DegNorm: normalization of generalized transcript degradation improves accuracy in RNA-seq analysis

Xiong, Bin; Yang, Yiben; Fineis, Frank R.; Wang, Ji Ping

doi:10.1186/s13059-019-1682-7

Cited by 36 publications

(48 citation statements)

References 40 publications

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“…98 Secondly, RNA sequencing was performed on samples with a wide range of RIN values. While we demonstrated that RIN was not a signi cant co-variant of differential expression, in the future methods such as DegNorm developed by Xiong et al 30 could be implemented to correct for expression variation due to differences in RNA degradation and potentially yield more accurate results. Thirdly, all samples were recruited from patients receiving cerebral imaging at a single center, which may introduce selection bias.…”

Section: Limitationsmentioning

confidence: 79%

Classification Models using Circulating Neutrophil Transcripts Can Detect Unruptured Intracranial Aneurysm

Poppenberg

Tutino

Li³

et al. 2020

Preprint

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Background: Intracranial aneurysms (IAs) are dangerous because of their potential to rupture. We previously found significant RNA expression differences in circulating neutrophils between patients with and without unruptured IAs and trained machine learning models to predict presence of IA using 40 neutrophil transcriptomes. Here, we aim to develop a predictive model for unruptured IA using neutrophil transcriptomes from a larger population and more robust machine learning methods. Methods: Neutrophil RNA extracted from the blood of 134 patients (55 with IA, 79 IA-free controls) was subjected to next-generation RNA sequencing. In a randomly-selected training cohort (n=94), the Least Absolute Shrinkage and Selection Operator (LASSO) selected transcripts, from which we constructed prediction models via 4 well-established supervised machine-learning algorithms (K-Nearest Neighbors, Random Forest, and Support Vector Machines with Gaussian and cubic kernels). We tested the models in the remaining samples (n=40) and assessed model performance by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (RT-qPCR) of 9 IA-associated genes was used to verify gene expression in a subset of 49 neutrophil RNA samples. We also examined the potential influence of demographics and comorbidities on model prediction. Results: Feature selection using LASSO in the training cohort identified 37 IA-associated transcripts. Models trained using these transcripts had a maximum accuracy of 90% in the testing cohort. The testing performance across all methods had an average area under ROC curve (AUC)=0.97, an improvement over our previous models. The Random Forest model performed best across both training and testing cohorts. RT-qPCR confirmed expression differences in 7 of 9 genes tested. Gene ontology and IPA network analyses performed on the 37 model genes reflected dysregulated inflammation, cell signaling, and apoptosis processes. In our data, demographics and comorbidities did not affect model performance. Conclusions: We improved upon our previous IA prediction models based on circulating neutrophil transcriptomes by increasing sample size and by implementing LASSO and more robust machine learning methods. Future studies are needed to validate these models in larger cohorts and further investigate effect of covariates.

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Section: Limitationsmentioning

confidence: 79%

Classification Models using Circulating Neutrophil Transcripts Can Detect Unruptured Intracranial Aneurysm

Poppenberg

Tutino

Li³

et al. 2020

Preprint

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“…16,44 , others have implemented statistical frameworks which account for gene-speci c biases. DegNorm, for example, accounts for the gene-speci c relative randomness in degradation in its correction approach 17 . Quality surrogate variable analysis (qSVA) speci cally improves differential expression by identifying transcript features associated with RNA degradation for its correction 28 .…”

Section: Discussionmentioning

confidence: 99%

“…Degradation introduces variability in signal and can be impacted by sample handling. Non-uniformity in degradation across genes and samples causes inaccurate normalization and transcript quanti cation 17 .…”

Section: Introductionmentioning

confidence: 99%

“…Despite this, many studies rely on RIN to correct for and assess sample quality confounders 16,29,30 . This is especially problematic in the case of transcript degradation because RIN scores are based on entire samples, while degradation effects can be transcript-speci c 17,3132 . Furthermore, existing studies on degradation are not simply generalizable to freeze-thaw, which has distinct and independent effects on sample quality and must be fully explored as such 16,33 .…”

Section: Introductionmentioning

confidence: 99%

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Multiple Freeze-thaw Cycles Lead to a Loss of Consistency in Poly(A)-enriched RNA Sequencing

