References¶
- Coifman05
Coifman et al. (2005), Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps, PNAS.
- Haghverdi15
Haghverdi et al. (2015), Diffusion maps for high-dimensional single-cell analysis of differentiation data, Bioinformatics.
- Haghverdi16
Haghverdi et al. (2016), Diffusion pseudotime robustly reconstructs branching cellular lineages, Nature Methods.
- Wolf18
Wolf et al. (2018), Scanpy: large-scale single-cell gene expression data analysis, Genome Biology.
- Pedregosa11
Pedregosa et al. (2011), Scikit-learn: Machine Learning in Python, JMLR.
- McInnes18
McInnes & Healy (2018), UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, arXiv.
- Weinreb16
Weinreb et al. (2016), SPRING: a kinetic interface for visualizing high dimensional single-cell expression data, bioRxiv.
- Satija15
Satija et al. (2015), Spatial reconstruction of single-cell gene expression data, Nature Biotechnology.
- Zheng17
Zheng et al. (2017), Massively parallel digital transcriptional profiling of single cells, Nature Communications.
- Weinreb17
Weinreb et al. (2016), SPRING: a kinetic interface for visualizing high dimensional single-cell expression data, bioRxiv.
- Blondel08
Blondel et al. (2008), Fast unfolding of communities in large networks, J. Stat. Mech..
- Levine15
Levine et al. (2015), Data-Driven Phenotypic Dissection of AML Reveals Progenitor–like Cells that Correlate with Prognosis, Cell.
- Traag17
(2017), Louvain, GitHub.
- Lambiotte09
Lambiotte et al. (2009) Laplacian Dynamics and Multiscale Modular Structure in Networks arXiv.