Impact Factor 4.151

Frontiers journals are at the top of citation and impact metrics

Original Research ARTICLE

Front. Genet., 16 September 2016 | https://doi.org/10.3389/fgene.2016.00164

In Papyro Comparison of TMM (edgeR), RLE (DESeq2), and MRN Normalization Methods for a Simple Two-Conditions-Without-Replicates RNA-Seq Experimental Design

  • Genomics and Biotechnology of the Fruits Laboratory, UMR 990 INRA/Institut National Polytechnique de Toulouse, Ecole Nationale Supérieure Agronomique de Toulouse, Université de Toulouse, Castanet-Tolosan, France

In the past 5 years, RNA-Seq has become a powerful tool in transcriptome analysis even though computational methods dedicated to the analysis of high-throughput sequencing data are yet to be standardized. It is, however, now commonly accepted that the choice of a normalization procedure is an important step in such a process, for example in differential gene expression analysis. The present article highlights the similarities between three normalization methods: TMM from edgeR R package, RLE from DESeq2 R package, and MRN. Both TMM and DESeq2 are widely used for differential gene expression analysis. This paper introduces properties that show when these three methods will give exactly the same results. These properties are proven mathematically and illustrated by performing in silico calculations on a given RNA-Seq data set.

1. Introduction

In the past 5 years, RNA-Seq approaches, based on high-throughput sequencing technologies, are becoming an essential tool in transcriptomics studies (cf. Wang et al., 2009). It is now commonly accepted that a normalization preprocessing step can significantly improve the quality of the analysis, in particular, for the differential gene expression analysis (cf. Bullard et al., 2010). Nevertheless, a gold standard normalization method has not yet been found.

This paper deals with two widely used and very important normalization methods and a third method related to these. The first method is the “Trimmed Mean of M-values” normalization (TMM) described in Robinson and Oshlack (2010) and implemented in the edgeR package (cf. Robinson et al., 2010). The second method is the “Relative Log Expression” normalization (RLE) implemented in the DESeq2 package (cf. Anders and Huber, 2010; Anders et al., 2013; Love et al., 2014). The third method is the “Median Ratio Normalization” (MRN) described in Maza et al. (2013). It has been shown that TMM and RLE give similar results both with real and simulated data sets (cf. Dillies et al., 2013; Maza et al., 2013; Rapaport et al., 2013; Li et al., 2015; Reddy, 2015). These two methods, as does MRN, deal efficiently with the intrinsic bias resulting from the relative size of studied transcriptomes. Also, it has even been shown that the MRN method performs slightly better on some simulated data sets (cf. Maza et al., 2013). Moreover, many studies have shown that LRE and/or TMM methods outperform other particular methods (cf. Dillies et al., 2013; Maza et al., 2013; Reddy, 2015; Zyprych-Walczak et al., 2015; Lin et al., 2016). Nevertheless, a comprehensive comparison study of differential expression analysis methods has used LRE or TMM for ten of the eleven compared tools (cf. Soneson and Delorenzi, 2013). Finally, other more sophisticated normalization methods have been carried out by iterating one of LRE or TMM methods (cf. Kadota et al., 2012; Sun et al., 2013; Tang et al., 2015).

In this paper, all theoretical results will be illustrated by in silico calculations carried out on a given real data set from the tomato fruit set (see Materials and Methods). In short, this data set consists of a matrix of counts: 34675 rows (genes) and 9 columns (samples from 3 stages and 3 biological replicates per stage). Normalization factors of these fruit set samples, obtained by each of the TMM, RLE, and MRN methods with default settings, are presented in Table 1. Figure 1 represents the scatter plot of obtained normalization factors and corresponding library sizes. Moreover, Figure 1 contains, for all three normalization methods, the regression lines estimated from a simple linear regression modeling the relationship between default normalization factors and library sizes. It is evident in both Table 1 and Figure 1 that the three methods (with default settings) do not give the same results. Indeed, it is known that TMM normalization factors do not take into account library sizes. This fact is illustrated in Figure 1 by an almost horizontal regression line. On the contrary, RLE and MRN factors are closer to each other, and share a positive correlation with the library size. The estimation of the regression parameters of regression lines above shows that the TMM slope is not statistically significant (at 5% type I error) which is the case of both LRE and MRN slopes (see Additional file 1).

