Showing 126 of total 126 results (show query)
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-atlassingle-cell-genomicssingle-cell-rna-seqcpp
75.5 match 2.4k stars 16.86 score 50k scripts 73 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
30.2 match 242 stars 8.75 score 1.1k scripts
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 22 days ago.
singlecellgeneexpressiondifferentialexpressionalignmentclusteringimmunooncologybatcheffectnormalizationqualitycontroldataimportgui
23.5 match 181 stars 10.16 score 252 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 4 months ago.
clonal-analysisimmune-repertoireimmune-systemscrna-seqscrnaseqseuratsingle-cellsingle-cell-genomicscpp
32.6 match 15 stars 6.45 score 15 scriptsbioc
clustifyr:Classifier for Single-cell RNA-seq Using Cell Clusters
Package designed to aid in classifying cells from single-cell RNA sequencing data using external reference data (e.g., bulk RNA-seq, scRNA-seq, microarray, gene lists). A variety of correlation based methods and gene list enrichment methods are provided to assist cell type assignment.
Maintained by Rui Fu. Last updated 5 months ago.
singlecellannotationsequencingmicroarraygeneexpressionassign-identitiesclustersmarker-genesrna-seqsingle-cell-rna-seq
20.2 match 119 stars 9.63 score 296 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 1 months ago.
biologicalquestionstatisticalmethodgeneexpressionsinglecelltranscriptomicsspatial
28.7 match 3 stars 6.50 scoresatijalab
SeuratObject:Data Structures for Single Cell Data
Defines S4 classes for single-cell genomic data and associated information, such as dimensionality reduction embeddings, nearest-neighbor graphs, and spatially-resolved coordinates. Provides data access methods and R-native hooks to ensure the Seurat object is familiar to other R users. 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>, and Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031> for more details.
Maintained by Paul Hoffman. Last updated 1 years ago.
15.6 match 25 stars 11.69 score 1.2k scripts 88 dependents
immunogenomics
harmony:Fast, Sensitive, and Accurate Integration of Single Cell Data
Implementation of the Harmony algorithm for single cell integration, described in Korsunsky et al <doi:10.1038/s41592-019-0619-0>. Package includes a standalone Harmony function and interfaces to external frameworks.
Maintained by Ilya Korsunsky. Last updated 4 months ago.
algorithmdata-integrationscrna-seqopenblascpp
12.6 match 554 stars 13.74 score 5.5k scripts 8 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
17.1 match 158 stars 9.66 score 398 scripts 1 dependents
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
24.9 match 24 stars 6.46 score 34 scripts
enblacar
SCpubr:Generate Publication Ready Visualizations of Single Cell Transcriptomics Data
A system that provides a streamlined way of generating publication ready plots for known Single-Cell transcriptomics data in a “publication ready” format. This is, the goal is to automatically generate plots with the highest quality possible, that can be used right away or with minimal modifications for a research article.
Maintained by Enrique Blanco-Carmona. Last updated 30 days ago.
softwaresinglecellvisualizationdata-visualizationggplot2publication-quality-plotsseuratsingle-cellsingle-cell-genomicssingle-cell-rna-seq
14.5 match 176 stars 8.71 score 194 scriptsbioc
UCell:Rank-based signature enrichment analysis for single-cell data
UCell is a package for evaluating gene signatures in single-cell datasets. UCell signature scores, based on the Mann-Whitney U statistic, are robust to dataset size and heterogeneity, and their calculation demands less computing time and memory than other available methods, enabling the processing of large datasets in a few minutes even on machines with limited computing power. UCell can be applied to any single-cell data matrix, and includes functions to directly interact with SingleCellExperiment and Seurat objects.
Maintained by Massimo Andreatta. Last updated 5 months ago.
singlecellgenesetenrichmenttranscriptomicsgeneexpressioncellbasedassays
8.1 match 143 stars 10.43 score 454 scripts 2 dependents
zjufanlab
scCATCH:Single Cell Cluster-Based Annotation Toolkit for Cellular Heterogeneity
An automatic cluster-based annotation pipeline based on evidence-based score by matching the marker genes with known cell markers in tissue-specific cell taxonomy reference database for single-cell RNA-seq data. See Shao X, et al (2020) <doi:10.1016/j.isci.2020.100882> for more details.
Maintained by Xin Shao. Last updated 2 years ago.
cell-markerscluster-annotationmarker-genesrna-seqsequencingseuratsingle-celltranscriptometranscriptomics
11.0 match 225 stars 7.13 score 75 scriptsstemangiola
tidygate:Interactively Gate Points
Interactively gate points on a scatter plot. Interactively drawn gates are recorded and can be applied programmatically to reproduce results exactly. Programmatic gating is based on the package gatepoints by Wajid Jawaid (who is also an author of this package).
Maintained by Stefano Mangiola. Last updated 6 months ago.
assaydomaininfrastructureclusteringdatavisdatavizdplyrdrawingfacsgateggplot2interactivepipeprogrammaticseuratsingle-cellsingle-cell-rna-seqtibbletidy-datatidyverse
11.0 match 23 stars 6.89 score 14 scripts 1 dependents
tomkellygenetics
leiden:R Implementation of Leiden Clustering Algorithm
Implements the 'Python leidenalg' module to be called in R. Enables clustering using the leiden algorithm for partition a graph into communities. See the 'Python' repository for more details: <https://github.com/vtraag/leidenalg> Traag et al (2018) From Louvain to Leiden: guaranteeing well-connected communities. <arXiv:1810.08473>.
Maintained by S. Thomas Kelly. Last updated 10 months ago.
