Memory Efficient Principal Component Analysis for the Dimensionality Reduction of Large Mass Spectrometry Imaging Data Sets
Memory Efficient Principal Component Analysis for the Dimensionality Reduction of Large Mass Spectrometry Imaging Data Sets
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DOI：
10.1021/ac302528v
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发表时间：
2013-03-19
影响因子：
7.4
通讯作者：
Bunch, Josephine
中科院分区：
文献类型：
--
作者：
Race, Alan M.;Steven, Rory T.;Bunch, Josephine
提出了一种大规模质谱成像数据主成分分析（PCA）计算的内存有效算法。质谱成像（MSI）可以对复杂样品（如完整组织）中的数百种未标记分子种类进行二维和三维概览。PCA与数据分箱或其他约简算法相结合，已广泛用于MSI数据的无监督处理中，并作为聚类和空间分割之前的维数约简方法。PCA的标准实现需要将数据存储在随机存取存储器中。这对可以处理的数据量施加了上限，需要在像素数量和要包括的峰值数量之间进行折衷。随着对大型3D多切片数据集的多变量分析的兴趣增加以及仪器的不断改进，保留所有像素和更多峰值的能力变得越来越重要。我们提出了一种新的方法，它没有限制的像素数量，并允许保留更多的峰值。新技术针对MATLAB（The MathWorks Inc.，Natick，马萨诸塞州）实施PCA（princomp），然后用于在不丢弃峰或像素的情况下减少从单个小鼠脑获取的多个连续切片，所述单个小鼠脑太大而不能用princomp分析。然后，k-means聚类进行约简数据集。我们进一步证明了83切片的模拟数据，包括每切片20 535像素，相当于44 GB的数据，新方法可以与现有的工具结合使用，以处理整个器官。MATLAB代码实现内存高效的PCA算法。
A memory efficient algorithm for the computation of principal component analysis (PCA) of large mass spectrometry imaging data sets is presented. Mass spectrometry imaging (MSI) enables two- and three-dimensional overviews of hundreds of unlabeled molecular species in complex samples such as intact tissue. PCA, in combination with data binning or other reduction algorithms, has been widely used in the unsupervised processing of MSI data and as a dimentionality reduction method prior to clustering and spatial segmentation. Standard implementations of PCA require the data to be stored in random access memory. This imposes an upper limit on the amount of data that can be processed, necessitating a compromise between the number of pixels and the number of peaks to include. With increasing interest in multivariate analysis of large 3D multislice data sets and ongoing improvements in instrumentation, the ability to retain all pixels and many more peaks is increasingly important. We present a new method which has no limitation on the number of pixels and allows an increased number of peaks to be retained. The new technique was validated against the MATLAB (The MathWorks Inc., Natick, Massachusetts) implementation of PCA (princomp) and then used to reduce, without discarding peaks or pixels, multiple serial sections acquired from a single mouse brain which was too large to be analyzed with princomp. Then, k-means clustering was performed on the reduced data set. We further demonstrate with simulated data of 83 slices, comprising 20 535 pixels per slice and equaling 44 GB of data, that the new method can be used in combination with existing tools to process an entire organ. MATLAB code implementing the memory efficient PCA algorithm is provided.