A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.
Aravind Subramanian,Rajiv Narayan,Steven M. Corsello,Steven M. Corsello,David Peck,Ted Natoli,Xiaodong Lu,Joshua Gould,John F. Davis,Andrew A. Tubelli,Jacob K. Asiedu,David L. Lahr,Jodi E. Hirschman,Zihan Liu,Melanie Donahue,Bina Julian,Mariya Khan,David Wadden,Ian Smith,Daniel D. Lam,Arthur Liberzon,Courtney Toder,Mukta Bagul,Marek Orzechowski,Oana M. Enache,Federica Piccioni,Sarah A. Johnson,Nicholas J. Lyons,Alice H. Berger,Alice H. Berger,Alykhan F. Shamji,Angela N. Brooks,Angela N. Brooks,Anita Vrcic,Corey Flynn,Jacqueline Rosains,David Y. Takeda,David Y. Takeda,Roger Hu,Desiree Davison,Justin Lamb,Kristin Ardlie,Larson Hogstrom,Peyton Greenside,Nathanael S. Gray,Nathanael S. Gray,Paul A. Clemons,Serena J. Silver,Xiaoyun Wu,Wen-Ning Zhao,Wen-Ning Zhao,Willis Read-Button,Xiaohua Wu,Stephen J. Haggarty,Stephen J. Haggarty,Lucienne Ronco,Jesse S. Boehm,Stuart L. Schreiber,Stuart L. Schreiber,Stuart L. Schreiber,John G. Doench,Joshua A. Bittker,David E. Root,Bang Wong,Todd R. Golub +64 more
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TLDR
The expanded CMap is reported, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that is shown to be highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts.About:
This article is published in Cell.The article was published on 2017-11-30 and is currently open access. It has received 1943 citations till now.read more
Citations
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Morphological Subprofile Analysis for Bioactivity Annotation of Small Molecules
Axel Pahl,Slava Ziegler +1 more
TL;DR: In this article , the authors introduce the concept of subprofile analysis to map the mode of action for both reference and unexplored compounds, and extract subprofiles that contain only a subset of morphological features to represent a consensus profile.
Journal ArticleDOI
Power analysis for RNA-Seq differential expression studies using generalized linear mixed effects models.
TL;DR: A novel simulation based procedure for power estimation of differential expression with the employment of generalized linear mixed effects models for correlated expression data is proposed and properly control the false positive rate at the nominal level.
Posted ContentDOI
MARSY: A multitask deep learning framework for prediction of drug combination synergy scores
TL;DR: MARSY is a deep learning multi-task model that incorporates information on gene expression profile of cancer cell lines, as well as the differential expression signature induced by each drug to predict drug-pair synergy scores, confirming the ability of MARSY in making accurate novel predictions.
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Cell-specific imputation of drug connectivity mapping with incomplete data
Diana Sapashnik,Rebecca Newman,Christopher Michael Pietras,Fangfang Qu,Lior Kofman,Sean Boudreau,Inbar Fried,Donna K. Slonim +7 more
TL;DR: Slonim et al. as discussed by the authors compared collaborative filtering with either neighborhood-based or SVD imputation methods to two naive approaches via cross-validation, and concluded that even for cells in which drug responses have not been fully characterized, it is possible to identify unassayed drugs that reverse in those cells the expression signatures observed in disease.
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Metabolomics paves the way for improved drug target identification
B Garana,Nicholas A. J. Graham +1 more
TL;DR: Proof‐of‐concept for a new way to identify drug–target interactions using high‐throughput metabolomics is demonstrated, potentially paving the way towards a universal method for predicting drug– target relationships.
References
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Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
Aravind Subramanian,Pablo Tamayo,Vamsi K. Mootha,Sayan Mukherjee,Benjamin L. Ebert,Michael A. Gillette,Amanda G. Paulovich,Scott L. Pomeroy,Todd R. Golub,Eric S. Lander,Jill P. Mesirov +10 more
TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Journal Article
Visualizing Data using t-SNE
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
Journal ArticleDOI
Gene Expression Omnibus: NCBI gene expression and hybridization array data repository
TL;DR: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data and provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-power gene expression and genomic hybridization experiments.
Journal ArticleDOI
BLAT—The BLAST-Like Alignment Tool
TL;DR: How BLAT was optimized is described, which is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences.
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Adjusting batch effects in microarray expression data using empirical Bayes methods
TL;DR: This paper proposed parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples.
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