scholarly journals Identifying signaling genes in spatial single-cell expression data

2020 ◽  
Author(s):  
Dongshunyi Li ◽  
Jun Ding ◽  
Ziv Bar-Joseph
Keyword(s):  
Single Cell ◽  
Cell Interactions ◽  
Excitatory Neuron ◽  
Expression Data ◽  
Mixture Of Experts ◽  
Prediction Of Response ◽  
Technological Advances ◽  
A Cell ◽  
Cell Expression ◽  
Cell Cell

Abstract Motivation Recent technological advances enable the profiling of spatial single-cell expression data. Such data present a unique opportunity to study cell–cell interactions and the signaling genes that mediate them. However, most current methods for the analysis of these data focus on unsupervised descriptive modeling, making it hard to identify key signaling genes and quantitatively assess their impact. Results We developed a Mixture of Experts for Spatial Signaling genes Identification (MESSI) method to identify active signaling genes within and between cells. The mixture of experts strategy enables MESSI to subdivide cells into subtypes. MESSI relies on multi-task learning using information from neighboring cells to improve the prediction of response genes within a cell. Applying the methods to three spatial single-cell expression datasets, we show that MESSI accurately predicts the levels of response genes, improving upon prior methods and provides useful biological insights about key signaling genes and subtypes of excitatory neuron cells. Availability and implementation MESSI is available at: https://github.com/doraadong/MESSI

scholarly journals Identifying signaling genes in spatial single cell expression data

2020 ◽  
Author(s):  
Dongshunyi Li ◽  
Jun Ding ◽  
Ziv Bar-Joseph
Keyword(s):  
Single Cell ◽  
Cell Interactions ◽  
Excitatory Neuron ◽  
Expression Data ◽  
Mixture Of Experts ◽  
Prediction Of Response ◽  
Technological Advances ◽  
A Cell ◽  
Cell Expression ◽  
Cell Cell

AbstractMotivationRecent technological advances enable the profiling of spatial single cell expression data. Such data presents a unique opportunity to study cell-cell interactions and the signaling genes that mediate them. However, most current methods for the analysis of this data focus on unsupervised descriptive modeling, making it hard to identify key signaling genes and quantitatively assess their impact.ResultsWe developed a Mixture of Experts for Spatial Signaling genes Identification (MESSI) method to identify active signaling genes within and between cells. The mixture of experts strategy enables MESSI to subdivide cells into subtypes. MESSI relies on multi-task learning using information from neighboring cells to improve the prediction of response genes within a cell. Applying the methods to three spatial single cell expression datasets, we show that MESSI accurately predicts the levels of response genes, improving upon prior methods and provides useful biological insights about key signaling genes and subtypes of excitatory neuron cells.AvailabilityMESSI is available at: https://github.com/doraadong/[email protected]

scholarly journals Discovery of rare cells from voluminous single cell expression data

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Aashi Jindal ◽  
Prashant Gupta ◽  
Jayadeva ◽  
Debarka Sengupta
Keyword(s):  
Single Cell ◽  
Expression Data ◽  
Rare Cells ◽  
Cell Expression

scholarly journals ArrayExpress update – from bulk to single-cell expression data

2018 ◽  
Vol 47 (D1) ◽  
pp. D711-D715 ◽  
Author(s):  
Awais Athar ◽  
Anja Füllgrabe ◽  
Nancy George ◽  
Haider Iqbal ◽  
Laura Huerta ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Data ◽  
Cell Expression

scholarly journals ESCO: single cell expression simulation incorporating gene co-expression

2020 ◽  
Author(s):  
Jinjin Tian ◽  
Jiebiao Wang ◽  
Kathryn Roeder
Keyword(s):  
Single Cell ◽  
R Package ◽  
Brain Cell ◽  
Gene Interactions ◽  
Cell Type ◽  
Imputation Methods ◽  
Biological Interest ◽  
A Cell ◽  
Cell Expression ◽  
Cell Data

AbstractMotivationGene-gene co-expression networks (GCN) are of biological interest for the useful information they provide for understanding gene-gene interactions. The advent of single cell RNA-sequencing allows us to examine more subtle gene co-expression occurring within a cell type. Many imputation and denoising methods have been developed to deal with the technical challenges observed in single cell data; meanwhile, several simulators have been developed for benchmarking and assessing these methods. Most of these simulators, however, either do not incorporate gene co-expression or generate co-expression in an inconvenient manner.ResultsTherefore, with the focus on gene co-expression, we propose a new simulator, ESCO, which adopts the idea of the copula to impose gene co-expression, while preserving the highlights of available simulators, which perform well for simulation of gene expression marginally. Using ESCO, we assess the performance of imputation methods on GCN recovery and find that imputation generally helps GCN recovery when the data are not too sparse, and the ensemble imputation method works best among leading methods. In contrast, imputation fails to help in the presence of an excessive fraction of zero counts, where simple data aggregating methods are a better choice. These findings are further verified with mouse and human brain cell data.AvailabilityThe ESCO implementation is available as R package SplatterESCO (https://github.com/JINJINT/SplatterESCO)[email protected]

Abstract 3391: Dissecting intratumoral cell-cell interactions in myeloid reprogramming by single cell RNA-seq

