Denoising large-scale biological data using network filters

Mapping Intimacies ◽

10.1101/2020.03.12.989244 ◽

2020 ◽

Author(s):

Andrew J. Kavran ◽

Aaron Clauset

Keyword(s):

Large Scale ◽

Interaction Network ◽

Biological Data ◽

Neuron Activity ◽

Data Sets ◽

Correlated Noise ◽

Life History Variation ◽

Real World Data ◽

Brain Connectome ◽

Wide Range

Large-scale biological data sets, e.g., transcriptomic, proteomic, or ecological, are often contaminated by noise, which can impede accurate inferences about underlying processes. Such measurement noise can arise from endogenous biological factors like cell cycle and life history variation, and from exogenous technical factors like sample preparation and instrument variation. Here we describe a general method for automatically reducing noise in large-scale biological data sets. This method uses an interaction network to identify groups of correlated or anti-correlated measurements that can be combined or “filtered” to better recover an underlying biological signal. Similar to the process of denoising an image, a single network filter may be applied to an entire system, or the system may be first decomposed into distinct modules and a different filter applied to each. Applied to synthetic data with known network structure and signal, network filters accurately reduce noise across a wide range of noise levels and structures. Applied to a machine learning task of predicting changes in human protein expression in healthy and cancerous tissues, network filtering prior to training increases accuracy up to 58% compared to using unfiltered data. These results indicate the broad potential utility of network-based filters to applications in systems biology.Author SummarySystem-wide measurements of many biological signals, whether derived from molecules, cells, or entire organisms, are often noisy. Removing or mitigating this noise prior to analysis can improve our understanding and predictions of biological phenomena. We describe a general way to denoise biological data that can account for both correlation and anti-correlation between different measurements. These “network filters” take as input a set of biological measurements, e.g., metabolite concentration, animal traits, neuron activity, or gene expression, and a network of how those measurements are biologically related, e.g., a metabolic network, food web, brain connectome, or protein-protein interaction network. Measurements are then “filtered” for correlated or anti-correlated noise using a set of other measurements that are identified using the network. We investigate the accuracy of these filters in synthetic and real-world data sets, and find that they can substantially reduce noise of different levels and structure. By denoising large-scale biological data sets, network filters have the potential to improve the analysis of many types of biological data.

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Denoising large-scale biological data using network filters

10.21203/rs.3.rs-66071/v2 ◽

2021 ◽

Author(s):

Andrew J Kavran ◽

Aaron Clauset

Keyword(s):

Large Scale ◽

Synthetic Data ◽

Interaction Network ◽

Learning Task ◽

Biological Data ◽

Data Sets ◽

Proteomics Data ◽

Life History Variation ◽

Wide Range ◽

Underlying Processes

Abstract Background: Large-scale biological data sets are often contaminated by noise, which can impede accurate inferences about underlying processes. Such measurement noise can arise from endogenous biological factors like cell cycle and life history variation, and from exogenous technical factors like sample preparation and instrument variation.Results: We describe a general method for automatically reducing noise in large-scale biological data sets. This method uses an interaction network to identify groups of correlated or anti-correlated measurements that can be combined or “ﬁltered” to better recover an underlying biological signal. Similar to the process of denoising an image, a single network ﬁlter may be applied to an entire system, or the system may be ﬁrst decomposed into distinct modules and a diﬀerent ﬁlter applied to each. Applied to synthetic data with known network structure and signal, network ﬁlters accurately reduce noise across a wide range of noise levels and structures. Applied to a machine learning task of predicting changes in human protein expression in healthy and cancerous tissues, network ﬁltering prior to training increases accuracy up to 43% compared to using unﬁltered data.Conclusions: Network ﬁlters are a general way to denoise biological data and can account for both correlation and anti-correlation between diﬀerent measurements. Furthermore, we ﬁnd that partitioning a network prior to ﬁltering can signiﬁcantly reduce errors in networks with heterogenous data and correlation patterns, and this approach outperforms existing diﬀusion based methods. Our results on proteomics data indicate the broad potential utility of network ﬁlters to applications in systems biology.

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Denoising Large-Scale Biological Data Using Network Filters

10.21203/rs.3.rs-66071/v1 ◽

2020 ◽

Author(s):

Andrew J Kavran ◽

Aaron Clauset

Keyword(s):

Large Scale ◽

Synthetic Data ◽

Interaction Network ◽

Learning Task ◽

Biological Data ◽

Data Sets ◽

Proteomics Data ◽

Life History Variation ◽

Wide Range ◽

Underlying Processes

Abstract Background: Large-scale biological data sets, e.g., transcriptomic, proteomic, or ecological, are often contaminated by noise, which can impede accurate inferences about underlying processes. Such measurement noise can arise from endogenous biological factors like cell cycle and life history variation, and from exogenous technical factors like sample preparation and instrument variation. Results: We describe a general method for automatically reducing noise in large-scale biological data sets. This method uses an interaction network to identify groups of correlated or anti-correlated measurements that can be combined or “filtered” to better recover an underlying biological signal. Similar to the process of denoising an image, a single network filter may be applied to an entire system, or the system may be first decomposed into distinct modules and a different filter applied to each. Applied to synthetic data with known network structure and signal, network filters accurately reduce noise across a wide range of noise levels and structures. Applied to a machine learning task of predicting changes in human protein expression in healthy and cancerous tissues, network filtering prior to training increases accuracy up to 58% compared to using unfiltered data. Conclusions: Network filters are a general way to denoise biological data and can account for both correlation and anti-correlation between different measurements. Furthermore, we find that partitioning a network prior to filtering can significantly reduce errors in networks with heterogenous data and correlation patterns, andthis approach outperforms existing diffusion based methods. Our results on proteomics data indicate the broad potential utility of network filters to applications in systems biology.

