scholarly journals Versatile knowledge guided network inference method for prioritizing key regulatory factors in multi-omics data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Christoph Ogris ◽  
Yue Hu ◽  
Janine Arloth ◽  
Nikola S. Müller
Keyword(s):  
Data Analysis ◽  
Network Inference ◽  
Comprehensive Evaluation ◽  
Integrated Analysis ◽  
Data Sets ◽  
Full Potential ◽  
Omics Data ◽  
General Applicability ◽  
Full Spectrum ◽  
Fully Integrated

AbstractConstantly decreasing costs of high-throughput profiling on many molecular levels generate vast amounts of multi-omics data. Studying one biomedical question on two or more omic levels provides deeper insights into underlying molecular processes or disease pathophysiology. For the majority of multi-omics data projects, the data analysis is performed level-wise, followed by a combined interpretation of results. Hence the full potential of integrated data analysis is not leveraged yet, presumably due to the complexity of the data and the lacking toolsets. We propose a versatile approach, to perform a multi-level fully integrated analysis: The Knowledge guIded Multi-Omics Network inference approach, KiMONo (https://github.com/cellmapslab/kimono). KiMONo performs network inference by using statistical models for combining omics measurements coupled to a powerful knowledge-guided strategy exploiting prior information from existing biological sources. Within the resulting multimodal network, nodes represent features of all input types e.g. variants and genes while edges refer to knowledge-supported and statistically derived associations. In a comprehensive evaluation, we show that our method is robust to noise and exemplify the general applicability to the full spectrum of multi-omics data, demonstrating that KiMONo is a powerful approach towards leveraging the full potential of data sets for detecting biomarker candidates.

scholarly journals Knowledge guided multi-level network inference

2020 ◽  
Author(s):  
Christoph Ogris ◽  
Yue Hu ◽  
Janine Arloth ◽  
Nikola S. Müller
Keyword(s):  
Data Analysis ◽  
Quantitative Trait ◽  
Network Inference ◽  
Integrated Analysis ◽  
Full Potential ◽  
Omics Data ◽  
Lasso Regression ◽  
Data Set ◽  
Level Data ◽  
Multi Level

AbstractConstantly decreasing costs of high-throughput profiling on many molecular levels generate vast amounts of so-called multi-omics data. Studying one biomedical question on two or more omic levels provides deeper insights into underlying molecular processes or disease pathophysiology. For the majority of multi-omics data projects, the data analysis is performed level-wise, followed by a combined interpretation of results. Few exceptions exist, for example the pairwise integration for quantitative trait analysis. However, the full potential of integrated data analysis is not leveraged yet, presumably due to the complexity of the data and the lacking toolsets. Here we propose a versatile approach, to perform a multi-level integrated analysis: The Knowledge guIded Multi-Omics Network inference approach, KiMONo. KiMONo performs network inference using statistical modeling on top of a powerful knowledge-guided strategy exploiting prior information from biological sources. Within the resulting network, nodes represent features of all input types and edges refer to associations between them, e.g. underlying a disease. Our method infers the network by combining sparse grouped-LASSO regression with a genomic position-confined Biogrid protein-protein interaction prior. In a comprehensive evaluation, we demonstrate that our method is robust to noise and still performs on low-sample size data. Applied to the five-level data set of the publicly available Pan-cancer collection, KiMONO integrated mutation, epigenetics, transcriptomics, proteomics and clinical information, detecting cancer specific omic features. Moreover, we analysed a four-level data set from a major depressive disorder cohort, including genetic, epigenetic, transcriptional and clinical data. Here we demonstrated KiMONo’s analytical power to identify expression quantitative trait methylation sites and loci and show it’s advantage to state-of-the-art methods. Our results show the general applicability to the full spectrum multi-omics data and demonstrating that KiMONo is a powerful approach towards leveraging the full potential of data sets. The method is freely available as an R package (https://github.com/cellmapslab/kimono).

scholarly journals Provision of an integrated data analysis platform for computational neuroscience experiments