7.8 match 38 stars 8.90 score 180 scripts 3 dependentsbioc
GeomxTools:NanoString GeoMx Tools
Tools for NanoString Technologies GeoMx Technology. Package provides functions for reading in DCC and PKC files based on an ExpressionSet derived object. Normalization and QC functions are also included.
Maintained by Maddy Griswold. Last updated 5 months ago.
geneexpressiontranscriptioncellbasedassaysdataimporttranscriptomicsproteomicsmrnamicroarrayproprietaryplatformsrnaseqsequencingexperimentaldesignnormalizationspatial
9.7 match 7.11 score 239 scripts 3 dependentsbioc
ReactomeGSA:Client for the Reactome Analysis Service for comparative multi-omics gene set analysis
The ReactomeGSA packages uses Reactome's online analysis service to perform a multi-omics gene set analysis. The main advantage of this package is, that the retrieved results can be visualized using REACTOME's powerful webapplication. Since Reactome's analysis service also uses R to perfrom the actual gene set analysis you will get similar results when using the same packages (such as limma and edgeR) locally. Therefore, if you only require a gene set analysis, different packages are more suited.
Maintained by Johannes Griss. Last updated 4 months ago.
genesetenrichmentproteomicstranscriptomicssystemsbiologygeneexpressionreactome
8.2 match 23 stars 8.05 score 67 scriptsbioc
scRepertoire:A toolkit for single-cell immune receptor profiling
scRepertoire is a toolkit for processing and analyzing single-cell T-cell receptor (TCR) and immunoglobulin (Ig). The scRepertoire framework supports use of 10x, AIRR, BD, MiXCR, Omniscope, TRUST4, and WAT3R single-cell formats. The functionality includes basic clonal analyses, repertoire summaries, distance-based clustering and interaction with the popular Seurat and SingleCellExperiment/Bioconductor R workflows.
Maintained by Nick Borcherding. Last updated 2 months ago.
softwareimmunooncologysinglecellclassificationannotationsequencingcpp
5.9 match 326 stars 10.49 score 240 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
11.0 match 8 stars 4.90 score 4 scripts
gfellerlab
SuperCell:Simplification of scRNA-seq data by merging together similar cells
Aggregates large single-cell data into metacell dataset by merging together gene expression of very similar cells.
Maintained by The package maintainer. Last updated 8 months ago.
softwarecoarse-grainingscrna-seq-analysisscrna-seq-data
6.4 match 72 stars 8.08 score 93 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 9 days ago.
7.7 match 102 stars 6.63 score 12 scripts
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 1 months ago.
8.6 match 2 stars 5.86 score 12 scripts 1 dependents
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 8 days ago.
17.3 match 2.70 score
mojaveazure
ggseurat:ggplot2 Bindings for Seurat Objects
Provides methods to allow Seurat objects to be utilized directly in the ggplot2 ecosystem.
Maintained by Paul Hoffman. Last updated 3 months ago.
12.9 match 4 stars 3.30 score 2 scriptsbioc
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 5 months ago.
datarepresentationdataimportsinglecellrnaseq
11.7 match 1 stars 3.40 score 3 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 2 months ago.
nonnegative-matrix-factorizationsingle-cellopenblascpp
3.6 match 402 stars 10.80 score 334 scripts 1 dependents
cran
VAM:Variance-Adjusted Mahalanobis
Contains logic for cell-specific gene set scoring of single cell RNA sequencing data.
Maintained by H. Robert Frost. Last updated 1 years ago.
7.8 match 4.78 score 4 dependentsbioc
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.
dimensionreductionbayesianvisualizationfactor-analysismofamulti-omics
3.5 match 319 stars 10.02 score 502 scripts
hayamizu-lab
treefit:The First Software for Quantitative Trajectory Inference
Perform two types of analysis: 1) checking the goodness-of-fit of tree models to your single-cell gene expression data; and 2) deciding which tree best fits your data.
Maintained by Kouhei Sutou. Last updated 1 months ago.
7.0 match 3 stars 4.95 score 1 scriptsbioc
Nebulosa:Single-Cell Data Visualisation Using Kernel Gene-Weighted Density Estimation
This package provides a enhanced visualization of single-cell data based on gene-weighted density estimation. Nebulosa recovers the signal from dropped-out features and allows the inspection of the joint expression from multiple features (e.g. genes). Seurat and SingleCellExperiment objects can be used within Nebulosa.
Maintained by Jose Alquicira-Hernandez. Last updated 5 months ago.
softwaregeneexpressionsinglecellvisualizationdimensionreductionsingle-cellsingle-cell-analysissingle-cell-multiomicssingle-cell-rna-seq
3.5 match 99 stars 9.66 score 494 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.
3.3 match 264 stars 10.09 score 594 scripts 1 dependents
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.
11.6 match 2 stars 2.78 score
igordot
scooter:Streamlined scRNA-Seq Analysis Pipeline
Streamlined scRNA-Seq analysis pipeline.
Maintained by Igor Dolgalev. Last updated 1 years ago.
12.8 match 4 stars 2.51 score 16 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
6.5 match 4.85 score 70 scripts
papatheodorou-group
scOntoMatch:Aligning Ontology Annotation Across Single Cell Datasets with 'scOntoMatch'
Unequal granularity of cell type annotation makes it difficult to compare scRNA-seq datasets at scale. Leveraging the ontology system for defining cell type hierarchy, 'scOntoMatch' aims to align cell type annotations to make them comparable across studies. The alignment involves two core steps: first is to trim the cell type tree within each dataset so each cell type does not have descendants, and then map cell type labels cross-studies by direct matching and mapping descendants to ancestors. Various functions for plotting cell type trees and manipulating ontology terms are also provided. In the Single Cell Expression Atlas hosted at EBI, a compendium of datasets with curated ontology labels are great inputs to this package.