TABLE 1
www.frontiersin.org

Table 1. Default normalization factors for the fruit set RNA-Seq data.

FIGURE 1
www.frontiersin.org

Figure 1. Normalization factors for the fruit set RNA-Seq data depending on corresponding library sizes. All three studied normalization methods are carried out with default settings. For all three methods, regression (dashed) lines are estimated from a simple linear regression modeling the relationship between default normalization factors and library sizes. Color key: TMM, RLE, and MRN are respectively colored in green, blue, and red. Key to symbols: Bud, Ant, and Pos stages are respectively drawn with circles, squares, and triangles.

The aim of this study is to provide a deeper understanding as to why the three normalization methods quoted above share a similar normalization approach. This paper also demonstrates that, in some cases, some shared parameters (such as relative size of transcriptomes or normalization factors) are strictly equal.

2. Materials and Methods

2.1. Tomato's RNA-Seq Data Set

To investigate the tomato transcriptome dynamics of fruit set, RNA were isolated from flower buds (Bud) and flowers at anthesis (Ant) and post-anthesis (Pos) stages. For each stage, cDNA libraries were generated from three biological replicates and subjected to Illumina mRNA-Seq technology sequencing. Then, after mapping reads to the tomato genome sequence, we obtained a table of raw counts with 34675 rows (genes) and 9 columns (3 stages and 3 replicates per stage). These technical procedures are described in Maza et al. (2013). In this paper, for sake of simplicity, the matrix (34675 × 9) containing raw counts is denoted by X.

2.2. Computations with R Packages

All computations were done within R environment (cf. R Development Core Team, 2011). All packages are available from R or Bioconductor websites (cf. Gentleman et al., 2004).

As described above, the matrix containing raw counts is denoted by X in all R command lines of given in silico examples.

The TMM normalization method is implemented in the edgeR package by means of the calcNormFactors function. For example, the default normalization factors obtained in Table 1 are obtained by the following command line:

> calcNormFactors(X)

The RLE normalization method is implemented in the DESeq2 package by means of the function estimateSizeFactorsForMatrix. For example, the default size factors obtained in Table 1 are obtained using the following command line:

> estimateSizeFactorsForMatrix(X)

The MRN normalization method is implemented in a homemade function called mrnFactors which is provided as an additional file (see Additional Files). For example, the default normalization factors obtained in Table 1 are obtained using the following command line:

> mrnFactors(X, rep(1:3 each=3))$normFactors

2.3. Definitions of Some Important Terms

We define hereafter some important terms that are used in the studied normalization methods. More detailed definitions are given in Robinson and Oshlack (2010), Anders and Huber (2010), and Maza et al. (2013).

Both M and A-values are defined for a given gene g and its expressions on two conditions Xg1 and Xg2. They represent respectively the fold change and the absolute expression level of the gene:

M=log2(Xg1/N1Xg2/N2)and A=12log2((Xg1/N1)(Xg2/N2)).

A trimmed mean of M (respectively A)-values corresponds to the calculation of the mean after discarding a given proportion of lower and higher values. A trimmed mean with a proportion of 50% corresponding obviously to the calculation of the median.

3. Results and Discussion

In this section, the three studied normalization methods are first described and compared. Then, three propositions are introduced and in silico results are provided to illustrate them. For the sake of clarity, mathematical proofs of these propositions are left to the end of the section.

We have to underline that we focus, in this paper, on so called “scaling normalization methods” but this is just one approach, which can be limited to some specific experimental situations (cf. Maza et al., 2013). Another alternative can be the use of control genes (see, for instance, Risso et al., 2014).

