Comparing partitions

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106 · see citing works

GSVA: gene set variation analysis for microarray and RNA-Seq data

Hänzelmann, S., Castelo, R., & Guinney, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-7 · Cited by 15,440 · see citing works

Scenic: single-cell regulatory network inference and clustering.

Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4463 · Cited by 6,520 · see citing works

Density-Based Clustering Based on Hierarchical Density Estimates

Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-37456-2_14 · Cited by 2,680 · see citing works

Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data

Monti, S., Tamayo, P., Mesirov, J., & Golub, T. (2003) · Machine Learning · doi:10.1023/a:1023949509487 · Cited by 2,116 · see citing works

Benchmarking atlas-level data integration in single-cell genomics

Luecken, M. D., Büttner, M., Chaichoompu, K., Danese, A., Interlandi, M., Mueller, M. F., Strobl, D. C., Zappia, L., Dugas, M., Colomé-Tatché, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01336-8 · Cited by 1,438 · see citing works

A benchmark of batch-effect correction methods for single-cell RNA sequencing data

Tran, H. T. N., Ang, K. S., Chevrier, M., Zhang, X., Lee, N. Y. S., Goh, M., & Chen, J. (2020) · Genome Biology · doi:10.1186/s13059-019-1850-9 · Cited by 1,113 · see citing works

Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors

Li, H., Courtois, E. T., Sengupta, D., Tan, Y., Chen, K. H., Goh, J. J. L., Kong, S. L., Chua, C., Hon, L. K., Tan, W. S., et al. (2017) · Nature Genetics · doi:10.1038/ng.3818 · Cited by 1,080 · see citing works

Cluster-wise assessment of cluster stability

Hennig, C. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2006.11.025 · Cited by 930 · see citing works

Political Polarization on Twitter

Conover, M., Ratkiewicz, J., Francisco, M., Goncalves, B., Menczer, F., & Flammini, A. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14126 · Cited by 626 · see citing works

Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex

Luo, C., Keown, C. L., Kurihara, L., Zhou, J., He, Y., Li, J., Castanon, R., Lucero, J., Nery, J. R., Sandoval, J. P., et al. (2017) · Science · doi:10.1126/science.aan3351 · Cited by 545 · see citing works

Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model

Townes, F. W., Hicks, S. C., Aryee, M. J., & Irizarry, R. A. (2019) · Genome Biology · doi:10.1186/s13059-019-1861-6 · Cited by 471 · see citing works

Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex

Arslan, S., Ktena, S. I., Makropoulos, A., Robinson, E. C., Rueckert, D., & Parisot, S. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.014 · Cited by 374 · see citing works

Anatomy of news consumption on Facebook

Schmidt, A. L., Zollo, F., Del Vicario, M., Bessi, A., Scala, A., Caldarelli, G., Stanley, H. E., & Quattrociocchi, W. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1617052114 · Cited by 305 · see citing works

A systematic performance evaluation of clustering methods for single-cell RNA-seq data

Duò, A., Robinson, M. D., & Soneson, C. (2018) · F1000Research · doi:10.12688/f1000research.15666.2 · Cited by 296 · see citing works

NEMO: cancer subtyping by integration of partial multi-omic data

Rappoport, N., & Shamir, R. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz058 · Cited by 286 · see citing works

Relating structure and function in the human brain: relative contributions of anatomy, stationary dynamics, and non-stationarities

Messé, A., Rudrauf, D., Benali, H., & Marrelec, G. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003530 · Cited by 215 · see citing works

A survey of Clustering Algorithms

Rokach, L. (2009) · Data Mining and Knowledge Discovery Handbook · doi:10.1007/978-0-387-09823-4_14 · Cited by 245 · see citing works

Genetic topography of brain morphology

Chen, C.-H., Fiecas, M., Gutiérrez, E. D., Panizzon, M. S., Eyler, L. T., Vuoksimaa, E., Thompson, W. K., Fennema-Notestine, C., Hagler, D. J., Jernigan, T. L., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1308091110 · Cited by 217 · see citing works

Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis

Sun, S., Zhu, J., Ma, Y., & Zhou, X. (2019) · Genome biology · doi:10.1186/s13059-019-1898-6 · Cited by 225 · see citing works

Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data

Freytag, S., Tian, L., Lönnstedt, I., Ng, M., & Bahlo, M. (2018) · F1000Research · doi:10.12688/f1000research.15809.1 · Cited by 220 · see citing works

Using neural networks for reducing the dimensions of single-cell RNA-Seq data

Lin, C., Jain, S., Kim, H., & Bar-Joseph, Z. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx681 · Cited by 197 · see citing works

Exploration of bacterial community classes in major human habitats

Zhou, Y., Mihindukulasuriya, K. A., Gao, H., La Rosa, P. S., Wylie, K. M., Martin, J. C., Kota, K., Shannon, W. D., Mitreva, M., Sodergren, E., et al. (2014) · Genome Biology · doi:10.1186/gb-2014-15-5-r66 · Cited by 149 · see citing works

Identification of depression subtypes and relevant brain regions using a data-driven approach

Tokuda, T., Yoshimoto, J., Shimizu, Y., Okada, G., Takamura, M., Okamoto, Y., Yamawaki, S., & Doya, K. (2018) · Scientific Reports · doi:10.1038/s41598-018-32521-z · Cited by 136 · see citing works

Fast and precise single-cell data analysis using a hierarchical autoencoder

Tran, D., Nguyen, H., Tran, B., La Vecchia, C., Luu, H. N., & Nguyen, T. (2021) · Nature Communications · doi:10.1038/s41467-021-21312-2 · Cited by 145 · see citing works

Detecting hierarchical genome folding with network modularity

Norton, H. K., Emerson, D. J., Huang, H., Kim, J., Titus, K. R., Gu, S., Bassett, D. S., & Phillips-Cremins, J. E. (2018) · Nature Methods · doi:10.1038/nmeth.4560 · Cited by 146 · see citing works

Epigenetic Identity in AML Depends on Disruption of Nonpromoter Regulatory Elements and Is Affected by Antagonistic Effects of Mutations in Epigenetic Modifiers

Glass, J. L., Hassane, D., Wouters, B. J., Kunimoto, H., Avellino, R., Garrett-Bakelman, F. E., Guryanova, O. A., Bowman, R., Redlich, S., Intlekofer, A. M., et al. (2017) · Cancer Discovery · doi:10.1158/2159-8290.cd-16-1032 · Cited by 142 · see citing works

Clustering of single-cell multi-omics data with a multimodal deep learning method

Lin, X., Tian, T., Wei, Z., & Hakonarson, H. (2022) · Nature Communications · doi:10.1038/s41467-022-35031-9 · Cited by 135 · see citing works

Meaning in Life in Emerging Adulthood: A Person‐Oriented Approach

Dezutter, J., Waterman, A. S., Schwartz, S. J., Luyckx, K., Beyers, W., Meca, A., Kim, S. Y., Whitbourne, S. K., Zamboanga, B. L., Lee, R. M., et al. (2014) · Journal of Personality · doi:10.1111/jopy.12033 · Cited by 133 · see citing works

Increasing sample size compensates for data problems in segmentation studies

Dolnicar, S., Grün, B., & Leisch, F. (2016) · Journal of Business Research · doi:10.1016/j.jbusres.2015.09.004 · Cited by 131 · see citing works

Oral Samples as non-invasive proxies for assessing the composition of the rumen microbial community

Tapio, I., Shingfield, K. J., McKain, N., Bonin, A., Fischer, D., Bayat, A. R., Vilkki, J., Taberlet, P., Snelling, T. J., & Wallace, R. J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0151220 · Cited by 125 · see citing works

Integrative phenotyping framework (iPF): integrative clustering of multiple omics data identifies novel lung disease subphenotypes

Kim, S., Herazo-Maya, J. D., Kang, D. D., Juan-Guardela, B. M., Tedrow, J., Martinez, F. J., Sciurba, F. C., Tseng, G. C., & Kaminski, N. (2015) · BMC Genomics · doi:10.1186/s12864-015-2170-4 · Cited by 123 · see citing works

A Monte Carlo evaluation of weighted community detection algorithms

Gates, K. M., Henry, T., Steinley, D., & Fair, D. A. (2016) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2016.00045 · Cited by 106 · see citing works

Blind PRNU-Based Image Clustering for Source Identification

Marra, F., Poggi, G., Sansone, C., & Verdoliva, L. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2701335 · Cited by 84 · see citing works

A Hierarchical Structure of Cortical Interneuron Electrical Diversity Revealed by Automated Statistical Analysis

Druckmann, S., Hill, S., Schürmann, F., Markram, H., & Segev, I. (2013) · Cerebral Cortex · doi:10.1093/cercor/bhs290 · Cited by 66 · see citing works

Exploring the molecular basis of age-related disease comorbidities using a multi-omics graphical model

Zierer, J., Pallister, T., Tsai, P.-C., Krumsiek, J., Bell, J. T., Lauc, G., Spector, T. D., Menni, C., & Kastenmüller, G. (2016) · Scientific Reports · doi:10.1038/srep37646 · Cited by 63 · see citing works

Automatic discovery of cell types and microcircuitry from neural connectomics

Jonas, E., & Kording, K. (2015) · eLife · doi:10.7554/elife.04250 · Cited by 51 · see citing works

Variational Bayes approximations for clustering via mixtures of normal inverse Gaussian distributions

Subedi, S., & McNicholas, P. D. (2014) · Advances in Data Analysis and Classification · doi:10.1007/s11634-014-0165-7 · Cited by 51 · see citing works

Representing high throughput expression profiles via perturbation barcodes reveals compound targets

Filzen, T. M., Kutchukian, P. S., Hermes, J. D., Li, J., & Tudor, M. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005335 · Cited by 31 · see citing works

Genetic and clinical correlates of AI-based brain aging patterns in cognitively unimpaired individuals

Skampardoni, I., Nasrallah, I. M., Abdulkadir, A., Wen, J., Melhem, R., Mamourian, E., Erus, G., Doshi, J., Singh, A., Yang, Z., et al. (2024) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2023.5599 · Cited by 28 · see citing works

Nonconvergence, covariance constraints, and class enumeration in growth mixture models.

McNeish, D., Harring, J. R., & Bauer, D. J. (2023) · Psychological Methods · doi:10.1037/met0000456 · Cited by 27 · see citing works

MONET: Multi-omic module discovery by omic selection

Rappoport, N., Safra, R., & Shamir, R. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008182 · Cited by 26 · see citing works

Nonlinear Network Reconstruction from Gene Expression Data Using Marginal Dependencies Measured by DCOL

Liu, H., Li, P., Zhu, M., Wang, X., Lu, J., & Yu, T. (2016) · PLOS ONE · doi:10.1371/journal.pone.0158247 · Cited by 17 · see citing works

On the robustness of graph-based clustering to random network alterations

Stacey, R. G., Skinnider, M. A., & Foster, L. J. (2021) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra120.002275 · Cited by 11 · see citing works

ICAT: a novel algorithm to robustly identify cell states following perturbations in single-cell transcriptomes

Hawkins, D. Y., Zuch, D. T., Huth, J., Rodriguez-Sastre, N., McCutcheon, K. R., Glick, A., Lion, A. T., Thomas, C. F., Descoteaux, A. E., Johnson, W. E., et al. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad278 · Cited by 10 · see citing works

Cultural Variations in Human-AI Partnership: Initial Cross-Cultural Validation of the Transactive Memory System with GenAI (TMS-GenAI) Measurement Tool

Akgun, M., & Toker, S. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790980 · Cited by 2 · see citing works

Frontotemporal dementia: does structural MRI-based clustering match clinical syndromes?

Singh-Reilly, N., Sintini, I., Ali, F., Duffy, J. R., Clark, H. M., Utianski, R. L., Meade, G., Machulda, M. M., Satoh, R., Lowe, V. J., et al. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1771092