2014 ◽  
Vol 16 (3) ◽  
pp. 150-169 ◽  
Author(s):  
Kamran Munir ◽  
Saad Liaquat Kiani ◽  
Khawar Hasham ◽  
Richard McClatchey ◽  
Andrew Branson ◽  
...  
Keyword(s):  
Data Analysis ◽  
Computational Neuroscience ◽  
Image Data ◽  
Building Blocks ◽  
Study Data ◽  
Scientific Workflow ◽  
Integrated Analysis ◽  
Data Sets ◽  
Biomedical Data ◽  
Content Type

Purpose – The purpose of this paper is to provide an integrated analysis base to facilitate computational neuroscience experiments, following a user-led approach to provide access to the integrated neuroscience data and to enable the analyses demanded by the biomedical research community. Design/methodology/approach – The design and development of the N4U analysis base and related information services addresses the existing research and practical challenges by offering an integrated medical data analysis environment with the necessary building blocks for neuroscientists to optimally exploit neuroscience workflows, large image data sets and algorithms to conduct analyses. Findings – The provision of an integrated e-science environment of computational neuroimaging can enhance the prospects, speed and utility of the data analysis process for neurodegenerative diseases. Originality/value – The N4U analysis base enables conducting biomedical data analyses by indexing and interlinking the neuroimaging and clinical study data sets stored on the grid infrastructure, algorithms and scientific workflow definitions along with their associated provenance information.

scholarly journals Accuracy, Robustness and Scalability of Dimensionality Reduction Methods for Single Cell RNAseq Analysis

2019 ◽  
Author(s):  
Shiquan Sun ◽  
Jiaqiang Zhu ◽  
Ying Ma ◽  
Xiang Zhou
Keyword(s):  
Data Analysis ◽  
Dimensionality Reduction ◽  
Single Cell ◽  
Comprehensive Evaluation ◽  
Computational Cost ◽  
Noise Removal ◽  
Data Sets ◽  
Vast Number ◽  
Cell Clustering ◽  
Reduction Methods

ABSTRACTBackgroundDimensionality reduction (DR) is an indispensable analytic component for many areas of single cell RNA sequencing (scRNAseq) data analysis. Proper DR can allow for effective noise removal and facilitate many downstream analyses that include cell clustering and lineage reconstruction. Unfortunately, despite the critical importance of DR in scRNAseq analysis and the vast number of DR methods developed for scRNAseq studies, however, few comprehensive comparison studies have been performed to evaluate the effectiveness of different DR methods in scRNAseq.ResultsHere, we aim to fill this critical knowledge gap by providing a comparative evaluation of a variety of commonly used DR methods for scRNAseq studies. Specifically, we compared 18 different DR methods on 30 publicly available scRNAseq data sets that cover a range of sequencing techniques and sample sizes. We evaluated the performance of different DR methods for neighborhood preserving in terms of their ability to recover features of the original expression matrix, and for cell clustering and lineage reconstruction in terms of their accuracy and robustness. We also evaluated the computational scalability of different DR methods by recording their computational cost.ConclusionsBased on the comprehensive evaluation results, we provide important guidelines for choosing DR methods for scRNAseq data analysis. We also provide all analysis scripts used in the present study atwww.xzlab.org/reproduce.html. Together, we hope that our results will serve as an important practical reference for practitioners to choose DR methods in the field of scRNAseq analysis.

scholarly journals Multiomics data analysis using tensor decomposition based unsupervised feature extraction --Comparison with DIABLO--

2019 ◽  
Author(s):  
Y-h. Taguchi
Keyword(s):  
Feature Extraction ◽  
Data Analysis ◽  
Tensor Decomposition ◽  
Integrated Analysis ◽  
Data Sets ◽  
Data Set ◽  
Advanced Method ◽  
Data Mining Tool ◽  
Mining Tool ◽  
Unsupervised Feature Extraction

AbstractMultiomics data analysis is the central issue of genomics science. In spite of that, there are not well defined methods that can integrate multomics data sets, which are formatted as matrices with different sizes. In this paper, I propose the usage of tensor decomposition based unsupervised feature extraction as a data mining tool for multiomics data set. It can successfully integrate miRNA expression, mRNA expression and proteome, which were used as a demonstration example of DIABLO that is the recently proposed advanced method for the integrated analysis of multiomics data set.

scholarly journals A comprehensive database for integrated analysis of omics data in autoimmune diseases