We notice here that, in order to be consistent, the first paragraph below (named “Notations and Experimental design”) reproduces information that have been already reported in detail in Maza et al. (2013).

3.1. Notations and Experimental Design

Let Xgkr be the observed number of reads (or count) of gene g ∈ {1, …, G} in condition k ∈ {1, …, K} for biological replicate r ∈ {1, …, R}. For the sake of simplicity, we deal with the same number of replicates for each given condition, but the following propositions are not altered by a more general design. Let μgk be the expectation of the true and unknown number of transcripts of a given cell for gene g in condition k; Lg the length of gene g; and Nkr the total number of reads in condition k for replicate r (or library size). As described in Robinson and Oshlack (2010), we can model the expected value of Xgkr as

(Xgkr)=μgkLgSkNkr

where Sk is the size of studied transcriptome in condition k, that is

Sk=g=1GμgkLg.

Then, for each gene g, an approximation of the expected value of the ratio between two conditions, say 1 and 2, is given by

E(Xg2rXg1r)μg2μg1S1S2N2rN1r.

We can easily see in the equation above that the ratio of interest, in a differential analysis point of view, i.e., μg2μg1, is not directly measured by ratios of raw counts because of library sizes and relative sizes of transcriptomes. As described in Maza et al. (2013), the three methods studied here aim at removing such biases.

3.2. Description of the Three Methods

Table 2 gives us the description of these three normalization methods. Both TMM and RLE are respectively implemented in edgeR and DESeq2 packages (see Materials and Methods). Differences and commonalities of these three methods are described here, step by step.

Step I Both TMM and MRN methods have a step of pre-normalization of counts by library sizes. The RLE method does not and works directly on raw counts.

Step II The three methods have a reference sample. For TMM, the reference sample has been chosen here arbitrarily with k = 1 and r = 1. The default for the edgeR package consists in choosing the library whose upper quartile is closest to the mean upper quartile for all the libraries. For MRN, the condition k = 1 is also arbitrarily chosen, as in Maza et al. (2013). Moreover, for MRN, by definition, all replicates of the chosen condition are used. For RLE, a geometric mean of all sample values is performed.

Step III For all three methods, relative sizes of transcriptomes and the reference sample are based on ratios of counts (or pre-normalized counts for TMM and MRN) and the reference sample. For TMM, the set Gkr* represents the set of not trimmed genes with valid M and A-values (cf. Robinson and Oshlack, 2010). By default, with the calcNormFactors function of the edgeR package, percentages of trimmed M and A-values are respectively of 30 and 5% (see Materials and Methods). In order to simplify, but still staying in the same vein, the relative scaling factor calculations are here described with an unweighted trimmed mean instead of the weighted one which is proposed by default on edgeR.

Step IV In this step, only the TMM method performs an adjustment of its relative scaling factors to multiply to 1.

Step V Only TMM and MRN methods take explicitly into account both relative scaling factors and library sizes.

Step VI In this step, it is clear that TMM normalization factors (as produced by the calcNormFactors function) do not take into account the library sizes but only the relative scaling factors. That explains the absence of correlation between normalization factors and library sizes in Figure 1 (see Introduction). In edgeR, these normalization factors are used as offset parameters in the statistical model for differential gene expression analysis. Once again, we underline here that values obtained in Table 1 are not estimations of the same theoretical parameters, and thus, these values can not be used in the same way, for instance, to normalize counts.

Step VII For normalization of counts, all three normalization methods take into account both relative sizes of transcriptomes and library sizes. Only the TMM method gives counts-per-million (CPM). Obviously, CPM values can easily be obtained for RLE and MRN methods by multiplying normalized counts by 106.

TABLE 2
www.frontiersin.org

Table 2. Description of the three normalization methods.

3.3. Properties of the Three Methods

After the above detailed descriptions of our three methods, we introduce below three properties showing particular cases where all three methods give the same result.

