Model-based prediction of spatial gene expression via generative linear mapping.
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ABSTRACT:
SUBMITTER: Okochi Y
PROVIDER: S-EPMC8211835 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Recent advances in various single-cell RNA sequencing (scRNA-seq) technologies have enabled profiling the gene expression level with the whole transcriptome at a single-cell resolution. However, it lacks the spatial context of tissues. The image-based transcriptomics in situ studies (e.g., MERFISH and seqFISH) maintain the cell spatial context at individual cell levels but can only measure a limited number of genes or transcripts (up to roughly 1,000 genes). Therefore, integrating scRNA-seq data and image-based transcriptomics data can potentially gain the complementary benefits of both. Here, we develop a computational method, SpatialMap, to bridge the gap, which primarily facilitates spatial mapping of unmeasured gene profiles in spatial transcriptomic data via integrating with scRNA-seq data from the same tissue. SpatialMap directly models the count nature of spatial gene expression data through generalized linear spatial models, which accounts for the spatial correlation among spatial locations using conditional autoregressive (CAR) prior. With a newly developed computationally efficient penalized quasi-likelihood (PQL)-based algorithm, SpatialMap can scale up to performing large-scale spatial mapping analysis. Finally, we applied the SpatialMap to four publicly available tissue-paired studies (i.e., scRNA-seq studies and image-based transcriptomics studies). The results demonstrate that the proposed method can accurately predict unmeasured gene expression profiles across various spatial and scRNA-seq dataset pairs of different species and technologies.
| S-EPMC9181802 | biostudies-literature
Project description:Cell reprogramming has played important roles in medical science, such as tissue repair, organ reconstruction, disease treatment, new drug development, and new species breeding. Oct4, a core pluripotency factor, has especially played a key role in somatic cell reprogramming through transcriptional control and affects the expression level of genes by its combination intensity. However, the quantitative relationship between Oct4 combination intensity and target gene expression is still not clear. Therefore, firstly, a generalized linear regression method was constructed to predict gene expression values in promoter regions affected by Oct4 combination intensity. Training data, including Oct4 combination intensity and target gene expression, were from promoter regions of genes with different cell development stages. Additionally, the quantitative relationship between gene expression and Oct4 combination intensity was analyzed with the proposed model. Then, the quantitative relationship between gene expression and Oct4 combination intensity at each stage of cell development was classified into high and low levels. Experimental analysis showed that the combination height of Oct4-inhibited gene expression decremented by a temporal exponential value, whereas the combination width of Oct4-promoted gene expression incremented by a temporal logarithmic value. Experimental results showed that the proposed method can achieve goodness of fit with high confidence.
| S-EPMC6409355 | biostudies-literature
Project description:Inferring missing links based on the currently observed network is known as link prediction, which has tremendous real-world applications in biomedicine, e-commerce, social media, and criminal intelligence. Numerous methods have been proposed to solve the link prediction problem. Yet, many of these methods are designed for undirected networks only and based on domain-specific heuristics. Here we developed a new link prediction method based on deep generative models, which does not rely on any domain-specific heuristic and works for general undirected or directed complex networks. Our key idea is to represent the adjacency matrix of a network as an image and then learn hierarchical feature representations of the image by training a deep generative model. Those features correspond to structural patterns in the network at different scales, from small subgraphs to mesoscopic communities. When applied to various real-world networks from different domains, our method shows overall superior performance against existing methods.
| S-EPMC7575873 | biostudies-literature
Project description:BackgroundTemporal analysis of gene expression data has been limited to identifying genes whose expression varies with time and/or correlation between genes that have similar temporal profiles. Often, the methods do not consider the underlying network constraints that connect the genes. It is becoming increasingly evident that interactions change substantially with time. Thus far, there is no systematic method to relate the temporal changes in gene expression to the dynamics of interactions between them. Information on interaction dynamics would open up possibilities for discovering new mechanisms of regulation by providing valuable insight into identifying time-sensitive interactions as well as permit studies on the effect of a genetic perturbation.ResultsWe present NETGEM, a tractable model rooted in Markov dynamics, for analyzing the dynamics of the interactions between proteins based on the dynamics of the expression changes of the genes that encode them. The model treats the interaction strengths as random variables which are modulated by suitable priors. This approach is necessitated by the extremely small sample size of the datasets, relative to the number of interactions. The model is amenable to a linear time algorithm for efficient inference. Using temporal gene expression data, NETGEM was successful in identifying (i) temporal interactions and determining their strength, (ii) functional categories of the actively interacting partners and (iii) dynamics of interactions in perturbed networks.ConclusionsNETGEM represents an optimal trade-off between model complexity and data requirement. It was able to deduce actively interacting genes and functional categories from temporal gene expression data. It permits inference by incorporating the information available in perturbed networks. Given that the inputs to NETGEM are only the network and the temporal variation of the nodes, this algorithm promises to have widespread applications, beyond biological systems.The source code for NETGEM is available from https://github.com/vjethava/NETGEM.
