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Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional programs of different cellular states by measuring the transcriptome of thousands of individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory networks and a number of methods with different learning frameworks have been developed to address this problem. Here, we present an expanded benchmarking study of eleven recent network inference methods on seven published scRNA-seq datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as on their ability to recover the network structure. We find that, while most methods have a modest recovery of experimentally derived interactions based on global metrics such as Area Under the Precision Recall curve, methods are able to capture targets of regulators that are relevant to the system under study. Among the top performing methods that use only expression were SCENIC, PIDC, MERLIN or Correlation. Addition of prior biological knowledge and the estimation of transcription factor activities resulted in the best overall performance with the Inferelator and MERLIN methods that use prior knowledge outperforming methods that use expression alone. We found that imputation for network inference did not improve network inference accuracy and could be detrimental. Comparisons of inferred networks for comparable bulk conditions showed that the networks inferred from scRNA-seq datasets are often better or at par with the networks inferred from bulk datasets. Our analysis should be beneficial in selecting methods for network inference. At the same time, this highlights the need for improved methods and better gold standards for regulatory network inference from scRNAseq datasets. © The Author(s) 2023. Published by Oxford University Press on behalf of the Genetics Society of America.

World University root node

Department of Computer Science & Engineering

G3: Genes, Genomes, Genetics

Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data

Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Single-cell technologies such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine cell-type specific gene regulation at unprecedented detail. However, current approaches to infer cell type-specific GRNs are limited in their ability to integrate scRNA-seq and scATAC-seq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the GRN for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated and real datasets, we show that scMTNI is a broadly applicable framework for linear and branching lineages that accurately infers GRN dynamics and identifies key regulators of fate transitions for diverse processes such as cellular reprogramming and differentiation. © 2023, The Author(s).

Fulltext

Nature Communications

Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets

Reconstruction of time-varying gene regulatory networks underlying a time-series gene expression data is a fundamental challenge in the computational systems biology. The challenge increases multi-fold if the target networks need to be constructed for hundreds to thousands of genes. There have been constant efforts to design an algorithm that can perform the reconstruction task correctly as well as can scale efficiently (with respect to both time and memory) to such a large number of genes. However, the existing algorithms either do not offer time-efficiency, or they offer it at other costs - memory-inefficiency or imposition of a constraint, known as the 'smoothly time-varying assumption'. In this article, two novel algorithms - 'an algorithm for reconstructing Time-varying Gene regulatory networks with Shortlisted candidate regulators - which is Light on memory' (TGS-Lite) and 'TGS-Lite Plus' (TGS-Lite+) - are proposed that are time-efficient, memory-efficient and do not impose the smoothly time-varying assumption. Additionally, they offer state-of-the-art reconstruction correctness as demonstrated with three benchmark datasets. Source Code: https://github.com/sap01/TGS-Lite-supplem/tree/master/sourcecode. © 2004-2012 IEEE.

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Rapid Reconstruction of Time-varying Gene Regulatory Networks with Limited Main Memory

Gene ontology (GO) is a comprehensive resource for the properties of gene products and their relationships. A similarity measure can be defined between two gene products by utilizing GO, and the corresponding similarity score can be treated as a likelihood to interact between them physically. However, GO is being updated regularly by the addition of new terms and removal/merging of obsolete terms. Therefore, the similarity score of interaction may differ from one instance of GO to another. In this paper, we systematically study the impact of the continuous evolution of GO on the performance of similarity measures for the task of scoring confidence of protein–protein interactions (PPIs). We find that the performance of a similarity measure gets affected due to the continuous evolution of GO. We further observe that the degree of robustness of a similarity measure is highly influenced by the particular setting we consider. © 2020, Springer Nature Singapore Pte Ltd.

SN Computer Science

Impact of the Continuous Evolution of Gene Ontology on the Performance of Similarity Measures for Scoring Confidence of Protein Interactions

Rapid advancements in high-throughput technologies have resulted in genome-scale time series datasets. Uncovering the temporal sequence of gene regulatory events, in the form of time-varying gene regulatory networks (GRNs), demands computationally fast, accurate, and scalable algorithms. The existing algorithms can be divided into two categories: ones that are time-intensive and hence unscalable; and others that impose structural constraints to become scalable. In this paper, a novel algorithm, namely 'an algorithm for reconstructing Time-varying Gene regulatory networks with Shortlisted candidate regulators' (TGS), is proposed. TGS is time-efficient and does not impose any structural constraints. Moreover, it provides such flexibility and time-efficiency, without losing its accuracy. TGS consistently outperforms the state-of-the-art algorithms in true positive detection, on three benchmark synthetic datasets. However, TGS does not perform as well in false positive rejection. To mitigate this issue, TGS+ is proposed. TGS+ demonstrates competitive false positive rejection power, while maintaining the superior speed and true positive detection power of TGS. Nevertheless, the main memory requirements of both TGS variants grow exponentially with the number of genes, which they tackle by restricting the maximum number of regulators for each gene. Relaxing this restriction remains a challenge as the actual number of regulators is not known a priori. © 2004-2012 IEEE.

Rapid Reconstruction of Time-Varying Gene Regulatory Networks

Gene Ontology (GO) is a taxonomy of biological terms related to the properties of genes and gene products. It can be used to define a similarity measure between two gene products and assign a confidence score to protein-protein interactions (PPIs). GO is being evolved regularly by the addition/deletion/merging of terms. However, there is no study which evaluates the robustness of a particular similarity measure over the evolution of GO. By robustness of a similarity measure, we mean it should either improve or keep its performance similar over the evolution of GO. In this paper, we systematically study the same for the task of scoring confidence of PPIs using GO-based similarity measures. We observe that the performance of similarity measures gets affected due to the regular updates of GO. We find that similarity measures are not robust in all conditions, rather they keep their performance quite similar over the evolution of GO in certain conditions. © 2019, Springer Nature Switzerland AG.