MIXnorm: normalizing RNA-seq data from formalin-fixed paraffin-embedded samples.
Ontology highlight
ABSTRACT: Motivation
Recent studies have shown that RNA-sequencing (RNA-seq) can be used to measure mRNA of sufficient quality extracted from formalin-fixed paraffin-embedded (FFPE) tissues to provide whole-genome transcriptome analysis. However, little attention has been given to the normalization of FFPE RNA-seq data, a key step that adjusts for unwanted biological and technical effects that can bias the signal of interest. Existing methods, developed based on fresh-frozen or similar-type samples, may cause suboptimal performance.Results
We proposed a new normalization method, labeled MIXnorm, for FFPE RNA-seq data. MIXnorm relies on a two-component mixture model, which models non-expressed genes by zero-inflated Poisson distributions and models expressed genes by truncated normal distributions. To obtain maximum likelihood estimates, we developed a nested EM algorithm, in which closed-form updates are available in each iteration. By eliminating the need for numerical optimization in the M-step, the algorithm is easy to implement and computationally efficient. We evaluated MIXnorm through simulations and cancer studies. MIXnorm makes a significant improvement over commonly used methods for RNA-seq expression data.Availability and implementation
R code available at https://github.com/S-YIN/MIXnorm.Contact
swang@smu.edu.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Yin S
PROVIDER: S-EPMC7267832 | biostudies-literature |
REPOSITORIES: biostudies-literature
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