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awesome-single-cell

Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.
https://github.com/seandavi/awesome-single-cell

Last synced: 1 day ago
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  • Software packages

    • Rare cell detection

    • RNA-seq

      • CellRanger - [Linux Binary] - Cell Ranger is a set of analysis pipelines that process Chromium single-cell RNA-seq output to align reads, generate gene-cell matrices and perform clustering and gene expression analysis. *Software requires registration with 10xgenomics.*
      • cellTree - [R] - Cell population analysis and visualization from single cell RNA-seq data using a Latent Dirichlet Allocation model.
      • countsimQC - [R] - Compare characteristics of one or more synthetic (e.g., RNA-seq) count matrices to a real count matrix, possibly the one based on which the synthetic data sets were generated.
      • CytoGuide - [C++,D3] - [CyteGuide: Visual Guidance for Hierarchical Single-Cell Analysis](http://ieeexplore.ieee.org/document/8017575/)
      • DeLorean - [R] - Bayesian pseudotime estimation algorithm that uses Gaussian processes to model gene expression profiles and provides a full posterior for the pseudotimes.
      • OncoNEM - [R] - OncoNEM is a probabilistic method for inferring intra-tumor evolutionarylineage trees from somatic single nucleotide variants of single cells. OncoNEM identifies homogeneous cellularsubpopulations and infers their genotypes as well as a tree describing their evolutionary relationships.
      • pcaReduce - [R] - hierarchical clustering of single cell transcriptional profiles.
      • scruff - [R] - An R package for preprocessing single cell RNA-seq (scRNA-seq) FASTQ reads generated by CEL-Seq and CEL-Seq2 protocols. It demultiplexes reads according to a predetermined list of cell barcodes, maps reads to reference genome using [Rsubread](https://bioconductor.org/packages/Rsubread), and reports filtered UMI (Unique Molecular Identifier) count matrix ready for downstream analysis. [scruff: an R/Bioconductor package for preprocessing single-cell RNA-sequencing data](https://doi.org/10.1186/s12859-019-2797-2).
      • Seurat - [R] - It contains easy-to-use implementations of commonly used analytical techniques, including the identification of highly variable genes, dimensionality reduction (PCA, ICA, t-SNE), standard unsupervised clustering algorithms (density clustering, hierarchical clustering, k-means), and the discovery of differentially expressed genes and markers.
      • sincera - [R] - R-based pipeline for single-cell analysis including clustering and visualization.
      • DecontX - [R] - DecontX is a Bayesian method to automatically estimate and remove read contamination in individual cells from scRNA-seq experiments even without learning any information from empty cell barcodes (identified by cell calling for droplet-based methods). [Decontamination of ambient RNA in single-cell RNA-seq with DecontX](https://doi.org/10.1186/s13059-020-1950-6). Included in package [celda](https://bioconductor.org/packages/celda).
      • alevin-fry - [Rust] - 🐟 Rapid, accurate and memory-frugal preprocessing of single-cell and single-nucleus RNA-seq data.
      • anchor - [Python] - ⚓ Find bimodal, unimodal, and multimodal features in your data
      • ascend - [R] - ascend is an R package comprised of fast, streamlined analysis functions optimized to address the statistical challenges of single cell RNA-seq. The package incorporates novel and established methods to provide a flexible framework to perform filtering, quality control, normalization, dimension reduction, clustering, differential expression and a wide-range of plotting.
      • bigSCale - [matlab] - An analytical framework for big-scale single cell data.
      • bonvoyage - [Python] - 📐 Transform percentage-based units into a 2d space to evaluate changes in distribution with both magnitude and direction.
