Zero-preserving imputation of single-cell RNA-seq data

[1]  Jamie L. Marshall,et al.  Compressed sensing for highly efficient imaging transcriptomics , 2021, Nature Biotechnology.

[2]  Lawrence A. David,et al.  Naught all zeros in sequence count data are the same. , 2020, Computational and structural biotechnology journal.

[3]  C. Conrad,et al.  SARS-CoV-2 receptor ACE2 and TMPRSS2 are primarily expressed in bronchial transient secretory cells. , 2020, The EMBO journal.

[4]  Roland Eils,et al.  SARS‐CoV‐2 receptor ACE2 and TMPRSS2 are primarily expressed in bronchial transient secretory cells , 2020, The EMBO journal.

[5]  Y. Kluger,et al.  Paracrine orchestration of intestinal tumorigenesis by a mesenchymal niche , 2020, Nature.

[6]  C. Lindskog,et al.  A genome-wide transcriptomic analysis of protein-coding genes in human blood cells , 2019, Science.

[7]  Paul J. Hoffman,et al.  Comprehensive Integration of Single-Cell Data , 2018, Cell.

[8]  Valentine Svensson,et al.  Droplet scRNA-seq is not zero-inflated , 2019, Nature Biotechnology.

[9]  Fabian J Theis,et al.  Single-cell RNA-seq denoising using a deep count autoencoder , 2019, Nature Communications.

[10]  Y. Kluger,et al.  Single-Cell Analysis Reveals a Hair Follicle Dermal Niche Molecular Differentiation Trajectory that Begins Prior to Morphogenesis. , 2019, Developmental cell.

[11]  Aaron Lun,et al.  Overcoming systematic errors caused by log-transformation of normalized single-cell RNA sequencing data , 2018, bioRxiv.

[12]  Angshul Majumdar,et al.  McImpute: Matrix Completion Based Imputation for Single Cell RNA-seq Data , 2018, bioRxiv.

[13]  Kevin R. Moon,et al.  Recovering Gene Interactions from Single-Cell Data Using Data Diffusion , 2018, Cell.

[14]  Nancy R. Zhang,et al.  SAVER: Gene expression recovery for single-cell RNA sequencing , 2018, Nature Methods.

[15]  Paul Hoffman,et al.  Integrating single-cell transcriptomic data across different conditions, technologies, and species , 2018, Nature Biotechnology.

[16]  Wei Vivian Li,et al.  An accurate and robust imputation method scImpute for single-cell RNA-seq data , 2018, Nature Communications.

[17]  Sydney M. Shaffer,et al.  Rare Cell Detection by Single-Cell RNA Sequencing as Guided by Single-Molecule RNA FISH. , 2018, Cell systems.

[18]  Allan R. Jones,et al.  Shared and distinct transcriptomic cell types across neocortical areas , 2017, bioRxiv.

[19]  Allon M. Klein,et al.  Single-Cell Analysis of Experience-Dependent Transcriptomic States in Mouse Visual Cortex , 2017, Nature Neuroscience.

[20]  E. Smits,et al.  CD56 in the Immune System: More Than a Marker for Cytotoxicity? , 2017, Front. Immunol..

[21]  H. Swerdlow,et al.  Large-scale simultaneous measurement of epitopes and transcriptomes in single cells , 2017, Nature Methods.

[22]  Jingshu Wang,et al.  Gene expression recovery for single cell RNA sequencing , 2017, bioRxiv.

[23]  Wei Chen,et al.  DIMM-SC: a Dirichlet mixture model for clustering droplet-based single cell transcriptomic data , 2017, Bioinform..

[24]  Yi Zhang,et al.  Single-Cell RNA-Seq Reveals Hypothalamic Cell Diversity. , 2017, Cell reports.

[25]  Samuel L. Wolock,et al.  A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure. , 2016, Cell systems.

[26]  Lars E. Borm,et al.  Molecular Diversity of Midbrain Development in Mouse, Human, and Stem Cells , 2016, Cell.

[27]  Steven L. Brunton,et al.  Randomized Matrix Decompositions using R , 2016, Journal of Statistical Software.

