SPRING: a kinetic interface for visualizing high dimensional single-cell expression data

By Caleb Weinreb, Samuel L. Wolock, Allon M. Klein. bioRxiv, 2017

305 citations recorded by the source corpus.

Motivation Single-cell gene expression profiling technologies can map the cell states in a tissue or organism. As these technologies become more common, there is a need for computational tools to explore the data they produce. In particular, existing data visualization approaches are imperfect for studying continuous gene expression topologies. Results Force-directed layouts of k-nearest-neighbor graphs can visualize continuous gene expression topologies in a manner that preserves high-dimensional relationships and allows manually exploration of different stable two-dimensional representations of the same data. We implemented an interactive web-tool to visualize single-cell data using force-directed graph layouts, called SPRING. SPRING reveals more detailed biological relationships than existing approaches when applied to branching gene expression trajectories from hematopoietic progenitor cells. Visualizations from SPRING are also more reproducible than those of stochastic visualization methods such as tSNE, a state-of-the-art tool. Availability https://kleintools.hms.harvard.edu/tools/spring.html,https://github.com/AllonKleinLab/SPRING/ Contact calebsw@gmail.com, allon_klein@hms.harvard.edu

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b6790d8030ebfe842d96732b091445d957ccc1fe

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Citations in the corpus

Successful anti-PD-1 cancer immunotherapy requires T cell-dendritic cell crosstalk involving the cytokines IFN-γ and IL-12 (2018, unknown): “The cell clusters, visualized with t-stochastic neighbor embedding (t-SNE; Figures 2A and S2A) or force-directed graph layouts (SPRING) (Figure S2B; Weinreb et al., 2018), identified the following populations: conventional T (Tconv) cells expressing Cd3e, regulatory T (Treg) cells expressing the tra”
Single-Cell Profiling and SCOPE-Seq Reveal Lineage Dynamics of Adult Ventricular-Subventricular Zone Neurogenesis and NOTUM as a Key Regulator (2020, methodology): “To address the complexity of adult neuronal lineage progression and regulation, we generated force-directed visualiza- tions (Weinreb et al., 2018) of neuronal lineage trajectories from V-SVZ astrocytes to OB neurons using both the NESFLPO and the GCERT2 datasets (12,334 and 7,903 cells,…”
A Unifying Perspective on Neighbor Embeddings along the Attraction-Repulsion Spectrum (2020, background): “…ForceAtlas2 (Jacomy et al., 2014) has recently gained popularity in the single-cell transcriptomic community to visualize datasets capturing cells at di ﬀ erent stages of development (Weinreb et al., 2018, 2020; Wagner et al., 2018a; Tusi et al., 2018; Kanton et al., 2019; Sharma et al., 2020).”
Single cell transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species (2019, methodology): “A uthor M anuscript A uthor M anuscript A uthor M anuscript A uthor M anuscript C, D. SPRING plots of lung immune cells from (C) human patients (34,450 cells) and (D) mice (15,939 cells).”
Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells (2017, methodology): “b, The same for data of Reference [19], where we annotated differentiated cells as follows: Cells with RNAseq and index data were analyzed using SPRING [16], a tool for generating graph-drawing visualizations.”
SCANPY: large-scale single-cell gene expression data analysis (2018, methodology): “Specifically, S CANPY provides preprocessing comparable to S EURAT [10] and C ELL R ANGER [6], visualization through T SNE [11, 12], graph-drawing [13– 15] and diffusion maps [11, 16, 17], clustering similar”
Lineage tracing on transcriptional landscapes links state to fate during differentiation (2018, methodology): “To specifically amplify barcode-containing GFP transcripts, we followed a protocol similar to (Wagner, Weinreb et al. 2018).”
Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming (2019, unknown): “The method: (1) Performs optimal-transport analyses on scRNA-seq data from a time course, by calculating temporal couplings and using them to find ancestors, descendants and trajectories; (2) Infers regulatory models that drive the temporal dynamics; (3) Uses ForceDirected Layout Embedding (FLE) to ”
Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data (2018, methodology): “Scrublet has also been incorporated into SPRING (kleintools.hms.harvard.edu/tools/spring.html), an interactive tool for single-cell data exploration (Weinreb et al., 2018).”