DeepMF: deciphering the latent patterns in omics profiles with a deep learning method
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Abstract
BACKGROUND: With recent advances in high-throughput technologies, matrix factorization techniques are increasingly being utilized for mapping quantitative omics profiling matrix data into low-dimensional embedding space, in the hope of uncovering insights in the underlying biological processes. Nevertheless, current matrix factorization tools fall short in handling noisy data and missing entries, both deficiencies that are often found in real-life data. RESULTS: Here, we propose DeepMF, a deep neural network-based factorization model. DeepMF disentangles the association between molecular feature-associated and sample-associated latent matrices, and is tolerant to noisy and missing values. It exhibited feasible cancer subtype discovery efficacy on mRNA, miRNA, and protein profiles of medulloblastoma cancer, leukemia cancer, breast cancer, and small-blue-round-cell cancer, achieving the highest clustering accuracy of 76%, 100%, 92%, and 100% respectively. When analyzing data sets with 70% missing entries, DeepMF gave the best recovery capacity with silhouette values of 0.47, 0.6, 0.28, and 0.44, outperforming other state-of-the-art MF tools on the cancer data sets Medulloblastoma, Leukemia, TCGA BRCA, and SRBCT. Its embedding strength as measured by clustering accuracy is 88%, 100%, 84%, and 96% on these data sets, which improves on the current best methods 76%, 100%, 78%, and 87%. CONCLUSION: DeepMF demonstrated robust denoising, imputation, and embedding ability. It offers insights to uncover the underlying biological processes such as cancer subtype discovery. Our implementation of DeepMF can be found at https://github.com/paprikachan/DeepMF.
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In evaluating DeepMF at 10%, 50%, and 70% missingness, Chen et al. found that DeepMF was consistently able to identify sample and feature subgroups with perfect accuracy.
“In evaluating the robustness of DeepMF to varying levels of missingness (10, 50, and 70%), Chen et al. (2019) found that DeepMF was consistently able to identify sample and feature subgroups with perfect accuracy.”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Chen et al., 2019
DeepMF's perfect-accuracy evaluations were completed in a simulated data setting in which data were simulated as missing completely at random (MCAR) and ground truth feature subgroups were generated with simple discrimination boundaries.
“However, it should be noted that these evaluations were completed in a simulated data setting in which data were simulated as missing completely at random (MCAR) and ground truth feature subgroups were generated with simple discrimination boundaries.”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Chen et al., 2019
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Cites
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458 · see citing works
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
Rousseeuw, P. J. (1987) · Journal of Computational and Applied Mathematics · doi:10.1016/0377-0427(87)90125-7 · Cited by 21,392 · see citing works
FactoMineR : An R Package for Multivariate Analysis
Lê, S., Josse, J., & Husson, F. (2008) · Journal of Statistical Software · doi:10.18637/jss.v025.i01 · Cited by 11,682 · see citing works
Signatures of mutational processes in human cancer
Initiative, A. P. C. G., Alexandrov, L. B., ICGC Breast Cancer Consortium, ICGC MMML-Seq Consortium, PedBrain, I., Nik-Zainal, S., Wedge, D. C., Aparicio, S. A. J. R., Behjati, S., Biankin, A. V., et al. (2013) · Nature · doi:10.1038/nature12477 · Cited by 9,660 · see citing works
Objective Criteria for the Evaluation of Clustering Methods
Rand, W. M. (1971) · Journal of the American Statistical Association · doi:10.1080/01621459.1971.10482356 · Cited by 6,493 · see citing works
Missing Value Estimation Methods for DNA Microarrays
Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., & Altman, R. B. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.6.520 · Cited by 4,230 · see citing works
mixOmics: An R package for ‘omics feature selection and multiple data integration
Rohart, F., Gautier, B., Singh, A., & Lê Cao, K.-A. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005752 · Cited by 4,056 · see citing works
Khan, J., Wei, J. S., Ringnér, M., Saal, L. H., Ladanyi, M., Westermann, F., Berthold, F., Schwab, M., Antonescu, C. R., Peterson, C., et al. (2001) · Nature Medicine · doi:10.1038/89044 · Cited by 2,644 · see citing works
Metagenes and molecular pattern discovery using matrix factorization
Brunet, J.-P., Tamayo, P., Golub, T. R., & Mesirov, J. P. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308531101 · Cited by 2,183 · see citing works
