Limits on inferring gene regulatory networks from single-cell measurements of unstable mRNA levels

Mahajan, Tarun; Saint-Antoine, Michael; Dar, Roy D.; Singh, Abhyudai

doi:10.1109/cdc51059.2022.9992359

Cited by 3 publications

(16 citation statements)

References 41 publications

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“…In this section, we briefly review and replicate results from Mahajan et al 2022 [21], which studied the feasibility of network inference from mRNA abundance data under conditions of only intrinsic noise. We consider a system with two genes: Gene 1 and Gene 2.…”

Section: Standard Activation Model No Extrinsic Noisementioning

confidence: 85%

“…There are some reasons to question the assumption that mRNA abundance data can be used as a reliable proxy for protein abundance. For example, Mahajan et al 2022 [21] shows though theoretical analysis and stochastic simulations that, under conditions of only intrinsic noise, the correlation between mRNA abundance and protein abundance even for the same gene becomes quite weak if there is a large difference between the mRNA stability and protein stability. Additionally, Liu et al 2016 [20] reviews the literature and reports a similar finding, that the correlation between mRNA levels and protein levels can be weak in some scenarios, and knowledge of mRNA transcript abundance alone is not always sufficient to predict protein abundance levels.…”

Section: Efficacy Of Network Inference Methodsmentioning

confidence: 99%

“…Part of the analysis in [21] involved calculating the correlation between the mRNA levels under different assumptions about the relative stability of the mRNA and protein. In our case, we are intereGsetneed1in the correlaGtieonne 2between M 1 and M 2 under different assumptions about the relative stability of mRENxtArin1sicand the Protein.…”

Section: Standard Activation Model No Extrinsic Noisementioning

confidence: 99%

“…A key finding in [21] was that in this model with only intrinsic noise, correlation between the mRNA levels is quite weak, and gets weaker as the ratio of stability between the protein and mRNA increases. Figure 2 shows our replication of this result: the correlation between M 1 and M 2 is quite weak, and drops to nearly 0 as the ratio of stability between the Protein and mRNA1 increases.…”

Section: Standard Activation Model No Extrinsic Noisementioning

confidence: 99%

“…Another key finding from Mahajan et al 2022 [21] related to the difficulty of distinguishing between scenarios in which one gene regulates another and scenarios in which both genes are regulated by a common upstream regulator. Both of these scenarios can yield a correlation between the mRNA levels, so there is a possibility of a false positive network inference error in the latter scenario.…”

Section: Distinguishing Between Cascade and Coregulationmentioning

confidence: 99%

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Limits on Inferring Gene Regulatory Networks Subjected to Different Noise Mechanisms

Saint-Antoine

Singh

2023

Preprint

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One of the most difficult and pressing problems in computational cell biology is the inference of gene regulatory network structure from transcriptomic data. Benchmarking network inference methods on model organism datasets has yielded mixed results, in which the methods sometimes perform reasonably well and other times fail to outperform random guessing. In this paper, we analyze the feasibility of network inference under different noise conditions using stochastic simulations. We show that gene regulatory interactions with extrinsic noise appear to be more amenable to inference than those with only intrinsic noise, especially when the extrinsic noise causes the system to switch between distinct expression states. Furthermore, we analyze the problem of false positives between genes that have no direct interaction but share a common upstream regulator, and explore a strategy for distinguishing between these false positives and true interactions based on noise profiles of mRNA expression levels. Lastly, we derive mathematical formulas for the mRNA noise levels and correlation using moment analysis techniques, and show how these levels change as the mean mRNA expression level changes.

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Section: Standard Activation Model No Extrinsic Noisementioning

confidence: 85%

Section: Efficacy Of Network Inference Methodsmentioning

confidence: 99%

Section: Standard Activation Model No Extrinsic Noisementioning

confidence: 99%

Section: Standard Activation Model No Extrinsic Noisementioning

confidence: 99%

Section: Distinguishing Between Cascade and Coregulationmentioning

confidence: 99%

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Limits on Inferring Gene Regulatory Networks Subjected to Different Noise Mechanisms

Saint-Antoine

Singh

2023

Preprint

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Benchmarking Gene Regulatory Network Inference Methods on Simulated and Experimental Data

Saint-Antoine

Singh

2023

Preprint

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Although the challenge of gene regulatory network inference has been studied for more than a decade, it is still unclear how well network inference methods work when applied to real data. Attempts to benchmark these methods on experimental data have yielded mixed results, in which sometimes even the best methods fail to outperform random guessing, and in other cases they perform reasonably well. So, one of the most valuable contributions one can currently make to the field of network inference is to benchmark methods on experimental data for which the true underlying network is already known, and report the results so that we can get a clearer picture of their efficacy. In this paper, we report results from the first, to our knowledge, benchmarking of network inference methods on single cell E. coli transcriptomic data. We report a moderate level of accuracy for the methods, better than random chance but still far from perfect. We also find that some methods that were quite strong and accurate on microarray and bulk RNA-seq data did not perform as well on the single cell data. Additionally, we benchmark a simple network inference method (Pearson correlation), on data generated through computer simulations in order to draw conclusions about general best practices in network inference studies. We predict that network inference would be more accurate using proteomic data rather than transcriptomic data, which could become relevant if high-throughput proteomic experimental methods are developed in the future. We also show through simulations that using a simplified model of gene expression that skips the mRNA step tends to substantially overestimate the accuracy of network inference methods, and advise against using this model for future in silico benchmarking studies.

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Modelling Pathogen Response of the Human Immune System in a Reduced State Space

Tajvar,

Forlin,

Brodin

et al. 2023

2023 62nd IEEE Conference on Decision and Control (CDC)

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The immune system response to pathogens is organized by a network of cells communicating through expression of a variety of proteins and signaling molecules. A high number of genes are involved in encoding these communicating agents, but the relatively low number of data points is a major challenge in modelling the gene expression response. In this work we propose a feature-selection approach based on gene expression distributions at the single-cell level that improves dynamics identification at the population level. We investigate common approaches to differential expression analysis and show that Earth Mover's Distance (EMD) is a relatively robust measure for gene selection as reflected by the coefficient of variation as well as accuracy of a naive Bayes classifier based on the selected genes. We ultimately propose the bootstrap standard deviation metric as an estimate of state uncertainty and show that statistically significant signals in pathogen response can be recovered in the reduced state space constructed with the selected genes.

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Limits on inferring gene regulatory networks from single-cell measurements of unstable mRNA levels

Cited by 3 publications

References 41 publications

Limits on Inferring Gene Regulatory Networks Subjected to Different Noise Mechanisms

Limits on Inferring Gene Regulatory Networks Subjected to Different Noise Mechanisms

Benchmarking Gene Regulatory Network Inference Methods on Simulated and Experimental Data

Modelling Pathogen Response of the Human Immune System in a Reduced State Space

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