Efficient Generation of Transcriptomic Profiles by Random Composite Measurements

[1]  Angela N. Brooks,et al.  A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles , 2017, Cell.

[2]  Thomas M. Norman,et al.  A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response , 2016, Cell.

[3]  Thomas M. Norman,et al.  Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens , 2016, Cell.

[4]  A. Regev,et al.  Revealing the vectors of cellular identity with single-cell genomics , 2016, Nature Biotechnology.

[5]  V. Jojic,et al.  Tradict enables accurate prediction of eukaryotic transcriptional states from 100 marker genes , 2016, Nature Communications.

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

[7]  Vladimir Jojic,et al.  Tradict enables high fidelity reconstruction of the eukaryotic transcriptome from 100 marker genes , 2016 .

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

[9]  Charles H. Yoon,et al.  Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq , 2016, Science.

[10]  Adele M Doyle,et al.  Fixed single-cell transcriptomic characterization of human radial glial diversity , 2015, Nature Methods.

[11]  Staci A. Sorensen,et al.  Adult Mouse Cortical Cell Taxonomy Revealed by Single Cell Transcriptomics , 2016 .

[12]  I. Amit,et al.  Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors , 2015, Cell.

[13]  Yonina C. Eldar,et al.  Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference , 2015, ArXiv.

[14]  Hans Clevers,et al.  Single-cell messenger RNA sequencing reveals rare intestinal cell types , 2015, Nature.

[15]  Hayder Radha,et al.  New guarantees for Blind Compressed Sensing , 2015, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[16]  Stephen Becker,et al.  Efficient dictionary learning via very sparse random projections , 2015, 2015 International Conference on Sampling Theory and Applications (SampTA).

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

[18]  Michael Q. Zhang,et al.  Integrative analysis of 111 reference human epigenomes , 2015, Nature.

[19]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.

[20]  Andrew D. Rouillard,et al.  LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures , 2014, Nucleic Acids Res..

[21]  Rona S. Gertner,et al.  Single cell RNA Seq reveals dynamic paracrine control of cellular variation , 2014, Nature.

[22]  P. Kharchenko,et al.  Bayesian approach to single-cell differential expression analysis , 2014, Nature Methods.

[23]  J. Buhmann,et al.  Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry , 2014, Nature Methods.

[24]  Sean C. Bendall,et al.  Multiplexed ion beam imaging of human breast tumors , 2014, Nature Medicine.

[25]  I. Amit,et al.  Massively Parallel Single-Cell RNA-Seq for Marker-Free Decomposition of Tissues into Cell Types , 2014, Science.

[26]  R. Sandberg,et al.  Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells , 2014, Science.

[27]  Thomas C. Evans,et al.  Efficient DNA ligation in DNA–RNA hybrid helices by Chlorella virus DNA ligase , 2013, Nucleic acids research.

[28]  Joshua M. Stuart,et al.  The Cancer Genome Atlas Pan-Cancer analysis project , 2013, Nature Genetics.

[29]  Ellen T. Gelfand,et al.  The Genotype-Tissue Expression (GTEx) project , 2013, Nature Genetics.

[30]  Rona S. Gertner,et al.  Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells , 2013, Nature.

[31]  D. Koller,et al.  Imputing gene expression from optimally reduced probe sets , 2012, Nature Methods.

[32]  Gitta Kutyniok,et al.  1 . 2 Sparsity : A Reasonable Assumption ? , 2012 .

[33]  Helga Thorvaldsdóttir,et al.  Molecular signatures database (MSigDB) 3.0 , 2011, Bioinform..

[34]  Sean C. Bendall,et al.  Single-Cell Mass Cytometry of Differential Immune and Drug Responses Across a Human Hematopoietic Continuum , 2011, Science.

[35]  Nathan Linial,et al.  Recovering key biological constituents through sparse representation of gene expression , 2011, Bioinform..

[36]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[37]  Yonina C. Eldar,et al.  Blind Compressed Sensing , 2010, IEEE Transactions on Information Theory.

[38]  C. Glass,et al.  Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. , 2010, Molecular cell.

[39]  Guillermo Sapiro,et al.  Online Learning for Matrix Factorization and Sparse Coding , 2009, J. Mach. Learn. Res..

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

[41]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[42]  M. Lustig,et al.  Compressed Sensing MRI , 2008, IEEE Signal Processing Magazine.

[43]  T. Golub,et al.  A method for high-throughput gene expression signature analysis , 2006, Genome Biology.

[44]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[45]  Emmanuel J. Candès,et al.  Decoding by linear programming , 2005, IEEE Transactions on Information Theory.

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

[47]  Sanjoy Dasgupta,et al.  An elementary proof of a theorem of Johnson and Lindenstrauss , 2003, Random Struct. Algorithms.

[48]  Sven Bergmann,et al.  Iterative signature algorithm for the analysis of large-scale gene expression data. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[49]  Roded Sharan,et al.  Discovering statistically significant biclusters in gene expression data , 2002, ISMB.

[50]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[51]  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.

[52]  George M. Church,et al.  Biclustering of Expression Data , 2000, ISMB.

[53]  D Haussler,et al.  Knowledge-based analysis of microarray gene expression data by using support vector machines. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[54]  P. Groenen,et al.  Modern Multidimensional Scaling: Theory and Applications , 1999 .

[55]  W. B. Johnson,et al.  Extensions of Lipschitz mappings into Hilbert space , 1984 .