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Slingshot was designed to model developmental trajectories in single-cell RNA sequencing data and serve as a component in an analysis pipeline after dimensionality reduction and clustering. For example, if you have three time points, you would run: scpa_out <- compare_pathways (samples = list (pop1, pop2, pop3), pathways = pathways) Additionally, our simulation study shows that Slingshot infers more accurate pseudotimes than other leading methods. Conclusions: Slingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories. Package ‘slingshot’ April 16, 2019 Title Tools for ordering single-cell sequencing Version 1.0.0 Description Provides functions for inferring continuous, branching lineage structures in low-dimensional data. Owens, Nature (2012) Single-cell data let us ask new questions. Slingshot: Cell lineage and pseudotime inference for single-cell transcriptomics Kelly Street1,8, Davide Risso2, Russell B. Fletcher 3, Diya Das , John Ngai3,6,7, Nir Yosef4,8, Elizabeth Purdom5,8, and Sandrine Dudoit 1,5,8 1Division of Biostatistics, School of Public Health, UC Berkeley. View Record in Scopus Google Scholar. Most scalable pseudotime ordering algorithm Chord: an ensemble machine learning algorithm to identify … 1, Step 1). b, c After processing the dataset for the pseudotime analysis using each doublet detection method, the top 20% of cells according to the doublet score were excluded. Pseudotime analysis with slingshot - GitHub Pages Slingshot: Cell lineage and pseudotime inference for single-cell transcriptomics Kelly Street1,8, Davide Risso2, Russell B. Fletcher 3, Diya Das , John Ngai3,6,7, Nir Yosef4,8, Elizabeth Purdom5,8, and Sandrine Dudoit 1,5,8 1Division of Biostatistics, School of Public Health, UC Berkeley. Slingshot: Trajectory Inference for Single-Cell Data
