Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Network modeling of single-cell omics data: challenges, opportunities, and progresses.

ABSTRACT: Single-cell multi-omics technologies are rapidly evolving, prompting both methodological advances and biological discoveries at an unprecedented speed. Gene regulatory network modeling has been used as a powerful approach to elucidate the complex molecular interactions underlying biological processes and systems, yet its application in single-cell omics data modeling has been met with unique challenges and opportunities. In this review, we discuss these challenges and opportunities, and offer an overview of the recent development of network modeling approaches designed to capture dynamic networks, within-cell networks, and cell-cell interaction or communication networks. Finally, we outline the remaining gaps in single-cell gene network modeling and the outlooks of the field moving forward.

SUBMITTER: Blencowe M

PROVIDER: S-EPMC7141415 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Network modeling of single-cell omics data: challenges, opportunities, and progresses.

Blencowe Montgomery M Arneson Douglas D Ding Jessica J Chen Yen-Wei YW Saleem Zara Z Yang Xia X

Emerging topics in life sciences 20190708 4

Single-cell multi-omics technologies are rapidly evolving, prompting both methodological advances and biological discoveries at an unprecedented speed. Gene regulatory network modeling has been used as a powerful approach to elucidate the complex molecular interactions underlying biological processes and systems, yet its application in single-cell omics data modeling has been met with unique challenges and opportunities. In this review, we discuss these challenges and opportunities, and offer an ...[more]

PMID: 32270049

Similar Datasets

Normalizing single-cell RNA sequencing data: challenges and opportunities.

Project description:Single-cell transcriptomics is becoming an important component of the molecular biologist's toolkit. A critical step when analyzing data generated using this technology is normalization. However, normalization is typically performed using methods developed for bulk RNA sequencing or even microarray data, and the suitability of these methods for single-cell transcriptomics has not been assessed. We here discuss commonly used normalization approaches and illustrate how these can produce misleading results. Finally, we present alternative approaches and provide recommendations for single-cell RNA sequencing users.

| S-EPMC5549838 | biostudies-literature

Challenges and Opportunities in Collecting and Modeling Ambulatory Electrodermal Activity Data.

Project description:Ambulatory assessment of electrodermal activity (EDA) is an emerging technique for capturing individuals' autonomic responses to real-life events. There is currently little guidance available for processing and analyzing such data in an ambulatory setting. This study aimed to describe and implement several methods for preprocessing and constructing features for use in modeling ambulatory EDA data, particularly for measuring stress. We used data from a study examining the effects of stressful tasks on EDA of adolescent mothers (AMs). A biosensor band recorded EDA 4 times per second and was worn during an approximately 2-hour assessment that included a 10-min mother-child videotaped interaction. The initial processing included filtering noise and motion artifacts. We constructed the features of the EDA data, including the number of peaks and their amplitude as well as EDA reactivity, quantified as the rate at which AMs returned to baseline EDA following an EDA peak. Although the pattern of EDA varied substantially across individuals, various features of EDA may be computed for all individuals enabling within- and between-individual analyses and comparisons. The algorithms we developed can be used to construct features for dry-electrode ambulatory EDA, which can be used by other researchers to study stress and anxiety.

| S-EPMC8653913 | biostudies-literature

Integrative Methods and Practical Challenges for Single-Cell Multi-omics.

Project description:Fast-developing single-cell multimodal omics (scMulti-omics) technologies enable the measurement of multiple modalities, such as DNA methylation, chromatin accessibility, RNA expression, protein abundance, gene perturbation, and spatial information, from the same cell. scMulti-omics can comprehensively explore and identify cell characteristics, while also presenting challenges to the development of computational methods and tools for integrative analyses. Here, we review these integrative methods and summarize the existing tools for studying a variety of scMulti-omics data. The various functionalities and practical challenges in using the available tools in the public domain are explored through several case studies. Finally, we identify remaining challenges and future trends in scMulti-omics modeling and analyses.

| S-EPMC7442857 | biostudies-literature

Progresses and challenges of Strongyloides spp. proteomics

Project description:The availability of high-quality data of helminth genomes provided over the last two decades has supported and accelerated large-scale ‘omics studies and, consequently, the achievement of a more in depth molecular characterisation of a number of pathogens. Strongyloides spp. are no less and since their genome was made available transcriptomics has been rather frequently applied to investigate gene expression regulation across their life cycle. Strongyloides proteomics characterisation has instead been somehow neglected, with only a few reports performing high-throughput or targeted analyses associated with protein identification by tandem mass spectrometry. Such investigations are however necessary in order to discern important aspects associated with human strongyloidiasis, including understanding parasite biology and the mechanisms of host-parasite interaction, but also to identify novel diagnostic and therapeutic targets. In this review article we will give an overview of the published proteomics studies investigating strongyloidiasis at different levels, spanning from the characterisation of the somatic proteome and excretory/secretory products of different parasite stages to the investigation of potentially immunogenic proteins. Moreover, in the effort to try to start filling the current gap in host-proteomics, we will also present the first serum proteomics analysis in patients suffering from human strongyloidiasis.

2024-01-26 | PXD041218 | Pride

Modeling SF3B1 Mutations in Cancer: Advances, Challenges, and Opportunities.

