The GCTx format and cmap{Py, R, M, J} packages: resources for optimized storage and integrated traversal of annotated dense matrices.
The GCTx format and cmap{Py, R, M, J} packages: resources for optimized storage and integrated traversal of annotated dense matrices.
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GCTX格式和CMAP {PY，R，M，J}软件包：用于优化注释密集矩阵的优化存储和集成遍历的资源。
DOI：
10.1093/bioinformatics/bty784
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发表时间：
2019-04-15
期刊：
影响因子：
--
通讯作者：
Subramanian A
中科院分区：
文献类型：
--
作者：
Enache OM;Lahr DL;Natoli TE;Litichevskiy L;Wadden D;Flynn C;Gould J;Asiedu JK;Narayan R;Subramanian A
在技术进步的推动下，药理学和遗传微扰数据集近年来已经发展到包括数百万个实验。共享和公开分发这些不同的数据为发现创造了许多机会，但近年来，所生成的数据规模空前，其复杂的相关元数据也带来了数据存储和集成方面的挑战。我们提出的GCTx文件格式和一套开源软件包的高效存储，序列化和密集的二维矩阵的分析。我们已经广泛使用连接图中的格式来组装和共享目前包含130万个实验的大规模数据集，我们预计该格式的通用性，与我们提供的代码库配对，将降低集成交叉分析和算法开发的障碍。软件包（Python、R、Matlab和Java版本）可在https://github.com/cmap上免费获得。其他说明、教程和数据集可在clue.io/code上获得。 补充数据可在Bioinformatics在线获得。
Facilitated by technological improvements, pharmacologic and genetic perturbational datasets have grown in recent years to include millions of experiments. Sharing and publicly distributing these diverse data creates many opportunities for discovery, but in recent years the unprecedented size of data generated and its complex associated metadata have also created data storage and integration challenges. We present the GCTx file format and a suite of open-source packages for the efficient storage, serialization and analysis of dense two-dimensional matrices. We have extensively used the format in the Connectivity Map to assemble and share massive datasets currently comprising 1.3 million experiments, and we anticipate that the format’s generalizability, paired with code libraries that we provide, will lower barriers for integrated cross-assay analysis and algorithm development. Software packages (available in Python, R, Matlab and Java) are freely available at https://github.com/cmap. Additional instructions, tutorials and datasets are available at clue.io/code. Supplementary data are available at Bioinformatics online.
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影响因子：
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作者：
Liberzon, Arthur;Subramanian, Aravind;Mesirov, Jill P.
通讯作者：
Mesirov, Jill P.
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Jaffe, Jacob D.
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通讯作者：
Pillai A
影响因子：
56.9
作者：
Lamb, Justin;Crawford, Emily D.;Golub, Todd R.
通讯作者：
Golub, Todd R.
影响因子：
56.9
作者：
Weinstein, JN;Myers, TG;Paull, KD
通讯作者：
Paull, KD