Photizo: an open-source library for cross-sample analysis of FTIR spectroscopy data
Photizo: an open-source library for cross-sample analysis of FTIR spectroscopy data
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Photizo：用于 FTIR 光谱数据跨样本分析的开源库
DOI：
10.1101/2022.02.25.481930
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Grant-Peters M
中科院分区：
文献类型：
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作者：
Grant-Peters M
随着仪器的不断改进，傅里叶变换红外（FTIR）显微光谱现在可以用来捕获样品化学表征的数千个高分辨率光谱。这种方法的空间分辨性质很好地适用于复杂生物标本的组织学分析。然而，目前的软件可以联合分析多个样品的挑战性，对于大型数据集，计算insufficient.ResultsTo克服这些限制，我们已经开发了Photizo-一个开源的Python库，使高通量的光谱数据预处理，可视化和下游分析，包括主成分分析，聚类，大分子定量和映射。Photizo可用于分析没有空间分量的数据，以及空间分辨数据，例如通过扫描模式红外显微光谱和焦平面阵列探测器的红外成像获得。可用性和实施本文的代码可在www.example.com上获得https://github.com/DendrouLab/Photizo并可访问www.example.com上的示例数据。https://zenodo.org/record/6417982#.Yk2O9TfMI6A
MotivationWith 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.ResultsTo 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 implementationThe 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.
影响因子：
4.6
作者：
Traag, V. A.;Waltman, L.;van Eck, N. J.
通讯作者：
van Eck, N. J.