Maintained by Yuyao Song. Last updated 1 years ago.
6.9 match 7 stars 4.54 score 6 scriptsbioc
monocle:Clustering, differential expression, and trajectory analysis for single- cell RNA-Seq
Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well.
Maintained by Cole Trapnell. Last updated 5 months ago.
immunooncologysequencingrnaseqgeneexpressiondifferentialexpressioninfrastructuredataimportdatarepresentationvisualizationclusteringmultiplecomparisonqualitycontrolcpp
3.4 match 8.89 score 1.6k scripts 2 dependentsbioc
schex:Hexbin plots for single cell omics data
Builds hexbin plots for variables and dimension reduction stored in single cell omics data such as SingleCellExperiment. The ideas used in this package are based on the excellent work of Dan Carr, Nicholas Lewin-Koh, Martin Maechler and Thomas Lumley.
Maintained by Saskia Freytag. Last updated 5 months ago.
softwaresequencingsinglecelldimensionreductionvisualizationimmunooncologydataimport
3.3 match 74 stars 8.96 score 102 scripts 2 dependentsbioc
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 3 months ago.
softwarespatialtranscriptomicstranscriptiongeneexpressionvisualizationdataimport10xgenomicsbioconductorspatial-transcriptomicsspatialexperimentspatiallibdvisium
5.3 match 1 stars 5.36 score 4 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.
4.4 match 12 stars 6.44 score 36 scripts 9 dependentsbioc
SimBu:Simulate Bulk RNA-seq Datasets from Single-Cell Datasets
SimBu can be used to simulate bulk RNA-seq datasets with known cell type fractions. You can either use your own single-cell study for the simulation or the sfaira database. Different pre-defined simulation scenarios exist, as are options to run custom simulations. Additionally, expression values can be adapted by adding an mRNA bias, which produces more biologically relevant simulations.
Maintained by Alexander Dietrich. Last updated 5 months ago.
4.0 match 14 stars 6.81 score 29 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
4.0 match 15 stars 6.73 score 20 scriptsbioc
projectR:Functions for the projection of weights from PCA, CoGAPS, NMF, correlation, and clustering
Functions for the projection of data into the spaces defined by PCA, CoGAPS, NMF, correlation, and clustering.
Maintained by Genevieve Stein-OBrien. Last updated 5 months ago.
functionalpredictiongeneregulationbiologicalquestionsoftware
3.2 match 62 stars 8.11 score 70 scriptsbioc
dittoSeq:User Friendly Single-Cell and Bulk RNA Sequencing Visualization
A universal, user friendly, single-cell and bulk RNA sequencing visualization toolkit that allows highly customizable creation of color blindness friendly, publication-quality figures. dittoSeq accepts both SingleCellExperiment (SCE) and Seurat objects, as well as the import and usage, via conversion to an SCE, of SummarizedExperiment or DGEList bulk data. Visualizations include dimensionality reduction plots, heatmaps, scatterplots, percent composition or expression across groups, and more. Customizations range from size and title adjustments to automatic generation of annotations for heatmaps, overlay of trajectory analysis onto any dimensionality reduciton plot, hidden data overlay upon cursor hovering via ggplotly conversion, and many more. All with simple, discrete inputs. Color blindness friendliness is powered by legend adjustments (enlarged keys), and by allowing the use of shapes or letter-overlay in addition to the carefully selected dittoColors().
Maintained by Daniel Bunis. Last updated 5 months ago.
softwarevisualizationrnaseqsinglecellgeneexpressiontranscriptomicsdataimport
3.4 match 7.56 score 760 scripts 2 dependentsbioc
nipalsMCIA:Multiple Co-Inertia Analysis via the NIPALS Method
Computes Multiple Co-Inertia Analysis (MCIA), a dimensionality reduction (jDR) algorithm, for a multi-block dataset using a modification to the Nonlinear Iterative Partial Least Squares method (NIPALS) proposed in (Hanafi et. al, 2010). Allows multiple options for row- and table-level preprocessing, and speeds up computation of variance explained. Vignettes detail application to bulk- and single cell- multi-omics studies.
Maintained by Maximilian Mattessich. Last updated 25 days ago.
softwareclusteringclassificationmultiplecomparisonnormalizationpreprocessingsinglecell
3.9 match 6 stars 6.60 score 10 scripts
person-c
easybio:Comprehensive Single-Cell Annotation and Transcriptomic Analysis Toolkit
Provides a comprehensive toolkit for single-cell annotation with the 'CellMarker2.0' database (see Xia Li, Peng Wang, Yunpeng Zhang (2023) <doi: 10.1093/nar/gkac947>). Streamlines biological label assignment in single-cell RNA-seq data and facilitates transcriptomic analysis, including preparation of TCGA<https://portal.gdc.cancer.gov/> and GEO<https://www.ncbi.nlm.nih.gov/geo/> datasets, differential expression analysis and visualization of enrichment analysis results. Additional utility functions support various bioinformatics workflows. See Wei Cui (2024) <doi: 10.1101/2024.09.14.609619> for more details.
Maintained by Wei Cui. Last updated 12 days ago.
limmageoqueryedgerfgseabioinformaticscellmarker2gsearna-seqsingle-cell
3.6 match 10 stars 6.62 score 35 scriptsbioc
edgeR:Empirical Analysis of Digital Gene Expression Data in R
Differential expression analysis of sequence count data. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models, quasi-likelihood, and gene set enrichment. Can perform differential analyses of any type of omics data that produces read counts, including RNA-seq, ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE, CAGE, metabolomics, or proteomics spectral counts. RNA-seq analyses can be conducted at the gene or isoform level, and tests can be conducted for differential exon or transcript usage.