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jordi Martorell-Marugán ◽  
Raúl López-Domínguez ◽  
Adrián García-Moreno ◽  
Daniel Toro-Domínguez ◽  
Juan Antonio Villatoro-García ◽  
...  
Keyword(s):  
Data Analysis ◽  
Autoimmune Diseases ◽  
Pathway Analysis ◽  
Molecular Mechanisms ◽  
Meta Analysis ◽  
Integrated Analysis ◽  
Omics Data ◽  
Difficult Diagnosis ◽  
Integrative Analyses ◽  
Public Repositories

Abstract Background Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field. Results Here, we present Autoimmune Diseases Explorer (https://adex.genyo.es), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis. Conclusions This is the first omics database focused on autoimmune diseases. This resource incorporates homogeneously processed data to facilitate integrative analyses among studies.

scholarly journals Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiajia Liu ◽  
Zhiwei Fan ◽  
Weiling Zhao ◽  
Xiaobo Zhou
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Network Inference ◽  
Rapid Development ◽  
Cell Interaction ◽  
Machine Intelligence ◽  
Cellular Heterogeneity ◽  
Integrated Analysis ◽  
Tree Inference ◽  
Cross Platform

The rapid development of single-cell technologies allows for dissecting cellular heterogeneity at different omics layers with an unprecedented resolution. In-dep analysis of cellular heterogeneity will boost our understanding of complex biological systems or processes, including cancer, immune system and chronic diseases, thereby providing valuable insights for clinical and translational research. In this review, we will focus on the application of machine learning methods in single-cell multi-omics data analysis. We will start with the pre-processing of single-cell RNA sequencing (scRNA-seq) data, including data imputation, cross-platform batch effect removal, and cell cycle and cell-type identification. Next, we will introduce advanced data analysis tools and methods used for copy number variance estimate, single-cell pseudo-time trajectory analysis, phylogenetic tree inference, cell–cell interaction, regulatory network inference, and integrated analysis of scRNA-seq and spatial transcriptome data. Finally, we will present the latest analyzing challenges, such as multi-omics integration and integrated analysis of scRNA-seq data.

Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets

2020 ◽  
Author(s):  
Zhuohui Wei ◽  
Yue Zhang ◽  
Wanlin Weng ◽  
Jiazhou Chen ◽  
Hongmin Cai
Keyword(s):  
Molecular Mechanisms ◽  
Genomic Data ◽  
Low Rank ◽  
Integrated Analysis ◽  
Data Sets ◽  
Omics Data ◽  
Data Types ◽  
Cancer Data ◽  
Cancer Types ◽  
Pan Cancer

Abstract The significance of pan-cancer categories has recently been recognized as widespread in cancer research. Pan-cancer categorizes a cancer based on its molecular pathology rather than an organ. The molecular similarities among multi-omics data found in different cancer types can play several roles in both biological processes and therapeutic developments. Therefore, an integrated analysis for various genomic data is frequently used to reveal novel genetic and molecular mechanisms. However, a variety of algorithms for multi-omics clustering have been proposed in different fields. The comparison of different computational clustering methods in pan-cancer analysis performance remains unclear. To increase the utilization of current integrative methods in pan-cancer analysis, we first provide an overview of five popular computational integrative tools: similarity network fusion, integrative clustering of multiple genomic data types (iCluster), cancer integration via multi-kernel learning (CIMLR), perturbation clustering for data integration and disease subtyping (PINS) and low-rank clustering (LRACluster). Then, a priori interactions in multi-omics data were incorporated to detect prominent molecular patterns in pan-cancer data sets. Finally, we present comparative assessments of these methods, with discussion over key issues in applying these algorithms. We found that all five methods can identify distinct tumor compositions. The pan-cancer samples can be reclassified into several groups by different proportions. Interestingly, each method can classify the tumors into categories that are different from original cancer types or subtypes, especially for ovarian serous cystadenocarcinoma (OV) and breast invasive carcinoma (BRCA) tumors. In addition, all clusters of the five computational methods show notable prognostic values. Furthermore, both the 9 recurrent differential genes and the 15 common pathway characteristics were identified across all the methods. The results and discussion can help the community select appropriate integrative tools according to different research tasks or aims in pan-cancer analysis.