      • bustools - [C++] - A suite of tools for manipulating BUS files for single cell RNA-Seq pre-processing. bustools can be used to error correct barcodes, collapse UMIs, produce gene count or transcript compatibility count matrices, and is useful for many other tasks.
      • Cell\_BLAST - [Python] - A BLAST-like toolkit for scRNA-seq data querying and automated annotation.
      • CellCNN - [Python] - Representation Learning for detection of phenotype-associated cell subsets
      • clusterExperiment - [R] - Functions for running and comparing many different clusterings of single-cell sequencing data. Meant to work with SCONE and slingshot.
      • Clustergrammer - [Python, JavaScript] - Interative web-based heatmap for visualizing and analyzing high dimensional biological data, including single-cell RNA-seq. Clustergrammer can be used within a Jupyter notebook as an interative widget that can be shared using GitHub and NBviewer, see [example notebook](http://nbviewer.jupyter.org/github/MaayanLab/CCLE_Clustergrammer/blob/master/notebooks/Clustergrammer_CCLE_Notebook.ipynb).
      • Clustergrammer2 - [Python, JavaScript] - Interative WebGL web-based heatmap for visualizing and analyzing single-cell high-dimensional and location-based biological data. Clustergrammer can be used within a Jupyter notebook as an interative widget that can be shared using GitHub and NBviewer, see [case studies](https://clustergrammer.readthedocs.io/case_studies.html).
      • CountClust - [R] - Functions for fitting Grade-of-Membership models, also known as "Topic models", to RNA-seq counts. These models generalize clustering methods to allow that each cell may belong to more than one cluster/topic.
      • cyclum - [python] - Cyclum is a novel AutoEncoder approach that characterizes circular trajectories in the high-dimensional gene expression space. Applying Cyclum to removing cell-cycle effects leads to substantially improved delineations of cell subpopulations, which is useful for establishing various cell atlases and studying tumor heterogeneity. [bioRxiv](https://www.biorxiv.org/content/10.1101/625566v1)
      • DESCEND - [R] - DESCEND deconvolves the true gene expression distribution across cells for UMI scRNA-seq counts. It provides estimates of several distribution based statistics (five distribution measurements and the coefficients of covariates (such as batches or cell size)).
      • dittoSeq - [R] - Bioconductor package offering user friendly visualization tools for single-cell and Bulk RNA Sequencing. Color blindness friendly by default; novice coder friendly; highly customizable and powerful enough to build publication-ready figures; universal in that it works directly with Seurat, SingleCellExperiment, and SummarizedExperiment objects and has import capabilities for edgeR DGElists.
      • dropkick - [Python] - Automated cell filtering for single-cell RNA sequencing data.
      • dynamo - [Python] - Inclusive model of expression dynamics with scSLAM-seq and multiomics, vector field reconstruction and potential landscape mapping.
      • embeddr - [R] - Embeddr creates a reduced dimensional representation of the gene space using a high-variance gene correlation graph and laplacian eigenmaps. It then fits a smooth pseudotime trajectory using principal curves.
      • FastProject - [Python] - Signature analysis on low-dimensional projections of single-cell expression data.
      • flotilla - [Python] - Reproducible machine learning analysis of gene expression and alternative splicing data
      • GPfates - [Python] - Model transcriptional cell fates as mixtures of Gaussian Processes
      • GSEApy - [Python] - GSEApy: Gene Set Enrichment Analysis in Python. GSEApy is a Python/Rust implementation for GSEA and wrapper for Enrichr. GSEApy can be used for RNA-seq, ChIP-seq, Microarray data. It can be used for convenient GO enrichment and to produce publication quality figures in python.
      • HocusPocus - [R] - Basic PCA-based workflow for analysis and plotting of single cell RNA-seq data.