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Denoising large-scale biological data using network filters

BMC Bioinformatics ◽

10.1186/s12859-021-04075-x ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Andrew J. Kavran ◽

Aaron Clauset

Keyword(s):

Large Scale ◽

Synthetic Data ◽

Interaction Network ◽

Learning Task ◽

Biological Data ◽

Data Sets ◽

Proteomics Data ◽

Life History Variation ◽

Wide Range ◽

Underlying Processes

Abstract Background Large-scale biological data sets are often contaminated by noise, which can impede accurate inferences about underlying processes. Such measurement noise can arise from endogenous biological factors like cell cycle and life history variation, and from exogenous technical factors like sample preparation and instrument variation. Results We describe a general method for automatically reducing noise in large-scale biological data sets. This method uses an interaction network to identify groups of correlated or anti-correlated measurements that can be combined or “filtered” to better recover an underlying biological signal. Similar to the process of denoising an image, a single network filter may be applied to an entire system, or the system may be first decomposed into distinct modules and a different filter applied to each. Applied to synthetic data with known network structure and signal, network filters accurately reduce noise across a wide range of noise levels and structures. Applied to a machine learning task of predicting changes in human protein expression in healthy and cancerous tissues, network filtering prior to training increases accuracy up to 43% compared to using unfiltered data. Conclusions Network filters are a general way to denoise biological data and can account for both correlation and anti-correlation between different measurements. Furthermore, we find that partitioning a network prior to filtering can significantly reduce errors in networks with heterogenous data and correlation patterns, and this approach outperforms existing diffusion based methods. Our results on proteomics data indicate the broad potential utility of network filters to applications in systems biology.

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mixOmics: an R package for ‘omics feature selection and multiple data integration

10.1101/108597 ◽

2017 ◽

Cited By ~ 19

Author(s):

Florian Rohart ◽

Benoît Gautier ◽

Amrit Singh ◽

Kim-Anh Lê Cao

Keyword(s):

Data Integration ◽

Large Scale ◽

Relevant Information ◽

R Package ◽

Biological Data ◽

Molecular Signature ◽

Single Type ◽

Data Sets ◽

Omics Data ◽

Wide Range

AbstractThe advent of high throughput technologies has led to a wealth of publicly available ‘omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions, but mainly for a single type of ‘omics. In addition, commonly used methods are univariate and consider each biological feature independently.We introducemixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a system biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous ‘omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple ‘omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latestmixOmicsintegrative frameworks for the multivariate analyses of ‘omics data available from the package.

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Comparative Cladistics: Fossils, Morphological Data Partitions and Lost Branches in the Fossil Tree of Life

10.31237/osf.io/7sa6d ◽

2017 ◽

Author(s):

Ross Mounce

Keyword(s):

Large Scale ◽

Phylogenetic Signal ◽

Scientific Progress ◽

Molecular Data ◽

Morphological Data ◽

Data Sets ◽

Phylogenetic Groups ◽

Use Of Data ◽

Wide Range ◽

Cladistic Analyses

In this thesis I attempt to gather together a wide range of cladistic analyses of fossil and extant taxa representing a diverse array of phylogenetic groups. I use this data to quantitatively compare the effect of fossil taxa relative to extant taxa in terms of support for relationships, number of most parsimonious trees (MPTs) and leaf stability. In line with previous studies I find that the effects of fossil taxa are seldom different to extant taxa – although I highlight some interesting exceptions. I also use this data to compare the phylogenetic signal within vertebrate morphological data sets, by choosing to compare cranial data to postcranial data. Comparisons between molecular data and morphological data have been previously well explored, as have signals between different molecular loci. But comparative signal within morphological data sets is much less commonly characterized and certainly not across a wide array of clades. With this analysis I show that there are many studies in which the evidence provided by cranial data appears to be be significantly incongruent with the postcranial data – more than one would expect to see just by the effect of chance and noise alone. I devise and implement a modification to a rarely used measure of homoplasy that will hopefully encourage its wider usage. Previously it had some undesirable bias associated with the distribution of missing data in a dataset, but my modification controls for this. I also take an in-depth and extensive review of the ILD test, noting it is often misused or reported poorly, even in recent studies. Finally, in attempting to collect data and metadata on a large scale, I uncovered inefficiencies in the research publication system that obstruct re-use of data and scientific progress. I highlight the importance of replication and reproducibility – even simple reanalysis of high profile papers can turn up some very different results. Data is highly valuable and thus it must be retained and made available for further re-use to maximize the overall return on research investment.

