Detecting molecular subtypes from multi-omics datasets using SUMO
Open accessAbstract
We present a data integration framework that uses non-negative matrix factorization of patient-similarity networks to integrate continuous multi-omics datasets for molecular subtyping. It is demonstrated to have the capability to handle missing data without using imputation and to be consistently among the best in detecting subtypes with differential prognosis and enrichment of clinical associations in a large number of cancers. When applying the approach to data from individuals with lower-grade gliomas, we identify a subtype with a significantly worse prognosis. Tumors assigned to this subtype are hypomethylated genome wide with a gain of AP-1 occupancy in demethylated distal enhancers. The tumors are also enriched for somatic chromosome 7 (chr7) gain, chr10 loss, and other molecular events that have been suggested as diagnostic markers for “ IDH wild type, with molecular features of glioblastoma” by the cIMPACT-NOW consortium but have yet to be included in the World Health Organization (WHO) guidelines.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
SUMO slightly outperformed NEMO in robustness to varying levels of missing samples, except at extreme missingness of 80% and greater.
“These comparisons indicated a slight outperformance by SUMO, except for in the cases of extreme missingness (80% and greater).”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Sienkiewicz et al., 2022
SUMO's range of adjusted rand index values was typically narrower than NEMO's, suggesting less variability in expected performance.
“As well, the range of adjusted rand index values (the performance metric used) was typically observed to be narrower for SUMO, suggesting less variability in expected performance.”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Sienkiewicz et al., 2022
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Normalized cuts and image segmentation
Shi, J., & Malik, J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868688 · Cited by 15,842 · see citing works
The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary
Louis, D. N., Perry, A., Reifenberger, G., von Deimling, A., Figarella‐Branger, D., Cavenee, W. K., Ohgaki, H., Wiestler, O. D., Kleihues, P., & Ellison, D. W. (2016) · Acta Neuropathologica · doi:10.1007/s00401-016-1545-1 · Cited by 15,703 · cited 1× · see citing works
“SUMO analysis of TCGA-LGG identifies a cluster of individuals with poor prognosis The 2016 World Health Organization (WHO) classification of diffuse gliomas recommends use of IDHmutation status to separate IDH mutant astrocytoma and oligodendroglioma from IDH wild type astrocytoma, which has a worse prognosis (Louis e…”
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
(2017) · HAL · Cited by 10,512 · see citing works
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658 · cited 1× · see citing works
“If batch information is known, we use ComBat (Johnson et al., 2007) to adjust for batch effects in this step.”
Integrated genomic analyses of ovarian carcinoma
Perou, C. M. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/hvp3-wg08 · Cited by 7,927 · see citing works
The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups
METABRIC Group, Curtis, C., Shah, S. P., Chin, S.-F., Turashvili, G., Rueda, O. M., Dunning, M. J., Speed, D., Lynch, A. G., Samarajiwa, S., et al. (2012) · Nature · doi:10.1038/nature10983 · Cited by 6,097 · cited 4× · see citing works
“We also downloaded two breast cancer datasets from METABRIC.”
InfluentialPaatero, P., & Tapper, U. (1994) · Environmetrics · doi:10.1002/env.3170050203 · Cited by 6,048 · cited 1× · see citing works
“The NMF technique aims to explain the observed data using a small number of basis components by factoring the data into the product of two non-negative matrices; one representing the basis components, and the other containing mixture coefficients (Paatero and Tapper, 1994).”
The Somatic Genomic Landscape of Glioblastoma
Brennan, C. W., Verhaak, R. G. W., McKenna, A., Campos, B., Noushmehr, H., Salama, S. R., Zheng, S., Chakravarty, D., Sanborn, J. Z., Berman, S. H., et al. (2013) · Cell · doi:10.1016/j.cell.2013.09.034 · Cited by 5,016 · cited 11× · see citing works
“We decided to apply SUMO to subtype the LGGs as a case study with the intent to evaluate the robustness and relevance of known and proposed glioma subtypes.”
