Grant-Peters M, Rich-Griffin C, Grant-Peters JE, Cinque G, Dendrou CA. Photizo: an open-source library for cross-sample analysis of FTIR spectroscopy data.
Bioinformatics 2022;
38:3490-3492. [PMID:
35608303 PMCID:
PMC9237726 DOI:
10.1093/bioinformatics/btac346]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 04/28/2022] [Accepted: 05/16/2022] [Indexed: 11/30/2022] Open
Abstract
MOTIVATION
With continually improved instrumentation, Fourier transform infrared (FTIR) microspectroscopy can now be used to capture thousands of high-resolution spectra for chemical characterization of a sample. The spatially resolved nature of this method lends itself well to histological profiling of complex biological specimens. However, current software can make joint analysis of multiple samples challenging and, for large datasets, computationally infeasible.
RESULTS
To overcome these limitations, we have developed Photizo-an open-source Python library enabling high-throughput spectral data pre-processing, visualization and downstream analysis, including principal component analysis, clustering, macromolecular quantification and mapping. Photizo can be used for analysis of data without a spatial component, as well as spatially resolved data, obtained e.g. by scanning mode IR microspectroscopy and IR imaging by focal plane array detector.
AVAILABILITY AND IMPLEMENTATION
The code underlying this article is available at https://github.com/DendrouLab/Photizo with access to example data available at https://zenodo.org/record/6417982#.Yk2O9TfMI6A.
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