Cluster ensemble based on Random Forests for genetic data

Abstract

BackgroundClustering plays a crucial role in several application domains, such as bioinformatics. In bioinformatics, clustering has been extensively used as an approach for detecting interesting patterns in genetic data. One application is population structure analysis, which aims to group individuals into subpopulations based on shared genetic variations, such as single nucleotide polymorphisms. Advances in DNA sequencing technology have facilitated the obtainment of genetic datasets with exceptional sizes. Genetic data usually contain hundreds of thousands of genetic markers genotyped for thousands of individuals, making an efficient means for handling such data desirable.ResultsRandom Forests (RFs) has emerged as an efficient algorithm capable of handling high-dimensional data. RFs provides a proximity measure that can capture different levels of co-occurring relationships between variables. RFs has been widely considered a supervised learning method, although it can be converted into an unsupervised learning method. Therefore, RF-derived proximity measure combined with a clustering technique may be well suited for determining the underlying structure of unlabeled data. This paper proposes, RFcluE, a cluster ensemble approach for determining the underlying structure of genetic data based on RFs. The approach comprises a cluster ensemble framework to combine multiple runs of RF clustering. Experiments were conducted on high-dimensional, real genetic dataset to evaluate the proposed approach. The experiments included an examination of the impact of parameter changes, comparing RFcluE performance against other clustering methods, and an assessment of the relationship between the diversity and quality of the ensemble and its effect on RFcluE performance.ConclusionsThis paper proposes, RFcluE, a cluster ensemble approach based on RF clustering to address the problem of population structure analysis and demonstrate the effectiveness of the approach. The paper also illustrates that applying a cluster ensemble approach, combining multiple RF clusterings, produces more robust and higher-quality results as a consequence of feeding the ensemble with diverse views of high-dimensional genetic data obtained through bagging and random subspace, the two key features of the RF algorithm.

Distance-based clustering challenges for unbiased benchmarking studies

“…In a non-model-based clustering method, a lasso-type penalty to selected features was used in the so-called sparse clustering 45 . As another option, Random forests provide a proximity measure that can capture different levels of co-occurring relationships between variables and can be converted into an unsupervised learning method, for which the derived proximity measure can be combined with a clustering approach 46 . However, there is no proof that methods that integrate feature selection in clustering outperform a two-stage approach in which the first stage screens for relevant features and the second stage applies conventional clustering methods on the pre-selected features 47 .…”

LANDMark: an ensemble approach to the supervised selection of biomarkers in high-throughput sequencing data

“…These counts were then divided by the total number of trees in the forest. To create a dissimilarity matrix the square root of each frequency was taken after it was subtracted from one [ 8 , 13 , 74 ]. A principal coordinates analysis (PCoA) of the dissimilarities was used for visualizing and analyzing these differences.…”

Decision Tree Ensembles Utilizing Multivariate Splits Are Effective at Investigating Beta-Diversity in Medically Relevant 16S Amplicon Sequencing Data

“…This co-occurrence, S ( x i , x j ), is a similarity and can be found using the following equation: Where x i and x j is the vector representation of all terminal node positions of samples x i and x j in the forest, and N is the total number of trees in the forest. The similarity matrix, S , is then converted into a dissimilarity matrix, D (Equation Two) (17). This dissimilarity measure, while not a metric such as the Jaccard distance (29), can be used to investigate beta-diversity and can be constructed using either a supervised or an unsupervised approach (17).…”

“…The similarity matrix, S , is then converted into a dissimilarity matrix, D (Equation Two) (17). This dissimilarity measure, while not a metric such as the Jaccard distance (29), can be used to investigate beta-diversity and can be constructed using either a supervised or an unsupervised approach (17). To use decision tree ensembles in an unsupervised manner a second dataset is created such that the columns (ASVs) are randomly permuted.…”