Batch effect detection and correction in RNA-seq data using machine-learning-based automated assessment of quality

The constant evolving and development of next-generation sequencing techniques lead to high throughput data composed of datasets that include a large number of biological samples. Although a large number of samples are usually experimentally processed by batches, scientific publications are often elusive about this information, which can greatly impact the quality of the samples and confound further statistical analyzes. Because dedicated bioinformatics methods developed to detect unwanted sources of variance in the data can wrongly detect real biological signals, such methods could benefit from using a quality-aware approach.

Researchers at Johannes Gutenberg-Universität Mainz have recently developed statistical guidelines and a machine learning tool to automatically evaluate the quality of a next-generation-sequencing sample. They leveraged this quality assessment to detect and correct batch effects in 12 publicly available RNA-seq datasets with available batch information. The researchers were able to distinguish batches by their quality score and used it to correct for some batch effects in sample clustering. Overall, the correction was evaluated as comparable to or better than the reference method that uses a priori knowledge of the batches (in 10 and 1 datasets of 12, respectively; total = 92%). When coupled to outlier removal, the correction was more often evaluated as better than the reference (comparable or better in 5 and 6 datasets of 12, respectively; total = 92%).


Fig. 1

Black boxes show components of the overall workflow. DeriveFeatures is a component that uses four bioinformatic tools to derive the four feature sets from the FASTQ files (.fastq): RAW, MAP, LOC, TSS. seqQscorer computes Plow, the probability of a sample to be of low quality. We used seqQscorer’s generic model, which is derived from 2642 labeled samples and uses a random forest as classification algorithm. We used the salmon tool to quantify gene expression and DESeq2 for rlog normalization

The researchers have shown the capabilities of their software to detect batches in public RNA-seq datasets from differences in the predicted quality of their samples. They also used these insights to correct the batch effect and observe the relation of sample quality and batch effect. These observations reinforce their expectation that while batch effects do correlate with differences in quality, batch effects also arise from other artifacts and are more suitably corrected statistically in well-designed experiments.

Sprang M, Andrade-Navarro MA, Fontaine JF. (2022) Batch effect detection and correction in RNA-seq data using machine-learning-based automated assessment of quality. BMC Bioinformatics 23(Suppl 6):279. [article]

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