Statistical guidelines for quality control of next-generation sequencing techniques

Sprang, Maximilian; Krüger, Matteo; Andrade‐Navarro, Miguel A.; Fontaine, Jean-Fred

doi:10.26508/lsa.202101113

Cited by 5 publications

(1 citation statement)

References 36 publications

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“…In previous studies, we used 2642 qualitylabeled FASTQ files from the ENCODE project to derive statistical features with different bioinformatics tools well known in the scientific community. We showed that these features have explanatory power over the quality of the data from which they were derived, and built a machine learning classification tool that uses these features as input [11,12]. With a grid search of multiple machine learning algorithms, from logistic regression to ensemble methods and multilayer perceptrons, we were able to provide a robust prediction of quality in FASTQ files.…”

Section: Introductionmentioning

confidence: 99%

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

2022

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Background 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. Results We recently developed statistical guidelines and a machine learning tool to automatically evaluate the quality of a next-generation-sequencing sample. We leveraged this quality assessment to detect and correct batch effects in 12 publicly available RNA-seq datasets with available batch information. We were able to distinguish batches by our 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%). Conclusions In this work, we show the capabilities of our software to detect batches in public RNA-seq datasets from differences in the predicted quality of their samples. We also use these insights to correct the batch effect and observe the relation of sample quality and batch effect. These observations reinforce our 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.

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Section: Introductionmentioning

confidence: 99%