An official website of the United States government
Abstract Single cell profiling techniques including multi-omics and spatial-omics technologies allow researchers to study cell-cell variation within a cell population. These variations extend to biological networks within cells, in particular, the gene regulatory networks (GRNs). GRNs rewire as the cells evolve, and different cells can have different governing GRNs. However, existing GRN inference methods usually infer a single GRN for a population of cells, without exploring the cell-cell variation in terms of their regulatory mechanisms. Recently, jointly profiled single cell transcriptomics and chromatin accessibility data have been used to infer GRNs. Although methods based on such multi-omics data were shown to improve over the accuracy of methods using only single cell RNA-seq (scRNA-seq) data, they do not take full advantage of the single cell resolution chromatin accessibility data. We propose CeSpGRN (CellSpecificGeneRegulatoryNetwork inference), which infers cell-specific GRNs from scRNA-seq, single cell multi-omics, or single cell spatial-omics data. CeSpGRN uses a Gaussian weighted kernel that allows the GRN of a given cell to be learned from the sequencing profile of itself and its neighboring cells in the developmental process. The kernel is constructed from the similarity of gene expressions or spatial locations between cells. When the chromatin accessibility data is available, CeSpGRN constructs cell-specific prior networks which are used to further improve the inference accuracy. We applied CeSpGRN to various types of real-world datasets and inferred various regulation changes that were shown to be important in cell development. We also quantitatively measured the performance of CeSpGRN on simulated datasets and compared with baseline methods. The results show that CeSpGRN has a superior performance in reconstructing the GRN for each cell, as well as in detecting the regulatory interactions that differ between cells. CeSpGRN is available athttps://github.com/PeterZZQ/CeSpGRN.
more »
« less
CLARIFY: cell–cell interaction and gene regulatory network refinement from spatially resolved transcriptomics
Abstract Motivation Gene regulatory networks (GRNs) in a cell provide the tight feedback needed to synchronize cell actions. However, genes in a cell also take input from, and provide signals to other neighboring cells. These cell–cell interactions (CCIs) and the GRNs deeply influence each other. Many computational methods have been developed for GRN inference in cells. More recently, methods were proposed to infer CCIs using single cell gene expression data with or without cell spatial location information. However, in reality, the two processes do not exist in isolation and are subject to spatial constraints. Despite this rationale, no methods currently exist to infer GRNs and CCIs using the same model. Results We propose CLARIFY, a tool that takes GRNs as input, uses them and spatially resolved gene expression data to infer CCIs, while simultaneously outputting refined cell-specific GRNs. CLARIFY uses a novel multi-level graph autoencoder, which mimics cellular networks at a higher level and cell-specific GRNs at a deeper level. We applied CLARIFY to two real spatial transcriptomic datasets, one using seqFISH and the other using MERFISH, and also tested on simulated datasets from scMultiSim. We compared the quality of predicted GRNs and CCIs with state-of-the-art baseline methods that inferred either only GRNs or only CCIs. The results show that CLARIFY consistently outperforms the baseline in terms of commonly used evaluation metrics. Our results point to the importance of co-inference of CCIs and GRNs and to the use of layered graph neural networks as an inference tool for biological networks. Availability and implementation The source code and data is available at https://github.com/MihirBafna/CLARIFY.
