Literature DB >> 19965880

DataPflex: a MATLAB-based tool for the manipulation and visualization of multidimensional datasets.

Bart S Hendriks1, Christopher W Espelin.   

Abstract

MOTIVATION: DataPflex is a MATLAB-based application that facilitates the manipulation and visualization of multidimensional datasets. The strength of DataPflex lies in the intuitive graphical user interface for the efficient incorporation, manipulation and visualization of high-dimensional data that can be generated by multiplexed protein measurement platforms including, but not limited to Luminex or Meso-Scale Discovery. Such data can generally be represented in the form of multidimensional datasets [for example (time x stimulation x inhibitor x inhibitor concentration x cell type x measurement)]. For cases where measurements are made in a combinational fashion across multiple dimensions, there is a need for a tool to efficiently manipulate and reorganize such data for visualization. DataPflex accepts data consisting of up to five arbitrary dimensions in addition to a measurement dimension. Data are imported from a simple .xls format and can be exported to MATLAB or .xls. Data dimensions can be reordered, subdivided, merged, normalized and visualized in the form of collections of line graphs, bar graphs, surface plots, heatmaps, IC50's and other custom plots. Open source implementation in MATLAB enables easy extension for custom plotting routines and integration with more sophisticated analysis tools. AVAILABILITY: DataPflex is distributed under the GPL license (http://www.gnu.org/licenses/) together with documentation, source code and sample data files at: http://code.google.com/p/datapflex. SUPPLEMENTARY INFORMATION: Supplementary data available at Bioinformatics online.

Entities:  

Mesh:

Year:  2009        PMID: 19965880     DOI: 10.1093/bioinformatics/btp667

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  5 in total

1.  Characterization of Torin2, an ATP-competitive inhibitor of mTOR, ATM, and ATR.

Authors:  Qingsong Liu; Chunxiao Xu; Sivapriya Kirubakaran; Xin Zhang; Wooyoung Hur; Yan Liu; Nicholas P Kwiatkowski; Jinhua Wang; Kenneth D Westover; Peng Gao; Dalia Ercan; Mario Niepel; Carson C Thoreen; Seong A Kang; Matthew P Patricelli; Yuchuan Wang; Tanya Tupper; Abigail Altabef; Hidemasa Kawamura; Kathryn D Held; Danny M Chou; Stephen J Elledge; Pasi A Janne; Kwok-Kin Wong; David M Sabatini; Nathanael S Gray
Journal:  Cancer Res       Date:  2013-02-22       Impact factor: 12.701

2.  Kinome-wide selectivity profiling of ATP-competitive mammalian target of rapamycin (mTOR) inhibitors and characterization of their binding kinetics.

Authors:  Qingsong Liu; Sivapriya Kirubakaran; Wooyoung Hur; Mario Niepel; Kenneth Westover; Carson C Thoreen; Jinhua Wang; Jing Ni; Matthew P Patricelli; Kurt Vogel; Steve Riddle; David L Waller; Ryan Traynor; Takaomi Sanda; Zheng Zhao; Seong A Kang; Jean Zhao; A Thomas Look; Peter K Sorger; David M Sabatini; Nathanael S Gray
Journal:  J Biol Chem       Date:  2012-01-05       Impact factor: 5.157

3.  Discovery of potent and selective covalent inhibitors of JNK.

Authors:  Tinghu Zhang; Francisco Inesta-Vaquera; Mario Niepel; Jianming Zhang; Scott B Ficarro; Thomas Machleidt; Ting Xie; Jarrod A Marto; NamDoo Kim; Taebo Sim; John D Laughlin; Hajeung Park; Philip V LoGrasso; Matt Patricelli; Tyzoon K Nomanbhoy; Peter K Sorger; Dario R Alessi; Nathanael S Gray
Journal:  Chem Biol       Date:  2012-01-27

4.  Adaptive informatics for multifactorial and high-content biological data.

Authors:  Bjorn L Millard; Mario Niepel; Michael P Menden; Jeremy L Muhlich; Peter K Sorger
Journal:  Nat Methods       Date:  2011-04-24       Impact factor: 28.547

5.  Systematically studying kinase inhibitor induced signaling network signatures by integrating both therapeutic and side effects.

Authors:  Hongwei Shao; Tao Peng; Zhiwei Ji; Jing Su; Xiaobo Zhou
Journal:  PLoS One       Date:  2013-12-05       Impact factor: 3.240

  5 in total

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