A systematic performance evaluation of clustering methods for single-cell RNA-seq data.
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[1] Karl Pearson F.R.S.. LIII. On lines and planes of closest fit to systems of points in space , 1901 .
[2] C. E. SHANNON,et al. A mathematical theory of communication , 1948, MOCO.
[3] W. Kruskal,et al. Use of Ranks in One-Criterion Variance Analysis , 1952 .
[4] J. H. Ward. Hierarchical Grouping to Optimize an Objective Function , 1963 .
[5] J. A. Hartigan,et al. A k-means clustering algorithm , 1979 .
[6] L. Hubert,et al. Comparing partitions , 1985 .
[7] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[8] Kurt Hornik,et al. A CLUE for CLUster Ensembles , 2005 .
[9] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[10] Steve Horvath,et al. WGCNA: an R package for weighted correlation network analysis , 2008, BMC Bioinformatics.
[11] Catalin C. Barbacioru,et al. mRNA-Seq whole-transcriptome analysis of a single cell , 2009, Nature Methods.
[12] Ulrike von Luxburg,et al. Clustering Stability: An Overview , 2010, Found. Trends Mach. Learn..
[13] Åsa K. Björklund,et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells , 2013, Nature Methods.
[14] Greg Finak,et al. Critical assessment of automated flow cytometry data analysis techniques , 2013, Nature Methods.
[15] Laurens van der Maaten,et al. Accelerating t-SNE using tree-based algorithms , 2014, J. Mach. Learn. Res..
[16] Cole Trapnell,et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells , 2014, Nature Biotechnology.
[17] Jüri Lember,et al. Bridging Viterbi and posterior decoding: a generalized risk approach to hidden path inference based on hidden Markov models , 2014, J. Mach. Learn. Res..
[18] A. Oudenaarden,et al. Validation of noise models for single-cell transcriptomics , 2014, Nature Methods.
[19] Aviv Regev,et al. Deconstructing transcriptional heterogeneity in pluripotent stem cells , 2014, Nature.
[20] A. Regev,et al. Spatial reconstruction of single-cell gene expression , 2015, Nature Biotechnology.
[21] Piet Demeester,et al. FlowSOM: Using self‐organizing maps for visualization and interpretation of cytometry data , 2015, Cytometry. Part A : the journal of the International Society for Analytical Cytology.
[22] S. Linnarsson,et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq , 2015, Science.
[23] Allon M. Klein,et al. Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells , 2015, Cell.
[24] Evan Z. Macosko,et al. Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets , 2015, Cell.
[25] Pang Wei Koh,et al. An atlas of transcriptional, chromatin accessibility, and surface marker changes in human mesoderm development , 2016, Scientific Data.
[26] J. Marioni,et al. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts , 2016, Genome Biology.
[27] Martin Hemberg,et al. Modelling dropouts allows for unbiased identification of marker genes in scRNASeq experiments , 2016 .
[28] Mauro J. Muraro,et al. De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data , 2016, Cell stem cell.
[29] Rhonda Bacher,et al. Design and computational analysis of single-cell RNA-sequencing experiments , 2016, Genome Biology.
[30] Mark D. Robinson,et al. Comparison of Clustering Methods for High-Dimensional Single-Cell Flow and Mass Cytometry Data , 2016, bioRxiv.
[31] Lior Pachter,et al. Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts , 2016, Genome Biology.
[32] Christopher Yau,et al. pcaReduce: hierarchical clustering of single cell transcriptional profiles , 2015, BMC Bioinformatics.
[33] Hongkai Ji,et al. TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis , 2016, Nucleic acids research.
[34] Lior Pachter,et al. Near-optimal probabilistic RNA-seq quantification , 2016, Nature Biotechnology.
[35] Milica Ng,et al. Cluster Headache: Comparing Clustering Tools for 10X Single Cell Sequencing Data , 2017, bioRxiv.
[36] Grace X. Y. Zheng,et al. Massively parallel digital transcriptional profiling of single cells , 2016, Nature Communications.
[37] M. Schaub,et al. SC3 - consensus clustering of single-cell RNA-Seq data , 2016, Nature Methods.
[38] Hannah A. Pliner,et al. Reversed graph embedding resolves complex single-cell trajectories , 2017, Nature Methods.
[39] David A. Knowles,et al. Batch effects and the effective design of single-cell gene expression studies , 2016, Scientific Reports.
[40] A. Oshlack,et al. Splatter: simulation of single-cell RNA sequencing data , 2017, Genome Biology.
[41] Benjamin Haibe-Kains,et al. Software for the integration of multi-omics experiments in Bioconductor , 2017, bioRxiv.
[42] Valentine Svensson,et al. Power Analysis of Single Cell RNA-Sequencing Experiments , 2016, Nature Methods.
[43] Aedín C. Culhane,et al. Software for the integration of multi-omics experiments in Bioconductor , 2017 .
[44] Aaron T. L. Lun,et al. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R , 2017, Bioinform..
[45] Joshua W. K. Ho,et al. CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data , 2016, Genome Biology.
[46] Xin Mei,et al. ascend: R package for analysis of single-cell RNA-seq data , 2017, bioRxiv.
[47] Christoph Ziegenhain,et al. Quantitative single-cell transcriptomics , 2018, Briefings in functional genomics.
[48] Vilas Menon,et al. Clustering single cells: a review of approaches on high-and low-depth single-cell RNA-seq data. , 2018, Briefings in functional genomics.
[49] S. Teichmann,et al. Exponential scaling of single-cell RNA-seq in the past decade , 2017, Nature Protocols.
[50] Mark D. Robinson,et al. Towards unified quality verification of synthetic count data with countsimQC , 2017, Bioinform..
[51] Luyi Tian,et al. Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data , 2018, F1000Research.
[52] Yuan Lin,et al. SAFE-clustering: Single-cell Aggregated (From Ensemble) Clustering for Single-cell RNA-seq Data , 2017, bioRxiv.
[53] Luyi Tian,et al. Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data , 2018, F1000Research.
[54] Vilas Menon,et al. Clustering single cells: a review of approaches on high-and low-depth single-cell RNA-seq data. , 2018, Briefings in functional genomics.
[55] Charlotte Soneson,et al. Bias, robustness and scalability in single-cell differential expression analysis , 2018, Nature Methods.
[56] M. Hemberg,et al. Identifying cell populations with scRNASeq. , 2017, Molecular aspects of medicine.
[57] R. Irizarry,et al. Missing data and technical variability in single‐cell RNA‐sequencing experiments , 2018, Biostatistics.
[58] Luke Zappia,et al. Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database , 2017, bioRxiv.
[59] D T Severson,et al. BEARscc determines robustness of single-cell clusters using simulated technical replicates , 2017, Nature Communications.
[60] Yuan Lin,et al. SAFE-clustering: Single-cell Aggregated (From Ensemble) Clustering for Single-cell RNA-seq Data , 2017, bioRxiv.