On the problem of confounders in modeling gene expression

Area of Science:

• Computational biology
• Genomics
• Transcriptional regulation

Background:

• Modeling transcription factor (TF) binding using ChIP-seq and chromatin accessibility data is common for generating hypotheses in transcriptional regulation.
• Existing methods lack a distinct approach for deriving TF binding scores from ChIP-seq and open chromatin experiments.
• Biases in scoring approaches can affect the interpretation and reliability of predictive gene expression models.

Purpose of the Study:

• To review biases in TF binding scoring approaches using ChIP-seq and chromatin accessibility data.
• To identify confounders in TF gene scores derived from ChIP-seq and DNase1-seq data.
• To evaluate correction approaches and quality control measures for improving the reliability of predictive gene expression models.

Main Methods:

• Generated predictive models for gene expression using ChIP-seq and DNase1-seq data from DEEP and ENCODE.
• Employed randomization experiments to identify confounders in TF gene scores.
• Reviewed and applied correction approaches for ChIP-seq and DNase1-seq data.

Main Results:

• Confounding factors in TF gene scores were identified through randomization experiments.
• Correction approaches effectively reduced the influence of identified confounders without negatively impacting model performance.
• Additional quality control measures were highlighted to enhance model reliability and prevent misinterpretation.

Conclusions:

• Standardized scoring and correction methods are crucial for accurate TF binding modeling.
• Implementing robust quality control measures alongside performance evaluation ensures reliable predictive gene expression models.
• This work provides a framework for improving the interpretation and reliability of computational models in transcriptional regulation studies.