Single-cell-data-visualization-tools

Collection of single-cell data visualization tools from https://github.com/mdozmorov/scRNA-seq_notes

• Kana - single-cell analysis in the browser, by Jayaram Kancherla (@jkanche), Aaron Lun (@LTLA). Input - 10X genomics CellRanger's output, matrix or .h5 files. Preprocessing (removal of low-quality cells, Normalization and log-transformation, Modelling of the mean-variance trend across genes), PCA, Clustering (t-SNE/UMAP), Marker detection, custom cluster definition and marker analysis. Works with scATAC-seq data. GitHub, Tweet.

• cellxgene - An interactive exploratory visualization tool for single-cell transcriptomics data, web and desktop versions. Input - matrix-form datasets, metadata, pre-computed embeddings/clustering. Compatible with Seurat, Scanpy, Bioconductor, scVI GitHub

Megill, Colin, Bruce Martin, Charlotte Weaver, Sidney Bell, Lia Prins, Seve Badajoz, Brian McCandless, et al. "Cellxgene: A Performant, Scalable Exploration Platform for High Dimensional Sparse Matrices" https://doi.org/10.1101/2021.04.05.438318 Preprint. Systems Biology, April 6, 2021.

• iCellR - Single (i) Cell R package (iCellR) is an interactive R package to work with high-throughput single cell sequencing technologies (i.e scRNA-seq, scVDJ-seq and CITE-seq).

Khodadadi-Jamayran, Alireza, Joseph Pucella, Hua Zhou, Nicole Doudican, John Carucci, Adriana Heguy, Boris Reizis, and Aristotelis Tsirigos. "ICellR: Combined Coverage Correction and Principal Component Alignment for Batch Alignment in Single-Cell Sequencing Analysis" https://www.biorxiv.org/content/10.1101/2020.03.31.019109v1.full BioRxiv, April 1, 2020

• Cerebro - interactive scRNA-seq visualization from a Seurat object (v2 or 3), dimensionality reduction, clustering, identification and visualization of marker genes, enriched pathways (EnrichR), signatures (MSigDb), expression of individual genes. cerebroPrepare R package saves the Seurat object, to be visualized with cerebroApp Shiny app. Standalone and Docker versions are available. GitHub.

Hillje, Roman, Pier Giuseppe Pelicci, and Lucilla Luzi. "Cerebro: Interactive Visualization of ScRNA-Seq Data" https://doi.org/10.1093/bioinformatics/btz877 Bioinformatics, 1 April 2020

• iS-CellR - a Shiny app for scRNA-seq analysis. Can be insalled locally, run from GitHub, Docker. Input - count matrix. Filtering, normalization, dimensionality reduction, clustering, differential expression, co-expression, reports.

Patel, Mitulkumar V. "IS-CellR: A User-Friendly Tool for Analyzing and Visualizing Single-Cell RNA Sequencing Data" https://doi.org/10.1093/bioinformatics/bty517 Bioinformatics 34, no. 24 (December 15, 2018)

• iSEE - Shiny app for interactive visualization of SummarizedExperiment scRNA-seq objects. GitHub, RNA-seq blog post, Workshop.

Rue-Albrecht, Kevin, Federico Marini, Charlotte Soneson, and Aaron T.L. Lun. "ISEE: Interactive SummarizedExperiment Explorer" https://doi.org/10.12688/f1000research.14966.1 F1000Research 7 (June 14, 2018)

• SPRING - a pipeline for data filtering, normalization and visualization using force-directed layout of k-nearest-neighbor graph. Web-based (10,000 cells max) and GitHub.

Weinreb, Caleb, Samuel Wolock, and Allon M. Klein. "SPRING: A Kinetic Interface for Visualizing High Dimensional Single-Cell Expression Data" https://doi.org/10.1093/bioinformatics/btx792 Bioinformatics (Oxford, England) 34, no. 7 (April 1, 2018)

• Granatum - web-based scRNA-seq analysis. list of modules, including plate merging and batch-effect removal, outlier-sample removal, gene-expression normalization, imputation, gene filtering, cell clustering, differential gene expression analysis, pathway/ontology enrichment analysis, protein network interaction visualization, and pseudo-time cell series reconstruction. Twitter.

Zhu, Xun, Thomas K. Wolfgruber, Austin Tasato, Cédric Arisdakessian, David G. Garmire, and Lana X. Garmire. "Granatum: A Graphical Single-Cell RNA-Seq Analysis Pipeline for Genomics Scientists" https://doi.org/10.1186/s13073-017-0492-3 Genome Medicine 9, no. 1 (December 2017).

• SCope - Fast visualization tool for large-scale and high dimensional single-cell data in .loom format. R and Python scripts for converting scRNA-seq data to .loom format.

• singleCellTK - R/Shiny package for an interactive scRNA-Seq analysis. Input, raw counts in SingleCellExperiment. Analysis: filtering raw results, clustering, batch correction, differential expression, pathway enrichment, and scRNA-Seq study design.

• scDataviz - single cell data vizualization and downstream analyses, by Kevin Blighe

• scOrange - visual pipeline builder for an in-depth analysis and visualization of scRNA-seq data. Works with 10X data, tab-delimited. Filtering, preprocessiong, differential gene expression, marker analysis, enrichment analysis, batch removal, clustering, tSNE. Screenshots, Short video tutorials. Python-based, Conda-installable. GitHub

• scCustomize - an R package, Collection of functions created and/or curated to aid in the visualization and analysis of single-cell data. Extends Seurat, Liger visualization, helper functions to enhance analysis of Seurat objects.

• UCSC Single Cell Browser - Python pipeline and Javascript scatter plot library for single-cell datasets. Pre-process an expression matrix by filtering, PCA, nearest-neighbors, clustering, t-SNE and UMAP and formats them for cbBuild. Demo that includes several landmark datasets