3MCor: an integrative web server for metabolome-microbiome-metadata correlation analysis

Abstract Motivation The metabolome and microbiome disorders are highly associated with human health and there are great demands for dual-omics interaction analysis. Here, we designed and developed an integrative platform, 3MCor, for metabolome and microbiome correlation analysis under the instruction of phenotype and with the consideration of confounders. Results Many traditional and novel correlation analysis methods were integrated for intra- and inter-correlation analysis. Three inter-correlation pipelines are provided for global, hierarchical, and pairwise analysis. The incorporated network analysis function is conducive to rapid identification of network clusters and key nodes from a complicated correlation network. Complete numerical results (csv files) and rich figures (pdf files) will be generated in minutes. To our knowledge, 3MCor is the first platform developed specifically for the correlation analysis of metabolome and microbiome. Its functions were compared with corresponding modules of existing omics data analysis platforms. A real-world data set was used to demonstrate its simple and flexible operation, comprehensive outputs, and distinctive contribution to dual-omics studies. Availability 3MCor is available at http://3mcor.cn and the backend R script is available at https://github.com/chentianlu/3MCorServer. Supplementary information Supplementary data are available at Bioinformatics online.

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3MCor: An Integrative Web Server for Metabolome-Microbiome-Metadata Correlation Analysis

10.21203/rs.3.rs-406175/v1 ◽

2021 ◽

Author(s):

Tao Sun ◽

Mengci Li ◽

Xiangtian Yu ◽

Dandan Liang ◽

Guoxiang Xie ◽

...

Keyword(s):

Correlation Analysis ◽

Web Server ◽

Public Library ◽

Rapid Identification ◽

Data Sets ◽

Real World Data ◽

Continuous Phenotype ◽

Flexible Operation ◽

Key Nodes ◽

The Web

Abstract Background: Mounting evidences have shown that microbiome and metabolome are closely linked to human health and dual-omics studies expanded our knowledge and understanding of health and life. Here, we designed and developed a full-function and easy-to-use platform, 3MCor (http://3mcor.cn/), for metabolome and microbiome correlation analysis under the instruction of phenotype and with the consideration of confounders.Results: Many traditional and newly reported correlation analysis methods were integrated for intra- and inter-correlation analysis. Three inter-correlation pipelines are provided for global, hierarchical, and pairwise analysis. Especially, the incorporated network analysis function is conducive to a rapid identification of network clusters and key nodes from a complicated correlation network. Complete numerical results (csv files) and rich figures (pdf files) will be generated in minutes. To our knowledge, 3MCor is the first platform developed specifically for the correlation analysis of metabolome and microbiome. Its functions were compared with corresponding modules of existing omics data analysis platforms. Results from 2 real-world data sets, one from a public library with a continuous phenotype and one from our lab with a categorical phenotype, were used to demonstrate its simple and flexible operation, comprehensive outputs, and distinctive contribution to dual-omics studies. Conclusions: 3MCor is powerful with complementary pipelines and comprehensive considerations of phenotypes, confounders, and the interactions among omics features. In addition to the web server, the backend R script is available at https://github.com/chentianlu/3MCorServer.

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Subtype-GAN: a deep learning approach for integrative cancer subtyping of multi-omics data

Bioinformatics ◽

10.1093/bioinformatics/btab109 ◽

2021 ◽

Author(s):

Hai Yang ◽

Rui Chen ◽

Dongdong Li ◽

Zhe Wang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Latent Variables ◽

Supplementary Information ◽

Learning Approach ◽

Data Sets ◽

Omics Data ◽

Data Set ◽

Benchmark Data ◽

Cancer Pathogenesis

Abstract Motivation The discovery of cancer subtyping can help explore cancer pathogenesis, determine clinical actionability in treatment, and improve patients' survival rates. However, due to the diversity and complexity of multi-omics data, it is still challenging to develop integrated clustering algorithms for tumor molecular subtyping. Results We propose Subtype-GAN, a deep adversarial learning approach based on the multiple-input multiple-output neural network to model the complex omics data accurately. With the latent variables extracted from the neural network, Subtype-GAN uses consensus clustering and the Gaussian Mixture model to identify tumor samples' molecular subtypes. Compared with other state-of-the-art subtyping approaches, Subtype-GAN achieved outstanding performance on the benchmark data sets consisting of ∼4,000 TCGA tumors from 10 types of cancer. We found that on the comparison data set, the clustering scheme of Subtype-GAN is not always similar to that of the deep learning method AE but is identical to that of NEMO, MCCA, VAE, and other excellent approaches. Finally, we applied Subtype-GAN to the BRCA data set and automatically obtained the number of subtypes and the subtype labels of 1031 BRCA tumors. Through the detailed analysis, we found that the identified subtypes are clinically meaningful and show distinct patterns in the feature space, demonstrating the practicality of Subtype-GAN. Availability The source codes, the clustering results of Subtype-GAN across the benchmark data sets are available at https://github.com/haiyang1986/Subtype-GAN. Supplementary information Supplementary data are available at Bioinformatics online.

