References
Amid & Warmuth (2019), TriMap: Large-scale Dimensionality Reduction Using Triplets, arXiv.
Amir et al. (2013), viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia, Nature Biotechnology.
Angerer et al. (2016), destiny – diffusion maps for large-scale single-cell data in R, Bioinformatics.
Bernstein et al. (2020), Solo: Doublet Identification in Single-Cell RNA-Seq via Semi-Supervised Deep Learning, Cell Systems.
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.
Eraslan and Simon et al. (2018), Single cell RNA-seq denoising using a deep count autoencoder, bioRxiv.
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.
Eulenberg et al. (2017), Reconstructing cell cycle and disease progression using deep learning Nature Communications.
PyPairs, GitHub.
Fruchterman & Reingold (1991), Graph drawing by force-directed placement, Software: Practice & Experience.
Gardner et al., (2000) Construction of a genetic toggle switch in Escherichia coli, Nature.
Hagberg et al. (2008), Exploring Network Structure, Dynamics, and Function using NetworkX, Scipy Conference.
Hastie et al. (2009), The Elements of Statistical Learning, Springer.
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.
Haghverdi et al. (2018), Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors, Nature Biotechnology.
Hie et al. (2019), Efficient integration of heterogeneous single-cell transcriptomes using Scanorama, Nature Biotechnology.
Huber et al. (2015), Orchestrating high-throughput genomic analysis with Bioconductor, 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.
Kang et al. (2018), Python Implementation of MNN correct, GitHub.
Korunsky et al. (2019),
Fast, sensitive and accurate integration of single-cell data with Harmony,
Nature Methods <https://doi.org/10.1038/s41592-019-0619-0> __.
Krumsiek et al. (2010), Odefy – From discrete to continuous models, BMC Bioinformatics.
Krumsiek et al. (2011), Hierarchical Differentiation of Myeloid Progenitors Is Encoded in the Transcription Factor Network, PLoS ONE.
Lambiotte et al. (2009) Laplacian Dynamics and Multiscale Modular Structure in Networks arXiv.
Lause et al. (2021) Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data, Genome Biology.
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.
Lopez et al. (2018), Deep generative modeling for single-cell transcriptomics Nature Methods.
Maaten & Hinton (2008), Visualizing data using t-SNE, JMLR.
McCarthy et al. (2017), scater: Single-cell analysis toolkit for gene expression data in R, Bioinformatics.
Moon et al. (2017), PHATE: A Dimensionality Reduction Method for Visualizing Trajectory Structures in High-Dimensional Biological Data, BioRxiv.
Satija et al. (2015), Spatial reconstruction of single-cell gene expression data, Nature Biotechnology.
La Manno et al. (2018), RNA velocity of single cells, Nature.
McInnes & Healy (2018), UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, arXiv.
Moignard et al. (2015), Decoding the regulatory network of early blood development from single-cell gene expression measurements, Nature Biotechnology.
Murphy (2012, Machine Learning: A Probabilisitc Perspective, MIT Press.
Ntranos et al. (2018), Identification of transcriptional signatures for cell types from single-cell RNA-Seq, bioRxiv.
Paul et al. (2015), Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors, Cell.
Pedersen (2012), Python implementation of ComBat GitHub.
Pedregosa et al. (2011), Scikit-learn: Machine Learning in Python, JMLR.
Polanski et al. (2019), BBKNN: fast batch alignment of single cell transcriptomes Bioinformatics.
Plass (2018), Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics Science.
Scialdone et al. (2015), Computational assignment of cell-cycle stage from single-cell transcriptome data Methods.
Setty et al. (2016), Wishbone identifies bifurcating developmental trajectories from single-cell data Nature Biotechnology.
Setty et al. (2018), Palantir characterizes cell fate continuities in human hematopoiesis Nature Biotechnology.
Stuart et al. (2019), Comprehensive integration of single-cell data Cell.
Svensson et al. (2020), Interpretable factor models of single-cell RNA-seq via variational autoencoders Bioinformatics.
Nowotschin et al. (2018), The emergent landscape of the mouse gut endoderm at single-cell resolution Nature.
(2017), Louvain, GitHub.
Tarashansky (2019), Self-assembling manifolds in single-cell RNA sequencing data, Elife.
Traag et al. (2018), From Louvain to Leiden: guaranteeing well-connected communities arXiv.
Ulyanov (2016), Multicore t-SNE, GitHub.
Van Dijk D et al. (2018), Recovering Gene Interactions from Single-Cell Data Using Data Diffusion, Cell.
Weinreb et al. (2016), SPRING: a kinetic interface for visualizing high dimensional single-cell expression data, bioRxiv.
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.
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.
Wolock et al. (2019), BBKNN: fast batch alignment of single cell transcriptomes Cell Systems.
Zheng et al. (2017), Massively parallel digital transcriptional profiling of single cells, Nature Communications.
Zunder et al. (2015), A continuous molecular roadmap to iPSC reprogramming through progression analysis of single-cell mass cytometry, Cell Stem Cell.