InfluentialVisualizing and interpreting cancer genomics data via the Xena platform
Goldman, M. J., Craft, B., Hastie, M., Repečka, K., McDade, F., Kamath, A., Banerjee, A., Luo, Y., Rogers, D., Brooks, A. N., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0546-8 · Cited by 4,481 · see citing works
xCell: digitally portraying the tissue cellular heterogeneity landscape
Aran, D., Hu, Z., & Butte, A. J. (2017) · Genome Biology · doi:10.1186/s13059-017-1349-1 · Cited by 4,397 · cited 1× · see citing works
“To investigate this, we downloaded the xCell scores corresponding to the enrichment of 64 different immune and stromal cell types in these TCGA samples (Aran et al., 2017).”
Liu, J., Lichtenberg, T., Hoadley, K. A., Poisson, L. M., Lazar, A. J., Cherniack, A. D., Kovatich, A. J., Benz, C. C., Levine, D. A., Lee, A. V., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.052 · Cited by 3,725 · cited 1× · see citing works
“Based on the recommendations by Liu et al., 2018, we used progression-free survival for DLBC, TCGT, and THYM.”
Ceccarelli, M., Barthel, F. P., Malta, T. M., Sabedot, T. S., Salama, S. R., Murray, B. A., Morozova, O., Newton, Y., Radenbaugh, A., Pagnotta, S. M., et al. (2016) · Cell · doi:10.1016/j.cell.2015.12.028 · Cited by 2,178 · cited 2× · see citing works
“Several studies have associated IDH somatic mutations with a more favorable course of the disease and have identified multiple subtypes with a poor clinical course (Eckel-Passow et al., 2015; Ceccarelli et al., 2016).”
Du, P., Zhang, X., Huang, C.-C., Jafari, N., Kibbe, W. A., Hou, L., & Lin, S. M. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-587 · Cited by 2,136 · cited 1× · see citing works
“In the case of methylation data, we use of log2 ratio of the methylated to unmethylated count, also referred to as M-values (Du et al., 2010).”
Similarity network fusion for aggregating data types on a genomic scale
Wang, B., Mezlini, A. M., Demir, F., Fiume, M., Tu, Z., Brudno, M., Haibe-Kains, B., & Goldenberg, A. (2014) · Nature Methods · doi:10.1038/nmeth.2810 · Cited by 2,036 · cited 6× · see citing works
“…extension of iClusterPlus v.1.26, LRAcluster v.1.0 (Wu et al., 2015), MCCA from PMA package v.1.2.1 (Witten and Tibshirani, 2009), NEMO v.0.1 (Rappoport and Shamir, 2019b), PINSPlus v.2.0.5 (Nguyen et al., 2017), SNF v.2.3 (Wang et al., 2014), and CIMLR v.1.0 (Ramazzotti et al., 2018).”
InfluentialGlioma Groups Based on 1p/19q, IDH , and TERT Promoter Mutations in Tumors
Eckel‐Passow, J. E., Lachance, D. H., Molinaro, A. M., Walsh, K. M., Decker, P. A., Sicotte, H., Pekmezci, M., Rice, T., Kosel, M., Smirnov, I. V., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1407279 · Cited by 1,981 · cited 1× · see citing works
“Several studies have associated IDH somatic mutations with a more favorable course of the disease and have identified multiple subtypes with a poor clinical course (Eckel-Passow et al., 2015; Ceccarelli et al., 2016).”
Brat, D. J., Aldape, K., Colman, H., Holland, E. C., Louis, D. N., Jenkins, R. B., Kleinschmidt-DeMasters, B. K., Perry, A., Reifenberger, G., Stupp, R., et al. (2018) · Acta Neuropathologica · doi:10.1007/s00401-018-1913-0 · Cited by 904 · cited 2× · see citing works
“Recent recommendations from the cIMPACT-NOW consortium suggest that EGFR amplification and combined chr7 gain and chr10 loss as well as TERT promoter mutation can be used to diagnose IDH wild type (WT) grade II/III gliomas that are likely to follow a more aggressive clinical disease course (Brat et al., 2018).”
InfluentialShen, R., Olshen, A. B., & Ladanyi, M. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp543 · Cited by 849 · cited 1× · see citing works
“More sophisticated methods (e.g., iCluster [Shen et al., 2009], iClusterPlus [Mo et al., 2013], and Bayesian consensus clustering [Lock and Dunson, 2013]) model the probabilistic distribution of each data type and infer subtypes by maximization of the likelihood of the observed data.”