      • HTSeq - [Python] - A Python library to facilitate programmatic analysis of data from high-throughput sequencing (HTS) experiments. A popular component of `HTSeq` is `htseq-count`, a script to quantify gene expression in bulk and single-cell RNA-Seq and similar experiments.
      • IA-SVA - [R] - Iteratively Adjusted Surrogate Variable Analysis (IA-SVA) is a statistical framework to uncover hidden sources of variation even when these sources are correlated with the biological variable of interest. IA-SVA provides a flexible methodology to i) identify a hidden factor for unwanted heterogeneity while adjusting for all known factors; ii) test the significance of the putative hidden factor for explaining the variation in the data; and iii), if significant, use the estimated factor as an additional known factor in the next iteration to uncover further hidden factors.
      • ICGS - [Python] - Iterative Clustering and Guide-gene Selection (Olsson et al. Nature 2016). Identify discrete, transitional and mixed-lineage states from diverse single-cell transcriptomics platforms. Integrated FASTQ pseudoalignment /quantification (Kallisto), differential expression, cell-type prediction and optional cell cycle exclusion analyses. Specialized methods for processing BAM and 10X Genomics spares matrix files. Associated single-cell splicing PSI methods (MultIPath-PSI). Apart of the AltAnalyze toolkit along with accompanying visualization methods (e.g., heatmap, t-SNE, SashimiPlots, network graphs). Easy-to-use graphical user and commandline interfaces.
      • ivis - [Python or R] - Structure-preserving dimensionality reduction in single-cell datasets.
      • kallisto - [C++] - kallisto is a program for quantifying abundances of transcripts or genes from bulk or single-cell RNA-Seq data, or more generally of target sequences using high-throughput sequencing reads. It is based on pseudoalignment for rapidly determining the compatibility of reads with targets, without the need for alignment.
      • kb-python - [Python] - `kb-python` is a python package for processing single-cell RNA-sequencing. It wraps the [`kallisto` | `bustools`](https://www.kallistobus.tools) single-cell RNA-seq command line tools in order to unify multiple processing workflows.
      • knn-smoothing - [python or R or matlab] - The algorithm is based on the observation that across protocols, the technical noise exhibited by UMI-filtered scRNA-Seq data closely follows Poisson statistics. Smoothing is performed by first identifying the nearest neighbors of each cell in a step-wise fashion, based on variance-stabilized and partially smoothed expression profiles, and then aggregating their transcript counts.
      • mfa - [R] - [Probabilistic modeling of bifurcations in single-cell gene expression data using a Bayesian mixture of factor analyzers](https://wellcomeopenresearch.org/articles/2-19/v1)
      • M3Drop - [R] - Michaelis-Menten Modelling of Dropouts for scRNASeq.
      • MetaCell - [R, C++] - Analysis of single cell RNA-seq data by computing partitions of a cell similarity graph into small homogeneous groups of cells called metacells.
      • MIMOSCA - [python] - A repository for the design and analysis of pooled single cell RNA-seq perturbation experiments (Perturb-seq).
      • Falco - [AWS cloud] - [Falco: A quick and flexible single-cell RNA-seq processing framework on the cloud](http://www.biorxiv.org/content/early/2016/07/15/064006.abstract).
      • sincell - [R] - Existing computational approaches for the assessment of cell-state hierarchies from single-cell data might be formalized under a general workflow composed of i) a metric to assess cell-to-cell similarities (combined or not with a dimensionality reduction step), and ii) a graph-building algorithm (optionally making use of a cells-clustering step). Sincell R package implements a methodological toolbox allowing flexible workflows under such framework.