| S-EPMC3228555 | biostudies-literature
Project description:Protein contact maps represent spatial pairwise inter-residue interactions, providing a protein's translationally and rotationally invariant topological representation. Accurate contact map prediction has been a critical driving force for improving protein structure determination. Contact maps can also be used as a stand-alone tool for varied applications such as prediction of protein-protein interactions, structure-aware thermal stability or physicochemical properties. We develop a novel hybrid contact map prediction model, CGAN-Cmap, that uses a generative adversarial neural network embedded with a series of modified squeeze and excitation residual networks. To exploit features of different dimensions, we introduce two parallel modules. This architecture improves the prediction by increasing receptive fields, surpassing redundant features and encouraging more meaningful ones from 1D and 2D inputs. We also introduce a new custom dynamic binary cross-entropy loss function to address the input imbalance problem for highly sparse long-range contacts in proteins with insufficient homologs. We evaluate the model's performance on CASP 11, 12, 13, 14, and CAMEO test sets. CGAN-Cmap outperforms state-of-the-art models, improving precision of medium and long-range contacts by at least 3.5%. As a direct assessment between our model and AlphaFold2, the leading available protein structure prediction model, we compare extracted contact maps from AlphaFold2 and predicted contact maps from CGAN-Cmap. The results show that CGAN-Cmap has a mean precision higher by 1% compared to AlphaFold2 for most ranges of contacts. These results demonstrate an efficient approach for highly accurate contact map prediction toward accurate characterization of protein structure, properties and functions from sequence.
| S-EPMC9674885 | biostudies-literature
Project description:A multivariate linear regression model was proposed to achieve short period prediction of PM2.5 (fine particles with an aerodynamic diameter of 2.5 μm or less). The main parameters for the proposed model included data on aerosol optical depth (AOD) obtained through remote sensing, meteorological factors from ground monitoring (wind velocity, temperature, and relative humidity), and other gaseous pollutants (SO2, NO2, CO, and O3). Beijing City was selected as a typical region for the case study. Data on the aforementioned variables for the city throughout 2015 were used to construct two regression models, which were discriminated by annual and seasonal data, respectively. The results indicated that the regression model based on annual data had (R2 = 0.766) goodness-of-fit and (R2 = 0.875) cross-validity. However, the regression models based on seasonal data for spring and winter were more effective, achieving 0.852 and 0.874 goodness-of-fit, respectively. Model uncertainties were also given, with the view of laying the foundation for further study.
| S-EPMC6062037 | biostudies-literature
Project description:Gene co-expression networks (GCNs) are constructed from Gene Expression Matrices (GEMs) in a bottom up approach where all gene pairs are tested for correlation within the context of the input sample set. This approach is computationally intensive for many current GEMs and may not be scalable to millions of samples. Further, traditional GCNs do not detect non-linear relationships missed by correlation tests and do not place genetic relationships in a gene expression intensity context. In this report, we propose EdgeScaping, which constructs and analyzes the pairwise gene intensity network in a holistic, top down approach where no edges are filtered. EdgeScaping uses a novel technique to convert traditional pairwise gene expression data to an image based format. This conversion not only performs feature compression, making our algorithm highly scalable, but it also allows for exploring non-linear relationships between genes by leveraging deep learning image analysis algorithms. Using the learned embedded feature space we implement a fast, efficient algorithm to cluster the entire space of gene expression relationships while retaining gene expression intensity. Since EdgeScaping does not eliminate conventionally noisy edges, it extends the identification of co-expression relationships beyond classically correlated edges to facilitate the discovery of novel or unusual expression patterns within the network. We applied EdgeScaping to a human tumor GEM to identify sets of genes that exhibit conventional and non-conventional interdependent non-linear behavior associated with brain specific tumor sub-types that would be eliminated in conventional bottom-up construction of GCNs. Edgescaping source code is available at https://github.com/bhusain/EdgeScaping under the MIT license.