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Chordomics: a visualization tool for linking function to phylogeny in microbiomes

Bioinformatics ◽

10.1093/bioinformatics/btz711 ◽

2019 ◽

Author(s):

Kevin McDonnell ◽

Nicholas Waters ◽

Enda Howley ◽

Florence Abram

Keyword(s):

Ecosystem Functioning ◽

Supplementary Information ◽

Visualization Tool ◽

Omics Data ◽

Microbial Function ◽

Microbiome Analysis ◽

Visualization Tools ◽

And Function ◽

Experimental Strategies ◽

Omics Data Analysis

Abstract Summary The overarching aim of microbiome analysis is to uncover the links between microbial phylogeny and function in order to access ecosystem functioning. This can be done using several experimental strategies targeting different biomolecules, including DNA (metagenomics), RNA (metatranscriptomics) and proteins (metaproteomics). Despite the importance of linking microbial function to phylogeny there are currently no visualization tools that effectively integrate this information. Chordomics is a Shiny-based application for linked -omics data analysis, allowing users to visualize microbial function and phylogeny on a single plot and compare datasets across time and environments. Availability and implementation Chordomics is available on GitHub: https://github.com/kevinmcdonnell6/chordomics; software is coded in R and JavaScript and a demonstration version is available at https://kmcd.shinyapps.io/ChordomicsDemo/. Supplementary information Supplementary data are available at Bioinformatics online.

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Evaluation of gene–drug common module identification methods using pharmacogenomics data

Briefings in Bioinformatics ◽

10.1093/bib/bbaa087 ◽

2020 ◽

Author(s):

Jie Huang ◽

Jiazhou Chen ◽

Bin Zhang ◽

Lei Zhu ◽

Hongmin Cai

Keyword(s):

Drug Interactions ◽

Drug Repositioning ◽

Simulated Data ◽

Supplementary Information ◽

Supplementary File ◽

Cancer Drugs ◽

Real World Data ◽

Data Set ◽

Module Identification ◽

Network Analyses

Abstract Accurately identifying the interactions between genomic factors and the response of cancer drugs plays important roles in drug discovery, drug repositioning and cancer treatment. A number of studies revealed that interactions between genes and drugs were ‘many-genes-to-many drugs’ interactions, i.e. common modules, opposed to ‘one-gene-to-one-drug’ interactions. Such modules fully explain the interactions between complex biological regulatory mechanisms and cancer drugs. However, strategies for effectively and robustly identifying the underlying common modules among pharmacogenomics data remain to be improved. In this paper, we aim to provide a detailed evaluation of three categories of state-of-the-art common module identification techniques from a machine learning perspective, including non-negative matrix factorization (NMF), partial least squares (PLS) and network analyses. We first evaluate the performance of six methods, namely SNMNMF, NetNMF, SNPLS, O2PLS, NSBM and HOGMMNC, using two series of simulated data sets with different noise levels and outlier ratios. Then, we conduct experiments using a real world data set of 2091 genes and 101 drugs in 392 cancer cell lines and compare the real experimental results from the aspect of biological process term enrichment, gene–drug and drug–drug interactions. Finally, we present interesting findings from our evaluation study and discuss the advantages and drawbacks of each method. Supplementary information: Supplementary file is available at Briefings in Bioinformatics online.

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Auto-sharing parameters for transfer learning based on multi-objective optimization

Integrated Computer-Aided Engineering ◽

10.3233/ica-210655 ◽

2021 ◽

pp. 1-13

Author(s):

Hailin Liu ◽

Fangqing Gu ◽

Zixian Lin

Keyword(s):

Transfer Learning ◽

Optimization Problem ◽

Data Sets ◽

Multi Objective Optimization ◽

Particle Swarm Optimizer ◽

Real World Data ◽

Data Set ◽

Target Task ◽

Main Research ◽

Multi Objective

Transfer learning methods exploit similarities between different datasets to improve the performance of the target task by transferring knowledge from source tasks to the target task. “What to transfer” is a main research issue in transfer learning. The existing transfer learning method generally needs to acquire the shared parameters by integrating human knowledge. However, in many real applications, an understanding of which parameters can be shared is unknown beforehand. Transfer learning model is essentially a special multi-objective optimization problem. Consequently, this paper proposes a novel auto-sharing parameter technique for transfer learning based on multi-objective optimization and solves the optimization problem by using a multi-swarm particle swarm optimizer. Each task objective is simultaneously optimized by a sub-swarm. The current best particle from the sub-swarm of the target task is used to guide the search of particles of the source tasks and vice versa. The target task and source task are jointly solved by sharing the information of the best particle, which works as an inductive bias. Experiments are carried out to evaluate the proposed algorithm on several synthetic data sets and two real-world data sets of a school data set and a landmine data set, which show that the proposed algorithm is effective.

