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Statistical significance of cluster membership for determination of cell identities in single cell genomics

https://doi.org/10.1101/248633
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Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells
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Spatial reconstruction of single-cell gene expression data
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Massively parallel digital transcriptional profiling of single cells
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Cluster analysis and display of genome-wide expression patterns
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Model-based clustering and data transformations for gene expression data
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Statistical significance of variables driving systematic variation in high-dimensional data
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Identification of cell types from single-cell transcriptomes using a novel clustering method
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Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R
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How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
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A Short Survey on Data Clustering Algorithms
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Least squares quantization in PCM
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Estimating the Number of Clusters in a Data Set Via the Gap Statistic
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Approximate Tests of Correlation in Time-Series
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Clustering by fast search and find of density peaks
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Massively Parallel Single-Cell RNA-Seq for Marker-Free Decomposition of Tissues into Cell Types
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Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
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Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
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A stability based method for discovering structure in clustered data
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Web-scale k-means clustering
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Concordant and discordant DNA methylation signatures of aging in human blood and brain
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Empirical Bayes Analysis of a Microarray Experiment
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Statistical Significance of Clustering for High-Dimension, Low–Sample Size Data
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Cluster Validation by Prediction Strength
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Optimal predictive model selection
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Size, power and false discovery rates
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Estimating the Dimension of a Model
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Bootstrap Methods: Another Look at the Jackknife
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SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis
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The Joint Null Criterion for Multiple Hypothesis Tests
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Algorithm AS 136: A K-Means Clustering Algorithm
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Dynamics of embryonic stem cell differentiation inferred from single-cell transcriptomics show a series of transitions through discrete cell states
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