    • Count modelling and normalization

      • BEARscc - [R] - BEARscc makes use of ERCC spike-in measurements to model technical variance as a function of gene expression and technical dropout effects on lowly expressed genes.

    • Quality control

      • SinQC - [R] - A Method and Tool to Control Single-cell RNA-seq Data Quality.
      • scater - [R] - Scater places an emphasis on tools for quality control, visualisation and pre-processing of data before further downstream analysis, filling a useful niche between raw RNA-sequencing count or transcripts-per-million data and more focused downstream modelling tools such as monocle, scLVM, SCDE, edgeR, limma and so on.

    • Dimension reduction

      • scvis - [python] - [Interpretable dimensionality reduction of single cell transcriptome data with deep generative models](https://doi.org/10.1101/178624)
      • destiny - [R] - Diffusion maps are spectral method for non-linear dimension reduction introduced by Coifman et al.(2005). Diffusion maps are based on a distance metric (diffusion distance) which is conceptually relevant to how differentiating cells follow noisy diffusion-like dynamics, moving from a pluripotent state towards more differentiated states.

    • Pseudotime and trajectory inference

      • DensityPath - [.] - DensityPath: a level-set algorithm to visualize and reconstruct cell developmental trajectories for large-scale single-cell RNAseq data
      • SCORPIUS - [R] - An accurate and easy tool for performing linear trajectory inference on single cells using single-cell RNA sequencing data. In addition, SCORPIUS provides functions for discovering the most important genes with respect to the reconstructed trajectory, as well as nice visualisation tools. Cannoodt et al. (2016) [doi:10.1101/079509](https://doi.org/10.1101/079509).
      • SPADE - [R] - Visualization and cellular hierarchy inference of single-cell data using SPADE.
      • TASIC - [matlab] - TASIC is a new method for determining temporal trajectories, branching and cell assignments in single cell time series experiments. Unlike prior approaches TASIC uses on a probabilistic graphical model to integrate expression and time information making it more robust to noise and stochastic variations.
      • VELOCYTO - [Python, R] - Estimating RNA velocity in single cell RNA sequencing datasets.

    • Epigenomics

      • Signac - [R] - Signac is an extension of Seurat for the analysis, interpretation, and exploration of single-cell chromatin datasets.
      • ArchR - [R] - ArchR is a full-featured R package for processing and analyzing single-cell ATAC-seq data. ArchR provides the most extensive suite of scATAC-seq analysis tools of any software available. [ArchR: An integrative and scalable software package for single-cell chromatin accessibility analysis](https://www.biorxiv.org/content/10.1101/2020.04.28.066498v1).
      • ChromVAR - [R] - Determine variations in chromatin accessibility across sets of annotations or peaks. Designed primarily for single-cell or sparse chromatin accessibility data, e.g. from scATAC-seq or sparse bulk ATAC or DNAse-seq experiments. [BioRxiv](https://www.biorxiv.org/content/early/2017/02/21/110346)
      • EpiScanpy - [python] - EpiScanpy is the epigenomic extension of scRNA-seq analysis tool Scanpy. It analyses single-cell open chromatin (scATAC-seq) and single-cell DNA methylation (for example scBS-seq) data. [EpiScanpy: integrated single-cell epigenomic analysis](https://www.nature.com/articles/s41467-021-25131-3)
      • ArchR - [R] - ArchR is a full-featured R package for processing and analyzing single-cell ATAC-seq data. ArchR provides the most extensive suite of scATAC-seq analysis tools of any software available. [ArchR: An integrative and scalable software package for single-cell chromatin accessibility analysis](https://www.biorxiv.org/content/10.1101/2020.04.28.066498v1).

    • Cell type identification and classification

      • CIPR - [R] - (Cluster Identity PRedictor-pronounced cy-per). A Shiny web applet (and R-package) that helps annotating the cluster identities in single-cell RNA-sequencing (scRNA-seq) experiments. The algorithm compares gene expression signature of experimental clusters with known reference datasets. In addition to 7 reference datasets implemented in CIPR (2 from mouse and 5 from human), users can upload custom high-throughput reference data for specialized studies. The CIPR pipeline can be further tailored to different analytical contexts by excluding irrelevant reference subsets and low-variance reference genes from the analysis. The manuscript describing CIPR and comparing its performance against other similar software was published in [BMC Bioinformatics](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-3538-2). CIPR's fast and computationally efficient calculations and graphical outputs will facilitate scRNA-seq analysis where the user wants to try different clustering parameters iteratively and examine the cluster identities. Source code for the [Shiny](https://github.com/atakanekiz/CIPR-Shiny) and [R-package](https://github.com/atakanekiz/CIPR-Package) implementations are available on GitHub.
      • SingleR - [R] - SingleR leverages reference transcriptomic datasets of pure cell types to infer the cell of origin of each of the single cells independently. [Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nature Immunology (2019)](https://www.nature.com/articles/s41590-018-0276-y)
      • Celltypist - [Python] - Celltypist is an automated cell type annotation tool for scRNA-seq datasets on the basis of logistic regression classifiers optimized by the stochastic gradient descent algorithm. Celltypist provides several different models for predictions, with a current focus on immune sub-populations, in order to assist in the accurate classification of different cell types and subtypes.