Maintained by Yunshun Chen. Last updated 4 days ago.
alternativesplicingbatcheffectbayesianbiomedicalinformaticscellbiologychipseqclusteringcoveragedifferentialexpressiondifferentialmethylationdifferentialsplicingdnamethylationepigeneticsfunctionalgenomicsgeneexpressiongenesetenrichmentgeneticsimmunooncologymultiplecomparisonnormalizationpathwaysproteomicsqualitycontrolregressionrnaseqsagesequencingsinglecellsystemsbiologytimecoursetranscriptiontranscriptomicsopenblas
1.7 match 13.40 score 17k scripts 255 dependents
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 30 days ago.
softwaresinglecellrnaseqatacseqnormalizationpreprocessingdimensionreductionvisualizationqualitycontrolclusteringclassificationannotationgeneexpressiondifferentialexpressionbioinformaticsgenomicsmachine-learningparameter-optimizationrobustnesssingle-cellunsupervised-learningcpp
3.8 match 22 stars 5.68 score 18 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
2.9 match 90 stars 7.04 score 41 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.
softwarenormalizationrnaseqsinglecellsequencinggeneexpressiontranscriptomicsregressioncellbasedassays
3.3 match 9 stars 6.02 score 13 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
3.0 match 28 stars 6.48 score 36 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 1 months ago.
3.6 match 5.30 scorebioc
tidySummarizedExperiment:Brings SummarizedExperiment to the Tidyverse
The tidySummarizedExperiment package provides a set of tools for creating and manipulating tidy data representations of SummarizedExperiment objects. SummarizedExperiment is a widely used data structure in bioinformatics for storing high-throughput genomic data, such as gene expression or DNA sequencing data. The tidySummarizedExperiment package introduces a tidy framework for working with SummarizedExperiment objects. It allows users to convert their data into a tidy format, where each observation is a row and each variable is a column. This tidy representation simplifies data manipulation, integration with other tidyverse packages, and enables seamless integration with the broader ecosystem of tidy tools for data analysis.
Maintained by Stefano Mangiola. Last updated 5 months ago.
assaydomaininfrastructurernaseqdifferentialexpressiongeneexpressionnormalizationclusteringqualitycontrolsequencingtranscriptiontranscriptomics
2.3 match 26 stars 8.44 score 196 scripts 1 dependents
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 1 years ago.
2.9 match 35 stars 6.40 score 145 scriptspapatheodorou-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 1 years ago.
3.6 match 9 stars 4.80 score 14 scripts
lazappi
clustree:Visualise Clusterings at Different Resolutions
Deciding what resolution to use can be a difficult question when approaching a clustering analysis. One way to approach this problem is to look at how samples move as the number of clusters increases. This package allows you to produce clustering trees, a visualisation for interrogating clusterings as resolution increases.
Maintained by Luke Zappia. Last updated 1 years ago.
clusteringclustering-treesvisualisationvisualization
1.5 match 219 stars 11.40 score 1.9k scripts 5 dependents
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.
4.0 match 21 stars 4.25 score 17 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
3.8 match 4.34 score 22 scriptsbioc
ASURAT:Functional annotation-driven unsupervised clustering for single-cell data
ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).
Maintained by Keita Iida. Last updated 5 months ago.
geneexpressionsinglecellsequencingclusteringgenesignalingcpp
3.8 match 4.32 score 21 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.
3.7 match 4 stars 4.30 score 5 scripts
erhard-lab
grandR:Comprehensive Analysis of Nucleotide Conversion Sequencing Data
Nucleotide conversion sequencing experiments have been developed to add a temporal dimension to RNA-seq and single-cell RNA-seq. Such experiments require specialized tools for primary processing such as GRAND-SLAM, (see 'Jürges et al' <doi:10.1093/bioinformatics/bty256>) and specialized tools for downstream analyses. 'grandR' provides a comprehensive toolbox for quality control, kinetic modeling, differential gene expression analysis and visualization of such data.
Maintained by Florian Erhard. Last updated 1 months ago.
2.3 match 11 stars 7.03 score 18 scripts 1 dependentsbioc
miloR:Differential neighbourhood abundance testing on a graph
Milo performs single-cell differential abundance testing. Cell states are modelled as representative neighbourhoods on a nearest neighbour graph. Hypothesis testing is performed using either a negative bionomial generalized linear model or negative binomial generalized linear mixed model.
Maintained by Mike Morgan. Last updated 5 months ago.
singlecellmultiplecomparisonfunctionalgenomicssoftwareopenblascppopenmp
1.5 match 357 stars 10.49 score 340 scripts 1 dependentsbioc
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 26 days ago.
singlecellgeneexpressionclusteringsequencingbayesianimmunooncologydataimportcppopenmp
1.5 match 147 stars 10.47 score 256 scripts 2 dependentsbioc
HGC:A fast hierarchical graph-based clustering method
HGC (short for Hierarchical Graph-based Clustering) is an R package for conducting hierarchical clustering on large-scale single-cell RNA-seq (scRNA-seq) data. The key idea is to construct a dendrogram of cells on their shared nearest neighbor (SNN) graph. HGC provides functions for building graphs and for conducting hierarchical clustering on the graph. The users with old R version could visit https://github.com/XuegongLab/HGC/tree/HGC4oldRVersion to get HGC package built for R 3.6.
Maintained by XGlab. Last updated 5 months ago.
singlecellsoftwareclusteringrnaseqgraphandnetworkdnaseqcpp
3.3 match 4.70 score 25 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
3.9 match 4.00 score 3 scriptsbioc
PIUMA:Phenotypes Identification Using Mapper from topological data Analysis
The PIUMA package offers a tidy pipeline of Topological Data Analysis frameworks to identify and characterize communities in high and heterogeneous dimensional data.
