TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis

By Zhicheng Ji, Hongkai Ji. Nucleic Acids Research, 2016

572 citations recorded by the source corpus.

When analyzing single-cell RNA-seq data, constructing a pseudo-temporal path to order cells based on the gradual transition of their transcriptomes is a useful way to study gene expression dynamics in a heterogeneous cell population. Currently, a limited number of computational tools are available for this task, and quantitative methods for comparing different tools are lacking. Tools for Single Cell Analysis (TSCAN) is a software tool developed to better support in silico pseudo-Time reconstruction in Single-Cell RNA-seq ANalysis. TSCAN uses a cluster-based minimum spanning tree (MST) approach to order cells. Cells are first grouped into clusters and an MST is then constructed to connect cluster centers. Pseudo-time is obtained by projecting each cell onto the tree, and the ordered sequence of cells can be used to study dynamic changes of gene expression along the pseudo-time. Clustering cells before MST construction reduces the complexity of the tree space. This often leads to improved cell ordering. It also allows users to conveniently adjust the ordering based on prior knowledge. TSCAN has a graphical user interface (GUI) to support data visualization and user interaction. Furthermore, quantitative measures are developed to objectively evaluate and compare different pseudo-time reconstruction methods. TSCAN is available at https://github.com/zji90/TSCAN and as a Bioconductor package.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c9be67e884ac8139f0538f623375c66d5d741680

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Citations in the corpus

Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics (2017, methodology): “, 2015) uses the principal curves method of Hastie and Stuetzle (1989) to infer lineages in a low-dimensional space obtained by a Laplacian eigenmap.”
A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor (2016, background): “…example, cells can be ordered in pseudotime (e.g., for progress along a differentiation pathway) with monocle (Trapnell et al., 2014) or TSCAN (Ji & Ji, 2016); cell-state hierarchies can be characterized with the sincell package (Julia et al., 2015); and oscillatory behaviour can be identified…”
SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation (2016, background): “The differentiation process can be reconstructed using dimension reduction [9, 10] and stochastic processes [11], for example, and the degree of differentiation (in pseudo-time) of each cell is characterized by the position in the reconstructed process.”
Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells (2017, methodology): “Spade [11], StemID 2 [13], Eclair [12], TSCAN [32] and Mpath [33] use different clustering algorithms such as k-means, k-medoids, hierarchical clustering or DBSCAN in a dimensionality-reduced space.”
Orchestrating single-cell analysis with Bioconductor (2019, background): “, slingshot [71], TSCAN [29], Monocle [69], 318 cellTree [72], and MFA [73]) were recently demonstrated to have excellent performance [74].”
Single-Cell RNA-Seq Technologies and Related Computational Data Analysis (2019, methodology): “…tools have been proposed for trajectory inference, e.g., Monocle (Trapnell et al., 2014), Waterfall (Shin et al., 2015), Wishbone (Setty et al., 2016), TSCAN (Ji and Ji, 2016), Monocle2 (Qiu et al., 2017), Slingshot (Street et al., 2018), and CellRouter (Lummertz da Rocha et al., 2018) ( Table 5 ).”
Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing. (2017, unknown): “, 2016), and TSCAN (Ji and Ji, 2016) to the same dataset and obtained comparable results (Figure S7A).”
DrImpute: imputing dropout events in single cell RNA sequencing data (2017, methodology): “In summary, these results suggested that imputing drop-out events using DrImpute also improved the performance of pseudotime inference using Monocle and TSCAN.”