    • Cell subsampling

      • geosketch - [Python] - Method to subsample massive scRNA-seq datasets while preserving rare cell states. Resulting “sketch” accelerates clustering, visualization, and integration analyses. [Paper](https://doi.org/10.1016/j.cels.2019.05.003)

    • Variant calling

      • monovar - [python] - Monovar is a single nucleotide variant (SNV) detection and genotyping algorithm for single-cell DNA sequencing data. It takes a list of bam files as input and outputs a vcf file containing the detected SNVs.
      • cerebra - [python] - Cerebra is a tool for high-throughput summarizing of vcf entries following traditional variant calling for a sequencing experiment. Helps to extract relevant mutation information from among tens of thousands of vcf lines.

    • Multi-assay data integration

      • scarf - [python] - 🧣 Toolkit for highly memory efficient analysis of single-cell RNA-Seq, scATAC-Seq and CITE-Seq data. Analyze atlas scale datasets with millions of cells on laptop. [Preprint](https://www.biorxiv.org/content/10.1101/2021.05.02.441899v1)

    • Other applications

      • SiFit - [Java] - [SiFit: A Method for Inferring Tumor Trees from Single-Cell Sequencing Data under Finite-site Models](http://biorxiv.org/content/early/2016/12/04/091595)

    • Spatial transcriptomics

      • Merfishtools - [Python] - MERFISHtools implement a Bayesian framework for accurately predicting gene or transcript expression from MERFISH data.
      • Starspace - [Python] - Defines a schema for gene or protein expression data containing spatially localized information. Converts data from a variety of assay types, including Spatial Transcriptomics, CODEX, In-situ Sequencing, MERFISH, osmFISH, and starMAP. Demonstrates how to visualize and interact with these data using common analysis packages, and convert the formats into loom and anndata objects, for downstream analysis in R and Python.

    • Simulation

      • splatter - [R] - Splatter is a package for the simulation of single-cell RNA sequencing count data. It provides a simple interface for creating complex simulations that are reproducible and well-documented.

  • Tutorials and workflows

    • Spatial transcriptomics

      • nf-core/scrnaseq - nf-core/scrnaseq is a bioinformatics best-practice analysis pipeline for processing 10x Genomics single-cell RNA-seq data. The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible.
      • Aaron Lun's Single Cell workflow on Bioconductor - [R] - This article describes a computational workflow for basic analysis of scRNA-seq data using software packages from the open-source Bioconductor project.
      • Clustering 3K PBMCs with Scanpy in Galaxy - Galaxy Training Material.
      • Gilad Lab Single Cell Data Exploration - R-based exploration of single cell sequence data. Lots of experimentation.
      • nf-core/scrnaseq - nf-core/scrnaseq is a bioinformatics best-practice analysis pipeline for processing 10x Genomics single-cell RNA-seq data. The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible.
      • nf-core/scflow - nf-core/scflow is a bioinformatics pipeline for scalable, reproducible, best-practice analyses of single-cell/nuclei RNA-sequencing data. The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner.
      • Orchestrating Single-Cell Analysis with Bioconductor - [R] - This blogdown book describes a comprehensive and reproducible workflow for the analysis of single-cell RNA-sequencing data.
      • Pre-processing of 10X Single-Cell RNA Datasets in Galaxy - Galaxy Training Material.
      • nf-core/scrnaseq - nf-core/scrnaseq is a bioinformatics best-practice analysis pipeline for processing 10x Genomics single-cell RNA-seq data. The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible.
      • Hemberg Lab scRNA-seq course materials
      • Using Seurat (v1.2) for spatial inference in single-cell data - 851 single cells from zebrafish embryogenesis (Satija*, Farrell* et al., Nature Biotechnology, 2015). Original tutorial using Seurat 1.2
      • nf-core/scrnaseq - nf-core/scrnaseq is a bioinformatics best-practice analysis pipeline for processing 10x Genomics single-cell RNA-seq data. The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible.
      • nf-core/scflow - nf-core/scflow is a bioinformatics pipeline for scalable, reproducible, best-practice analyses of single-cell/nuclei RNA-sequencing data. The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner.
      • Orchestrating Single-Cell Analysis with Bioconductor - [R] - This blogdown book describes a comprehensive and reproducible workflow for the analysis of single-cell RNA-sequencing data.

