Diverse, high-dimensional modalities collected in large cohorts present new opportunities for the formulation and testing of integrative scientific hypotheses. Similarity-driven multi-view linear reconstruction (SiMLR) is an algorithm that exploits inter-modality relationships to transform large scientific datasets into smaller, more well-powered and interpretable low-dimensional spaces. SiMLR contributes an objective function to identify joint signal regularization based on sparse matrices representing prior within-modality relationships and an implementation that permits application to joint reduction of large data matrices. We demonstrate that SiMLR outperlforms closely related methods on supervised learning problems in simulation data, a multi-omics cancer survival prediction dataset and multiple modality neuroimaging datasets. Taken together, this collection of results shows that SiMLR may be applied to joint signal estimation from disparate modalities and may yield practically useful results in a variety of application domains.
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All visualized plots in the main manuscript are generated from our code capsule, which contains both the specific data sources and software calls necessary to reproduce the figures80.
The simulation data are built dynamically in R. The scripts that generate the data are publicly available in our code capsule80. We downloaded evaluation data from the multi-omic cancer benchmark47 website at http://acgt.cs.tau.ac.il/multi_omic_benchmark/download.html. Data are available in our code capsule80 along with the relevant statistical details and calls needed to reproduce the results reported here. The data are free to use with no restrictions. The brain age data used here were obtained from PTBP81. These data were originally downloaded from https://figshare.com/articles/dataset/The_Pediatric_Template_of_Brain_Perfusion_PTBP_/923555. The relevant subset is available in our code capsule80. The data are free to use with no restrictions. Supplementary data used here were obtained from the PING study database (https://chd.ucsd.edu/research/ping-study.html). PING requires a user to register and request data. The review of the request may also require institutional support and justification of data use. We originally gained access to these data in 2013 as part of the PING-in-a-box service, which is now defunct. Data used here were also obtained from the ADNI database (http://adni.loni.usc.edu). ADNI was launched in 2003 as a public-private partnership, led by M. W. Weiner. The primary goal of ADNI has been to test whether serial magnetic resonance imaging, positron emission tomography, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment and early Alzheimer’s disease. For up-to-date information, see http://adni.loni.usc.edu. The investigators within ADNI contributed to the design and implementation of ADNI and/or provided data, but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Authorship_List.pdf. ADNI requires a user to register and request data. The review of the request may also require institutional support and justification of data use. We originally gained access to these data in 2008. The version used in the Supplementary Information was downloaded in August 2020 from LONI.
ANTsR is open source and freely available at https://github.com/ANTsX/ANTsR. The development of the code available on GitHub is ongoing. The specific release version of the code and scripts used for the analysis and generation of figures in the main body of this manuscript are available in our code capsule80.
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This work is supported by a combined grant from Cohen Veterans Bioscience (CVB-461) and the Office of Naval Research (N00014-18-1-2440) as well as the National Institutes of Health (K01-ES025432-01).
Supplementary data used in the preparation of this article were obtained from the PING study database (https://chd.ucsd.edu/research/ping-study.html). The investigators within PING contributed to the design and implementation of the PING database and/or provided data, but did not participate in the analysis or writing of this report. A complete listing of investigators of the PING study can be found at ref. 79.
Supplementary data collection and sharing for this project was funded by ADNI (National Institutes of Health Grant U01 AG024904) and the Department of Defense ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica; Biogen; Bristol Myers Squibb; CereSpir; Cogstate; Eisai; Elan Pharmaceuticals; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche and its affiliated company Genentech; Fujirebio; GE Healthcare; IXICO; Janssen Alzheimer Immunotherapy Research & Development; Johnson & Johnson Pharmaceutical Research & Development; Lumosity; Lundbeck; Merck & Co.; Meso Scale Diagnostics; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (https://fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory of Neuro Imaging at the University of Southern California.
The authors declare no competing interests.
Peer review information Nature Computational Science thanks Steve Marron, Cathy Philippe and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Fernando Chirigati was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
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Avants, B.B., Tustison, N.J. & Stone, J.R. Similarity-driven multi-view embeddings from high-dimensional biomedical data. Nat Comput Sci 1, 143–152 (2021). https://doi.org/10.1038/s43588-021-00029-8
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