| S-EPMC6684082 | biostudies-literature
Project description:BackgroundElucidating cellular metabolism led to many breakthroughs in biotechnology, synthetic biology, and health sciences. To date, deriving metabolic fluxes by 13C tracer experiments is the most prominent approach for studying metabolic fluxes quantitatively, often with high accuracy and precision. However, the technique has a high demand for experimental resources. Alternatively, flux balance analysis (FBA) has been employed to estimate metabolic fluxes without labeling experiments. It is less informative but can benefit from the low costs and low experimental efforts and gain flux estimates in experimentally difficult conditions. Methods to integrate relevant experimental data have been emerged to improve FBA flux estimations. Data from transcription profiling is often selected since it is easy to generate at the genome scale, typically embedded by a discretization of differential and non-differential expressed genes coding for the respective enzymes.ResultWe established the novel method Linear Programming based Gene Expression Model (LPM-GEM). LPM-GEM linearly embeds gene expression into FBA constraints. We implemented three strategies to reduce thermodynamically infeasible loops, which is a necessary prerequisite for such an omics-based model building. As a case study, we built a model of B. subtilis grown in eight different carbon sources. We obtained good flux predictions based on the respective transcription profiles when validating with 13C tracer based metabolic flux data of the same conditions. We could well predict the specific carbon sources. When testing the model on another, unseen dataset that was not used during training, good prediction performance was also observed. Furthermore, LPM-GEM outperformed a well-established model building methods.ConclusionEmploying LPM-GEM integrates gene expression data efficiently. The method supports gene expression-based FBA models and can be applied as an alternative to estimate metabolic fluxes when tracer experiments are inappropriate.
| S-EPMC9188260 | biostudies-literature
Project description:Tumour heterogeneity in breast cancer poses challenges in predicting outcome and response to therapy. Spatial transcriptomics technologies may address these challenges, as they provide a wealth of information about gene expression at the cell level, but they are expensive, hindering their use in large-scale clinical oncology studies. Predicting gene expression from hematoxylin and eosin stained histology images provides a more affordable alternative for such studies. Here we present BrST-Net, a deep learning framework for predicting gene expression from histopathology images using spatial transcriptomics data. Using this framework, we trained and evaluated four distinct state-of-the-art deep learning architectures, which include ResNet101, Inception-v3, EfficientNet (with six different variants), and vision transformer (with two different variants), all without utilizing pretrained weights for the prediction of 250 genes. To enhance the generalisation performance of the main network, we introduce an auxiliary network into the framework. Our methodology outperforms previous studies, with 237 genes identified with positive correlation, including 24 genes with a median correlation coefficient greater than 0.50. This is a notable improvement over previous studies, which could predict only 102 genes with positive correlation, with the highest correlation values ranging from 0.29 to 0.34.
| S-EPMC10442349 | biostudies-literature
Project description:SummaryMeasurement of single-cell gene expression at different timepoints enables the study of cell development. However, due to the resource constraints and technical challenges associated with the single-cell experiments, researchers can only profile gene expression at discrete and sparsely sampled timepoints. This missing timepoint information impedes downstream cell developmental analyses. We propose scNODE, an end-to-end deep learning model that can predict in silico single-cell gene expression at unobserved timepoints. scNODE integrates a variational autoencoder with neural ordinary differential equations to predict gene expression using a continuous and nonlinear latent space. Importantly, we incorporate a dynamic regularization term to learn a latent space that is robust against distribution shifts when predicting single-cell gene expression at unobserved timepoints. Our evaluations on three real-world scRNA-seq datasets show that scNODE achieves higher predictive performance than state-of-the-art methods. We further demonstrate that scNODE's predictions help cell trajectory inference under the missing timepoint paradigm and the learned latent space is useful for in silico perturbation analysis of relevant genes along a developmental cell path.Availability and implementationThe data and code are publicly available at https://github.com/rsinghlab/scNODE.
| S-EPMC11373355 | biostudies-literature