scholarly journals MatrixQCvis: shiny-based interactive data quality exploration for omics data

2021 ◽  
Author(s):  
Thomas Naake ◽  
Wolfgang Huber
Keyword(s):  
Data Analysis ◽  
Data Quality ◽  
Quality Metrics ◽  
Quality Data ◽  
Data Sets ◽  
Omics Data ◽  
Data Types ◽  
Analysis Workflow ◽  
Data Quality Metrics

Motivation: First-line data quality assessment and exploratory data analysis are integral parts of any data analysis workflow. In high-throughput quantitative omics experiments (e.g. transcriptomics, proteomics, metabolomics), after initial processing, the data are typically presented as a matrix of numbers (feature IDs x samples). Efficient and standardized data-quality metrics calculation and visualization are key to track the within-experiment quality of these rectangular data types and to guarantee for high-quality data sets and subsequent biological question-driven inference. Results: We present MatrixQCvis, which provides interactive visualization of data quality metrics at the per-sample and per-feature level using R's shiny framework. It provides efficient and standardized ways to analyze data quality of quantitative omics data types that come in a matrix-like format (features IDs x samples). MatrixQCvis builds upon the Bioconductor SummarizedExperiment S4 class and thus facilitates the integration into existing workflows. Availability: MatrixQCVis is implemented in R. It is available via Bioconductor and released under the GPL v3.0 license.

scholarly journals OmicsOne: associate omics data with phenotypes in one-click

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Hui Zhang ◽  
Minghui Ao ◽  
Arianna Boja ◽  
Michael Schnaubelt ◽  
Yingwei Hu
Keyword(s):  
Quality Control ◽  
Data Analysis ◽  
Association Analysis ◽  
Lung Squamous Cell Carcinoma ◽  
Data Sets ◽  
Omics Data ◽  
Data Set ◽  
Cancer Data ◽  
Public Data ◽  
Potential Biomarkers

Abstract Background The rapid advancements of high throughput “omics” technologies have brought a massive amount of data to process during and after experiments. Multi-omic analysis facilitates a deeper interrogation of a dataset and the discovery of interesting genes, proteins, lipids, glycans, metabolites, or pathways related to the corresponding phenotypes in a study. Many individual software tools have been developed for data analysis and visualization. However, it still lacks an efficient way to investigate the phenotypes with multiple omics data. Here, we present OmicsOne as an interactive web-based framework for rapid phenotype association analysis of multi-omic data by integrating quality control, statistical analysis, and interactive data visualization on ‘one-click’. Materials and methods OmicsOne was applied on the previously published proteomic and glycoproteomic data sets of high-grade serous ovarian carcinoma (HGSOC) and the published proteome data set of lung squamous cell carcinoma (LSCC) to confirm its performance. The data was analyzed through six main functional modules implemented in OmicsOne: (1) phenotype profiling, (2) data preprocessing and quality control, (3) knowledge annotation, (4) phenotype associated features discovery, (5) correlation and regression model analysis for phenotype association analysis on individual features, and (6) enrichment analysis for phenotype association analysis on interested feature sets. Results We developed an integrated software solution, OmicsOne, for the phenotype association analysis on multi-omics data sets. The application of OmicsOne on the public data set of ovarian cancer data showed that the software could confirm the previous observations consistently and discover new evidence for HNRNPU and a glycopeptide of HYOU1 as potential biomarkers for HGSOC data sets. The performance of OmicsOne was further demonstrated in the Tumor and NAT comparison study on the proteome data set of LSCC. Conclusions OmicsOne can effectively simplify data analysis and reveal the significant associations between phenotypes and potential biomarkers, including genes, proteins, and glycopeptides, in minutes to assist users to understand aberrant biological processes.

DTI data analysis: application of fiber tracking to group averaged data sets

2010 ◽  
Vol 41 (01) ◽  
Author(s):  
HP Müller ◽  
A Unrath ◽  
A Riecker ◽  
AC Ludolph ◽  
J Kassubek
Keyword(s):  
Data Analysis ◽  
Fiber Tracking ◽  
Data Sets ◽  
Analysis Application
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