Maintained by Mattia Chiesa. Last updated 5 months ago.
clusteringgraphandnetworkdimensionreductionnetworkclassification
3.0 match 4 stars 5.08 score 2 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 5 months ago.
3.2 match 4.58 score 38 scriptsbioc
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 2 months ago.
preprocessingsinglecellrnaseqatacseqdoubletssingle-cell
1.2 match 184 stars 12.34 score 888 scripts 1 dependentswelch-lab
CytoSimplex:Simplex Visualization of Cell Fate Similarity in Single-Cell Data
Create simplex plots to visualize the similarity between single-cells and selected clusters in a 1-/2-/3-simplex space. Velocity information can be added as an additional layer. See Liu J, Wang Y et al (2023) <doi:10.1101/2023.12.07.570655> for more details.
Maintained by Yichen Wang. Last updated 6 months ago.
3.5 match 1 stars 4.18 score 3 scriptscran
RESET:Reconstruction Set Test
Contains logic for sample-level variable set scoring using randomized reduced rank reconstruction error. Frost, H. Robert (2023) "Reconstruction Set Test (RESET): a computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error" <doi:10.1101/2023.04.03.535366>.
Maintained by H. Robert Frost. Last updated 1 years ago.
6.3 match 2.30 score
dgrun
RaceID:Identification of Cell Types, Inference of Lineage Trees, and Prediction of Noise Dynamics from Single-Cell RNA-Seq Data
Application of 'RaceID' allows inference of cell types and prediction of lineage trees by the 'StemID2' algorithm (Herman, J.S., Sagar, Grun D. (2018) <DOI:10.1038/nmeth.4662>). 'VarID2' is part of this package and allows quantification of biological gene expression noise at single-cell resolution (Rosales-Alvarez, R.E., Rettkowski, J., Herman, J.S., Dumbovic, G., Cabezas-Wallscheid, N., Grun, D. (2023) <DOI:10.1186/s13059-023-02974-1>).
Maintained by Dominic Grün. Last updated 4 months ago.
3.0 match 4.74 score 110 scriptsbioc
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 1 months ago.
datarepresentationtranscriptomicsspatial
1.5 match 49 stars 9.40 score 322 scripts 1 dependentsbioc
zinbwave:Zero-Inflated Negative Binomial Model for RNA-Seq Data
Implements a general and flexible zero-inflated negative binomial model that can be used to provide a low-dimensional representations of single-cell RNA-seq data. The model accounts for zero inflation (dropouts), over-dispersion, and the count nature of the data. The model also accounts for the difference in library sizes and optionally for batch effects and/or other covariates, avoiding the need for pre-normalize the data.
Maintained by Davide Risso. Last updated 5 months ago.
immunooncologydimensionreductiongeneexpressionrnaseqsoftwaretranscriptomicssequencingsinglecell
1.3 match 43 stars 10.53 score 190 scripts 6 dependentskharchenkolab
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-correctionscrna-seqsingle-cell-rna-seqopenblascppopenmp
1.9 match 204 stars 7.32 score 258 scriptsbioc
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 5 months ago.
softwarevisualizationdifferentialexpressiongeneexpressiontranscriptionrnaseqsinglecellsequencingclustering
2.7 match 2 stars 4.85 score 2 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.
3.3 match 3.90 score
cozygene
BisqueRNA:Decomposition of Bulk Expression with Single-Cell Sequencing
Provides tools to accurately estimate cell type abundances from heterogeneous bulk expression. A reference-based method utilizes single-cell information to generate a signature matrix and transformation of bulk expression for accurate regression based estimates. A marker-based method utilizes known cell-specific marker genes to measure relative abundances across samples. For more details, see Jew and Alvarez et al (2019) <doi:10.1101/669911>.
Maintained by Brandon Jew. Last updated 4 years ago.
1.8 match 72 stars 6.95 score 124 scripts
chanzuckerberg
cellxgene.census:CZ CELLxGENE Discover Cell Census
API to facilitate the use of the CZ CELLxGENE Discover Census. For more information about the API and the project visit https://github.com/chanzuckerberg/cellxgene-census/
Maintained by Chan Zuckerberg Initiative Foundation. Last updated 5 months ago.
1.9 match 96 stars 6.60 score 15 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 5 months ago.
sequencinggeneticvariabilitysinglecellcpp
2.5 match 2 stars 4.85 score 3 scriptsbioc
scone:Single Cell Overview of Normalized Expression data
SCONE is an R package for comparing and ranking the performance of different normalization schemes for single-cell RNA-seq and other high-throughput analyses.
Maintained by Davide Risso. Last updated 24 days ago.
immunooncologynormalizationpreprocessingqualitycontrolgeneexpressionrnaseqsoftwaretranscriptomicssequencingsinglecellcoverage
1.3 match 53 stars 9.12 score 104 scriptsbioc
APL:Association Plots
APL is a package developed for computation of Association Plots (AP), a method for visualization and analysis of single cell transcriptomics data. The main focus of APL is the identification of genes characteristic for individual clusters of cells from input data. The package performs correspondence analysis (CA) and allows to identify cluster-specific genes using Association Plots. Additionally, APL computes the cluster-specificity scores for all genes which allows to rank the genes by their specificity for a selected cell cluster of interest.
Maintained by Clemens Kohl. Last updated 4 months ago.
statisticalmethoddimensionreductionsinglecellsequencingrnaseqgeneexpression
1.8 match 15 stars 6.31 score 15 scriptsbioc
ontoProc:processing of ontologies of anatomy, cell lines, and so on
Support harvesting of diverse bioinformatic ontologies, making particular use of the ontologyIndex package on CRAN. We provide snapshots of key ontologies for terms about cells, cell lines, chemical compounds, and anatomy, to help analyze genome-scale experiments, particularly cell x compound screens. Another purpose is to strengthen development of compelling use cases for richer interfaces to emerging ontologies.