2019 ◽  
Author(s):  
Qianqian Song ◽  
Gregory A. Hawkins ◽  
Leonard Wudel ◽  
Ping-Chieh Chou ◽  
Elizabeth Forbes ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Interactions ◽  
Rna Seq ◽  
Cell Cell

scholarly journals Machine learning of stem cell identities from single-cell expression data via regulatory network archetypes

2017 ◽  
Author(s):  
Patrick S Stumpf ◽  
Ben D MacArthur
Keyword(s):  
Machine Learning ◽  
Stem Cells ◽  
Stem Cell ◽  
Single Cell ◽  
Regulatory Network ◽  
Cell Biology ◽  
Embryonic Stem ◽  
Network Activity ◽  
Expression Data ◽  
Cell Expression

AbstractThe molecular regulatory network underlying stem cell pluripotency has been intensively studied, and we now have a reliable ensemble model for the ‘average’ pluripotent cell. However, evidence of significant cell-to-cell variability suggests that the activity of this network varies within individual stem cells, leading to differential processing of environmental signals and variability in cell fates. Here, we adapt a method originally designed for face recognition to infer regulatory network patterns within individual cells from single-cell expression data. Using this method we identify three distinct network configurations in cultured mouse embryonic stem cells – corresponding to naïve and formative pluripotent states and an early primitive endoderm state – and associate these configurations with particular combinations of regulatory network activity archetypes that govern different aspects of the cell’s response to environmental stimuli, cell cycle status and core information processing circuitry. These results show how variability in cell identities arise naturally from alterations in underlying regulatory network dynamics and demonstrate how methods from machine learning may be used to better understand single cell biology, and the collective dynamics of cell communities.

scholarly journals Using approximate Bayesian computation to quantify cell-cell adhesion parameters in a cell migratory process

2016 ◽  
Author(s):  
Robert J. H. Ross ◽  
R. E. Baker ◽  
Andrew Parker ◽  
M. J. Ford ◽  
R. L. Mort ◽  
...  
Keyword(s):  
Cell Adhesion ◽  
Approximate Bayesian Computation ◽  
Cell Interactions ◽  
Model Parameters ◽  
Bayesian Computation ◽  
Accurate Identification ◽  
A Cell ◽  
Approximate Bayesian ◽  
Set Up ◽  
Cell Cell

AbstractIn this work we implement approximate Bayesian computational methods to improve the design of a wound-healing assay used to quantify cell-cell interactions. This is important as cell-cell interactions, such as adhesion and repulsion, have been shown to play an important role in cell migration. Initially, we demonstrate with a model of an ideal experiment that we are able to identify model parameters for agent motility and adhesion, given we choose appropriate summary statistics. Following this, we replace our model of an ideal experiment with a model representative of a practically realisable experiment. We demonstrate that, given the current (and commonly used) experimental set-up, model parameters cannot be accurately identified using approximate Bayesian computation methods. We compare new experimental designs through simulation, and show more accurate identification of model parameters is possible by expanding the size of the domain upon which the experiment is performed, as opposed to increasing the number of experimental repeats. The results presented in this work therefore describe time and cost-saving alterations for a commonly performed experiment for identifying cell motility parameters. Moreover, the results presented in this work will be of interest to those concerned with performing experiments that allow for the accurate identification of parameters governing cell migratory processes, especially cell migratory processes in which cell-cell adhesion or repulsion are known to play a significant role.

scholarly journals Spatial analysis of ligand-receptor interactions in skin cancer at genome-wide and single-cell resolution

2020 ◽  
Author(s):  
M Tran ◽  
S Yoon ◽  
ST Min ◽  
S Andersen ◽  
K Devitt ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Single Cell ◽  
Molecular Mechanisms ◽  
Digital Pcr ◽  
Droplet Digital Pcr ◽  
Cell Interactions ◽  
Genome Wide ◽  
Receptor Interactions ◽  
Cell Cell

AbstractThe ability to study cancer-immune cell communication across the whole tumor section without tissue dissociation is important to understand molecular mechanisms of cancer immunotherapy and drug targets. Current experimental methods such as immunohistochemistry allow researchers to investigate a small number of cells or a limited number of ligand-receptor pairs at tissue scale with limited cellular resolution. In this work, we developed a powerful experimental and analytical pipeline that allows for the genome-wide discovery and targeted validation of cellular communication. By profiling thousands of genes, spatial transcriptomic and single-cell RNA sequencing data show genes that are possibly involved in interactions. The expression of the candidate genes could be visualized by single-molecule in situ hybridization and droplet digital PCR. We developed a computational pipeline called STRISH that enables us to quantitatively model cell-cell interactions by automatically scanning for local expression of RNAscope data to recapitulate an interaction landscape across the whole tissue. Furthermore, we showed the strong correlation of microscopic RNAscope imaging data analyzed by STRISH with the gene expression values measured by droplet digital PCR. We validated the unique ability of this approach to discover new cell-cell interactions in situ through analysis of two types of cancer, basal cell carcinoma and squamous cell carcinoma. We expect that the approach described here will help to discover and validate ligand receptor interactions in different biological contexts such as immune-cancer cell interactions within a tumor.

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