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Title: An accessible, scalable ecosystem for enabling and sharing diverse mass spectrometry imaging analyses

Mass spectrometry imaging (MSI) is used in an increasing number of biological applications. Typical MSI datasets contain unique, high-resolution mass spectra from tens of thousands of spatial locations, resulting in raw data sizes of tens of gigabytes per sample. In this paper, we review technical progress that is enabling new biological applications and that is driving an increase in the complexity and size of MSI data. Handling such data often requires specialized computational infrastructure, software, and expertise. OpenMSI, our recently described platform, makes it easy to explore and share MSI datasets via the web – even when larger than 50 GB. Here we describe the integration of OpenMSI with IPython notebooks for transparent, sharable, and replicable MSI research. An advantage of this approach is that users do not have to share raw data along with analyses; instead, data is retrieved via OpenMSI's web API. The IPython notebook interface provides a low-barrier entry point for data manipulation that is accessible for scientists without extensive computational training. Via these notebooks, analyses can be easily shared without requiring any data movement. We provide example notebooks for several common MSI analysis types including data normalization, plotting, clustering, and classification, and image registration.
Authors:
 [1] ;  [1] ;  [1]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Report Number(s):
LBNL-1005721
Journal ID: ISSN 0003-9861; ir:1005721
Type:
Published Article
Journal Name:
Archives of Biochemistry and Biophysics
Additional Journal Information:
Journal Volume: 589; Journal ID: ISSN 0003-9861
Publisher:
Elsevier
Research Org:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; 96 KNOWLEDGE MANAGEMENT AND PRESERVATION; Mass spectrometry imaging; Maldi; Ipython; Jupyter; Openmsi; Metabolomics
OSTI Identifier:
1333754
Alternate Identifier(s):
OSTI ID: 1378570

Fischer, Curt R., Ruebel, Oliver, and Bowen, Benjamin P.. An accessible, scalable ecosystem for enabling and sharing diverse mass spectrometry imaging analyses. United States: N. p., Web. doi:10.1016/j.abb.2015.08.021.
Fischer, Curt R., Ruebel, Oliver, & Bowen, Benjamin P.. An accessible, scalable ecosystem for enabling and sharing diverse mass spectrometry imaging analyses. United States. doi:10.1016/j.abb.2015.08.021.
Fischer, Curt R., Ruebel, Oliver, and Bowen, Benjamin P.. 2015. "An accessible, scalable ecosystem for enabling and sharing diverse mass spectrometry imaging analyses". United States. doi:10.1016/j.abb.2015.08.021.
@article{osti_1333754,
title = {An accessible, scalable ecosystem for enabling and sharing diverse mass spectrometry imaging analyses},
author = {Fischer, Curt R. and Ruebel, Oliver and Bowen, Benjamin P.},
abstractNote = {Mass spectrometry imaging (MSI) is used in an increasing number of biological applications. Typical MSI datasets contain unique, high-resolution mass spectra from tens of thousands of spatial locations, resulting in raw data sizes of tens of gigabytes per sample. In this paper, we review technical progress that is enabling new biological applications and that is driving an increase in the complexity and size of MSI data. Handling such data often requires specialized computational infrastructure, software, and expertise. OpenMSI, our recently described platform, makes it easy to explore and share MSI datasets via the web – even when larger than 50 GB. Here we describe the integration of OpenMSI with IPython notebooks for transparent, sharable, and replicable MSI research. An advantage of this approach is that users do not have to share raw data along with analyses; instead, data is retrieved via OpenMSI's web API. The IPython notebook interface provides a low-barrier entry point for data manipulation that is accessible for scientists without extensive computational training. Via these notebooks, analyses can be easily shared without requiring any data movement. We provide example notebooks for several common MSI analysis types including data normalization, plotting, clustering, and classification, and image registration.},
doi = {10.1016/j.abb.2015.08.021},
journal = {Archives of Biochemistry and Biophysics},
number = ,
volume = 589,
place = {United States},
year = {2015},
month = {9}
}