Objective Criteria for the Evaluation of Clustering Methods
Rand, W. M. (1971) · Journal of the American Statistical Association · doi:10.2307/2284239 · Cited by 1,863 · see citing works
A flexible R package for nonnegative matrix factorization
Gaujoux, R., & Seoighe, C. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-367 · Cited by 1,554 · see citing works
Deciphering Signatures of Mutational Processes Operative in Human Cancer
Alexandrov, L. B., Nik‐Zainal, S., Wedge, D. C., Campbell, P. J., & Stratton, M. R. (2013) · Cell Reports · doi:10.1016/j.celrep.2012.12.008 · Cited by 1,277 · see citing works
Natural Scales in Geographical PatternsPreprint
Menezes, T., & Roth, C. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01036 · Cited by 1,126 · see citing works
Deep Matrix Factorization Models for Recommender Systems
Xue, H.-J., Dai, X., Zhang, J., Huang, S., & Chen, J. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/447 · Cited by 982 · see citing works
Network-based stratification of tumor mutations
Hofree, M., Shen, J. P., Carter, H. K., Gross, A. M., & Ideker, T. E. (2013) · Nature Methods · doi:10.1038/nmeth.2651 · Cited by 726 · see citing works
The Repertoire of Mutational Signatures in Human CancerPreprint
Alexandrov, L. B., Kim, J., Haradhvala, N. J., Huang, M. N., Ng, A. W. T., Wu, Y., Boot, A., Covington, K. R., Gordenin, D. A., Bergstrom, E. N., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/322859 · Cited by 529 · see citing works
Application of independent component analysis to microarrays
Lee, S., & Batzoglou, S. (2003) · Genome biology · doi:10.1186/gb-2003-4-11-r76 · Cited by 260 · see citing works
Enter the Matrix: Factorization Uncovers Knowledge from Omics
Stein-O’Brien, G., Arora, R., Culhane, A. C., Favorov, A. V., Garmire, L. X., Greene, C. S., Goff, L. A., Li, Y., Ngom, A., Ochs, M. F., et al. (2018) · Trends in Genetics · doi:10.1016/j.tig.2018.07.003 · Cited by 235 · see citing works
Review of processing and analysis methods for DNA methylation array data
Wilhelm-Benartzi, C. S., Koestler, D. C., Karagas, M. R., Flanagan, J. M., Christensen, B. C., Kelsey, K. T., Marsit, C. J., Houseman, E. A., & Brown, R. E. (2013) · British Journal of Cancer · doi:10.1038/bjc.2013.496 · Cited by 202 · see citing works
Dealing with missing values in large-scale studies: microarray data imputation and beyond
Aittokallio, T. A. (2009) · Briefings in Bioinformatics · doi:10.1093/bib/bbp059 · Cited by 171 · see citing works
Zhao, W., Luo, J., & Jiao, S. (2014) · Scientific Reports · doi:10.1038/srep06591 · Cited by 123 · see citing works
CoGAPS: an R/C++ package to identify patterns and biological process activity in transcriptomic data
Fertig, E. J., Ding, J., Favorov, A. V., Parmigiani, G., & Ochs, M. F. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq503 · Cited by 91 · see citing works
Ochs, M. F., Rink, L. A., Tarn, C., Mburu, S., Taguchi, T., Eisenberg, B. L., & Godwin, A. K. (2009) · Cancer Research · doi:10.1158/0008-5472.can-09-1709 · Cited by 58 · see citing works
Gene Expression Classification of Lung Adenocarcinoma into Molecular Subtypes
Hu, F., Zhou, Y., Wang, Q., Yang, Z., Shi, Y. H., & Chi, Q. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2905553 · Cited by 50 · see citing works
Matrix factorization for transcriptional regulatory network inference
Ochs, M. F., & Fertig, E. J. (2012) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb.2012.6217256 · Cited by 40 · see citing works
An integrative imputation method based on multi-omics datasets
Lin, D., Zhang, J., Li, J., Xu, C., Deng, H., & Wang, Y. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1122-6 · Cited by 39 · see citing works
Fertig, E. J., Marković, A. K., Danilova, L., Gaykalova, D. A., Cope, L., Chung, C. H., Ochs, M. F., & Califano, J. A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0078127 · Cited by 35 · see citing works
Fertig, E. J., Favorov, A. V., & Ochs, M. F. (2013) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2013.2263390 · Cited by 11 · see citing works
Deep Matrix Factorization for Recommender Systems with Missing Data not at Random
Zhang, F., Song, J., & Peng, S. (2018) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1060/1/012001 · Cited by 8 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Missing data in multi-omics integration: Recent advances through artificial intelligence
Flores, J. E., Claborne, D. M., Weller, Z. D., Webb-Robertson, B.-J. M., Waters, K. M., & Bramer, L. M. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1098308 · Cited by 181 · see citing works
“DeepMF (Chen et al., 2019) casts traditional matrix factorization within a deep learning framework to map concatenated multiomics data to a lower-dimensional latent space.”
