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Cdev: a ground-truth based measure to evaluate RNA-seq normalization performance.

ABSTRACT: Normalization of RNA-seq data has been an active area of research since the problem was first recognized a decade ago. Despite the active development of new normalizers, their performance measures have been given little attention. To evaluate normalizers, researchers have been relying on ad hoc measures, most of which are either qualitative, potentially biased, or easily confounded by parametric choices of downstream analysis. We propose a metric called condition-number based deviation, or cdev, to quantify normalization success. cdev measures how much an expression matrix differs from another. If a ground truth normalization is given, cdev can then be used to evaluate the performance of normalizers. To establish experimental ground truth, we compiled an extensive set of public RNA-seq assays with external spike-ins. This data collection, together with cdev, provides a valuable toolset for benchmarking new and existing normalization methods.

SUBMITTER: Tran DT

PROVIDER: S-EPMC8496462 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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cdev: a ground-truth based measure to evaluate RNA-seq normalization performance.

Tran Diem-Trang DT Might Matthew M

PeerJ 20211004

Normalization of RNA-seq data has been an active area of research since the problem was first recognized a decade ago. Despite the active development of new normalizers, their performance measures have been given little attention. To evaluate normalizers, researchers have been relying on <i>ad hoc</i> measures, most of which are either qualitative, potentially biased, or easily confounded by parametric choices of downstream analysis. We propose a metric called condition-number based deviation, o ...[more]

PMID: 34707933

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