MUREN: a Robust and Multi-reference Approach of RNA-seq Transcript Normalization
Normalization of RNA-seq expression data aims at identifying biological expression differentiation between samples by removing unwanted confounding factors. Explicitly or implicitly, the justification of normalization requires a set of housekeeping genes, as provided by biological a priori knowledge. However, the existence of housekeeping genes common for a very large collection of samples, especially under a wide range of conditions, is questionable.
We propose to carry out pairwise normalization with respect to multiple references, selected from representative samples. Then the pairwise intermediates are integrated based on a linear model that adjusts the reference effects. Motivated by the notion of housekeeping genes and their statistical counterparts, we adopt the robust least trimmed squares (LTS) regression in pairwise normalization. The effectiveness of the proposed method (MUREN) is compared with other existing tools using some standard data sets. The goodness of normalization emphasizes on preserving possible asymmetric differentiation, whose biological significance is exemplified by a single cell cycle data.
MUREN performs the RNA-seq normalization using a two-step statistical regression induced from a general principle. We propose that the densities of pairwise differentiations are used to evaluate the goodness of normalization. MUREN adjusts the mode of differentiation toward zero while preserves the skewness due to biological asymmetric differentiation. Moreover, by robustly integrating pre-normalized counts with respect to multiple reference, MUREN is immune to individual outlier samples.
remotes::install_github("hippo-yf/MUREN")
- R (>= 4.0.2)
- assertthat (>= 0.2.1)
- doSNOW (>=1.0.18)
- iterators (>=1.0.12)
- magrittr (>=1.5)
- MASS (>=7.3-51.6)
- matrixStats (>=0.56.0)
A rat toxicogenomics RNA-seq data from Munro et al (2014)
library(MUREN)
data(rat_tox_thi)Normalized counts
thi_norm = muren_norm(rat_tox_thi)or, scaling coefficients
(thi_coeff = muren_norm(rat_tox_thi, res_return = 'scaling_coeff'))The following code chunk shows how to combine MUREN and edgeR for differential gene (DE) calling.
The data rat_tox_thi has 3 treatment and control samples respectively
library(edgeR)
group = rep(c('treatment', 'control'), each = 3)
thi_de = DGEList(counts = rat_tox_thi[-1], genes = rat_tox_thi[1], group = group)norm.factors by TMM
thi_de = calcNormFactors(thi_de)norm.factos by MUREN (coerce the median same with that of TMM, the re-adjustment here is only used for comparison)
factors_muren = thi_coeff/thi_de$samples$lib.size
factors_muren_adj = factors_muren/median(factors_muren)*median(thi_de$samples$norm.factors)The plot shows the comparison, where the line is identical
Substitute the norm.factors
thi_de$samples$norm.factors = factors_murenDesign matrix
design <- model.matrix(~group)Dispersion
thi_de = estimateDisp(thi_de, design, robust=TRUE)Differential expression
fit <- glmFit(thi_de, design)
lrt <- glmLRT(fit)
topTags(lrt)Feng, Y., Li, L.M. MUREN: a robust and multi-reference approach of RNA-seq transcript normalization. BMC Bioinformatics 22, 386 (2021). https://doi.org/10.1186/s12859-021-04288-0
Munro SA, Lund SP, Pine PS, et al. Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures. Nat Commun. 2014;5:5125. doi:10.1038/ncomms6125
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139-140.
McCarthy DJ, Chen Y, Smyth GK. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 2012;40(10):4288-4297.
GPL-3
- fix bug of double-parameter model
- fix bug of input data type
- optimize memory usage
- first release