SVD based initialization: A head start for nonnegative matrix factorization
Boutsidis, C., & Gallopoulos, E. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.09.010 · Cited by 729 · cited 1× · see citing works
“Both initialization and convergence speeds are important factors to consider when formulating the appropriate factorization algorithms (Boutsidis and Gallopoulos, 2008).”
The UCSC Xena platform for public and private cancer genomics data visualization and interpretationPreprint
Goldman, M., Craft, B., Hastie, M., Repečka, K., Kamath, A., McDade, F., Rogers, D., Brooks, A. N., Zhu, J., & Haussler, D. H. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/326470 · Cited by 604 · cited 1× · see citing works
“We used SUMO to integrate the processed level 3 gene expression, DNA methylation, and miRNA expression data for the TCGA-LGG cohort downloaded from the UCSC Xena platform (Goldman et al., 2019) (and preprocessed as described in the Data preprocessing part of the STAR Methods).”
Shen, R., Olshen, A. B., & Ladanyi, M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp659 · Cited by 566 · see citing works
Extensions of Sparse Canonical Correlation Analysis with Applications to Genomic Data
Witten, D., & Tibshirani, R. J. (2009) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1470 · Cited by 551 · see citing works
Multi-omic and multi-view clustering algorithms: review and cancer benchmark
Rappoport, N., & Ron Shamir, A. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky889 · Cited by 549 · cited 2× · see citing works
“We use a recent benchmark (Rappoport and Shamir, 2018) and datasets from TCGA and METABRIC (Curtis et al.”
Pattern discovery and cancer gene identification in integrated cancer genomic data
Mo, Q., Wang, S., Seshan, V. E., Olshen, A. B., Schultz, N., Sander, C., Powers, R. S., Ladanyi, M., & Shen, R. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1208949110 · Cited by 474 · cited 1× · see citing works
“More sophisticated methods (e.g., iCluster [Shen et al., 2009], iClusterPlus [Mo et al., 2013], and Bayesian consensus clustering [Lock and Dunson, 2013]) model the probabilistic distribution of each data type and infer subtypes by maximization of the likelihood of the observed data.”
Gene therapy of experimental brain tumors using neural progenitor cells
Benedetti, S., Pirola, B., Pollo, B., Magrassi, L., Bruzzone, M. G., Rigamonti, D., Galli, R., Selleri, S., DiMeco, F., De‐Fraja, C., et al. (2000) · Nature Medicine · doi:10.1038/74710 · Cited by 456 · cited 1× · see citing works
“Subtypes 4 and 5 are enriched for G-CIMP-high samples, although subtype 5 is enriched for mutations in ATRX (post hoc test of residuals for c(2) test, p < 10 (5)) and shows a higher enrichment for mast cells (Figure 7), which are known to induce release of selective inflammatory cytokines, such as interleukin-4 (IL-4)…”
Symmetric Nonnegative Matrix Factorization for Graph Clustering
Kuang, D., Ding, C. H. Q., & Park, H. (2012) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972825.10 · Cited by 452 · cited 1× · see citing works
“In this work, we used a variant of NMF called Symmetric NMF, in which the decomposition is done on a symmetrical matrix that contains pairwise similarity values between the data points instead of being done directly on the data points (Kuang et al., 2012).”
Rivera, A. L., Pelloski, C. E., Gilbert, M. R., Colman, H., Cruz, C., Sulman, E. P., Bekele, B. N., & Aldape, K. (2009) · Neuro-Oncology · doi:10.1093/neuonc/nop020 · Cited by 432 · cited 1× · see citing works
“MGMT promoter methylation is associated with a better response to alkylating chemotherapy, suggesting that individuals assigned to subtype 1 are more likely to respond to temozolomide (Rivera et al., 2010).”
Critical limitations of consensus clustering in class discovery
Şenbabaoğlu, Y., Michailidis, G. C., & Li, J. Z. (2014) · Scientific Reports · doi:10.1038/srep06207 · Cited by 380 · see citing works
Stichel, D., Ebrahimi, A., Reuß, D., Schrimpf, D., Ono, T., Shirahata, M., Reifenberger, G., Weller, M., Hänggi, D., Wick, W., et al. (2018) · Acta Neuropathologica · doi:10.1007/s00401-018-1905-0 · Cited by 311 · cited 2× · see citing works
“Our results support the proposed inclusion of additional diagnosis criteria, in particular chr7 gain and chr10 loss, which could lead to reclassification of several LGGs into GBMs (Stichel et al., 2018).”