more »
« less
- Award ID(s):
- 2145736
- PAR ID:
- 10451235
- Date Published:
- Journal Name:
- Bioinformatics
- Volume:
- 39
- Issue:
- Supplement_1
- ISSN:
- 1367-4803
- Page Range / eLocation ID:
- i484 to i493
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Inference of gene regulatory networks (GRNs) can reveal cell state transitions from single-cell genomics data. However, obstacles to temporal inference from snapshot data are difficult to overcome. Single-nuclei multiomic data offer a means to bridge this gap and derive temporal information using joint measurements of gene expression and chromatin accessibility in the same single cells. We developed popInfer to infer networks that characterize lineage-specific dynamic cell state transitions from joint gene expression and chromatin accessibility data. Benchmarking against alternative methods for GRN inference, we showed that popInfer achieves higher accuracy in the GRNs inferred. popInfer was applied to study single-cell multiomics data characterizing hematopoietic stem cells (HSCs) and the transition from HSC to a multipotent progenitor cell state during murine hematopoiesis across age and dietary conditions. From the networks predicted by popInfer, we discovered gene interactions controlling entry to/exit from HSC quiescence that are perturbed in response to diet or aging.more » « less
-
Inferring gene regulatory networks (GRNs) from single-cell gene expression datasets is a challenging task. Existing methods are often designed heuristically for specific datasets and lack the flexibility to incorporate additional information or compare against other algorithms. Further, current GRN inference methods do not provide uncertainty estimates with respect to the interactions that they predict, making inferred networks challenging to interpret. To overcome these challenges, we introduce Probabilistic Matrix Factorization for Gene Regulatory Network inference (PMF-GRN). PMF-GRN uses single-cell gene expression data to learn latent factors representing transcription factor activity as well as regulatory relationships between transcription factors and their target genes. This approach incorporates available experimental evidence into prior distributions over latent factors and scales well to single-cell gene expression datasets. By utilizing variational inference, we facilitate hyperparameter search for principled model selection and direct comparison to other generative models. To assess the accuracy of our method, we evaluate PMF-GRN using the model organisms Saccharomyces cerevisiae and Bacillus subtilis, benchmarking against database-derived gold standard interactions. We discover that, on average, PMF-GRN infers GRNs more accurately than current state-of-the-art single-cell GRN inference methods. Moreover, our PMF-GRN approach offers well-calibrated uncertainty estimates, as it performs gene regulatory network (GRN) inference in a probabilistic setting. These estimates are valuable for validation purposes, particularly when validated interactions are limited or a gold standard is incomplete.more » « less
-
Abstract The inference of gene regulatory networks (GRNs) is crucial to understanding the regulatory mechanisms that govern biological processes. GRNs may be represented as edges in a graph, and hence, it have been inferred computationally for scRNA-seq data. A wisdom of crowds approach to integrate edges from several GRNs to create one composite GRN has demonstrated improved performance when compared with individual algorithm implementations on bulk RNA-seq and microarray data. In an effort to extend this approach to scRNA-seq data, we present COFFEE (COnsensus single cell-type speciFic inFerence for gEnE regulatory networks), a Borda voting-based consensus algorithm that integrates information from 10 established GRN inference methods. We conclude that COFFEE has improved performance across synthetic, curated, and experimental datasets when compared with baseline methods. Additionally, we show that a modified version of COFFEE can be leveraged to improve performance on newer cell-type specific GRN inference methods. Overall, our results demonstrate that consensus-based methods with pertinent modifications continue to be valuable for GRN inference at the single cell level. While COFFEE is benchmarked on 10 algorithms, it is a flexible strategy that can incorporate any set of GRN inference algorithms according to user preference. A Python implementation of COFFEE may be found on GitHub: https://github.com/lodimk2/coffeemore » « less
-
Abstract Inferring gene regulatory networks (GRNs) from single-cell data is challenging due to heuristic limitations. Existing methods also lack estimates of uncertainty. Here we present Probabilistic Matrix Factorization for Gene Regulatory Network Inference (PMF-GRN). Using single-cell expression data, PMF-GRN infers latent factors capturing transcription factor activity and regulatory relationships. Using variational inference allows hyperparameter search for principled model selection and direct comparison to other generative models. We extensively test and benchmark our method using real single-cell datasets and synthetic data. We show that PMF-GRN infers GRNs more accurately than current state-of-the-art single-cell GRN inference methods, offering well-calibrated uncertainty estimates.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1093/bioinformatics/btad269
