Parallel Factor Analysis Enables Quantification and Identification of Highly Convolved Data-Independent-Acquired Protein Spectra

Filip Buric; Jan Zrimec; Aleksej Zelezniak

doi:10.1016/j.patter.2020.100137

Parallel Factor Analysis Enables Quantification and Identification of Highly Convolved Data-Independent-Acquired Protein Spectra

Patterns (N Y). 2020 Nov 5;1(9):100137. doi: 10.1016/j.patter.2020.100137. eCollection 2020 Dec 11.

Authors

Filip Buric¹, Jan Zrimec¹, Aleksej Zelezniak^{1

2}

Affiliations

¹ Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, Gothenburg 412 96, Sweden.
² Science for Life Laboratory, Tomtebodavägen 23a, Stockholm 171 65, Sweden.

Abstract

High-throughput data-independent acquisition (DIA) is the method of choice for quantitative proteomics, combining the best practices of targeted and shotgun approaches. The resultant DIA spectra are, however, highly convolved and with no direct precursor-fragment correspondence, complicating biological sample analysis. Here, we present CANDIA (canonical decomposition of data-independent-acquired spectra), a GPU-powered unsupervised multiway factor analysis framework that deconvolves multispectral scans to individual analyte spectra, chromatographic profiles, and sample abundances, using parallel factor analysis. The deconvolved spectra can be annotated with traditional database search engines or used as high-quality input for de novo sequencing methods. We demonstrate that spectral libraries generated with CANDIA substantially reduce the false discovery rate underlying the validation of spectral quantification. CANDIA covers up to 33 times more total ion current than library-based approaches, which typically use less than 5% of total recorded ions, thus allowing quantification and identification of signals from unexplored DIA spectra.

Keywords: PARAFAC; big data; canonical decomposition; data-independent acquisition; deconvolution; mass spectrometry; proteomics; tensor factorization.