Critical limitations of consensus clustering in class discovery
By Y. Şenbabaoğlu, George Michailidis, Jun Z. Li. Scientific Reports, 2014
335 citations recorded by the source corpus.
Consensus clustering (CC) has been adopted for unsupervised class discovery in many genomic studies. It calculates how frequently two samples are grouped together in repeated clustering runs and uses the resulting pairwise "consensus rates" for visual demonstration that clusters exist, for comparing cluster stability and for estimating the optimal cluster number (K). However, the sensitivity and specificity of CC have not been systemically assessed. Through simulations we find that CC is able to divide randomly generated unimodal data into apparently stable clusters for a range of K, essentially reporting chance partitions of cluster-less data. For data with known structure, the common implementations of CC perform poorly in identifying the true K. These results suggest that CC should be applied and interpreted with caution. We found that a new metric based on CC, the proportion of ambiguously clustered pairs (PAC), infers K equally or more reliably than similar methods in simulated data with known K. Our overall approach involves the use of realistic null distributions based on the observed gene-gene correlation structure in a given study and the implementation of PAC to more accurately estimate K. We discuss the strength of our approach in the context of other ensemble-based methods.
Subfields: Genetics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/55fc2f009c9a1d7a6d46e2396ba07ccf174af5d9
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Citations in the corpus
- A Survey on Multi-View Clustering (2017, neutral): “, [118], [119], [120], [121], [49] can be applied to MVC.”
- Ensemble deep learning: A review (2021, methodology): “To create diverse clusters, several approaches can be applied: using different sampling data, using different subsets of the original features, and employing different clustering methods[130].”
- Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer (2022, methodology): “The cumulative distribution function (CDF) curves of the consensus score matrix and proportion of ambiguous clustering (PAC) statistic 23 indicated that the optimal number was obtained when k = 2 (Fig.”
- Dimension reduction techniques for the integrative analysis of multi-omics data (2016, background): “Overlapping clusters have been identified in many tumors including glioblastoma and serous ovarian cancer [6, 7].”