  • People

  • Citation

  • Web portals, apps, and databases

    • Web portals and databases

      • CELLxGENE - CELLxGENE is a suite of tools that help scientists to find, download, explore, analyze, annotate, and publish single cell datasets. It includes several powerful tools with various features to help you to engage with single cell data.
      • conquer - A repository of consistently processed, analysis-ready single-cell RNA-seq data sets.
      • Curated Database of single-cell studies - Available as a `tsv` download. Over 500 single cell transcriptomics studies have been published to date. Many of these have data available, but the links between data, study, and systems studied can be hard to identify through literature search. This manuscript describes a nearly exhaustive and manually curated database of single cell transcriptomics studies with descriptions of what kind of data and what biological systems have been studied. [bioRxiv](https://doi.org/10.1101/742304).
      • EBI Single Cell Expression Atlas - The Single Cell Expression Atlas contains uniformly re-analysed single cell expression data across different species and provides interactive visualizations to explore that data.
      • Galaxy Single Cell Omics Workbench - dedicated Galaxy server for analyzing single cell data.
      • IRIS3 - IRIS3 (integrated cell-type-specific regulon inference server from single-cell RNA-Seq) is an easy-to-use server empowered by over 20 functionalities to support comprehensive interpretations and graphical visualizations of identified cell-type-specific regulons.
      • JingleBells - A repository of standardized single cell RNA-Seq datasets for analysis and visualization in IGV at the single cell level. Currently focused on immune cells (http://www.jimmunol.org/content/198/9/3375.long).
      • scRNASeqDB - A database aggregating human single-cell RNA-seq datasets. [ref](http://biorxiv.org/content/early/2017/01/31/104810)
      • Single Cell Portal - The Single-Cell Portal was developed to facilitate open data and open science in Single-cell Genomics. The portal currently focuses on sharing scientific results interactively, and sharing associated datasets.
      • dseqr - Dseqr runs end-to-end multi-sample single-cell and bulk RNA-seq analyses using a user friendly web app built around best practices from the OSCA handbook. Features include pseudobulk differential expression analysis, automated cluster annotation, reference mapping with Azimuth, Gene Ontology analysis, and drug connectivity mapping. Projects can either be analysed online or locally using the [dseqr R package](https://github.com/hms-dbmi/dseqr).
      • Cell\_BLAST - A Web portal powered by Cell\_BLAST (scRNA-seq querying tool) and ACA (scRNA-seq database).