Lock, E. F., & Dunson, D. B. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt425 · Cited by 308 · cited 1× · see citing works
“More sophisticated methods (e.g., iCluster [Shen et al., 2009], iClusterPlus [Mo et al., 2013], and Bayesian consensus clustering [Lock and Dunson, 2013]) model the probabilistic distribution of each data type and infer subtypes by maximization of the likelihood of the observed data.”
Gill, B. J. A., Pisapia, D. J., Malone, H. R., Goldstein, H. E., Lei, L., Sonabend, A. M., Yun, J. J., Samanamud, J. L., Sims, J. S., Banu, M. A., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1405839111 · Cited by 299 · cited 1× · see citing works
“The NE subtype has been related previously to the tumor margin, where increased normal NE tissue is likely to be detected (Gill et al., 2014).”
NEMO: cancer subtyping by integration of partial multi-omic data
Rappoport, N., & Shamir, R. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz058 · Cited by 286 · cited 9× · see citing works
“A more recent method, NEMO (Rappoport and Shamir, 2019b), calculates an average similarity matrix and then detects the clusters using spectral clustering.”
InfluentialPrecision oncology: origins, optimism, and potential
Prasad, V. K., Fojo, T., & Brada, M. (2016) · The Lancet Oncology · doi:10.1016/s1470-2045(15)00620-8 · Cited by 257 · cited 1× · see citing works
“Given that each assay reveals a snapshot of certain cellular aspects of a disease, integrative analyses are often necessary for a complete understanding of its molecular etiology and for discovering its molecular subtypes and biomarkers (Prasad et al., 2016).”
Wu, D., Wang, D., Zhang, M. Q., & Gu, J. (2015) · BMC Genomics · doi:10.1186/s12864-015-2223-8 · Cited by 229 · cited 1× · see citing works
“Throughout the study, whenever appropriate, we compare SUMO v.0.2.6 with the iClusterBayes extension of iClusterPlus v.1.26, LRAcluster v.1.0 (Wu et al., 2015), MCCA from PMA package v.1.2.1 (Witten and Tibshirani, 2009), NEMO v.0.1 (Rappoport and Shamir, 2019b), PINSPlus v.2.0.5 (Nguyen et al.,…”
InfluentialMulti-omic tumor data reveal diversity of molecular mechanisms that correlate with survival
Ramazzotti, D., Lal, A., Wang, B., Batzoglou, S., & Sidow, A. (2018) · Nature Communications · doi:10.1038/s41467-018-06921-8 · Cited by 212 · cited 1× · see citing works
“…extension of iClusterPlus v.1.26, LRAcluster v.1.0 (Wu et al., 2015), MCCA from PMA package v.1.2.1 (Witten and Tibshirani, 2009), NEMO v.0.1 (Rappoport and Shamir, 2019b), PINSPlus v.2.0.5 (Nguyen et al., 2017), SNF v.2.3 (Wang et al., 2014), and CIMLR v.1.0 (Ramazzotti et al., 2018).”
InfluentialA novel approach for data integration and disease subtypingPreprint
Nguyen, T., Tagett, R., Diaz, D., & Drăghici, S. (2017) · Genome Research · doi:10.1101/gr.215129.116 · Cited by 178 · cited 3× · see citing works
“PINSPlus results varied significantly in terms of survival and enrichment of clinical labels.”
Influentialde Souza, C. F., Sabedot, T. S., Malta, T. M., Stetson, L. C., Morozova, O., Sokolov, A., Laird, P. W., Wiznerowicz, M., Iavarone, A., Snyder, J., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.107 · Cited by 178 · cited 1× · see citing works
“These results are in agreement with other published studies that show that AP-1 binds to demethylated regions in G-CIMP-low tumors, but we find this to be true for all samples assigned to subtype 2 (Souza et al., 2018).”
Louis, D. N., Aldape, K. D., Brat, D. J., Capper, D., Ellison, D. W., Hawkins, C. E., Paulus, W., Perry, A., Reifenberger, G., Figarella-Branger, D. F., et al. (2016) · Acta Neuropathologica · doi:10.1007/s00401-016-1646-x · Cited by 155 · cited 1× · see citing works
“The cIMPACT-NOW (the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy) initiative was established to evaluate and recommend changes to future CNS tumor classifications based on consensus review of novel diagnostically relevant data (Louis et al., 2017).”