[28]  Evan Z. Macosko,et al.  Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics , 2016, Cell.

[29]  Grace X. Y. Zheng,et al.  Massively parallel digital transcriptional profiling of single cells , 2016, Nature Communications.

[30]  G. Alterovitz,et al.  Gene expression prediction using low-rank matrix completion , 2016, BMC Bioinformatics.

[31]  Matt Thomson,et al.  Low Dimensionality in Gene Expression Data Enables the Accurate Extraction of Transcriptional Programs from Shallow Sequencing. , 2016, Cell systems.

[32]  Andreas Ziegler,et al.  ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R , 2015, 1508.04409.

[33]  Evan Z. Macosko,et al.  Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets , 2015, Cell.

[34]  S. Linnarsson,et al.  Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq , 2015, Science.

[35]  Yan Guo,et al.  A Cell-Based Systems Biology Assessment of Human Blood to Monitor Immune Responses after Influenza Vaccination , 2015, PloS one.

[36]  John D. Storey,et al.  Statistical significance of variables driving systematic variation in high-dimensional data , 2013, Bioinform..

[37]  Sujay Sanghavi,et al.  Completing any low-rank matrix, provably , 2013, J. Mach. Learn. Res..

[38]  A. Onatski.  Determining the Number of Factors from Empirical Distribution of Eigenvalues , 2010, The Review of Economics and Statistics.

[39]  Eric T. Wang,et al.  An Abundance of Ubiquitously Expressed Genes Revealed by Tissue Transcriptome Sequence Data , 2009, PLoS Comput. Biol..

[40]  Emmanuel J. Candès,et al.  The Power of Convex Relaxation: Near-Optimal Matrix Completion , 2009, IEEE Transactions on Information Theory.

[41]  B. Nadler.  Finite sample approximation results for principal component analysis: a matrix perturbation approach , 2009, 0901.3245.

[42]  Andrea Montanari,et al.  Matrix completion from a few entries , 2009, 2009 IEEE International Symposium on Information Theory.

[43]  B. Nadler,et al.  Determining the number of components in a factor model from limited noisy data , 2008 .

[44]  D. Koller,et al.  The Immunological Genome Project: networks of gene expression in immune cells , 2008, Nature Immunology.

[45]  Emmanuel J. Candès,et al.  Exact Matrix Completion via Convex Optimization , 2008, Found. Comput. Math..

[46]  Peter D. Hoff,et al.  Model Averaging and Dimension Selection for the Singular Value Decomposition , 2006, math/0609042.

[47]  D. Pe’er,et al.  Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data , 2003, Nature Genetics.

[48]  Philippe Soriano,et al.  Evolutionary Divergence of Platelet-Derived Growth Factor Alpha Receptor Signaling Mechanisms , 2003, Molecular and Cellular Biology.

[49]  J. Delabie,et al.  The Value of Anti-Pax-5 Immunostaining in Routinely Fixed and Paraffin-Embedded Sections: A Novel Pan Pre-B and B-Cell Marker , 2002, The American journal of surgical pathology.

[50]  R. Germain.  T-cell development and the CD4–CD8 lineage decision , 2002, Nature Reviews Immunology.

[51]  S. Seki,et al.  Systematic characterization of human CD8+ T cells with natural killer cell markers in comparison with natural killer cells and normal CD8+ T cells , 2001, Immunology.

[52]  D. Botstein,et al.  Singular value decomposition for genome-wide expression data processing and modeling. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[53]  R. Cattell.  The Scree Test For The Number Of Factors. , 1966, Multivariate behavioral research.

[54]  Y. Kluger,et al.  Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding , 2017, ArXiv.

[55]  Longbo Chen,et al.  FINDING STRUCTURE WITH RANDOMNESS : PROBABILISTIC ALGORITHMS FOR CONSTRUCTING , 2016 .

[56]  Xiaoming Yuan,et al.  Matrix completion via an alternating direction method , 2012 .

[57]  D. Paul.  ASYMPTOTICS OF SAMPLE EIGENSTRUCTURE FOR A LARGE DIMENSIONAL SPIKED COVARIANCE MODEL , 2007 .