# References¶

Amid19

Amid & Warmuth (2019), TriMap: Large-scale Dimensionality Reduction Using Triplets, arXiv.

Amir13

Amir et al. (2013), viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia, Nature Biotechnology.

Angerer16

Angerer et al. (2016), destiny – diffusion maps for large-scale single-cell data in R, Bioinformatics.

Blondel08

Blondel et al. (2008), Fast unfolding of communities in large networks, J. Stat. Mech..

ForceAtlas2 for Python and NetworkX, GitHub.

Coifman05

Coifman et al. (2005), Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps, PNAS.

Csardi06

Csardi et al. (2006), The igraph software package for complex network research, InterJournal Complex Systems.

Eraslan18

Eraslan and Simon et al. (2018), Single cell RNA-seq denoising using a deep count autoencoder, bioRxiv.

Ester96

Ester et al. (1996), A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR.

Eulenberg17

Eulenberg et al. (2017), Reconstructing cell cycle and disease progression using deep learning Nature Communications.

Fechtner18

PyPairs, GitHub.

Fruchterman91

Fruchterman & Reingold (1991), Graph drawing by force-directed placement, Software: Practice & Experience.

Gardner00

Gardner et al., (2000) Construction of a genetic toggle switch in Escherichia coli, Nature.

Hagberg08

Hagberg et al. (2008), Exploring Network Structure, Dynamics, and Function using NetworkX, Scipy Conference.

Hastie09

Hastie et al. (2009), The Elements of Statistical Learning, Springer.

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.

Haghverdi18

Haghverdi et al. (2018), Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors, Nature Biotechnology.

Huber15

Huber et al. (2015), Orchestrating high-throughput genomic analysis with Bioconductor, Nature Methods.

Islam11

Islam et al. (2011), Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq, Genome Research.

Jacomy14

Jacomy et al. (2014), ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software PLOS One.

Johnson07

Johnson, Li & Rabinovic (2007), Adjusting batch effects in microarray expression data using empirical Bayes methods, Biostatistics.

Kang18

Kang et al. (2018), Python Implementation of MNN correct, GitHub.

Krumsiek10

Krumsiek et al. (2010), Odefy – From discrete to continuous models, BMC Bioinformatics.

Krumsiek11

Krumsiek et al. (2011), Hierarchical Differentiation of Myeloid Progenitors Is Encoded in the Transcription Factor Network, PLoS ONE.

Lambiotte09

Lambiotte et al. (2009) Laplacian Dynamics and Multiscale Modular Structure in Networks arXiv.

Leek12

Leek et al. (2012), sva: Surrogate Variable Analysis. R package Bioconductor.

Levine15

Levine et al. (2015), Data-Driven Phenotypic Dissection of AML Reveals Progenitor–like Cells that Correlate with Prognosis, Cell.

Maaten08

Maaten & Hinton (2008), Visualizing data using t-SNE, JMLR.

McCarthy17

McCarthy et al. (2017), scater: Single-cell analysis toolkit for gene expression data in R, Bioinformatics.

Moon17

Moon et al. (2017), PHATE: A Dimensionality Reduction Method for Visualizing Trajectory Structures in High-Dimensional Biological Data, BioRxiv.

Satija15

Satija et al. (2015), Spatial reconstruction of single-cell gene expression data, Nature Biotechnology.

Manno18

La Manno et al. (2018), RNA velocity of single cells, Nature.

McInnes18

McInnes & Healy (2018), UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, arXiv.

Moignard15

Moignard et al. (2015), Decoding the regulatory network of early blood development from single-cell gene expression measurements, Nature Biotechnology.

Murphy12

Murphy (2012, Machine Learning: A Probabilisitc Perspective, MIT Press.

Ntranos18

Ntranos et al. (2018), Identification of transcriptional signatures for cell types from single-cell RNA-Seq, bioRxiv.

Paul15

Paul et al. (2015), Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors, Cell.

Pedersen12

Pedersen (2012), Python implementation of ComBat GitHub.

Pedregosa11

Pedregosa et al. (2011), Scikit-learn: Machine Learning in Python, JMLR.

Polanski19

Polanski et al. (2019), BBKNN: fast batch alignment of single cell transcriptomes Bioinformatics.

Plass18

Plass (2018), Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics Science.

Scialdone15

Scialdone et al. (2015), Computational assignment of cell-cycle stage from single-cell transcriptome data Methods.

Setty18

Setty et al. (2018), Palantir characterizes cell fate continuities in human hematopoiesis bioRxiv.

Traag17

Traag (2017), Louvain, GitHub.

Traag18

Traag et al. (2018), From Louvain to Leiden: guaranteeing well-connected communities arXiv.

Ulyanov16

Ulyanov (2016), Multicore t-SNE, GitHub.

vanDijk18

Van Dijk D et al. (2018), Recovering Gene Interactions from Single-Cell Data Using Data Diffusion, Cell.

Weinreb17

Weinreb et al. (2016), SPRING: a kinetic interface for visualizing high dimensional single-cell expression data, bioRxiv.

Wittmann09

Wittmann et al. (2009), Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling, BMC Systems Biology.

Waskom et al. (2017), Seaborn, Zenodo.

Wolf18

Wolf et al. (2018), Scanpy: large-scale single-cell gene expression data analysis, Genome Biology.

Wolf19

Wolf et al. (2019), PAGA: Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biology, bioRxiv.

Zheng17

Zheng et al. (2017), Massively parallel digital transcriptional profiling of single cells, Nature Communications.

Zunder15

Zunder et al. (2015), A continuous molecular roadmap to iPSC reprogramming through progression analysis of single-cell mass cytometry, Cell Stem Cell.