TooManyCellsInteractive: A visualization tool for dynamic exploration of single-cell data

As single-cell sequencing data sets grow in size, visualizations of large cellular populations become difficult to parse and require extensive processing to identify subpopulations of cells. Managing many of these charts is laborious for technical users and unintuitive for non-technical users. To address this issue, we developed TooManyCellsInteractive (TMCI), a browser-based JavaScript application for visualizing hierarchical cellular populations as an interactive radial tree. TMCI allows users to explore, filter, and manipulate hierarchical data structures through an intuitive interface while also enabling batch export of high-quality custom graphics. Here we describe the software architecture and illustrate how TMCI has identified unique survival pathways among drug-tolerant persister cells in a pan-cancer analysis. TMCI will help guide increasingly large data visualizations and facilitate multi-resolution data exploration in a user-friendly way.

[1]  David S. Fischer,et al.  The scverse project provides a computational ecosystem for single-cell omics data analysis , 2023, Nature Biotechnology.

[2]  C. Prieto,et al.  SingleCAnalyzer: Interactive Analysis of Single Cell RNA-Seq Data on the Cloud , 2022, Frontiers in Bioinformatics.

[3]  Z. Bar-Joseph,et al.  Interactive single-cell data analysis using Cellar , 2022, Nature Communications.

[4]  M. Nykter,et al.  Single-cell ATAC and RNA sequencing reveal pre-existing and persistent cells associated with prostate cancer relapse , 2021, Nature Communications.

[5]  L. Pachter,et al.  The specious art of single-cell genomics , 2021, bioRxiv.

[6]  Dylan Kotliar,et al.  Sciviewer enables interactive visual interrogation of single-cell RNA-Seq data from the Python programming environment , 2021, bioRxiv.

[7]  Gregory W. Schwartz,et al.  TooManyPeaks identifies drug-resistant-specific regulatory elements from single-cell leukemic epigenomes , 2021, Cell reports.

[8]  Sidney M. Bell,et al.  cellxgene: a performant, scalable exploration platform for high dimensional sparse matrices , 2021, bioRxiv.

[9]  Xiaowen Chen,et al.  A Comparison for Dimensionality Reduction Methods of Single-Cell RNA-seq Data , 2021, Frontiers in Genetics.

[10]  N. López-Bigas,et al.  Single-cell transcriptional changes associated with drug tolerance and response to combination therapies in cancer , 2021, Nature communications.

[11]  Jordan G Bryan,et al.  An Embryonic Diapause-like Adaptation with Suppressed Myc Activity Enables Tumor Treatment Persistence. , 2021, Cancer cell.

[12]  Trevor J Pugh,et al.  Colorectal Cancer Cells Enter a Diapause-like DTP State to Survive Chemotherapy , 2021, Cell.

[13]  Thomas E. Yankeelov,et al.  Integrating transcriptomics and bulk time course data into a mathematical framework to describe and predict therapeutic resistance in cancer , 2020, Physical biology.

[14]  Irene Papatheodorou,et al.  UCSC Cell Browser: visualize your single-cell data , 2020, bioRxiv.

[15]  Aviv Regev,et al.  Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq , 2020, Nature Methods.

[16]  Eric J. Deeds,et al.  A novel metric reveals previously unrecognized distortion in dimensionality reduction of scRNA-seq data , 2019, bioRxiv.

[17]  Pier Giuseppe Pelicci,et al.  Cerebro: interactive visualization of scRNA-seq data , 2019, bioRxiv.

[18]  Simon Anders,et al.  Exploring dimension-reduced embeddings with Sleepwalk , 2019, bioRxiv.

[19]  Olga Tanaseichuk,et al.  Metascape provides a biologist-oriented resource for the analysis of systems-level datasets , 2019, Nature Communications.

[20]  Gregory W. Schwartz,et al.  TooManyCells identifies and visualizes relationships of single-cell clades , 2019, Nature Methods.

[21]  Aviv Regev,et al.  scSVA: an interactive tool for big data visualization and exploration in single-cell omics , 2019, bioRxiv.

[22]  Philipp Berens,et al.  The art of using t-SNE for single-cell transcriptomics , 2018, Nature Communications.

[23]  Vincent A. Traag,et al.  From Louvain to Leiden: guaranteeing well-connected communities , 2018, Scientific Reports.

[24]  James T. Webber,et al.  Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris , 2018, Nature.

[25]  Gary D Bader,et al.  scClustViz – Single-cell RNAseq cluster assessment and visualization , 2018, F1000Research.

[26]  M. Renfree,et al.  The history of the discovery of embryonic diapause in mammals , 2018, Biology of Reproduction.

[27]  Leland McInnes,et al.  UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction , 2018, ArXiv.

[28]  Fabian J Theis,et al.  SCANPY: large-scale single-cell gene expression data analysis , 2018, Genome Biology.

[29]  Martin Wattenberg,et al.  How to Use t-SNE Effectively , 2016 .

[30]  Grace X. Y. Zheng,et al.  Massively parallel digital transcriptional profiling of single cells , 2016, Nature Communications.

[31]  A. Regev,et al.  Spatial reconstruction of single-cell gene expression , 2015, Nature Biotechnology.

[32]  M. Glickman,et al.  Converting Cancer Therapies into Cures: Lessons from Infectious Diseases , 2012, Cell.

[33]  Helga Thorvaldsdóttir,et al.  Molecular signatures database (MSigDB) 3.0 , 2011, Bioinform..

[34]  Weiyi Meng,et al.  Efficient SPectrAl Neighborhood blocking for entity resolution , 2011, 2011 IEEE 27th International Conference on Data Engineering.

[35]  Ben S. Wittner,et al.  A Chromatin-Mediated Reversible Drug-Tolerant State in Cancer Cell Subpopulations , 2010, Cell.

[36]  J. Mesirov,et al.  From the Cover: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005 .

[37]  G. Landberg,et al.  High ID2 protein expression correlates with a favourable prognosis in patients with primary breast cancer and reduces cellular invasiveness of breast cancer cells , 2005, International journal of cancer.

[38]  Y. Itahana,et al.  Role of Id-2 in the maintenance of a differentiated and noninvasive phenotype in breast cancer cells. , 2003, Cancer research.

[39]  M. Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[40]  Frank V. Celeste Induction of Multiple Alternative Mitogenic Signaling Pathways Accompanies Emergence of Slowly Growing Drug-Tolerant Cancer Cells , 2022 .

[41]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .