A hyperparameter-randomized ensemble approach for robust clustering across diverse datasets
Goggin, S.; Zunder, E.
Show abstract
Clustering analysis is widely used to group objects by similarity, but for complex datasets such as those produced by single-cell analysis, the currently available clustering methods are limited by accuracy, robustness, ease of use, and interpretability. To address these limitations, we developed an ensemble clustering method with hyperparameter randomization that outperforms other methods across a broad range of single-cell and synthetic datasets, without the need for manual hyperparameter selection. In addition to hard cluster labels, it also outputs soft cluster memberships to characterize continuum-like regions and per cell overlap scores to quantify the uncertainty in cluster assignment. We demonstrate the improved clustering interpretability from these features by tracing the intermediate stages between handwritten digits in the MNIST dataset, and between tanycyte subpopulations in the hypothalamus. This approach improves the quality of clustering and subsequent downstream analyses for single-cell datasets, and may also prove useful in other fields of data analysis.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- scAlign: a tool for alignment, integration and rare cell identification from scRNA-seq data 96%
- Smoother: A Unified and Modular Framework for Incorporating Structural Dependency in Spatial Omics Data 96%
- scBFA: modeling detection patterns to mitigate technical noise in large-scale single cell genomics data 96%
Similar papers in this journal
- Density-Preserving Data Visualization Unveils Dynamic Patterns of Single-Cell Transcriptomic Variability 97%
- BayesSpace enables the robust characterization of spatial gene expression architecture in tissue sections at increased resolution 96%
- Comparative analysis of cell-cell communication at single-cell resolution 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.