    • Interactive visualization and analysis

      • ChromSCape - [R] - Interactive & complete analysis of single-cell epigenomic landscapes with Shiny. Includes counting, QC, filtering, dimensionality reduction, clustering, visualisation, coverage, peak calling, differential & gene set analysis) - (scChIP-seq, scCUT&TAG, scATAC-seq, scChIC-seq...). ([Prompsy et al. 2020](https://doi.org/10.1038/s41467-020-19542-x))
      • CReSCENT - [R, Javascript, Python] - CReSCENT: CanceR Single Cell ExpressioN Toolkit ([Mohanraj et al. 2020](https://doi.org/10.1093/nar/gkaa437)), is an intuitive and scalable web portal incorporating a containerized pipeline execution engine for standardized analysis of cancer scRNA-seq data and associated metadata. CReSCENT uses public data sets and preconfigured pipelines that are accessible to computational biology non-experts and are user-editable to allow optimization, comparison, and reanalysis for specific experiments. Users can also upload their own scRNA-seq data for analysis and results can be kept private or shared with other users.
      • NASQAR - Nucleic Acid SeQuence Analysis Resource, a web-based platform that provides an intuitive interface for popular tools (like DESeq2, Seurat, and others) to perform standard downstream analysis workflows for RNAseq data. The portal hosts a number of R Shiny apps.
      • ShinyCortex - a resource that brings together data from recent scRNA-seq studies of the developing cortex for further analysis. ShinyCortex is based in R and displays recently published scRNA-seq data from the human and mouse cortex in a comprehensible, dynamic and accessible way, suitable for data exploration by biologists. [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5962798/)
      • iSEE - [R] - iSEE, interactive SummarizedExperiment Explorer. The iSEE package aims to provide an interactive user interface for exploring data in objects derived from the SummarizedExperiment class. Particular focus will be given to single-cell data in the SingleCellExperiment derived class. The interface is implemented with RStudio's Shiny, with a multi-panel setup for ease of navigation. Features include: dynamically linked charts, support for reproducibility by recording the exact code for every output, as well as guided tours to learn step-by-step the salient features of the user interface and of the data. A demo instance of the app is available at this address: http://shiny.imbei.uni-mainz.de:3838/iSEE.
      • Ginkgo - [R, C] - Ginkgo is a web application for single-cell copy-number variation analysis and visualization.
      • STREAM - STREAM is an interactive computational pipeline for reconstructing complex celluar developmental trajectories from sc-qPCR, scRNA-seq or scATAC-seq data. [preprint](https://doi.org/10.1101/302554).
      • scRNAseqApp - The scRNAseqApp is a Shiny app package designed for interactive visualization of single-cell data. It is an enhanced version derived from the [ShinyCell](https://github.com/SGDDNB/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.

  • Journal articles of general interest

Programming Languages
Python 14 R 11 Jupyter Notebook 3 C 2 HTML 1 JavaScript 1 Rust 1 java 1 python 1 r 1
Categories
Software packages 103 People 33 Web portals, apps, and databases 19 Tutorials and workflows 14 Journal articles of general interest 11 Citation 1
Sub Categories
RNA-seq 47 Rare cell detection 30 Spatial transcriptomics 16 Male 16 Female 16 Web portals and databases 11 Interactive visualization and analysis 8 Methods comparisons 7 Epigenomics 5 Pseudotime and trajectory inference 5 Paper collections 3 Cell type identification and classification 3 Dimension reduction 2 Experimental design 2 Quality control 2 Variant calling 2 Count modelling and normalization 1 Multi-assay data integration 1 Cell subsampling 1 Other applications 1 Simulation 1
Keywords
single-cell-rna-seq 8 single-cell 6 rna-seq 4 python 3 visualization 3 heatmap 2 rust 2 transcriptomics 2 r 2 kallisto 2 clustering 2 biology 2 scrna-seq 2 color-blindness 1 color-blind 1 trajectory-inference-methods 1 latent-process-inference 1 artifical-neural-network 1 webgl 1 regl 1 cell-fate 1 differential-geometry 1 dynamical-systems 1 dynamo 1 nasc-seq 1 potential-landscape 1 rna-acceleration 1 rna-curvature 1 10x 1 quantification 1 rpackages 1 scrna-seq-analysis 1 scrnaseq 1 singlecell 1 bioinformatics 1 deep-learning 1 d3 1 jupyter-notebook 1 jupyter-widget 1 binder 1 binder-ready 1 javascript 1 jupyter-widgets 1 marker-gene-analysis 1 network-analysis 1 pagerank 1 pathway-analysis 1 rna-seq-data 1 scrna-seq-data 1 single-cell-splicing 1