Dataset Information

A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.

ABSTRACT: We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs, and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.

SUBMITTER: Subramanian A

PROVIDER: S-EPMC5990023 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Publications

A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.

Subramanian Aravind A Narayan Rajiv R Corsello Steven M SM Peck David D DD Natoli Ted E TE Lu Xiaodong X Gould Joshua J Davis John F JF Tubelli Andrew A AA Asiedu Jacob K JK Lahr David L DL Hirschman Jodi E JE Liu Zihan Z Donahue Melanie M Julian Bina B Khan Mariya M Wadden David D Smith Ian C IC Lam Daniel D Liberzon Arthur A Toder Courtney C Bagul Mukta M Orzechowski Marek M Enache Oana M OM Piccioni Federica F Johnson Sarah A SA Lyons Nicholas J NJ Berger Alice H AH Shamji Alykhan F AF Brooks Angela N AN Vrcic Anita A Flynn Corey C Rosains Jacqueline J Takeda David Y DY Hu Roger R Davison Desiree D Lamb Justin J Ardlie Kristin K Hogstrom Larson L Greenside Peyton P Gray Nathanael S NS Clemons Paul A PA Silver Serena S Wu Xiaoyun X Zhao Wen-Ning WN Read-Button Willis W Wu Xiaohua X Haggarty Stephen J SJ Ronco Lucienne V LV Boehm Jesse S JS Schreiber Stuart L SL Doench John G JG Bittker Joshua A JA Root David E DE Wong Bang B Golub Todd R TR

Cell 20171101 6

We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs, and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference o ...[more]

PMID: 29195078

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Project description:In this report, the authors introduce the general concept of the completeness map, as a means to evaluate the completeness of data acquired by a given CT system design (architecture and scan mode). They illustrate the utility of completeness map by applying the completeness map concept to a number of candidate CT system designs, as part of a study to advance the state-of-the-art in cardiac CT.In order to optimally reconstruct a point within a volume of interest (VOI), the Radon transform on all possible planes through that point should be measured. The authors quantified the extent to which this ideal condition is satisfied for the entire image volume. They first determined a Radon completeness number for each point in the VOI, as the percentage of possible planes that is actually measured. A completeness map is then defined as a 3D matrix of the completeness numbers for the entire VOI. The authors proposed algorithms to analyze the projection datasets in Radon space and compute the completeness number for a fixed point and apply these algorithms to various architectures and scan modes that they are evaluating. In this report, the authors consider four selected candidate architectures, operating with different scan modes, for a total of five system design alternatives. Each of these alternatives is evaluated using completeness map.If the detector size and cone angle are large enough to cover the entire cardiac VOI, a single-source circular scan can have ≥99% completeness over the entire VOI. However, only the central z-slice can be exactly reconstructed, which corresponds to 100% completeness. For a typical single-source architecture, if the detector is limited to an axial dimension of 40 mm, a helical scan needs about five rotations to form an exact reconstruction region covering the cardiac VOI, while a triple-source helical scan only requires two rotations, leading to a 2.5x improvement in temporal resolution. If the source and detector of an inverse-geometry (IGCT) system have the same axial extent, and the spacing of source points in the axial and transaxial directions is sufficiently small, the IGCT can also form an exact reconstruction region for the cardiac VOI. If the VOI can be covered by the x-ray beam in any view, a composite-circling scan can generate an exact reconstruction region covering the VOI.The completeness map evaluation provides useful information for selecting the next-generation cardiac CT system design. The proposed completeness map method provides a practical tool for analyzing complex scanning trajectories, where the theoretical image quality for some complex system designs is impossible to predict, without yet-undeveloped reconstruction algorithms.

Dataset Information

A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.

Publications

A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.

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