Using neural networks for reducing the dimensions of single-cell RNA-Seq data
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[1] C. Mallows,et al. A Method for Comparing Two Hierarchical Clusterings , 1983 .
[2] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[3] Russ B. Altman,et al. Missing value estimation methods for DNA microarrays , 2001, Bioinform..
[4] Jill P. Mesirov,et al. Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data , 2003, Machine Learning.
[5] George C Tseng,et al. Tight Clustering: A Resampling‐Based Approach for Identifying Stable and Tight Patterns in Data , 2005, Biometrics.
[6] Yann LeCun,et al. Learning a similarity metric discriminatively, with application to face verification , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[7] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[8] Mike Tyers,et al. BioGRID: a general repository for interaction datasets , 2005, Nucleic Acids Res..
[9] Julia Hirschberg,et al. V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure , 2007, EMNLP.
[10] Sergei Vassilvitskii,et al. k-means++: the advantages of careful seeding , 2007, SODA '07.
[11] John Varga,et al. The transcriptional coactivator and acetyltransferase p300 in fibroblast biology and fibrosis , 2007, Journal of cellular physiology.
[12] M. Hendrix,et al. IRF6 in development and disease: A mediator of quiescence and differentiation , 2008, Cell cycle.
[13] Sandhya Rani,et al. Human Protein Reference Database—2009 update , 2008, Nucleic Acids Res..
[14] Yoshua Bengio,et al. Why Does Unsupervised Pre-training Help Deep Learning? , 2010, AISTATS.
[15] James Bailey,et al. Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance , 2010, J. Mach. Learn. Res..
[16] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[17] Geoffrey E. Hinton,et al. Using very deep autoencoders for content-based image retrieval , 2011, ESANN.
[18] K. Mclaughlin,et al. Mouse chimeras as a system to investigate development, cell and tissue function, disease mechanisms and organ regeneration , 2011, Cell cycle.
[19] Haiyuan Yu,et al. Detecting overlapping protein complexes in protein-protein interaction networks , 2012, Nature Methods.
[20] Ziv Bar-Joseph,et al. DREM 2.0: Improved reconstruction of dynamic regulatory networks from time-series expression data , 2012, BMC Systems Biology.
[21] Sean R. Davis,et al. NCBI GEO: archive for functional genomics data sets—update , 2012, Nucleic Acids Res..
[22] Ziv Bar-Joseph,et al. Identifying proteins controlling key disease signaling pathways , 2013, Bioinform..
[23] Geoffrey C Gurtner,et al. The role of focal adhesion complexes in fibroblast mechanotransduction during scar formation. , 2013, Differentiation; research in biological diversity.
[24] Rona S. Gertner,et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells , 2013, Nature.
[25] Cole Trapnell,et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells , 2014, Nature Biotechnology.
[26] I. Amit,et al. Massively Parallel Single-Cell RNA-Seq for Marker-Free Decomposition of Tissues into Cell Types , 2014, Science.
[27] N. Neff,et al. Reconstructing lineage hierarchies of the distal lung epithelium using single cell RNA-seq , 2014, Nature.
[28] Rona S. Gertner,et al. Single cell RNA Seq reveals dynamic paracrine control of cellular variation , 2014, Nature.
[29] Christine A. Wells,et al. Single-Cell Gene Expression Profiles Define Self-Renewing, Pluripotent, and Lineage Primed States of Human Pluripotent Stem Cells , 2014, Stem cell reports.
[30] Aman Gupta,et al. Learning structure in gene expression data using deep architectures, with an application to gene clustering , 2015 .
[31] A. Blais,et al. Transcriptional control of stem cell fate by E2Fs and pocket proteins , 2015, Front. Genet..
[32] S. Linnarsson,et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq , 2015, Science.
[33] Angela Yan,et al. Non-alcoholic fatty liver disease induces signs of Alzheimer’s disease (AD) in wild-type mice and accelerates pathological signs of AD in an AD model , 2016, Journal of Neuroinflammation.
[34] E. Pierson,et al. ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis , 2015, Genome Biology.
[35] S. Quake,et al. A survey of human brain transcriptome diversity at the single cell level , 2015, Proceedings of the National Academy of Sciences.
[36] Allon M. Klein,et al. Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells , 2015, Cell.
[37] David W. Nauen,et al. Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis. , 2015, Cell stem cell.
[38] Fabian J Theis,et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells , 2015, Nature Biotechnology.
[39] S. Linnarsson,et al. Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing , 2014, Nature Neuroscience.
[40] C. Tyler-Smith,et al. Ancient DNA and the rewriting of human history: be sparing with Occam’s razor , 2016, Genome Biology.
[41] Chen Xu,et al. Identification of cell types from single-cell transcriptomes using a novel clustering method , 2015, Bioinform..
[42] Hui Wang,et al. SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis , 2015, PLoS Comput. Biol..
[43] Aleksandra A. Kolodziejczyk,et al. Single Cell RNA-Sequencing of Pluripotent States Unlocks Modular Transcriptional Variation , 2015, Cell stem cell.
[44] M. Cugmas,et al. On comparing partitions , 2015 .
[45] Pornpimol Charoentong,et al. Computational genomics tools for dissecting tumour–immune cell interactions , 2016, Nature Reviews Genetics.
[46] Hedi Peterson,et al. g:Profiler—a web server for functional interpretation of gene lists (2016 update) , 2016, Nucleic Acids Res..
[47] J. Marioni,et al. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts , 2016, Genome Biology.
[48] Jens Hjerling-Leffler,et al. Disentangling neural cell diversity using single-cell transcriptomics , 2016, Nature Neuroscience.
[49] Christopher Yau,et al. pcaReduce: hierarchical clustering of single cell transcriptional profiles , 2015, BMC Bioinformatics.
[50] Paul C. Blainey,et al. A microfluidic platform enabling single-cell RNA-seq of multigenerational lineages , 2016, Nature Communications.
[51] Charles C. Kim,et al. Brain trauma elicits non-canonical macrophage activation states , 2016, Journal of Neuroinflammation.
[52] Yi Li,et al. Gene expression inference with deep learning , 2015, bioRxiv.
[53] Lan Bao,et al. Somatosensory neuron types identified by high-coverage single-cell RNA-sequencing and functional heterogeneity , 2016, Cell Research.
[54] C. Greene,et al. ADAGE-Based Integration of Publicly Available Pseudomonas aeruginosa Gene Expression Data with Denoising Autoencoders Illuminates Microbe-Host Interactions , 2016, mSystems.
[55] Zhigang Xue,et al. Simultaneous profiling of transcriptome and DNA methylome from a single cell , 2016, Genome Biology.
[56] Bo Wang,et al. Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning , 2016, Nature Methods.
[57] Christine Nardini,et al. Missing value estimation methods for DNA methylation data , 2019, Bioinform..