Position-dependent motif characterization using non-negative matrix factorization
Hutchins, L. N., Murphy, S. M., Singh, P., & Graber, J. H. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn526 · Cited by 143 · cited 1× · see citing works
“We evaluated the solutions with 2–19 clusters according to the proportion of ambiguously clustered pairs (PACs) (Şenbabaoğlu et al., 2014) and the cophenetic correlation (Hutchins et al., 2008; see STAR Methods for details).”
Silva, T. C., Coetzee, S. G., Gull, N., Yao, L., Hazelett, D. J., Noushmehr, H., Lin, D., & Berman, B. P. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty902 · Cited by 131 · cited 2× · see citing works
“We used ELMER (Silva et al., 2019) in an unsupervised mode to compare subtype 2 tumors with the other LGGs. ELMER identified 382 probes overlapping putative distal enhancers that were hypomethylated in subtype 2 samples (adjusted p < 0.01; methylation difference between means of the groups, >0.3).”
InfluentialIntegrated genomic analyses of ovarian carcinoma
The Cancer Genome Atlas Research Network (2011) · Nature · doi:10.1038/nature10166 · Cited by 129 · cited 1× · see citing works
“This group of individuals with a poor prognosis is enriched for those with mesenchymal tumors that are known to lead to worse outcomes (Cancer Genome Atlas Research Network, 2011).”
Developmentally regulated signaling pathways in glioma invasion
Mehta, S. V., & Cascio, C. L. (2017) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-017-2608-8 · Cited by 90 · cited 1× · see citing works
“Therefore, FOS proteins have been implicated as regulators of cell proliferation, differentiation, and transformation (Mehta and Lo Cascio, 2018).”
A survey and systematic assessment of computational methods for drug response prediction
Chen, J., & Zhang, L. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz164 · Cited by 85 · see citing works
TumorMap: Exploring the Molecular Similarities of Cancer Samples in an Interactive Portal
Newton, Y., Novak, A. M., Swatloski, T., McColl, D., Chopra, S., Graim, K. S., Weinstein, A., Baertsch, R., Salama, S. R., Ellrott, K., et al. (2017) · Cancer Research · doi:10.1158/0008-5472.can-17-0580 · Cited by 66 · cited 1× · see citing works
“The largest study of diffuse grade II-III-IV gliomas to date used TumorMap (Newton et al., 2017) to integrate gene expression and DNA methylation data from around 1,000 individuals, and they, too, identified IDH status as the primary driver of two macro-clusters (Ceccarelli et al., 2016).”
jNMFMA: a joint non-negative matrix factorization meta-analysis of transcriptomics data
Wang, H., Zheng, C.-H., & Zhao, X. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu679 · Cited by 56 · cited 1× · see citing works
“NMF has been successfully used as a clustering method in image and pattern recognition (Leuschner et al., 2019), text-mining (Chen et al., 2015), and bioinformatics (Wang et al., 2015).”
Dynamic Regulation of AP-1 Transcriptional Complexes Directs Trophoblast Differentiation
Kubota, K., Kent, L. N., Rumi, M. A. K., Roby, K. F., & Soares, M. J. (2015) · Molecular and Cellular Biology · doi:10.1128/mcb.00118-15 · Cited by 51 · cited 1× · see citing works
“More specifically, we find that the expression of FOSL1, which contributes to regulation of placenta development, is significantly higher in subtype 2 tumors, and higher expression of the gene is associated with a worse prognosis (Kubota et al., 2015).”
Supervised non-negative matrix factorization methods for MALDI imaging applications
Leuschner, J., Schmidt, M., Fernsel, P., Lachmund, D., Boskamp, T., & Maaß, P. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty909 · Cited by 51 · cited 1× · see citing works
“NMF has been successfully used as a clustering method in image and pattern recognition (Leuschner et al., 2019), text-mining (Chen et al., 2015), and bioinformatics (Wang et al., 2015).”
Weakly supervised nonnegative matrix factorization for user-driven clustering
Choo, J., Lee, C., Reddy, C. K., & Park, H. (2014) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0384-8 · Cited by 32 · cited 9× · see citing works
“The NMF technique aims to explain the observed data using a small number of basis components by factoring the data into the product of two non-negative matrices; one representing the basis components, and the other containing mixture coefficients (Paatero and Tapper, 1994).”
