Network embedding-based representation learning for single cell RNA-seq data.
Network embedding-based representation learning for single cell RNA-seq data.
复制标题
基于网络嵌入的单细胞 RNA-seq 数据表示学习
DOI：
10.1093/nar/gkx750
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发表时间：
2017-11-02
影响因子：
14.9
通讯作者：
Zhang MQ
中科院分区：
文献类型：
--
作者：
Li X;Chen W;Chen Y;Zhang X;Gu J;Zhang MQ
单细胞RNA-seq （scRNA-seq）技术可以揭示细胞间异质性的宝贵见解。一般来说，将高维数据投影到低维子空间是挖掘此类大数据的有力策略。然而，与传统的bulk RNA-seq相比，scRNA-seq具有较高的噪声和较低的覆盖范围，从而给计算带来了新的困难。一个主要的挑战是如何处理频繁的退学事件。这些事件通常是由基因转录中的随机突发效应和RNA转录物捕获的技术故障引起的，往往使传统的降维方法效率低下。为了克服这个问题，我们开发了一种新的基于网络嵌入的单细胞表示学习（SCRL）方法。该方法可以有效地实现数据驱动的非线性投影，并结合先前的生物学知识（如通路信息）来学习更有意义的细胞和基因的低维表示。基准测试结果表明，在最近的几个scRNA-seq数据集上，SCRL优于其他降维方法。
Single cell RNA-seq (scRNA-seq) techniques can reveal valuable insights of cell-to-cell heterogeneities. Projection of high-dimensional data into a low-dimensional subspace is a powerful strategy in general for mining such big data. However, scRNA-seq suffers from higher noise and lower coverage than traditional bulk RNA-seq, hence bringing in new computational difficulties. One major challenge is how to deal with the frequent drop-out events. The events, usually caused by the stochastic burst effect in gene transcription and the technical failure of RNA transcript capture, often render traditional dimension reduction methods work inefficiently. To overcome this problem, we have developed a novel Single Cell Representation Learning (SCRL) method based on network embedding. This method can efficiently implement data-driven non-linear projection and incorporate prior biological knowledge (such as pathway information) to learn more meaningful low-dimensional representations for both cells and genes. Benchmark results show that SCRL outperforms other dimensional reduction methods on several recent scRNA-seq datasets.
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DOI：
10.1042/bj20091439
发表时间：
2010-02-09
期刊：
The Biochemical journal
影响因子：
--
作者：
Lee J;Go Y;Kang I;Han YM;Kim J
通讯作者：
Kim J
影响因子：
48
作者：
Wu, Angela R.;Neff, Norma F.;Kalisky, Tomer;Dalerba, Piero;Treutlein, Barbara;Rothenberg, Michael E.;Mburu, Francis M.;Mantalas, Gary L.;Sim, Sopheak;Clarke, Michael F.;Quake, Stephen R.
通讯作者：
Quake, Stephen R.
人类原始生殖细胞的转录组和 DNA 甲基化组景观
影响因子：
64.5
作者：
Guo, Fan;Yan, Liying;Qiao, Jie
通讯作者：
Qiao, Jie
影响因子：
14.8
作者：
通讯作者：
--
影响因子：
5.6
作者：
Mu, Xinyi;Wen, Jing;Xia, Guoliang
通讯作者：
Xia, Guoliang