Proposition 1(concerning TMM and MRN). Let's assume that the following assumptions hold for the calculus of the TMM normalization method:

The reference sample is (arbitrarily) the first one (k = 1 and r = 1).

Trimming parameters of M and A-values are respectively equal to 50 and 0%.

Calculation of the trimmed mean is done without computing weights (unweighted mean).

Moreover, let R = 1 (no replicates). Then, the relative scaling factors of TMM and MRN methods are equal:

τk1TMM=τkMRN.

An example illustrating Proposition 1 is given in Table 3. Calculations are carried out by means of R functions calNormFactors (from the edgeR package) for the TMM method and mrnFactors (see Materials and Methods) for the MRN method, as follows:

> calcNormFactors(X, refColumn=1,
logratioTrim=0.499, sumTrim=0,
doWeighting=FALSE)
> mrnFactors(X, 1:9)$medianRatios

TABLE 3
www.frontiersin.org

Table 3. Normalization factors of tomato fruit set samples, obtained from TMM and MRN normalization methods with parameters of Proposition 1.

We can clearly see in Table 3 that, with function arguments corresponding to the assumptions of Proposition 1, the adjusted relative scaling factors produced by TMM and MRN methods are equal up to the third or fourth decimal place for almost all of the values (only one of the values has just two decimal places equal). This slight difference is due to the logratioTrim argument that cannot be strictly equal to 50%.

Proposition 2 (concerning RLE and MRN). Let K = 2 conditions and no replicates (R = 1). Then, the size factors calculated from the RLE and MRN methods are equal:

fk1RLE=fk1MRN.

We illustrate Proposition 2 by calculating the size factors for some pairs of samples (see Table 4). Calculations are carried out by means of R functions estimateSizeFactorsForMatrix (from the DESeq2 package) for the RLE method and mrnFactors (see Materials and Methods) for the MRN method, as follows:

> estimateSizeFactorsForMatrix(X[ , c(1,
2)])
> mrnFactors(X[ , c(1, 2)], c(1,
2))$normFactors
> estimateSizeFactorsForMatrix(X[ , c(3,
5)])
> mrnFactors(X[ , c(3, 5)], c(1,
2))$normFactors
> estimateSizeFactorsForMatrix(X[ , c(4,
7)])
> mrnFactors(X[ , c(4, 7)], c(1,
2))$normFactors

TABLE 4
www.frontiersin.org

Table 4. Normalization factors of some pairs of tomato fruit set samples, obtained from RLE and MRN normalization methods with parameters of Proposition 2.

We can see in Table 4 that, as introduced in Proposition 2, normalization results from both methods are equal.

We must note here that, with K > 2, Proposition 2 does not hold: with more than two samples, the reference sample of the RLE method takes into account all raw counts and this is obviously not the case for the MRN method. This can be checked by straightforward calculations with more than two samples.

Proposition 3 (concerning RLE, TMM, and MRN). Let's assume that the assumptions of both Proposition 1 and Proposition 2 are satisfied. Then, normalized counts of the RLE and MRN methods, and counts-per-million of the TMM method (up to a constant) are equal:

Zgk1TMM×N11N21106=Zgk1RLE=Zgk1MRN.

3.4. Calculation of RLE Normalization Factors with edgeR

We note here that the calcNormFactors function contains an argument called “method” that can also be used to calculate RLE normalization factors (cf. the description of the calcNormFactors function in the edgeR package). The user should however be careful! Indeed, as calculated below, calcNormFactors with method="RLE" and estimateSizeFactorsForMatrix functions do not give the same results. What happens is that the calcNormFactors function does not work with raw counts but with pre-normalized ones (see Step I of Table 2). Moreover, the calcNormFactors function proceeds to an adjustment of values (see Step IV of Table 2). In order to find the same values with both functions, we have to proceed as follows:

> calcNormFactors(X, method="RLE")
> estimateSizeFactorsForMatrix(X)
> f=estimateSizeFactorsForMatrix(X%*%diag
(1/colSums(X)))
> f/prod(f) ^(1/length(f))

3.5. Proofs of Propositions

Proof of Proposition 1. We first note that, with R = 1, i.e., with no biological replicates, the reference samples of both TMM and MRN methods are equal:

YgTMM=YgMRN.

