References#
Amir et al. (2013), viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia, Nature Biotechnology.
Blondel et al. (2008), Fast unfolding of communities in large networks, J. Stat. Mech..
ForceAtlas2 for Python and NetworkX, GitHub.
Coifman et al. (2005), Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps, PNAS.
Csardi et al. (2006), The igraph software package for complex network research, InterJournal Complex Systems.
Fruchterman & Reingold (1991), Graph drawing by force-directed placement, Software: Practice & Experience.
Haghverdi et al. (2015), Diffusion maps for high-dimensional single-cell analysis of differentiation data, Bioinformatics.
Haghverdi et al. (2016), Diffusion pseudotime robustly reconstructs branching cellular lineages, Nature Methods.
Islam et al. (2011), Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq, Genome Research.
Jacomy et al. (2014), ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software PLOS One.
Johnson, Li & Rabinovic (2007), Adjusting batch effects in microarray expression data using empirical Bayes methods, Biostatistics.
Lambiotte et al. (2009) Laplacian Dynamics and Multiscale Modular Structure in Networks arXiv.
Leek et al. (2012), sva: Surrogate Variable Analysis. R package Bioconductor.
Levine et al. (2015), Data-Driven Phenotypic Dissection of AML Reveals Progenitor–like Cells that Correlate with Prognosis, Cell.
Maaten & Hinton (2008), Visualizing data using t-SNE, JMLR.
McInnes & Healy (2018), UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, arXiv.
Pedersen (2012), Python implementation of ComBat GitHub.
Pedregosa et al. (2011), Scikit-learn: Machine Learning in Python, JMLR.
(2017), Louvain, GitHub.
Traag et al. (2018), From Louvain to Leiden: guaranteeing well-connected communities arXiv.
Weinreb et al. (2016), SPRING: a kinetic interface for visualizing high dimensional single-cell expression data, bioRxiv.
Wolf et al. (2018), Scanpy: large-scale single-cell gene expression data analysis, Genome Biology.
Wolf et al. (2019), PAGA: Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biology, bioRxiv.
Zunder et al. (2015), A continuous molecular roadmap to iPSC reprogramming through progression analysis of single-cell mass cytometry, Cell Stem Cell.