scholarly journals Multiple-platform data integration method with application to combined analysis of microarray and proteomic data

2012 ◽  
Vol 13 (1) ◽  
pp. 320 ◽  
Author(s):  
Shicheng Wu ◽  
Yawen Xu ◽  
Zeny Feng ◽  
Xiaojian Yang ◽  
Xiaogang Wang ◽  
...  
2010 ◽  
Vol 30 (9) ◽  
pp. 2370-2373 ◽  
Author(s):  
Jiong LANG ◽  
Yan-bing LIU ◽  
Shi-yong XIONG

Author(s):  
G Sriman Narayana ◽  
Kuruva Arjun Kumar

In privacy-enhancing technology, it has been inevitably challenging to strike a maintain balance between privacy, efficiency and usability (utility). We propose a highly practical and efficient approach for privacy-preserving integration and sharing of datasets among a group of participants. At the heart of our solution is a new interactive protocol, Secure Channel. Through Secure Channel, each participant is able to randomize their datasets via an independent and untrusted third party, such that the resulting dataset can be merged with other randomized datasets contributed by other participants group in a privacy-preserving manner. Our process does not require any public or key sharing between participants in order to integrate different datasets. This, in turn, leads to a user can understand and use easily and scalable solution. Moreover, the accuracy of a randomized dataset which are returned by the third party can be securely verified by the other participant of group. We further demonstrate Secure Channel’s general utilities, using it to construct a structure preserving data integration protocol. This is mainly useful for, good quality integration of network traffic data.


2015 ◽  
Vol 20 (6) ◽  
pp. 483-489
Author(s):  
Xinming Wang ◽  
Haoxiang Tan ◽  
Kaijun Chen ◽  
Hua Tang ◽  
Gansen Zhao ◽  
...  

2018 ◽  
Author(s):  
Jong-Eun Park ◽  
Krzysztof Polański ◽  
Kerstin Meyer ◽  
Sarah A. Teichmann

AbstractIncreasing numbers of large scale single cell RNA-Seq projects are leading to a data explosion, which can only be fully exploited through data integration. Therefore, efficient computational tools for combining diverse datasets are crucial for biology in the single cell genomics era. A number of methods have been developed to assist data integration by removing technical batch effects, but most are computationally intensive. To overcome the challenge of enormous datasets, we have developed BBKNN, an extremely fast graph-based data integration method. We illustrate the power of BBKNN for dimensionalityreduced visualisation and clustering in multiple biological scenarios, including a massive integrative study over several murine atlases. BBKNN successfully connects cell populations across experimentally heterogeneous mouse scRNA-Seq datasets, which reveals global markers of cell type and organspecificity and provides the foundation for inferring the underlying transcription factor network. BBKNN is available at https://github.com/Teichlab/bbknn.


2012 ◽  
Vol 12 (9) ◽  
pp. 2866-2874 ◽  
Author(s):  
Lihui Peng ◽  
Bowen Zhang ◽  
Huichao Zhao ◽  
Sonh Aubin Stephane ◽  
Hiroaki Ishikawa ◽  
...  

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