Moreover, if we assume that (i) the trimmed mean of the TMM method is done with an unweighted mean as described in the Step III of the edgeR method in Table 2, and that (ii) the trimming values are equal to 50% of genes with upper M-values and 50% of genes with lower M-values, then we obtain that

τk1TMM=1#Gk1*gGk1*Ygk1YgTMM=mediang(Ygk1YgTMM).

Then

τk1TMM=mediang(Ygk1YgMRN)=τkMRN.

Proof of Proposition 2. Let's first describe the RLE method calculations by following steps of Table 2. For K = 2 and R = 1, the pseudo-reference sample is the following:

YgRLE=Xg11Xg21.

Then, we directly have that

f11RLE=τ11RLE=mediang(Xg11Xg21)

and

f21RLE=τ21RLE=mediang(Xg21Xg11).

Let's then describe calculations for the MRN method. For K = 2 and R = 1, the reference sample is simply the first sample:

YgMRN=Yg11.

Then, the relative sizes are the following:

τ1MRN=1andτ2MRN=mediang(Yg21Yg11)=mediang(Xg21Xg11)N11N21.

That leads to

e11MRN=N11and e21MRN=mediang(Xg21Xg11)N11.

Finally, the calculation of the geometric mean of these values, i.e.,

MRN=mediang(Xg21Xg11)N11

implies that

f11MRN=mediang(Xg11Xg21)

and

f21MRN=mediang(Xg21Xg11).

It follows that

fk1MRN=fk1RLE.

Proof of Proposition 3. We have already proven that, assuming the assumptions of Proposition 2, i.e., K = 2 and R = 1, the RLE and MRN methods produce the same normalization factors. Then, obviously, normalized counts are equal. Let's then prove that TMM and MRN normalized counts are equal up to a constant.

For the TMM method, assuming the assumptions of Proposition 1, the relative scaling factors are the following:

τ11TMM=τ1MRN=1

and

τ21TMM=τ2MRN=mediang(Xg21Xg11)N11N21.

Then, with the following geometric mean of these values:

τ~TMM=τ11TMMτ21TMM=mediang(Xg21Xg11)N11N21

the adjusted relative scaling factors are the following:

τ~11TMM=mediang(Xg11Xg21)N21N11

and

τ~21TMM=mediang(Xg21Xg11)N11N21.

We can then calculate the effective library sizes:

e11TMM=τ~11TMMN11=mediang(Xg11Xg21)N11N21

and

e21TMM=τ~21TMMN21=mediang(Xg21Xg11)N11N21.

Hence, these effective library sizes are equal (up to a constant) to the normalization factors obtained from RLE (and MRN) methods:

e11TMM=f11RLEN11N21

and

e21TMM=f21RLEN11N21.

And the proposition is proved.     □

4. Conclusions and Further Work

This paper focus on two widely used normalization methods for RNA-Seq data and a third method related to these, that seem to give similar results and outperform many other classical methods if we consider all references given in the Introduction. Better understanding these methods is then an important issue dealt by this paper.

We highlight in this paper that the three considered normalization methods deal with similar underlying ideas. Moreover, we prove that these methods give exactly the same result in some simple experimental designs. For instance, Proposition 3 shows that for two given samples, normalized counts are (up to a constant) equal.

It has also been shown in this paper that the user should carefully use and not mix these normalization methods and R packages as all concepts are not equal. For instance, the so called “normalization factors” from edgeR and “size factors” from DESeq2 are not the same theoretical parameters.