Liu, M., Li, S., Yuan, H., Ong, M. E. H., Ning, Y., Xie, F., Saffari, S. E., Shang, Y., Volovici, V., Chakraborty, B., et al. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102587 · Cited by 166 · see citing works
Park, Y., Heider, D., & Hauschild, A.-C. (2021) · Cancers · doi:10.3390/cancers13133148 · Cited by 41 · see citing works
From genetics to systems biology of stress-related mental disorders
Dalvie, S., Chatzinakos, C., Zoubi, O. A., Georgiadis, F., Lancashire, L., & Daskalakis, N. P. (2021) · Neurobiology of Stress · doi:10.1016/j.ynstr.2021.100393 · Cited by 28 · see citing works
Integrative computational epigenomics to build data-driven gene regulation hypotheses
Chen, T., & Tyagi, S. (2020) · GigaScience · doi:10.1093/gigascience/giaa064 · Cited by 25 · see citing works
Moving towards genome-wide data integration for patient stratification with Integrate Any Omics
Ma, S., Zeng, A. G. X., Haibe‐Kains, B., Goldenberg, A., Dick, J. E., & Wang, B. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-024-00942-3 · Cited by 23 · see citing works
Incorporating cell hierarchy to decipher the functional diversity of single cells
Chen, L., & Li, S. C. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac1044 · Cited by 18 · see citing works
Chen, L., Qing, Y., Li, R., Li, C., Li, H., Feng, X., & Li, S. C. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab452 · Cited by 12 · see citing works
A Review of Deep Learning Methods for Multi-omics Integration in Precision Medicine
Zaghlool, S. B., & Attallah, O. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995099 · Cited by 10 · see citing works
SMURF: embedding single-cell RNA-seq data with matrix factorization preserving self-consistency
Pu, J., Wang, B., Liu, X., Chen, L., & Li, S. C. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad026 · Cited by 7 · see citing works
Fan, M., You, Z. P., Fu, Z., Xu, M., Wang, S., Xie, S., Gao, X., Wang, Y., & Li, L. (2021) · Medical Physics · doi:10.1002/mp.15316 · Cited by 6 · see citing works
Advancements in precision nutrition: Targeted strategies for personalized health
Zheng, H., Zhang, X., Su, W., Zaky, A. A., EL-Aty, A. M. A., Xu, E., Liu, D., & Tan, M. (2026) · The Innovation Life · doi:10.59717/j.xinn-life.2026.100191 · Cited by 6 · see citing works
Enhancing Recommendation Diversity and Novelty with Bi-LSTM and Mean Shift Clustering
Yuan, Y., Zhou, Y., Chen, X., Xiong, Q., & Okere, H. C. (2024) · Electronics · doi:10.3390/electronics13193841 · Cited by 5 · see citing works
Li, D., Gu, H., Chang, Q., Wang, J., & Qin, P. (2022) · Soft Computing · doi:10.1007/s00500-022-07180-y · Cited by 5 · see citing works
An Ensemble Matrix Completion Model for Predicting Potential Drugs Against SARS-CoV-2
Wen, L., Wang, S., & Xu, J. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.694534 · Cited by 5 · see citing works
scSVAS: CNV clonal visualization online platform for large scale single-cell genomicsPreprint
Chen, L., Qing, Y., Li, R., Li, C., Li, H., Feng, X., & Li, S. C. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.10.437122 · Cited by 2 · see citing works
Muhammad, B. L., Kim, H., Aliyu, I., Shehu, H. A., & Ki, J. (2026) · Toxins · doi:10.3390/toxins18010026 · Cited by 1 · see citing works
Halawani, R., Büchert, M., & Chen, Y. P. (2024) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2024.3503761 · Cited by 3 · see citing works
Fan, M., Yuan, W., Liu, W., Gao, X., Xu, M., Wang, S., & Li, L. (2021) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac3a25 · Cited by 1 · see citing works
Breast Cancer Subtypes Prediction Using Omics Data and Machine Learning Models
binali, S. A., Meshoul, S., & Shaiba, H. A. (2022) · Algorithms for intelligent systems · doi:10.1007/978-981-19-1653-3_45 · Cited by 1 · see citing works
ResNetMF: Enhancing Recommendation Systems with Residual Network Matrix FactorizationPreprint
Payandenick, M., & Wang, Y. C. (2023) · Preprints.org · doi:10.20944/preprints202310.0302.v1 · Cited by 1 · see citing works
Ye, P., Kong, W., & Wang, S. (2025) · Current Bioinformatics · doi:10.2174/0115748936367139250801163310
- Authors
- Chen, L., Xu, J., & Li, S. C.
- Affiliations
- City University of Hong Kong (HK)
- Corresponding author
- Li, Shuai Cheng
- Venue
- BMC Bioinformatics
- Published
- 2019-12
- Volume
- 20
- Issue
- S23
- Pages
- 648-648
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC6933662
- OpenAlex
- W2997597306
- Cited by
- 23
- References
- 29
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 2 · 2023: 5 · 2022: 4 · 2021: 7 · 2020: 1
- FWCI
- 0.99
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- CityU Scholars (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- City University of Hong Kong (11257316)
- Universiti Tunku Abdul Rahman
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- matrix factorization, omics data, missing data imputation, gene expression profiles, microRNA expression profiles, protein profile, medulloblastoma, leukemia, breast cancer, small round cell tumor, cancer clustering
- MeSH terms
- Deep Learning, Algorithms, Computer Simulation, Humans, Neoplasms, RNA, Messenger, Software, Neural Networks, Computer, Genomics, Databases, Genetic, MicroRNAs
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