Linked matrix factorization.
data integration
dimension reduction
massive data sets
missing data imputation
principal components analysis
Journal
Biometrics
ISSN: 1541-0420
Titre abrégé: Biometrics
Pays: United States
ID NLM: 0370625
Informations de publication
Date de publication:
06 2019
06 2019
Historique:
received:
10
02
2018
accepted:
16
08
2018
pubmed:
6
12
2018
medline:
30
1
2020
entrez:
6
12
2018
Statut:
ppublish
Résumé
Several recent methods address the dimension reduction and decomposition of linked high-content data matrices. Typically, these methods consider one dimension, rows or columns, that is shared among the matrices. This shared dimension may represent common features measured for different sample sets (horizontal integration) or a common sample set with features from different platforms (vertical integration). We introduce an approach for simultaneous horizontal and vertical integration, Linked Matrix Factorization (LMF), for the general case where some matrices share rows (e.g., features) and some share columns (e.g., samples). Our motivating application is a cytotoxicity study with accompanying genomic and molecular chemical attribute data. The toxicity matrix (cell lines
Substances chimiques
Cytotoxins
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
582-592Subventions
Organisme : NCRR NIH HHS
ID : UL1 RR033183
Pays : United States
Organisme : NCRR NIH HHS
ID : KL2 RR033182
Pays : United States
Informations de copyright
© 2019 International Biometric Society.
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