Maintained by Vincent Carey. Last updated 5 days ago.
infrastructuregobioinformaticsgenomicsontology
1.7 match 3 stars 6.37 score 75 scripts 2 dependentsbioc
dreamlet:Scalable differential expression analysis of single cell transcriptomics datasets with complex study designs
Recent advances in single cell/nucleus transcriptomic technology has enabled collection of cohort-scale datasets to study cell type specific gene expression differences associated disease state, stimulus, and genetic regulation. The scale of these data, complex study designs, and low read count per cell mean that characterizing cell type specific molecular mechanisms requires a user-frieldly, purpose-build analytical framework. We have developed the dreamlet package that applies a pseudobulk approach and fits a regression model for each gene and cell cluster to test differential expression across individuals associated with a trait of interest. Use of precision-weighted linear mixed models enables accounting for repeated measures study designs, high dimensional batch effects, and varying sequencing depth or observed cells per biosample.
Maintained by Gabriel Hoffman. Last updated 5 months ago.
rnaseqgeneexpressiondifferentialexpressionbatcheffectqualitycontrolregressiongenesetenrichmentgeneregulationepigeneticsfunctionalgenomicstranscriptomicsnormalizationsinglecellpreprocessingsequencingimmunooncologysoftwarecpp
1.3 match 12 stars 8.09 score 128 scriptsbioc
sccomp:Tests differences in cell-type proportion for single-cell data, robust to outliers
A robust and outlier-aware method for testing differences in cell-type proportion in single-cell data. This model can infer changes in tissue composition and heterogeneity, and can produce realistic data simulations based on any existing dataset. This model can also transfer knowledge from a large set of integrated datasets to increase accuracy further.
Maintained by Stefano Mangiola. Last updated 15 days ago.
bayesianregressiondifferentialexpressionsinglecellbatch-correctioncompositioncytofdifferential-proportionmicrobiomemultilevelproportionsrandom-effectssingle-cellunwanted-variation
1.3 match 99 stars 8.41 score 69 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
1.9 match 5.41 score 258 scripts
xiyupeng
SpaTopic:Topic Inference to Identify Tissue Architecture in Multiplexed Images
A novel spatial topic model to integrate both cell type and spatial information to identify the complex spatial tissue architecture on multiplexed tissue images without human intervention. The Package implements a collapsed Gibbs sampling algorithm for inference. 'SpaTopic' is scalable to large-scale image datasets without extracting neighborhood information for every single cell. For more details on the methodology, see <https://xiyupeng.github.io/SpaTopic/>.
Maintained by Xiyu Peng. Last updated 7 days ago.
1.8 match 8 stars 5.67 score 13 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.
5.5 match 1.70 score 3 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 15 days ago.
visualizationsinglecellrnaseqinteractive-visualizationsmultiple-usersshiny-appssingle-cell-rna-seq
1.6 match 4 stars 5.78 score 3 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
1.9 match 4 stars 4.60 score 3 scriptsbioc
lemur:Latent Embedding Multivariate Regression
Fit a latent embedding multivariate regression (LEMUR) model to multi-condition single-cell data. The model provides a parametric description of single-cell data measured with treatment vs. control or more complex experimental designs. The parametric model is used to (1) align conditions, (2) predict log fold changes between conditions for all cells, and (3) identify cell neighborhoods with consistent log fold changes. For those neighborhoods, a pseudobulked differential expression test is conducted to assess which genes are significantly changed.
Maintained by Constantin Ahlmann-Eltze. Last updated 5 months ago.
transcriptomicsdifferentialexpressionsinglecelldimensionreductionregressionopenblascpp
1.1 match 87 stars 7.80 score 81 scripts
felixthestudent
cellpypes:Cell Type Pipes for Single-Cell RNA Sequencing Data
Annotate single-cell RNA sequencing data manually based on marker gene thresholds. Find cell type rules (gene+threshold) through exploration, use the popular piping operator '%>%' to reconstruct complex cell type hierarchies. 'cellpypes' models technical noise to find positive and negative cells for a given expression threshold and returns cell type labels or pseudobulks. Cite this package as Frauhammer (2022) <doi:10.5281/zenodo.6555728> and visit <https://github.com/FelixTheStudent/cellpypes> for tutorials and newest features.
Maintained by Felix Frauhammer. Last updated 1 years ago.
celltype-annotationclassification-algorithmscrnaseqsingle-cell-rna-seq
1.9 match 51 stars 4.41 score 8 scripts
scmethods
scregclust:Reconstructing the Regulatory Programs of Target Genes in scRNA-Seq Data
Implementation of the scregclust algorithm described in Larsson, Held, et al. (2024) <doi:10.1038/s41467-024-53954-3> which reconstructs regulatory programs of target genes in scRNA-seq data. Target genes are clustered into modules and each module is associated with a linear model describing the regulatory program.
Maintained by Felix Held. Last updated 2 months ago.
clusteringregulatory-programsscrna-seq-analysiscppopenmp
1.3 match 9 stars 6.45 score 21 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
1.3 match 63 stars 6.30 score 16 scriptsbioc
CoGAPS:Coordinated Gene Activity in Pattern Sets
Coordinated Gene Activity in Pattern Sets (CoGAPS) implements a Bayesian MCMC matrix factorization algorithm, GAPS, and links it to gene set statistic methods to infer biological process activity. It can be used to perform sparse matrix factorization on any data, and when this data represents biomolecules, to do gene set analysis.
