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SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data

Creator: Welch, Joshua D, Hartemink, Alexander J, Prins, Jan

Abstract Single cell experiments provide an unprecedented opportunity to reconstruct a sequence of changes in a biological process from individual “snapshots” of cells. However, nonlinear gene expression changes, genes unrelated to the process, and (more)

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13059_2016_article_975.pdf

Collection: BioMed Central

Date Deposited: 2016-05-27

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Additional file 10: Figure S10. Correlation matrices for genes selected by SLICER

Collection: BioMed Central

Date Deposited: 2016-05-27

Date Created: 2016-05-27

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Additional file 1: Figure S1. Selecting k using the a-convex hull

Collection: BioMed Central

Date Deposited: 2016-05-27

Date Created: 2016-05-27

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Additional file 2: Figure S2. Computing geodesic entropy of a trajectory

Collection: BioMed Central

Date Deposited: 2016-05-27

Date Created: 2016-05-27

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Additional file 5: Figure S5. Additional marker genes for mouse lung dataset

Collection: BioMed Central

Date Deposited: 2016-05-27

Date Created: 2016-05-27

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Additional file 9: Figure S9. Three-dimensional LLE results for biological datasets

Collection: BioMed Central

Date Deposited: 2016-05-27

Date Created: 2016-05-27

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