scholarly journals Model-based prediction of spatial gene expression via generative linear mapping

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yasushi Okochi ◽  
Shunta Sakaguchi ◽  
Ken Nakae ◽  
Takefumi Kondo ◽  
Honda Naoki
Keyword(s):  
Gene Expression ◽  
Expectation Maximization Algorithm ◽  
Gaussian Mixture ◽  
Linear Mapping ◽  
Model Based ◽  
Model Free ◽  
Mammalian Liver ◽  
Interpretable Model ◽  
Mouse Visual Cortex

AbstractDecoding spatial transcriptomes from single-cell RNA sequencing (scRNA-seq) data has become a fundamental technique for understanding multicellular systems; however, existing computational methods lack both accuracy and biological interpretability due to their model-free frameworks. Here, we introduce Perler, a model-based method to integrate scRNA-seq data with reference in situ hybridization (ISH) data. To calibrate differences between these datasets, we develop a biologically interpretable model that uses generative linear mapping based on a Gaussian mixture model using the Expectation–Maximization algorithm. Perler accurately predicts the spatial gene expression of Drosophila embryos, zebrafish embryos, mammalian liver, and mouse visual cortex from scRNA-seq data. Furthermore, the reconstructed transcriptomes do not over-fit the ISH data and preserved the timing information of the scRNA-seq data. These results demonstrate the generalizability of Perler for dataset integration, thereby providing a biologically interpretable framework for accurate reconstruction of spatial transcriptomes in any multicellular system.

scholarly journals Model-based prediction of spatial gene expression via generative linear mapping

2020 ◽  
Author(s):  
Yasushi Okochi ◽  
Shunta Sakaguchi ◽  
Ken Nakae ◽  
Takefumi Kondo ◽  
Honda Naoki
Keyword(s):  
Gene Expression ◽  
Expectation Maximization Algorithm ◽  
Gaussian Mixture ◽  
Linear Mapping ◽  
Model Based ◽  
Model Free ◽  
Mammalian Liver ◽  
Interpretable Model ◽  
Mouse Visual Cortex

AbstractDecoding spatial transcriptomes from single-cell RNA sequencing (scRNA-seq) data has become a fundamental technique for understanding multicellular systems; however, existing computational methods lack both accuracy and biological interpretability due to their model-free frameworks. Here, we introduced Perler, a model-based method to integrate scRNA-seq data with reference in situ hybridization (ISH) data. To calibrate differences between these datasets, we developed a biologically interpretable model that uses generative linear mapping based on a Gaussian-mixture model using the Expectation-Maximization algorithm. Perler accurately predicted the spatial gene expression of Drosophila embryos, zebrafish embryos, mammalian liver, and mouse visual cortex from scRNA-seq data. Furthermore, the reconstructed transcriptomes did not over-fit the ISH data and preserved the timing information of the scRNA-seq data. These results demonstrated the generalizability of Perler for dataset integration, thereby providing a biologically interpretable framework for accurate reconstruction of spatial transcriptomes in any multicellular system.

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

Molecular and Microscopic Analysis of Altered Gene Expression in Transgenic and Gene Targeted Mice

1996 ◽  
Vol 54 ◽  
pp. 12-13
Author(s):  
W. K. Jones ◽  
J. Robbins
Keyword(s):  
Gene Expression ◽  
Pulmonary Veins ◽  
Microscopic Analysis ◽  
Mouse Tissue ◽  
Adult Heart ◽  
Heavy Chains ◽  
Altered Gene Expression ◽  
Altered Gene ◽  
Pulmonary Myocardium

Two myosin heavy chains (MyHC) are expressed in the mammalian heart and are differentially regulated during development. In the mouse, the α-MyHC is expressed constitutively in the atrium. At birth, the β-MyHC is downregulated and replaced by the α-MyHC, which is the sole cardiac MyHC isoform in the adult heart. We have employed transgenic and gene-targeting methodologies to study the regulation of cardiac MyHC gene expression and the functional and developmental consequences of altered α-MyHC expression in the mouse.We previously characterized an α-MyHC promoter capable of driving tissue-specific and developmentally correct expression of a CAT (chloramphenicol acetyltransferase) marker in the mouse. Tissue surveys detected a small amount of CAT activity in the lung (Fig. 1a). The results of in situ hybridization analyses indicated that the pattern of CAT transcript in the adult heart (Fig. 1b, top panel) is the same as that of α-MyHC (Fig. 1b, lower panel). The α-MyHC gene is expressed in a layer of cardiac muscle (pulmonary myocardium) associated with the pulmonary veins (Fig. 1c). These studies extend our understanding of α-MyHC expression and delimit a third cardiac compartment.

Model-Based and Model-Free Social Cognition

2019 ◽  
Author(s):  
Leor M Hackel ◽  
Jeffrey Jordan Berg ◽  
Björn Lindström ◽  
David Amodio
Keyword(s):  
Reinforcement Learning ◽  
Social Cognition ◽  
Learning Strategies ◽  
Memory Systems ◽  
Learning Task ◽  
Financial Advisors ◽  
Model Based ◽  
Model Free ◽  
Systems Model ◽  
Task Assessment

Do habits play a role in our social impressions? To investigate the contribution of habits to the formation of social attitudes, we examined the roles of model-free and model-based reinforcement learning in social interactions—computations linked in past work to habit and planning, respectively. Participants in this study learned about novel individuals in a sequential reinforcement learning paradigm, choosing financial advisors who led them to high- or low-paying stocks. Results indicated that participants relied on both model-based and model-free learning, such that each independently predicted choice during the learning task and self-reported liking in a post-task assessment. Specifically, participants liked advisors who could provide large future rewards as well as advisors who had provided them with large rewards in the past. Moreover, participants varied in their use of model-based and model-free learning strategies, and this individual difference influenced the way in which learning related to self-reported attitudes: among participants who relied more on model-free learning, model-free social learning related more to post-task attitudes. We discuss implications for attitudes, trait impressions, and social behavior, as well as the role of habits in a memory systems model of social cognition.

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