Noise Robustness of Transcript-Based Estimators for Properties of Interactions

Area of Science:

• Systems biology
• Computational neuroscience
• Statistical inference

Background:

• Transcript-based methods are crucial for inferring biological interactions.
• Understanding the impact of noise on these estimators is vital for reliable biological network reconstruction.

Purpose of the Study:

• To evaluate the robustness of transcript-based interaction estimators against various noise types.
• To determine the impact of noise on distinguishing coupling regimes and inferring interaction direction.

Main Methods:

• Simulated noisy transcriptomic data with controlled noise levels (colored, isospectral, symmetric, asymmetric).
• Analysis of transcript-based estimators' performance across different signal-to-noise ratios.
• Assessment of estimator sensitivity and accuracy in characterizing coupling and directionality.

Main Results:

• All tested estimators showed sensitivity to symmetric and asymmetric noise contamination.
• Significant noise impact was observed at signal-to-noise ratios far exceeding typical biological conditions.
• Different coupling regimes were generally distinguishable, but interaction direction estimation was severely affected.

Conclusions:

• Transcript-based estimators exhibit limited robustness to noise, particularly for inferring interaction direction.
• Careful consideration of noise impact is necessary for accurate biological network inference and interpretation.
• Future research should focus on developing noise-resilient inference methods for transcriptomic data.