Multi-view clustering for multi-omics data using unified embedding

Abstract

In real world applications, data sets are often comprised of multiple views, which provide consensus and complementary information to each other. Embedding learning is an effective strategy for nearest neighbour search and dimensionality reduction in large data sets. This paper attempts to learn a unified probability distribution of the points across different views and generates a unified embedding in a low-dimensional space to optimally preserve neighbourhood identity. Probability distributions generated for each point for each view are combined by conflation method to create a single unified distribution. The goal is to approximate this unified distribution as much as possible when a similar operation is performed on the embedded space. As a cost function, the sum of Kullback-Leibler divergence over the samples is used, which leads to a simple gradient adjusting the position of the samples in the embedded space. The proposed methodology can generate embedding from both complete and incomplete multi-view data sets. Finally, a multi-objective clustering technique ( AMOSA ) is applied to group the samples in the embedded space. The proposed methodology, Multi-view Neighbourhood Embedding ( MvNE ), shows an improvement of approximately 2−3% over state-of-the-art models when evaluated on 10 omics data sets.

Transfer learning data adaptation using conflation of low‐level textural features

“…Since we are dealing with the same unknown quantity presented in the form of distribution, we can merge the distributions using the conflation of distributions method, which minimises the loss of Shannon information as the information in the probability distributions is merged 56 …”

“…In a study by Mitra, Saha and Hasanuzzaman, 56 they approximate a unified probability distribution in embedded learning for nearest neighbour search and dimensionality reduction in large datasets. This conflation aims to generate a unified embedding in low‐dimensional space that preserves the neighbourhood identity of the datasets in multiple views.…”

Missing data in multi-omics integration: Recent advances through artificial intelligence

“…A similarly motivated approach, the Multi-view Neighborhood Embedding (MvNE) method introduced by Mitra et al ( 2020 ), integrates and maps multi-omics data to a lower-dimensional subspace to facilitate cluster analyses. The proposed model first estimates a unified probability distribution that describes the likelihood of neighboring points across the full-dimensional space of the original multi-omics data.…”

Convex Multi-View Clustering Via Robust Low Rank Approximation With Application to Multi-Omic Data

“…a For our method and all of the benchmark methods described above, we evaluate the clustering performance by measuring cluster purity, Normalized Mutual Information (NMI) [43], and Adjusted Rand Index (ARI) [42]. They have been used before in [19], [38], [41] to measure the performance of their multi-view clustering methods for cancer subtype clustering. These evaluation metrics measure the similarity between the predicted cluster labels and the true class labels.…”