Nevertheless, it has been shown in Maza et al. (2013) that the MRN method performs slightly better on some simulated data sets with a standard experimental design of two conditions with replicates. The present paper does not explain why it performs better but attempts to give some hypotheses, inspired by the proved propositions, by focusing on what differ between the three normalization methods. We give hereafter some of these hypotheses. (i) For instance, the reference sample of the MRN method, as a mean of all replicates of a given condition, is a more robust estimation of mean counts in a given condition (more robust than the TMM method). (ii) Also, for the TMM method, the trimming parameter of M-values should perhaps (by default) be chosen around 50% in order to have a more robust estimation of the relative size of transcriptomes. (iii) Moreover, in the same way, for the MRN method, the relative sizes of transcriptomes are not sample-specific but condition-specific. Indeed, for the MRN method, these relative sizes are the same for all replicates of a condition. This should perhaps give a more robust estimation than for TMM and RLE relative sizes. All these hypotheses, among others, should be explored in forthcoming work.

Finally, we conclude here that for a very simple experimental design, i.e., about two conditions and no replicates, users can use any of the three studied normalization methods with no impact on results. But, for a more complex experimental design, the results described in Maza et al. (2013) tend to indicate that the MRN method could be adopted. However, obviously, this last hypothesis should be proved rigorously in further work.

Author Contributions

EM has carried out the calculations, performed the analysis, and written the paper.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer CK and handling Editor declared their shared affiliation, and the handling Editor states that the process nevertheless met the standards of a fair and objective review.

Acknowledgments

I thank Sarah Chrisment for the thorough revision of my manuscript which help me to fix the grammatical errors and to improve the overall readability of the text. This work was supported by the Laboratoire d'Excellence (LABEX) TULIP (ANR-10-LABX-41). This work benefited from the networking activities within the European funded COST ACTION FA1106 “Qualityfruit.”

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/article/10.3389/fgene.2016.00164

Additional Files

Additional file 1 — Script of R Commands

This first additional file contains the R code of all calculations carried out in this paper. This file can be obviously executed on R directly (see Materials and Methods) but it can also be opened with a simple text viewer.

Additional file 2 — Script of mrnFactors Function

The second additional file contains the R code for the mrnFactors function (see Materials and Methods). As the additional file above, this file can be executed on R directly and can be opened with a simple text viewer. This file is called by Additional file 1.

Additional file 3 — Fruit Set Data

This file contains the fruit set data used in Additional file 1. This file is a simple text file.

References

Anders, S., and Huber, W. (2010). Differential expression analysis for sequence count data. Genome Biol. 11:R106. doi: 10.1186/gb-2010-11-10-r106

PubMed Abstract | CrossRef Full Text | Google Scholar

Anders, S., McCarthy, D. J., Chen, Y., Okoniewski, M., Smyth, G. K., Huber, W., et al. (2013). Count-based differential expression analysis of RNA sequencing data using R and Bioconductor. Nat. Protoc. 8, 1765–1786. doi: 10.1038/nprot.2013.099

PubMed Abstract | CrossRef Full Text | Google Scholar

Bullard, J. H., Purdom, E., Hansen, K. D., and Dudoit, S. (2010). Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinform. 11:94. doi: 10.1186/1471-2105-11-94

PubMed Abstract | CrossRef Full Text

Dillies, M.-A., Rau, A., Aubert, J., Hennequet-Antier, C., Jeanmougin, M., Servant, N., et al. (2013). A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief. Bioinform. 14, 671–683. doi: 10.1093/bib/bbs046

PubMed Abstract | CrossRef Full Text | Google Scholar

Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B., Dettling, M., Dudoit, S., et al. (2004). Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5:R80. doi: 10.1186/gb-2004-5-10-r80