Project description:In this issue of Cancer Cell, Obeng et al. identify the consequences of expressing the most common mutation in the spliceosomal gene SF3B1 on hematopoiesis. The knockin mouse model described represents a valuable tool to dissect the effects of SF3B1 mutations on transformation, splicing, and less well-characterized functions of SF3B1.

| S-EPMC5569387 | biostudies-other

Clustering single-cell multi-omics data with MoClust.

Project description:MotivationSingle-cell multi-omics sequencing techniques have rapidly developed in the past few years. Clustering analysis with single-cell multi-omics data may give us novel perspectives to dissect cellular heterogeneity. However, multi-omics data have the properties of inherited large dimension, high sparsity and existence of doublets. Moreover, representations of different omics from even the same cell follow diverse distributions. Without proper distribution alignment techniques, clustering methods will encounter less separable clusters easily affected by less informative omics data.ResultsWe developed MoClust, a novel joint clustering framework that can be applied to several types of single-cell multi-omics data. A selective automatic doublet detection module that can identify and filter out doublets is introduced in the pretraining stage to improve data quality. Omics-specific autoencoders are introduced to characterize the multi-omics data. A contrastive learning way of distribution alignment is adopted to adaptively fuse omics representations into an omics-invariant representation. This novel way of alignment boosts the compactness and separableness of clusters, while accurately weighting the contribution of each omics to the clustering object. Extensive experiments, over both simulated and real multi-omics datasets, demonstrated the powerful alignment, doublet detection and clustering ability features of MoClust.Availability and implementationAn implementation of MoClust is available from https://doi.org/10.5281/zenodo.7306504.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC9805570 | biostudies-literature

Clustering single-cell multimodal omics data with jrSiCKLSNMF.

Project description:Introduction: The development of multimodal single-cell omics methods has enabled the collection of data across different omics modalities from the same set of single cells. Each omics modality provides unique information about cell type and function, so the ability to integrate data from different modalities can provide deeper insights into cellular functions. Often, single-cell omics data can prove challenging to model because of high dimensionality, sparsity, and technical noise. Methods: We propose a novel multimodal data analysis method called joint graph-regularized Single-Cell Kullback-Leibler Sparse Non-negative Matrix Factorization (jrSiCKLSNMF, pronounced "junior sickles NMF") that extracts latent factors shared across omics modalities within the same set of single cells. Results: We compare our clustering algorithm to several existing methods on four sets of data simulated from third party software. We also apply our algorithm to a real set of cell line data. Discussion: We show overwhelmingly better clustering performance than several existing methods on the simulated data. On a real multimodal omics dataset, we also find our method to produce scientifically accurate clustering results.

| S-EPMC10288154 | biostudies-literature

Data-driven challenges and opportunities in crystallography.

Project description:Structural biology is in the midst of a revolution fueled by faster and more powerful instruments capable of delivering orders of magnitude more data than their predecessors. This increased pace in data gathering introduces new experimental and computational challenges, frustrating real-time processing and interpretation of data and requiring long-term solutions for data archival and retrieval. This combination of challenges and opportunities is driving the exploration of new areas of structural biology, including studies of macromolecular dynamics and the investigation of molecular ensembles in search of a better understanding of conformational landscapes. The next generation of instruments promises to yield even greater data rates, requiring a concerted effort by institutions, centers and individuals to extract meaning from every bit and make data accessible to the community at large, facilitating data mining efforts by individuals or groups as analysis tools improve.

| S-EPMC7289006 | biostudies-literature

Big data: survey, technologies, opportunities, and challenges.

Project description:Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over 2 billion people worldwide are connected to the Internet, and over 5 billion individuals own mobile phones. By 2020, 50 billion devices are expected to be connected to the Internet. At this point, predicted data production will be 44 times greater than that in 2009. As information is transferred and shared at light speed on optic fiber and wireless networks, the volume of data and the speed of market growth increase. However, the fast growth rate of such large data generates numerous challenges, such as the rapid growth of data, transfer speed, diverse data, and security. Nonetheless, Big Data is still in its infancy stage, and the domain has not been reviewed in general. Hence, this study comprehensively surveys and classifies the various attributes of Big Data, including its nature, definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a data life cycle that uses the technologies and terminologies of Big Data. Future research directions in this field are determined based on opportunities and several open issues in Big Data domination. These research directions facilitate the exploration of the domain and the development of optimal techniques to address Big Data.

| S-EPMC4127205 | biostudies-other

Mapping gene regulatory networks from single-cell omics data.

Project description:Single-cell techniques are advancing rapidly and are yielding unprecedented insight into cellular heterogeneity. Mapping the gene regulatory networks (GRNs) underlying cell states provides attractive opportunities to mechanistically understand this heterogeneity. In this review, we discuss recently emerging methods to map GRNs from single-cell transcriptomics data, tackling the challenge of increased noise levels and data sparsity compared with bulk data, alongside increasing data volumes. Next, we discuss how new techniques for single-cell epigenomics, such as single-cell ATAC-seq and single-cell DNA methylation profiling, can be used to decipher gene regulatory programmes. We finally look forward to the application of single-cell multi-omics and perturbation techniques that will likely play important roles for GRN inference in the future.

| S-EPMC6063279 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data