InfluentialInjury narrative text classification using factorization model
Chen, L., Vallmuur, K., & Nayak, R. (2015) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-15-s1-s5 · Cited by 30 · cited 1× · see citing works
“NMF has been successfully used as a clustering method in image and pattern recognition (Leuschner et al., 2019), text-mining (Chen et al., 2015), and bioinformatics (Wang et al., 2015).”
MIRA: an R package for DNA methylation-based inference of regulatory activity
Lawson, J. T., Tomazou, E. M., Bock, C., & Sheffield, N. C. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty083 · Cited by 22 · cited 2× · see citing works
“xCell scores are derived from gene expression, but we observe similar results in analysis of methylation data using MIRA (Lawson et al., 2018).”
Cai, S., Georgakilas, G. K., Johnson, J. L., & Vahedi, G. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00319 · Cited by 16 · cited 1× · see citing works
“For example, cosine similarity has been shown to be a better metric for the calculation of similarity between single cells in the single-cell sequencing for transposase accessible chromatin (scATAC-seq) (Cai et al., 2018).”
Inaccuracy of the log‐rank approximation in cancer data analysis
Rappoport, N., & Shamir, R. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188754 · Cited by 15 · cited 1× · see citing works
“In this study, we report the p values of the log rank test using exact permutation tests because p values based on the c(2) approximation are highly inaccurate in evaluating clustering solutions on real cancer datasets (Rappoport and Shamir, 2019a).”
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
“As well, the range of adjusted rand index values (the performance metric used) was typically observed to be narrower for SUMO, suggesting less variability in expected performance.”
BackgroundInfluentialDeep learning in cancer genomics and histopathology
Unger, M., & Kather, J. N. (2024) · Genome Medicine · doi:10.1186/s13073-024-01315-6 · Cited by 124 · see citing works
“Articles such as Sienkie-wicz et al. [68] (Fig.”
Heterogeneous data integration methods for patient similarity networks
Gliozzo, J., Mesiti, M., Notaro, M., Petrini, A., Patak, A., Gallardo, A. P., Paccanaro, A., Valentini, G., & Casiraghi, E. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac207 · Cited by 36 · see citing works
“Consensus clustering has also been successfully applied by the recently published SUMO [62], an integrative clustering algorithm that starts by computing several unimodal PSNs by using a scaled-normalized Euclidean kernel similar to the one exploited by SNF [3].”
InfluentialHagenberg, J., BeCOME study group, OPTIMA Study Group, Brückl, T. M., Erhart, M., Kopf‐Beck, J., Ködel, M., Rehawi, G., Roeh, S., Sauer, S., et al. (2024) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2024.09.013 · Cited by 31 · see citing works
“The residuals from this regression were then used for down-stream analysis.”
MethodVieira, F. G., Bispo, R., & Lopes, M. B. (2024) · Bioinformatics and Biology Insights · doi:10.1177/11779322241249563 · Cited by 20 · see citing works
“1 To understand the heterogeneity among gliomas, tremendous efforts have been made to identify the different adult-type diffuse types, 2-4 which can be broadly classified as lower-grade gliomas (LGGs) according to a lower proliferative activity or glioblastoma (GBM) which exhibits high malignancy…”
He, W., Huang, Y., Shi, X., Wang, Q., Wu, M., Li, H., Liu, Q., Zhang, X., Huang, C., & Li, X. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04300-6 · Cited by 12 · see citing works
Gliozzo, J., Gomez, M. A. S., Bonometti, A., Patak, A., Casiraghi, E., & Valentini, G. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf150 · Cited by 8 · see citing works
“Advantages over SOTA methods: unlike techniques tailored to specific tasks (e.g. clustering with MaDDA (Vitali et al. 2018) or SUMO (Sienkiewicz et al. 2022), classification with DeepIMV (Lee and van der Schaar 2021) or MOGDx (Ryan et al. 2024)), the miss-SNF algorithm produces a general-purpose integrated matrix suit…”
