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Quantifying and correcting bias in transcriptional parameter inference from single-cell data.

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Summary

This study reveals biases in estimating gene transcription parameters from single-cell data using the two-state model. The research develops a theory to correct these biases, improving accuracy for mammalian gene expression analysis.

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Area of Science:

  • Molecular Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Single-cell RNA sequencing and FISH measure mRNA counts, often analyzed with the two-state telegraph model.
  • This model estimates mRNA synthesis, switching on, and switching off rates, assuming no cell-to-cell variation (extrinsic noise).
  • The accuracy of these population-average parameter estimates in the presence of extrinsic noise is not well understood.

Purpose of the Study:

  • To develop a theoretical framework explaining estimation bias in transcriptional parameters derived from single-cell data.
  • To identify how different sources of extrinsic noise and transcriptional activity modes affect parameter estimation bias.
  • To provide a method for correcting existing estimates of gene transcription parameters in mammalian genes.

Main Methods:

  • Developed a theory to analyze estimation bias in the two-state telegraph model for single-cell gene expression data.
  • Investigated bias signatures based on the source of extrinsic noise (synthesis, on-rate, or off-rate variability).
  • Estimated extrinsic noise from sequencing data covariance matrices and applied the theory to correct published parameter estimates.

Main Results:

  • Non-bursty expression: Overestimation of synthesis and switching off rates, underestimation of switching on rate, with potential infinities at critical noise thresholds.
  • Bursty expression: Overestimation of mean burst size and underestimation of mean burst frequency.
  • Successfully corrected previously published mammalian gene transcription parameter estimates using the developed theory and noise estimations.

Conclusions:

  • Extrinsic noise significantly biases estimates from the standard two-state telegraph model, with specific patterns depending on noise source and expression mode.
  • The developed theory provides a quantitative understanding of these biases and a method for correction.
  • This work enhances the reliability of single-cell-based transcriptional parameter inference for mammalian genes.