Maintained by Elana J. Fertig. Last updated 5 months ago.
geneexpressiontranscriptiongenesetenrichmentdifferentialexpressionbayesianclusteringtimecoursernaseqmicroarraymultiplecomparisondimensionreductionimmunooncologycpp
1.2 match 6.72 score 104 scripts
castleli
scBSP:A Fast Tool for Single-Cell Spatially Variable Genes Identifications on Large-Scale Data
Identifying spatially variable genes is critical in linking molecular cell functions with tissue phenotypes. This package utilizes a granularity-based dimension-agnostic tool, single-cell big-small patch (scBSP), implementing sparse matrix operation and KD tree methods for distance calculation, for the identification of spatially variable genes on large-scale data. The detailed description of this method is available at Wang, J. and Li, J. et al. 2023 (Wang, J. and Li, J. (2023), <doi:10.1038/s41467-023-43256-5>).
Maintained by Jinpu Li. Last updated 1 months ago.
1.8 match 18 stars 4.43 score 2 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 7 days ago.
7.7 match 1.00 score 2 scriptsbioc
jazzPanda:Finding spatially relevant marker genes in image based spatial transcriptomics data
This package contains the function to find marker genes for image-based spatial transcriptomics data. There are functions to create spatial vectors from the cell and transcript coordiantes, which are passed as inputs to find marker genes. Marker genes are detected for every cluster by two approaches. The first approach is by permtuation testing, which is implmented in parallel for finding marker genes for one sample study. The other approach is to build a linear model for every gene. This approach can account for multiple samples and backgound noise.
Maintained by Melody Jin. Last updated 13 days ago.
spatialgeneexpressiondifferentialexpressionstatisticalmethodtranscriptomicscorrelationlinear-modelsmarker-genesspatial-transcriptomics
1.5 match 2 stars 5.00 scorebioc
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 26 days ago.
1.5 match 4.94 score 29 scriptsfeiyoung
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 12 months ago.
dimension-reductionselfsupervisedspatial-clusteringspatial-transcriptomicsopenblascpp
1.2 match 5 stars 6.12 score 29 scripts 2 dependentsmojaveazure
pbmc3k:Raw and Processed Matrices of the PBMC 3k Dataset
What the package does (one paragraph).
Maintained by Paul Hoffman. Last updated 9 months ago.
7.0 match 1.00 scorebioc
GRaNIE:GRaNIE: Reconstruction cell type specific gene regulatory networks including enhancers using single-cell or bulk chromatin accessibility and RNA-seq data
Genetic variants associated with diseases often affect non-coding regions, thus likely having a regulatory role. To understand the effects of genetic variants in these regulatory regions, identifying genes that are modulated by specific regulatory elements (REs) is crucial. The effect of gene regulatory elements, such as enhancers, is often cell-type specific, likely because the combinations of transcription factors (TFs) that are regulating a given enhancer have cell-type specific activity. This TF activity can be quantified with existing tools such as diffTF and captures differences in binding of a TF in open chromatin regions. Collectively, this forms a gene regulatory network (GRN) with cell-type and data-specific TF-RE and RE-gene links. Here, we reconstruct such a GRN using single-cell or bulk RNAseq and open chromatin (e.g., using ATACseq or ChIPseq for open chromatin marks) and optionally (Capture) Hi-C data. Our network contains different types of links, connecting TFs to regulatory elements, the latter of which is connected to genes in the vicinity or within the same chromatin domain (TAD). We use a statistical framework to assign empirical FDRs and weights to all links using a permutation-based approach.
Maintained by Christian Arnold. Last updated 5 months ago.
softwaregeneexpressiongeneregulationnetworkinferencegenesetenrichmentbiomedicalinformaticsgeneticstranscriptomicsatacseqrnaseqgraphandnetworkregressiontranscriptionchipseq
1.3 match 5.40 score 24 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.
3.5 match 1.73 score 18 scripts
iame-researchcenter
PFIM:Population Fisher Information Matrix
Evaluate or optimize designs for nonlinear mixed effects models using the Fisher Information matrix. Methods used in the package refer to Mentré F, Mallet A, Baccar D (1997) <doi:10.1093/biomet/84.2.429>, Retout S, Comets E, Samson A, Mentré F (2007) <doi:10.1002/sim.2910>, Bazzoli C, Retout S, Mentré F (2009) <doi:10.1002/sim.3573>, Le Nagard H, Chao L, Tenaillon O (2011) <doi:10.1186/1471-2148-11-326>, Combes FP, Retout S, Frey N, Mentré F (2013) <doi:10.1007/s11095-013-1079-3> and Seurat J, Tang Y, Mentré F, Nguyen TT (2021) <doi:10.1016/j.cmpb.2021.106126>.
Maintained by Romain Leroux. Last updated 5 months ago.
2.1 match 2.78 score 9 scriptscarmonalab
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 1 months ago.
filteringmarker-genesscgatesignaturessingle-cell
0.5 match 106 stars 8.38 score 163 scriptscran
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.
1.8 match 2.30 scorebioc
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
0.5 match 47 stars 7.59 score 18 scripts
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.
3.6 match 1.00 score 7 scripts
junjunlab
ClusterGVis:One-Step to Cluster and Visualize Gene Expression Data
Streamlining the clustering and visualization of time-series gene expression data from RNA-Seq experiments, this tool supports fuzzy c-means and k-means clustering algorithms. It is compatible with outputs from widely-used packages such as 'Seurat', 'Monocle', and 'WGCNA', enabling seamless downstream visualization and analysis. See Lokesh Kumar and Matthias E Futschik (2007) <doi:10.6026/97320630002005> for more details.