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Community Detection in Large-Scale Bipartite Biological Networks

Frontiers in Genetics ◽

10.3389/fgene.2021.649440 ◽

2021 ◽

Vol 12 ◽

Author(s):

Genís Calderer ◽

Marieke L. Kuijjer

Keyword(s):

Biological Networks ◽

Large Scale ◽

Interaction Network ◽

Gene Interaction ◽

Community Structures ◽

Gene Interaction Network ◽

Structure Detection ◽

Wide Range ◽

Disease Associations ◽

Or Gene

Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks—such as those that represent regulatory interactions, drug-gene, or gene-disease associations—are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a “hairball” of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.

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Usage and Scaling of an Open-Source Spiking Multi-Area Model of Monkey Cortex

Lecture Notes in Computer Science - Brain-Inspired Computing ◽

10.1007/978-3-030-82427-3_4 ◽

2021 ◽

pp. 47-59

Author(s):

Sacha J. van Albada ◽

Jari Pronold ◽

Alexander van Meegen ◽

Markus Diesmann

Keyword(s):

Open Source ◽

Large Scale ◽

Network Models ◽

Macaque Monkey ◽

Source Model ◽

Model Specification ◽

Data Sets ◽

Neural Network Models ◽

Wide Range ◽

Ict Infrastructure

AbstractWe are entering an age of ‘big’ computational neuroscience, in which neural network models are increasing in size and in numbers of underlying data sets. Consolidating the zoo of models into large-scale models simultaneously consistent with a wide range of data is only possible through the effort of large teams, which can be spread across multiple research institutions. To ensure that computational neuroscientists can build on each other’s work, it is important to make models publicly available as well-documented code. This chapter describes such an open-source model, which relates the connectivity structure of all vision-related cortical areas of the macaque monkey with their resting-state dynamics. We give a brief overview of how to use the executable model specification, which employs NEST as simulation engine, and show its runtime scaling. The solutions found serve as an example for organizing the workflow of future models from the raw experimental data to the visualization of the results, expose the challenges, and give guidance for the construction of an ICT infrastructure for neuroscience.

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Characterising RDF data sets

Journal of Information Science ◽

10.1177/0165551516677945 ◽

2017 ◽

Vol 44 (2) ◽

pp. 203-229 ◽

Cited By ~ 6

Author(s):

Javier D Fernández ◽

Miguel A Martínez-Prieto ◽

Pablo de la Fuente Redondo ◽

Claudio Gutiérrez

Keyword(s):

Data Structures ◽

Large Scale ◽

Open Data ◽

Structural Features ◽

Data Sets ◽

Data Set ◽

Wide Range ◽

Rdf Data ◽

Description Framework ◽

Resource Description

The publication of semantic web data, commonly represented in Resource Description Framework (RDF), has experienced outstanding growth over the last few years. Data from all fields of knowledge are shared publicly and interconnected in active initiatives such as Linked Open Data. However, despite the increasing availability of applications managing large-scale RDF information such as RDF stores and reasoning tools, little attention has been given to the structural features emerging in real-world RDF data. Our work addresses this issue by proposing specific metrics to characterise RDF data. We specifically focus on revealing the redundancy of each data set, as well as common structural patterns. We evaluate the proposed metrics on several data sets, which cover a wide range of designs and models. Our findings provide a basis for more efficient RDF data structures, indexes and compressors.

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NEW CHALLENGES FACING INTEGRATIVE BIOLOGICAL SCIENCE IN THE POST-GENOMIC ERA

Journal of Biological System ◽

10.1142/s0218339006001805 ◽

2006 ◽

Vol 14 (02) ◽

pp. 275-293 ◽

Cited By ~ 2

Author(s):

CHRISTOPHER S. OEHMEN ◽

TJERK P. STRAATSMA ◽

GORDON A. ANDERSON ◽

GALYA ORR ◽

BOBBIE-JO M. WEBB-ROBERTSON ◽

...

Keyword(s):

Paradigm Shift ◽

Large Scale ◽

Experimental Testing ◽

Spatial Scales ◽

Geographical Area ◽

Biological Data ◽

Biological Research ◽

Complex Data ◽

Discovery Research ◽

Wide Range

The future of biology will be increasingly driven by the fundamental paradigm shift from hypothesis-driven research to data-driven discovery research employing the growing volume of biological data coupled to experimental testing of new discoveries. But hardware and software limitations in the current workflow infrastructure make it impossible or intractible to use real data from disparate sources for large-scale biological research. We identify key technological developments needed to enable this paradigm shift involving (1) the ability to store and manage extremely large datasets which are dispersed over a wide geographical area, (2) development of novel analysis and visualization tools which are capable of operating on enormous data resources without overwhelming researchers with unusable information, and (3) formalisms for integrating mathematical models of biosystems from the molecular level to the organism population level. This will require the development of algorithms and tools which efficiently utilize high-performance compute power and large storage infrastructures. The end result will be the ability of a researcher to integrate complex data from many different sources with simulations to analyze a given system at a wide range of temporal and spatial scales in a single conceptual model.

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