[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..
[Chippada18]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.
[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.
[Park18]Park et al. (2018), Fast Batch Alignment of Single Cell Transcriptomes Unifies Multiple Mouse Cell Atlases into an Integrated Landscape bioRxiv.
[Pedersen12]Pedersen (2012), Python implementation of ComBat GitHub.
[Pedregosa11]Pedregosa et al. (2011), Scikit-learn: Machine Learning in Python, JMLR.
[Paul15]Paul et al. (2015), Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors, Cell.
[Scialdone15]Scialdone et al. (2015), Computational assignment of cell-cycle stage from single-cell transcriptome data Methods.
[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.
[Waskom16]Waskom et al. (2017), Seaborn, Zenodo.
[Wolf17]Wolf et al. (2018), Scanpy: large-scale single-cell gene expression data analysis, Genome Biology.
[Wolf17i]Wolf et al. (2017), Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. 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.