PubMed Abstract | CrossRef Full Text

Kadota, K., Nishiyama, T., and Shimizu, K. (2012). A normalization strategy for comparing tag count data. Algorithms Mol. Biol. 7:5. doi: 10.1186/1748-7188-7-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, P., Piao, Y., Shon, H. S., and Ryu, K. H. (2015). Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data. BMC Bioinform. 16:347. doi: 10.1186/s12859-015-0778-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Lin, Y., Golovnina, K., Chen, Z.-X., Lee, H. N., Negron, Y. L. S., Sultana, H., et al. (2016). Comparison of normalization and differential expression analyses using RNA-Seq data from 726 individual Drosophila melanogaster. BMC Genomics 17:28. doi: 10.1186/s12864-015-2353-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Love, M. I., Huber, W., and Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15:550. doi: 10.1186/s13059-014-0550-8

PubMed Abstract | CrossRef Full Text

Maza, E., Frasse, P., Senin, P., Bouzayen, M., and Zouine, M. (2013). Comparison of normalization methods for differential gene expression analysis in RNA-Seq experiments: a matter of relative size of studied transcriptomes. Commun. Integr. Biol. 6:e25849. doi: 10.4161/cib.25849

PubMed Abstract | CrossRef Full Text | Google Scholar

Rapaport, F., Khanin, R., Liang, Y., Pirun, M., Krek, A., Zumbo, P., et al. (2013). Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biol. 14:R95. doi: 10.1186/gb-2013-14-9-r95

PubMed Abstract | CrossRef Full Text | Google Scholar

R Development Core Team (2011). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing.

Reddy, R. (2015). A comparison of methods: normalizing high-throughput RNA sequencing data. bioRxiv. doi: 10.1101/026062

PubMed Abstract | CrossRef Full Text | Google Scholar

Risso, D., Ngai, J., Speed, T. P., and Dudoit, S. (2014). Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32, 896–902. doi: 10.1038/nbt.2931

PubMed Abstract | CrossRef Full Text | Google Scholar

Robinson, M. D., McCarthy, D. J., and Smyth, G. K. (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140. doi: 10.1093/bioinformatics/btp616

PubMed Abstract | CrossRef Full Text | Google Scholar

Robinson, M. D., and Oshlack, A. (2010). A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11:R25. doi: 10.1186/gb-2010-11-3-r25

PubMed Abstract | CrossRef Full Text

Soneson, C., and Delorenzi, M. (2013). A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics 14:91. doi: 10.1186/1471-2105-14-91

PubMed Abstract | CrossRef Full Text | Google Scholar

Sun, J., Nishiyama, T., Shimizu, K., and Kadota, K. (2013). TCC: an R package for comparing tag count data with robust normalization strategies. BMC Bioinformatics 14:219. doi: 10.1186/1471-2105-14-219

PubMed Abstract | CrossRef Full Text | Google Scholar

Tang, M., Sun, J., Shimizu, K., and Kadota, K. (2015). Evaluation of methods for differential expression analysis on multi-group RNA-seq count data. BMC Bioinformatics 16:361. doi: 10.1186/s12859-015-0794-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, Z., Gerstein, M., and Snyder, M. (2009). RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63. doi: 10.1038/nrg2484

PubMed Abstract | CrossRef Full Text | Google Scholar

Zyprych-Walczak, J., Szabelska, A., Handschuh, L., Górczak, K., Klamecka, K., Figlerowicz, M., et al. (2015). The impact of normalization methods on RNA-Seq data analysis. BioMed Res. Int. 2015, 1–10. doi: 10.1155/2015/621690

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: RNA-seq data, normalization, comparison of methods, DESeq2, edgeR

Citation: Maza E (2016) In Papyro Comparison of TMM (edgeR), RLE (DESeq2), and MRN Normalization Methods for a Simple Two-Conditions-Without-Replicates RNA-Seq Experimental Design. Front. Genet. 7:164. doi: 10.3389/fgene.2016.00164

Received: 04 April 2016; Accepted: 02 September 2016;
Published: 16 September 2016.

Edited by:

Celia M. T. Greenwood, McGill University, Canada

Reviewed by:

Claudia L. Kleinman, McGill University, Canada
Pingzhao Hu, University of Manitoba, Canada

Copyright © 2016 Maza. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Elie Maza, elie.maza@ensat.fr