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Empirical evaluation of feature subset selection based on a real-world data set

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2004.03.005 ◽

2004 ◽

Vol 17 (3) ◽

pp. 285-288 ◽

Cited By ~ 5

Author(s):

Petra Perner ◽

Chid Apte

Keyword(s):

Real World ◽

Empirical Evaluation ◽

Subset Selection ◽

Feature Subset Selection ◽

Feature Subset ◽

Real World Data ◽

Data Set ◽

World Data

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

Biotechnology Advances ◽

10.1016/j.biotechadv.2021.107739 ◽

2021 ◽

Vol 49 ◽

pp. 107739

Author(s):

Parminder S. Reel ◽

Smarti Reel ◽

Ewan Pearson ◽

Emanuele Trucco ◽

Emily Jefferson

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Omics Data ◽

Learning Approaches ◽

Omics Data Analysis

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Time-ResNeXt for epilepsy recognition based on EEG signals in wireless networks

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-020-01810-5 ◽

2020 ◽

Vol 2020 (1) ◽

Cited By ~ 1

Author(s):

Shaoqiang Wang ◽

Shudong Wang ◽

Song Zhang ◽

Yifan Wang

Keyword(s):

Deep Learning ◽

Network Structure ◽

Signal Recognition ◽

Eeg Signals ◽

Real World Data ◽

Data Set ◽

Practical Applications ◽

Epilepsy Diagnosis ◽

Single Data ◽

Electroencephalogram Eeg

Abstract To automatically detect dynamic EEG signals to reduce the time cost of epilepsy diagnosis. In the signal recognition of electroencephalogram (EEG) of epilepsy, traditional machine learning and statistical methods require manual feature labeling engineering in order to show excellent results on a single data set. And the artificially selected features may carry a bias, and cannot guarantee the validity and expansibility in real-world data. In practical applications, deep learning methods can release people from feature engineering to a certain extent. As long as the focus is on the expansion of data quality and quantity, the algorithm model can learn automatically to get better improvements. In addition, the deep learning method can also extract many features that are difficult for humans to perceive, thereby making the algorithm more robust. Based on the design idea of ResNeXt deep neural network, this paper designs a Time-ResNeXt network structure suitable for time series EEG epilepsy detection to identify EEG signals. The accuracy rate of Time-ResNeXt in the detection of EEG epilepsy can reach 91.50%. The Time-ResNeXt network structure produces extremely advanced performance on the benchmark dataset (Berne-Barcelona dataset) and has great potential for improving clinical practice.

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SMILE: Mutual Information Learning for Integration of Single-cell Omics Data

Bioinformatics ◽

10.1093/bioinformatics/btab706 ◽

2021 ◽

Author(s):

Yang Xu ◽

Priyojit Das ◽

Rachel Patton McCord

Keyword(s):

Deep Learning ◽

Mutual Information ◽

Single Cell ◽

Learning Algorithm ◽

Cellular Systems ◽

Supplementary Information ◽

Omics Data ◽

Learning Approaches ◽

Rna Seq ◽

Integrate Data

Abstract Motivation Deep learning approaches have empowered single-cell omics data analysis in many ways and generated new insights from complex cellular systems. As there is an increasing need for single cell omics data to be integrated across sources, types, and features of data, the challenges of integrating single-cell omics data are rising. Here, we present an unsupervised deep learning algorithm that learns discriminative representations for single-cell data via maximizing mutual information, SMILE (Single-cell Mutual Information Learning). Results Using a unique cell-pairing design, SMILE successfully integrates multi-source single-cell transcriptome data, removing batch effects and projecting similar cell types, even from different tissues, into the shared space. SMILE can also integrate data from two or more modalities, such as joint profiling technologies using single-cell ATAC-seq, RNA-seq, DNA methylation, Hi-C, and ChIP data. When paired cells are known, SMILE can integrate data with unmatched feature, such as genes for RNA-seq and genome wide peaks for ATAC-seq. Integrated representations learned from joint profiling technologies can then be used as a framework for comparing independent single source data. Supplementary information Supplementary data are available at Bioinformatics online. The source code of SMILE including analyses of key results in the study can be found at: https://github.com/rpmccordlab/SMILE.

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