‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling

Using machine learning approaches for multi-omics data analysis: A review

High-throughput next-generation sequencing now makes it possible to generate a vast amount of multi-omics data for various applications. These data have revolutionized biomedical research by providing a more comprehensive understanding of the biological systems and molecular mechanisms of disease development. Recently, deep learning (DL) algorithms have become one of the most promising methods in multi-omics data analysis, due to their predictive performance and capability of capturing nonlinear and hierarchical features. While integrating and translating multi-omics data into useful functional insights remain the biggest bottleneck, there is a clear trend towards incorporating multi-omics analysis in biomedical research to help explain the complex relationships between molecular layers. Multi-omics data have a role to improve prevention, early detection and prediction; monitor progression; interpret patterns and endotyping; and design personalized treatments. In this review, we outline a roadmap of multi-omics integration using DL and offer a practical perspective into the advantages, challenges and barriers to the implementation of DL in multi-omics data.

A roadmap for multi-omics data integration using deep learning.

Cancer is a complex disease that deregulates cellular functions at various molecular levels (e.g., DNA, RNA, and proteins). Integrated multi-omics analysis of data from these levels is necessary to understand the aberrant cellular functions accountable for cancer and its development. In recent years, Deep Learning (DL) approaches have become a useful tool in integrated multi-omics analysis of cancer data. However, high dimensional multi-omics data are generally imbalanced with too many molecular features and relatively few patient samples. This imbalance makes a DL based integrated multi-omics analysis difficult. DL-based dimensionality reduction technique, including variational autoencoder (VAE), is a potential solution to balance high dimensional multi-omics data. However, there are few VAE-based integrated multi-omics analyses, and they are limited to pancancer. In this work, we did an integrated multi-omics analysis of ovarian cancer using the compressed features learned through VAE and an improved version of VAE, namely Maximum Mean Discrepancy VAE (MMD-VAE). First, we designed and developed a DL architecture for VAE and MMD-VAE. Then we used the architecture for mono-omics, integrated di-omics and tri-omics data analysis of ovarian cancer through cancer samples identification, molecular subtypes clustering and classification, and survival analysis. The results show that MMD-VAE and VAE-based compressed features can respectively classify the transcriptional subtypes of the TCGA datasets with an accuracy in the range of 93.2-95.5% and 87.1-95.7%. Also, survival analysis results show that VAE and MMD-VAE based compressed representation of omics data can be used in cancer prognosis. Based on the results, we can conclude that (i) VAE and MMD-VAE outperform existing dimensionality reduction techniques, (ii) integrated multi-omics analyses perform better or similar compared to their mono-omics counterparts, and (iii) MMD-VAE performs better than VAE in most omics dataset.

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Integrated multi-omics analysis of ovarian cancer using variational autoencoders.

A fused method using a combination of multi-omics data enables a comprehensive study of complex biological processes and highlights the interrelationship of relevant biomolecules and their functions. Driven by high-throughput sequencing technologies, several promising deep learning methods have been proposed for fusing multi-omics data generated from a large number of samples.In this study, 16 representative deep learning methods are comprehensively evaluated on simulated, single-cell, and cancer multi-omics datasets. For each of the datasets, two tasks are designed: classification and clustering. The classification performance is evaluated by using three benchmarking metrics including accuracy, F1 macro, and F1 weighted. Meanwhile, the clustering performance is evaluated by using four benchmarking metrics including the Jaccard index (JI), C-index, silhouette score, and Davies Bouldin score. For the cancer multi-omics datasets, the methods' strength in capturing the association of multi-omics dimensionality reduction results with survival and clinical annotations is further evaluated. The benchmarking results indicate that moGAT achieves the best classification performance. Meanwhile, efmmdVAE, efVAE, and lfmmdVAE show the most promising performance across all complementary contexts in clustering tasks.Our benchmarking results not only provide a reference for biomedical researchers to choose appropriate deep learning-based multi-omics data fusion methods, but also suggest the future directions for the development of more effective multi-omics data fusion methods. The deep learning frameworks are available at https://github.com/zhenglinyi/DL-mo . 

/pdf/a-benchmark-study-of-deep-learning-based-multi-omics-data-jw3lhip6.pdf

A benchmark study of deep learning-based multi-omics data fusion methods for cancer

Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration.

Over the last few decades, a large amount of research work has been carried on genomic data. The cancer disease make cells in specific tissues in the body undergo uncontrolled division which results in the malignant growth or tumour. Today, DNA microarray technologies allow us to simultaneously monitor the expression pattern of thousands of genes. Microarray gene expression data are characterised by a very high dimensionality genes, and a relatively small number of samples observations. If one wants to identify all those genes from these thousands of gene expressions which are responsible for the disease like cancer, then it is useful to rank the genes. In this paper, we have proposed a novel gene ranking method based on Wilcoxon Rank Sum Test and genetic algorithm. WRST has been used for reducing dimensionality and genetic algorithm for finding out those differentially expressed genes. The final subset of genes has been cross-validated using k fold LOOCV k varied for different dataset method and thereafter used for classification of data using SVM with linear kernel. At first the proposed method has been applied on two relatively new benchmark datasets, like GDS4382 colorectal cancer dataset and GDS4794 small cell lung cancer dataset and the results show that the proposed method can reach up to 100% classification accuracy with very few dominant genes, which indirectly validates the biological and statistical significance of the proposed method. After that it is also applied on five real-life datasets and the results are compared with one of the recent state of the art approach on the basis of % of Accuracy, Sensitivity, and Specificity etc.

A novel gene ranking method using Wilcoxon rank sum test and genetic algorithm

The smartphone has become quite ubiquitous and an indispensable part of our lives in the modern day. It has many sensors which capture several minute details pertaining to our activities. So, it is but inevitable that human desire creeps in to augment and improve one's own actions by studying such behaviour captured through the instrumentalities of the smart-phone. In this context, study of data on human activities captured through accelerometer and gyroscope get primal significance. In this paper, we have attempted to apply machine learning and deep learning techniques on a publicly available dataset. Initially, classification algorithms like K-Nearest Neighbours and Random Forest are applied. The classification accuracies observed are 90.46% and 92.97% respectively. Using benchmark feature selection and dimensionality reduction techniques does not improve the model accuracies to a large extent - with reported accuracies of 91.48% and 92.56% respectively. However, on employing deep neural network techniques, an accuracy of 97.32% is achieved, which indicates suitability of deep learning techniques over traditional machine learning techniques for the task of human activity recognition using mobile sensor data.

Human Activity Recognition using Deep Neural Network

Gene — Gene Interaction is a logical interaction between two genes that affects the observable behavior of one organism. This genetic interaction helps to identify pathways of associated genes for various diseases. In this paper we have used two metrics, like correlation & entropy to find the level of interaction between the genes applied on Gene Interaction networks. We have applied our algorithm on three benchmark cancer datasets Colorectal, Leukaemia and CML. Results show some weighted graphs, where the weights along each edge represents the level of interaction between two genes in a particular network.

Dibyendu Bikash Seal

Papers

Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration.

A novel gene ranking method using Wilcoxon rank sum test and genetic algorithm

Human Activity Recognition using Deep Neural Network

Gene — Gene Interaction: A clustering, correlation & entropy based approach

CASSL: A cell-type annotation method for single cell transcriptomics data using semi-supervised learning