Showing 79 of total 79 results (show query)
bioc
destiny:Creates diffusion maps
Create and plot diffusion maps.
Maintained by Philipp Angerer. Last updated 5 months ago.
cellbiologycellbasedassaysclusteringsoftwarevisualizationdiffusion-mapsdimensionality-reductioncpp
82 stars 11.44 score 792 scripts 1 dependentsbioc
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.
softwarecopynumbervariationvariantdetectionstructuralvariationgenomicvariationgeneticstranscriptomicsstatisticalmethodbayesianhiddenmarkovmodelsinglecelljagscpp
601 stars 10.92 score 674 scriptsbioc
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.
singlecellgeneexpressiondifferentialexpressionalignmentclusteringimmunooncologybatcheffectnormalizationqualitycontroldataimportgui
182 stars 10.17 score 252 scripts
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
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.
assaydomaininfrastructurernaseqdifferentialexpressiongeneexpressionnormalizationclusteringqualitycontrolsequencingtranscriptiontranscriptomicsdplyrggplot2pcapurrrsctseuratsingle-cellsingle-cell-rna-seqtibbletidyrtidyversetranscriptstsneumap
159 stars 9.48 score 398 scripts 1 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.
customizationggplot2scrna-seqseuratsingle-cellsingle-cell-genomicssingle-cell-rna-seqvisualization
246 stars 8.45 score 1.1k 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.
filteringmarker-genesscgatesignaturessingle-cell
106 stars 8.38 score 163 scriptsbioc
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.
dimensionreductiongeneexpressionsinglecellsoftwarespatialtranscriptomicsvisualizationopenblascppopenmp
47 stars 7.59 score 18 scriptsbioc
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.
softwaretimecourseexperimentaldesignrnaseqcpp
9 stars 7.46 score 11 scriptsbioc
tidytof:Analyze High-dimensional Cytometry Data Using Tidy Data Principles
This package implements an interactive, scientific analysis pipeline for high-dimensional cytometry data built using tidy data principles. It is specifically designed to play well with both the tidyverse and Bioconductor software ecosystems, with functionality for reading/writing data files, data cleaning, preprocessing, clustering, visualization, modeling, and other quality-of-life functions. tidytof implements a "grammar" of high-dimensional cytometry data analysis.
Maintained by Timothy Keyes. Last updated 5 months ago.
singlecellflowcytometrybioinformaticscytometrydata-sciencesingle-celltidyversecpp
18 stars 7.24 score 35 scriptsbioc
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.
assaydomaininfrastructurernaseqdifferentialexpressiongeneexpressionnormalizationclusteringqualitycontrolsequencingtranscriptiontranscriptomicsdatabaseduckdbhdf5human-cell-atlassingle-cellsinglecellexperimenttidyverse
90 stars 7.04 score 41 scriptsbioc
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.
geneexpressiontranscriptomicsclusteringdatarepresentationbenchmarkbioconductorpipeline-benchmarkingpipelinessingle-cell-rna-seq
41 stars 7.02 score 43 scriptsbioc
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.
systemsbiologytranscriptomicsgeneexpressionsinglecell
16 stars 6.85 score 96 scriptsbioc
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.
singlecelltranscriptomicsgeneexpressionsupportvectormachineclassificationsoftware
15 stars 6.61 score 20 scriptscarmonalab
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 scriptsbioc
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.
dataimportrnaseqsequencinggeneexpressionspatialsinglecelltranscriptomicspreprocessingrna-seqspatial-transcriptomics
31 stars 6.52 score 36 scriptsbioc
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 17 days ago.
biologicalquestionstatisticalmethodgeneexpressionsinglecelltranscriptomicsspatial
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-phenotypesseuratsingle-cell-rna-seq
25 stars 6.47 score 34 scripts
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 5 months ago.
clonal-analysisimmune-repertoireimmune-systemscrna-seqscrnaseqseuratsingle-cellsingle-cell-genomicscpp
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 8 days ago.
37 stars 6.43 score 145 scriptsbioc
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.
singlecellimmunooncologyrnaseqgeneexpressiontranscriptionflowcytometrymassspectrometrydataimport
63 stars 6.30 score 16 scriptsbioc
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.
assaydomaininfrastructurernaseqdifferentialexpressiongeneexpressionnormalizationclusteringqualitycontrolsequencingtranscriptiontranscriptomicscytometrygenomicstidyverse
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-reductionselfsupervisedspatial-clusteringspatial-transcriptomicsopenblascpp
5 stars 6.12 score 29 scripts 2 dependentsbioc
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.
softwarenormalizationrnaseqsinglecellsequencinggeneexpressiontranscriptomicsregressioncellbasedassays
9 stars 6.02 score 13 scriptsfeiyoung
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.
