ago.

1.00 score 7 scripts

xiayh17

scRNAstat:A Pipeline to Process Single Cell RNAseq Data

A pipeline that can process single or multiple Single Cell RNAseq samples primarily specializes in Clustering and Dimensionality Reduction. Meanwhile we use common cell type marker genes for T cells, B cells, Myeloid cells, Epithelial cells, and stromal cells (Fiboblast, Endothelial cells, Pericyte, Smooth muscle cells) to visualize the Seurat clusters, to facilitate labeling them by biological names. Once users named each cluster, they can evaluate the quality of them again and find the de novo marker genes also.

Maintained by Yonghe Xia. Last updated 25 days ago.

1.00 score 2 scripts

mohmedsoudy

sccca:Single-Cell Correlation Based Cell Type Annotation

Performing cell type annotation based on cell markers from a unified database. The approach utilizes correlation-based approach combined with association analysis using Fisher-exact and phyper statistical tests (Upton, Graham JG. (1992) <DOI:10.2307/2982890>).

Maintained by Mohamed Soudy. Last updated 1 years ago.

1.00 score

Seurat:Tools for Single Cell Genomics

uwot:The Uniform Manifold Approximation and Projection (UMAP) Method for Dimensionality Reduction

scran:Methods for Single-Cell RNA-Seq Data Analysis

scDblFinder:scDblFinder

mia:Microbiome analysis

PCAtools:PCAtools: Everything Principal Components Analysis

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

CATALYST:Cytometry dATa anALYSis Tools

infercnv:Infer Copy Number Variation from Single-Cell RNA-Seq Data

rliger:Linked Inference of Genomic Experimental Relationships

muscat:Multi-sample multi-group scRNA-seq data analysis tools

celda:CEllular Latent Dirichlet Allocation

singleCellTK:Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

BASiCS:Bayesian Analysis of Single-Cell Sequencing data

SoupX:Single Cell mRNA Soup eXterminator

MOFA2:Multi-Omics Factor Analysis v2

DropletUtils:Utilities for Handling Single-Cell Droplet Data

scMerge:scMerge: Merging multiple batches of scRNA-seq data

tidyseurat:Brings Seurat to the Tidyverse

SpatialFeatureExperiment:Integrating SpatialExperiment with Simple Features in sf

embed:Extra Recipes for Encoding Predictors

BatchQC:Batch Effects Quality Control Software

Banksy:Spatial transcriptomic clustering

scPipe:Pipeline for single cell multi-omic data pre-processing

dexter:Data Management and Analysis of Tests

Voyager:From geospatial to spatial omics

miaViz:Microbiome Analysis Plotting and Visualization

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

scCustomize:Custom Visualizations & Functions for Streamlined Analyses of Single Cell Sequencing

lefser:R implementation of the LEfSE method for microbiome biomarker discovery

scGate:Marker-Based Cell Type Purification for Single-Cell Sequencing Data

fastTopics:Fast Algorithms for Fitting Topic Models and Non-Negative Matrix Factorizations to Count Data

flashier:Empirical Bayes Matrix Factorization

malariasimulation:An individual based model for malaria

POMA:Tools for Omics Data Analysis

FLAMES:FLAMES: Full Length Analysis of Mutations and Splicing in long read RNA-seq data

pagoda2:Single Cell Analysis and Differential Expression

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

BayesSpace:Clustering and Resolution Enhancement of Spatial Transcriptomes

ggsc:Visualizing Single Cell and Spatial Transcriptomics

MGnifyR:R interface to EBI MGnify metagenomics resource

MOSim:Multi-Omics Simulation (MOSim)

netSmooth:Network smoothing for scRNAseq

conos:Clustering on Network of Samples

melt:Multiple Empirical Likelihood Tests

rnndescent:Nearest Neighbor Descent Method for Approximate Nearest Neighbors

CuratedAtlasQueryR:Queries the Human Cell Atlas

pipeComp:pipeComp pipeline benchmarking framework

musicatk:Mutational Signature Comprehensive Analysis Toolkit

COTAN:COexpression Tables ANalysis

BioTIP:BioTIP: An R package for characterization of Biological Tipping-Point

fwildclusterboot:Fast Wild Cluster Bootstrap Inference for Linear Models

epiregulon:Gene regulatory network inference from single cell epigenomic data

scAnnotatR:Pretrained learning models for cell type prediction on single cell RNA-sequencing data

CiteFuse:CiteFuse: multi-modal analysis of CITE-seq data

GeneNMF:Non-Negative Matrix Factorization for Single-Cell Omics

tricycle:tricycle: Transferable Representation and Inference of cell cycle

SpotClean:SpotClean adjusts for spot swapping in spatial transcriptomics data

condiments:Differential Topology, Progression and Differentiation

CatsCradle:This package provides methods for analysing spatial transcriptomics data and for discovering gene clusters

SignacX:Cell Type Identification and Discovery from Single Cell Gene Expression Data

sccore:Core Utilities for Single-Cell RNA-Seq

APackOfTheClones:Visualization of Clonal Expansion for Single Cell Immune Profiles

nebula:Negative Binomial Mixed Models Using Large-Sample Approximation for Differential Expression Analysis of ScRNA-Seq Data

scDHA:Single-Cell Decomposition using Hierarchical Autoencoder

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

CellMixS:Evaluate Cellspecific Mixing

scDataviz:scDataviz: single cell dataviz and downstream analyses

iSEEtree:Interactive visualisation for microbiome data

tidyomics:Easily install and load the tidyomics ecosystem

DR.SC:Joint Dimension Reduction and Spatial Clustering

peco:A Supervised Approach for **P**r**e**dicting **c**ell Cycle Pr**o**gression using scRNA-seq data

CRISPRball:Shiny Application for Interactive CRISPR Screen Visualization, Exploration, Comparison, and Filtering

Dino:Normalization of Single-Cell mRNA Sequencing Data

EmbedSOM:Fast Embedding Guided by Self-Organizing Map

dar:Differential Abundance Analysis by Consensus

SingleCellSignalR:Cell Signalling Using Single Cell RNAseq Data Analysis

ProFAST:Probabilistic Factor Analysis for Spatially-Aware Dimension Reduction

SpaNorm:Spatially-aware normalisation for spatial transcriptomics data

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

peco:A Supervised Approach for Predicting cell Cycle Progression using scRNA-seq data