This blog and the work it’s based on want to draw attention to an often overlooked aspect of RNA-Seq analysis. Recent research shows the importance of recognizing and addressing these imbalances, as they can significantly skew downstream analyses, consequently leading to false positives or negatives—akin to the more familiar batch effects, though potentially less pronounced.

Why Should You Care About Quality Imbalances?

RNA-seq studies compare gene expression between different groups, often contrasting diseased and healthy samples. However, as demonstrated by Sprang et al.  [1], quality imbalances between these groups can undermine the validity of the analysis. In their study of 40 clinically relevant RNA-seq datasets, a substantial 35% exhibited significant quality imbalances. This discrepancy can inflate the number of differentially expressed genes, many of which may be artifacts of poor data quality rather than actual biological signals. Such imbalances can cause erroneous conclusions, reducing the reproducibility of the findings across studies.

Quality Imbalances: A Subtle but Serious Issue

While batch effects have been widely acknowledged and methods developed to mitigate them, quality imbalances remain a less discussed but still critical issue. Like batch effects, these imbalances can distort the results of RNA-seq studies. The study found that the higher the quality imbalance, the greater the number of differentially expressed genes, with some datasets showing a fourfold increase in false positives. This means that without addressing quality imbalances, your analysis could be driven more by the data quality than by the biological differences you’re aiming to investigate.

To help researchers manage these issues, tools like the seqQscorer, a machine learning-based software, have been developed [2]. seqQscorer was used in the study by Sprang et al. 2024 [1] and provides an automatic quality control procedure that uses tree-based and deep-learning classification algorithms to statistically characterize NGS quality features. It has been validated across various functional genomics datasets, demonstrating its ability to generalize to data from unseen species, making it a valuable resource for identifying and mitigating quality issues in RNA-seq data and other functional genomics assays, including ChIP-seq, DNAse-seq, and ATAC-seq data.

Former work has also shown some connection between quality imbalances and batch effects, showing that they co-occur in some datasets and pointing at possibly similar sources for these artifacts [3].

Extending Beyond RNA-seq: The Case of ChIP-seq

The issue isn’t confined to RNA-seq alone. Sprang et al. also conducted preliminary analyses on ChIP-seq data and observed similar trends, suggesting that quality imbalances may be a pervasive problem across different types of sequencing data. This opens the door for further research, which is necessary to develop robust strategies to identify and correct these imbalances in various genomic studies [1].

A Call to Action: Improve Data Transparency and Reporting

Another critical issue that Sprang et al. highlighted is the lack of comprehensive metadata in publicly available datasets. Many datasets in public repositories lack detailed information about data quality, batch effects, and even crucial medical or biological annotations. This missingness makes it challenging to assess the reliability of the data and complicates efforts to replicate studies. Therefore, researchers are urged to provide thorough documentation when biological sequencing data, including quality control metrics and detailed metadata.

Conclusion

Hidden quality imbalances in RNA-seq data are a subtle yet significant source of bias that can compromise the validity of your research findings. To ensure the reliability and reproducibility of your RNA-seq studies, it’s essential to check for and address these imbalances. Additionally, the scientific community must advocate for better data transparency and reporting standards, particularly in public repositories, to foster high-quality, reproducible research.

Remember: Your RNA-seq data might be biased by hidden quality imbalances. Best check for it! 😊

seqQscorer is available on Github: salbrec/seqQscorer (github.com). Future work will include an RNA-seq workflow that implements these works’ quality analyses and makes them more easily accessible to the research community.

References:

1. Sprang, M., Möllmann, J., Andrade-Navarro, M.A. et al. Overlooked poor-quality patient samples in sequencing data impair reproducibility of published clinically relevant datasets. Genome Biol25, 222 (2024). https://doi.org/10.1186/s13059-024-03331-6
2. Albrecht, S., Sprang, M., Andrade-Navarro, M.A. et al. seqQscorer: automated quality control of next-generation sequencing data using machine learning. Genome Biol22, 75 (2021). https://doi.org/10.1186/s13059-021-02294-2
3. Sprang, M., Andrade-Navarro, M.A. & Fontaine, JF. Batch effect detection and correction in RNA-seq data using machine-learning-based automated assessment of quality. BMC Bioinformatics23(Suppl 6), 279 (2022). https://doi.org/10.1186/s12859-022-04775-y