2 stars 5.86 score 12 scripts 1 dependentsbioc
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.
visualizationclusteringsinglecelltranscriptomicsrnaseqbig-databigdatascrna-seqscrna-seq-analysisvisualvisual-exploration
6 stars 5.82 score 8 scriptsbioc
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 1 months ago.
softwareimmunooncologysinglecell
8 stars 5.81 score 7 scriptsbioc
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.
metagenomicsmicrobiomedifferentialexpressionmultiplecomparisonnormalizationpreprocessingsoftwarebenchmarkdifferential-abundance-methods
8 stars 5.78 score 8 scriptsbioc
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.
visualizationsinglecellrnaseqinteractive-visualizationsmultiple-usersshiny-appssingle-cell-rna-seq
4 stars 5.76 score 3 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.
softwaresinglecellrnaseqatacseqnormalizationpreprocessingdimensionreductionvisualizationqualitycontrolclusteringclassificationannotationgeneexpressiondifferentialexpressionbioinformaticsgenomicsmachine-learningparameter-optimizationrobustnesssingle-cellunsupervised-learningcpp
23 stars 5.70 score 18 scriptsbioc
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.
cellbasedassayssinglecellspatialsoftwaretranscriptomics
11 stars 5.69 score 15 scripts
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.
softwarevisualizationdifferentialexpressiongeneexpressiontranscriptionrnaseqsinglecellsequencingclustering
7 stars 5.45 score 2 scriptsbioc
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.
singlecellrnaseqregressiongeneexpression
5.41 score 258 scriptsbioc
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.
dataimportrnaseqsinglecellsequencinggeneexpressiontranscriptomicspreprocessingclustering
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
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 13 hours ago.
cross-species-analysisscrna-seq
12 stars 5.23 score 6 scriptsbioc
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.
singlecellsoftwareclusteringvisualizationsequencingrnaseqcellbasedassays
5 stars 5.00 score 4 scriptsbioc
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 2 months ago.
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-analysisseurat-objectssingle-cellsingle-cell-atac-seqsingle-cell-rna-seq
8 stars 4.90 score 4 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.
sequencinggeneticvariabilitysinglecellcpp
2 stars 4.85 score 3 scriptsbioc
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.
rnaseqsinglecelldimensionreductionclusteringgenesetenrichmentgeneexpressionatacseqopenblascppopenmp
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 scriptsbioc
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.
transcriptomicsclusteringspatialbiocviewssoftware
4 stars 4.60 score 3 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-integrationdata-miningrna-seqsingle-cellsingle-cell-rna-seq
26 stars 4.41 score 20 scriptsbioc
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.
singlecellrnaseqqualitycontrolpreprocessingnormalizationvisualizationtranscriptomicsgeneexpressionsequencingsoftwaredataimportdatarepresentationinfrastructurecoverageclustering
4.34 score 22 scriptsbioc
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.
singlecellclusteringfeatureextractiongraphandnetworkgenetargetrnaseq
4.34 score 11 scriptsbioc
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.
spatialtranscriptomicsreactomekegg
4.30 score 8 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 scriptsbioc
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.
singlecelltranscriptomicsdimensionreductiongeneexpressionatacseqbatcheffectkeggqualitycontrol
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 scriptsbioc
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.
softwarecopynumbervariationhiddenmarkovmodelsinglecellclassification
4.18 score 4 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-analysisrna-seq-pipelinesingle-cellsingle-cell-rna-seqvariable-selection
5 stars 4.18 score 6 scriptsbioc
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.
visualizationinfrastructureguisinglecell
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 2 days ago.
3.98 score
matei-ionita
Cleanet:Automated doublet detection and classification for cytometry data
Automated method for doublet detection in flow or mass cytometry data, based on simulating doublets and finding events whose protein expression patterns are similar to the simulated doublets.
Maintained by Matei Ionita. Last updated 4 months ago.
3.70 score
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-deconvolutiondeconvolutiondifferential-expressionffpegeneset-enrichment-analysisscdeconrsingle-cell
4 stars 3.60 score 4 scripts
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.
datarepresentationdataimportsinglecellrnaseq
1 stars 3.48 score 3 scripts
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
obenno
scSpotlight:A Single Cell Analysis Shiny App
A single cell analysis (viewer) app based on Seurat.
Maintained by Zhixia Xiao. Last updated 8 months ago.
2 stars 2.78 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
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
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
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
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
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
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
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 26 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
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