Kellman

Baghdassarian

Pramparo

et al. 2020

Preprint

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RNA-Seq is ubiquitous, but depending on the study, sub-optimal sample handling may be required, resulting in repeated freeze-thaw cycles. However, little is known about how each cycle impacts downstream analyses, due to a lack of study and known limitations in common RNA quality metrics, e.g., RIN, at quantifying RNA degradation following repeated freeze-thaws. Here we quantify the impact of repeated freeze-thaw on the reliability of RNA-Seq. To do so, we developed a method to estimate the relative noise between technical replicates independently of RIN. Using this approach we inferred the effect of both RIN and the number of freeze-thaw cycles on sample noise. We find that RIN is unable to fully account for the change in sample noise due to freeze-thaw cycles. Additionally, freeze-thaw is detrimental to sample quality and differential expression (DE) reproducibility, approaching zero after three cycles for poly(A)-enriched samples, wherein the inherent 3’ bias in read coverage is more exacerbated by freeze-thaw cycles, while ribosome-depleted samples are less affected by freeze-thaws. The use of poly(A)-enrichment for RNA sequencing is pervasive in library preparation of frozen tissue, and thus, it is important during experimental design and data analysis to consider the impact of repeated freeze-thaw cycles on reproducibility.

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“…Between IA and control groups in our study, there was no statistically significant difference in RIN (p = 0.18, Student’s t-test). Yet, given that some samples had low RIN and others had high RIN, we performed co-variate correlation analysis as shown in Xiong et al [ 30 ] in order to determine if RNA quality could have affected the expression levels of differentially expressed transcripts. A correlation between gene expression and RIN was considered if it had both a Pearson correlation coefficient r > 0.80 and a p-value < 0.01.…”

Section: Methodsmentioning

confidence: 99%

Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm

et al. 2020

View full text Add to dashboard Cite

Background Intracranial aneurysms (IAs) are dangerous because of their potential to rupture. We previously found significant RNA expression differences in circulating neutrophils between patients with and without unruptured IAs and trained machine learning models to predict presence of IA using 40 neutrophil transcriptomes. Here, we aim to develop a predictive model for unruptured IA using neutrophil transcriptomes from a larger population and more robust machine learning methods. Methods Neutrophil RNA extracted from the blood of 134 patients (55 with IA, 79 IA-free controls) was subjected to next-generation RNA sequencing. In a randomly-selected training cohort (n = 94), the Least Absolute Shrinkage and Selection Operator (LASSO) selected transcripts, from which we constructed prediction models via 4 well-established supervised machine-learning algorithms (K-Nearest Neighbors, Random Forest, and Support Vector Machines with Gaussian and cubic kernels). We tested the models in the remaining samples (n = 40) and assessed model performance by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (RT-qPCR) of 9 IA-associated genes was used to verify gene expression in a subset of 49 neutrophil RNA samples. We also examined the potential influence of demographics and comorbidities on model prediction. Results Feature selection using LASSO in the training cohort identified 37 IA-associated transcripts. Models trained using these transcripts had a maximum accuracy of 90% in the testing cohort. The testing performance across all methods had an average area under ROC curve (AUC) = 0.97, an improvement over our previous models. The Random Forest model performed best across both training and testing cohorts. RT-qPCR confirmed expression differences in 7 of 9 genes tested. Gene ontology and IPA network analyses performed on the 37 model genes reflected dysregulated inflammation, cell signaling, and apoptosis processes. In our data, demographics and comorbidities did not affect model performance. Conclusions We improved upon our previous IA prediction models based on circulating neutrophil transcriptomes by increasing sample size and by implementing LASSO and more robust machine learning methods. Future studies are needed to validate these models in larger cohorts and further investigate effect of covariates.

show abstract

DegNorm: normalization of generalized transcript degradation improves accuracy in RNA-seq analysis

Cited by 36 publications

References 40 publications

Classification Models using Circulating Neutrophil Transcripts Can Detect Unruptured Intracranial Aneurysm

Classification Models using Circulating Neutrophil Transcripts Can Detect Unruptured Intracranial Aneurysm

Multiple Freeze-thaw Cycles Lead to a Loss of Consistency in Poly(A)-enriched RNA Sequencing

Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm

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