Maintained by Jun Zhang. Last updated 5 days ago.
sequencingclusterprofilersummarizedexperimentmfuzzcomplexheatmapgene-clusteringgene-expressionvisualization
0.5 match 281 stars 6.80 score 30 scripts
jdmde
scellpam:Applying Partitioning Around Medoids to Single Cell Data with High Number of Cells
PAM (Partitioning Around Medoids) algorithm application to samples of single cell sequencing techniques with a high number of cells (as many as the computer memory allows). The package uses a binary format to store matrices (either full, sparse or symmetric) in files written in the disk that can contain any data type (not just double) which allows its manipulation when memory is sufficient to load them as int or float, but not as double. The PAM implementation is done in parallel, using several/all the cores of the machine, if it has them. This package shares a great part of its code with packages 'jmatrix' and 'parallelpam' but their functionality is included here so there is no need to install them.
Maintained by Juan Domingo. Last updated 8 months ago.
1.2 match 2.78 score 9 scriptsbioc
escape:Easy single cell analysis platform for enrichment
A bridging R package to facilitate gene set enrichment analysis (GSEA) in the context of single-cell RNA sequencing. Using raw count information, Seurat objects, or SingleCellExperiment format, users can perform and visualize ssGSEA, GSVA, AUCell, and UCell-based enrichment calculations across individual cells.
Maintained by Nick Borcherding. Last updated 2 months ago.
softwaresinglecellclassificationannotationgenesetenrichmentsequencinggenesignalingpathways
0.5 match 5.92 score 138 scriptsbioc
FEAST:FEAture SelcTion (FEAST) for Single-cell clustering
Cell clustering is one of the most important and commonly performed tasks in single-cell RNA sequencing (scRNA-seq) data analysis. An important step in cell clustering is to select a subset of genes (referred to as “features”), whose expression patterns will then be used for downstream clustering. A good set of features should include the ones that distinguish different cell types, and the quality of such set could have significant impact on the clustering accuracy. FEAST is an R library for selecting most representative features before performing the core of scRNA-seq clustering. It can be used as a plug-in for the etablished clustering algorithms such as SC3, TSCAN, SHARP, SIMLR, and Seurat. The core of FEAST algorithm includes three steps: 1. consensus clustering; 2. gene-level significance inference; 3. validation of an optimized feature set.
Maintained by Kenong Su. Last updated 5 months ago.
sequencingsinglecellclusteringfeatureextraction
0.5 match 10 stars 5.97 score 47 scripts
gabrielelubatti
CIARA:Cluster Independent Algorithm for Rare Cell Types Identification
Identification of markers of rare cell types by looking at genes whose expression is confined in small regions of the expression space <https://github.com/ScialdoneLab>.
Maintained by Gabriele Lubatti. Last updated 3 years ago.
1.1 match 2.70 score 7 scriptsbioc
scQTLtools:An R package for single-cell eQTL analysis and visualization
This package specializes in analyzing and visualizing eQTL at the single-cell level. It can read gene expression matrices or Seurat data, or SingleCellExperiment object along with genotype data. It offers a function for cis-eQTL analysis to detect eQTL within a given range, and another function to fit models with three methods. Using this package, users can also generate single-cell level visualization result.
Maintained by Xiaofeng Wu. Last updated 2 months ago.
softwaregeneexpressiongeneticvariabilitysnpdifferentialexpressiongenomicvariationvariantdetectiongeneticsfunctionalgenomicssystemsbiologyregressionsinglecellnormalizationvisualizationrna-seqsc-eqtl
0.5 match 3 stars 4.95 score
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
0.5 match 26 stars 4.41 score 20 scripts
xudonghan-bio
scapGNN:Graph Neural Network-Based Framework for Single Cell Active Pathways and Gene Modules Analysis
It is a single cell active pathway analysis tool based on the graph neural network (F. Scarselli (2009) <doi:10.1109/TNN.2008.2005605>; Thomas N. Kipf (2017) <arXiv:1609.02907v4>) to construct the gene-cell association network, infer pathway activity scores from different single cell modalities data, integrate multiple modality data on the same cells into one pathway activity score matrix, identify cell phenotype activated gene modules and parse association networks of gene modules under multiple cell phenotype. In addition, abundant visualization programs are provided to display the results.
Maintained by Xudong Han. Last updated 2 years ago.
1.2 match 2.00 score 7 scriptsbioc
EasyCellType:Annotate cell types for scRNA-seq data
We developed EasyCellType which can automatically examine the input marker lists obtained from existing software such as Seurat over the cell markerdatabases. Two quantification approaches to annotate cell types are provided: Gene set enrichment analysis (GSEA) and a modified versio of Fisher's exact test. The function presents annotation recommendations in graphical outcomes: bar plots for each cluster showing candidate cell types, as well as a dot plot summarizing the top 5 significant annotations for each cluster.
Maintained by Ruoxing Li. Last updated 5 months ago.
singlecellsoftwaregeneexpressiongenesetenrichment
0.5 match 4.30 score 6 scripts
castleli
GrabSVG:Granularity-Based Spatially Variable Genes Identifications
Identifying spatially variable genes is critical in linking molecular cell functions with tissue phenotypes. This package implemented a granularity-based dimension-agnostic tool for the identification of spatially variable genes. The detailed description of this method is available at Wang, J. and Li, J. et al. 2023 (Wang, J. and Li, J. (2023), <doi:10.1038/s41467-023-43256-5>).
Maintained by Jinpu Li. Last updated 1 years ago.
1.8 match 1.00 scoremojaveazure
pbmc3k.sce:PBMC 3k Dataset as a SingleCellExperiment
The PBMC 3k dataset provided as a SingleCellExperiment object. Also includes a processed version, pbmc3k.sce.final, using Bioc-equivalents of the Seurat standard workflow
Maintained by Paul Hoffman. Last updated 10 months ago.
0.5 match 1.30 score