Processing Next-Generation Mass Spectrometry Imaging Data: Principal Component Analysis at Scale.

Abstract

Mass spectrometry imaging (MSI) is constantly improving in spatial resolving power, throughput and mass resolution. Although beneficial, these improvements increase data set size and content. The larger data requires correspondingly fast computer-based analyses. However, these analyses often do not scale well with increased data size. Principal component analysis (PCA) is an important analytical tool commonly used with MSI data; however, most PCA algorithms load and process the entire data set within random access memory (RAM) which is most often insufficient for large data sets. PCA algorithms that use less RAM than the data set exist but are usually much slower or sacrifice precision and are rarely used for MSI data processing. Incremental PCA (IPCA) is an alternative algorithm that avoids large RAM allocations while also preserving speed and analytical precision. Here, we demonstrate and benchmark the use of differing implementations of IPCA, PCA, and commercial software on large and often complex MSI data sets. We show that using an already-published Python-based IPCA algorithm, IPCA can be successfully applied to MSI data sets too large to fit with RAM. Furthermore, our benchmarks demonstrate that, contrary to expectations, IPCA is faster than all other tested PCA implementations on all large data sets that can be directly compared.