Subtype-WGME enables whole-genome-wide multi-omics cancer subtyping
Yang, H., Zhao, L., Li, D., An, C., Fang, X., Chen, Y., Liu, J., Xiao, T., & Wang, Z. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100781 · Cited by 8 · see citing works
MoNETA: MultiOmics Network Embedding for SubType Analysis
Scala, G., Ferraro, L., Brandi, A., Guo, Y. P., Majello, B., & Ceccarelli, M. (2024) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqae141 · Cited by 4 · see citing works
Protocol for integrative subtyping of lower-grade gliomas using the SUMO pipeline
Sienkiewicz, K., & Ratan, A. (2022) · STAR Protocols · doi:10.1016/j.xpro.2021.101110 · Cited by 4 · see citing works
Tobiasz, J., & Polańska, J. (2023) · Cancers · doi:10.3390/cancers15174230 · Cited by 3 · see citing works
Multi-omics Profiles are Applicable to Human Diseases and Drug Development
Espinosa‐González, A. M., Benítez-Flores, J. D. C., Gómez-Verján, J. C., Rivero-Segura, N. A., Castro, I. P., Cabrera, J. C. R., & Estrella-Parra, E. A. (2024) · Recent advances in biotechnology · doi:10.2174/9789815223163124090003 · Cited by 2 · see citing works
Manuilova, I., Bossenz, J., Weise, A. B., Boehm, D., Döbel, M., Werle, S. D., Ustjanzew, A., Reimer, N., Strantz, C., Unberath, P., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/71906 · Cited by 1 · see citing works
“In the previewed publications, NMF [76-78] or NMTF [41,79] were used to enhance clustering approaches.”
Connection and Curation of Corpus (Labeled and Unlabeled)
Dev, S., & Sharan, A. (2024) · Transactions on computer systems and networks · doi:10.1007/978-981-97-3962-2_4
He, W., Huang, Y., Shi, X., Wang, Q., Wu, M., Li, H., Liu, Q., Zhang, X., Huang, C., & Li, X. (2023) · Figshare · doi:10.6084/m9.figshare.23639929
He, W., Huang, Y., Shi, X., Wang, Q., Wu, M., Li, H., Liu, Q., Zhang, X., Huang, C., & Li, X. (2023) · Figshare · doi:10.6084/m9.figshare.23639926
Bairwa, A. K., Bhat, S., Sawant, T., Varma, P., & Kushwaha, S. S. (2026) · Discover Computing · doi:10.1007/s10791-026-10173-2
Assessing current capabilities for incorporating lipidomics in multiomics data integration
Ross, D. H., Jain, R., Kim, H., Flores, J. E., Sarkar, S., Clendinen, C. S., Kyle, J., Liu, T., & Gosline, S. J. C. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag276
“Similarity Network Fusion (SNF) [ 43], SUMO [ 44], and NEMO [ 45] exemplify this approach ( Fig.”
- Authors
- Sienkiewicz, K., Chen, J., Chatrath, A., Lawson, J. T., Sheffield, N. C., Zhang, L., & Ratan, A.
- Affiliations
- University of Virginia (US)
- National University of Singapore (SG)
- University of Virginia Cancer Center (US)
- Corresponding author
- Ratan, Aakrosh
- ORCID
- Venue
- Cell Reports Methods
- Publisher
- Elsevier BV
- Published
- 2022-01
- Volume
- 2
- Issue
- 1
- Pages
- 100152
- ISSN
- 2667-2375 (ISSN-L)
- PMC
- PMC8865426
- OpenAlex
- W4210728747
- Cited by
- 18
- References
- 51
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 7 · 2023: 5 · 2022: 2
- FWCI
- 1.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cell Reports Methods (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (P30CA044579)
- National Institute of General Medical Sciences (GM128636)
- National Research Foundation Singapore (NRF2016NRF-NSFC001\u2013026, NRF2016NRF-NSFC001)
- UVA Cancer Center (T32 GM007267)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Health Sciences › Medicine › Genetics › Glioma Diagnosis and Treatment
- Keywords
- molecular subtyping, multi-omics integration, non-negative matrix factorization, patient similarity networks, missing data, tumor subtypes, prognosis, lower-grade glioma, DNA methylation, distal enhancers, chromosome 7 gain, chromosome 10 loss, IDH-wildtype, SUMO
- MeSH terms
- Multiomics, Chromosome Aberrations, Glioblastoma, Glioma, Humans, Prognosis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review