Gene Regulatory Network Inference in the Presence of Dropouts: A Causal View

Alapfogalmak

Introducing a causal graphical model to characterize dropout mechanisms for gene regulatory network inference.

Kivonat

The content discusses the challenges of gene regulatory network inference (GRNI) due to dropouts in single-cell RNA sequencing data. It introduces a causal dropout model to address these challenges, providing a principled framework for GRNI in the presence of dropouts. The method involves test-wise deletion of samples with zero values for conditioned variables to maintain accurate conditional independence relations. Extensive experiments on synthetic, curated, and real-world experimental data demonstrate the efficacy of the proposed approach.
Abstract:

Gene regulatory network inference is challenging due to dropouts in single-cell RNA sequencing data.
Introduces a causal graphical model, Causal Dropout Model, to address dropout issues.
Test-wise deletion of samples with zero values maintains accurate conditional independence relations.
Empirical evaluation demonstrates the effectiveness of the method on various datasets.
Introduction:

GRNs represent causal relationships among genes essential for understanding biological processes.
Single-cell RNA sequencing enables comprehensive studies but faces challenges like dropouts.
Existing approaches handle dropouts through imputation or probabilistic models, lacking theoretical guarantees.
Data Extraction:

"Dealing with dropouts in scRNA-seq data has been approached through two main strategies."
"Various factors contribute to the occurrence of dropouts in scRNA-seq data."
Quotations:

"We introduce a causal graphical model to characterize the dropout mechanism."
"Empirical evaluation on synthetic, curated, and real-world experimental transcriptomic data demonstrate the efficacy of our method."

Statisztikák

Dealing with dropouts in scRNA-seq data has been approached through two main strategies.
Various factors are commonly acknowledged to contribute to the occurrence of dropouts.

Idézetek

"We introduce a causal graphical model to characterize the dropout mechanism."
"Empirical evaluation on synthetic, curated, and real-world experimental transcriptomic data demonstrate the efficacy of our method."

Főbb Kivonatok

Gene Regulatory Network Inference in the Presence of Dropouts

by Haoyue Dai,I... : arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15500.pdf

Mélyebb kérdések

How can this causal dropout model be applied to other types of biological datasets

The causal dropout model proposed in the context of gene regulatory networks can be applied to other types of biological datasets by adapting the framework to suit the specific characteristics of the data. For instance, in epigenetics research, where understanding the regulation of gene expression through modifications like DNA methylation and histone acetylation is crucial, this model could be modified to account for these additional layers of regulation. By incorporating known mechanisms and interactions specific to epigenetic processes into the causal graphical model, researchers can infer causal relationships between genes and their regulators while considering potential dropouts or missing data.

What are potential limitations or biases introduced by using imputation methods for handling dropouts

Using imputation methods for handling dropouts in biological datasets may introduce limitations and biases due to several reasons:

Unidentifiability: Imputation assumes that missing values are randomly distributed across samples, which may not hold true in biological data with systematic patterns or non-random dropouts.
Model Assumptions: Parametric imputation models make assumptions about the underlying distribution of gene expressions, leading to potential misspecification if these assumptions do not align with the true data distribution.
Spurious Relations: Imputed values can introduce spurious correlations or relations between variables that do not reflect actual biological interactions, impacting downstream analyses such as network inference.

These limitations highlight why a more principled approach like test-wise deletion based on a causal graphical model may offer a more reliable way to handle dropouts without introducing unnecessary biases.

How can this approach be extended to study dynamic changes in gene regulatory networks over time

To study dynamic changes in gene regulatory networks over time using this approach, one could incorporate temporal information into the causal dropout model. By extending the existing framework to include time-series data from different experimental time points or conditions, researchers can analyze how gene regulations evolve over time and under varying stimuli.
This extension would involve modifying the causal graphical model to capture temporal dependencies between genes at different time points. The test-wise deletion procedure could then be adapted to consider conditional independence relations not only within each time point but also across multiple time points. This enhanced approach would enable researchers to uncover dynamic changes in regulatory networks and identify key regulators driving temporal responses within biological systems.