This is a comment for a required course. The initial paper of interest is Dudoit’s paper Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Though the paper itself is over a decade old, it provides a necessary introduction to oligonucleotide array technology and some of the errors associated with it. Due to the awesomeness of inkjet printing, custom oligo arrays can be built for any kind of DNA reporter, but they can further be used for combinatorial mutagenesis. In the intervening years, RNA-seq has become the tool of choice for investigating gene expression data; and while the principles outlined in Dudoit 2002 have remained the same, the theory and practice of differential *omics are much evolved. Note that differential expression is but one of many A/B testing experiments that follow the same pattern; others include differential methylation (bisulfite sequencing), differential histone modification (ChIP-seq), differential chromatin state (ATAC-seq), and so on and so forth. Quite honestly, old-fashioned GWAS falls into this category too. While each specific application of A/B testing has its own particular concerns, these concerns fall into categories with near-perfect overlap.

**Part 1: Units**

Chips yield intensity values, sequencing yields read counts. That’s about it, right? Well, think about DNA-based studies (SNP/Indel/CNV) for a moment. These studies don’t “operate” on the level of intensity (chip) or read counts (seq), they operate on the level of the genotype. Hom-ref, hom-var, or het. The unit we want to test here is the allele. There’s an entire layer of inference between the measured units (intensity or read counts) and the allele: things like Birdseed or zCall will infer allelic state from raw oligo intensities; while programs like the GATK Haplotype Caller or Freebayes will perform this inference from aligned reads.

But genotypes turn out to be the only place you can really do this. In theory, methylation should follow the same process, but since typically cell populations (not single cells) are sequenced, there are very many CpG sites which are intermediately-methylated; this makes it more similar to *pooled *DNA sequencing experiments than it is to standard exome/genome-seq. The unit becomes the methylation *frequency*; and similar maximum-likelihood approaches such as MLML can be used to estimate these. For methylation arrays (such as the Illumina Infinium HM450 chip), this estimation is not performed; instead the raw log(R/G) values are used after suitable data normalization (see Part 2).This is a general trend for chip data.

What’s the ideal unit for mRNA? I think it should be the concentration of each mRNA in solution. That is, the ideal unit is mol/L. Note that this is an *absolute* measure: as stoichiometry is in general nonlinear, there are regimes where the same relative concentration ([transcript mRNA]/[all mRNA]) may result in vastly different translational dynamics, particularly in the presence of translational regulators such as FMRP. The estimation of absolute concentration from RNA-seq does not appear to have been attempted; and typically library preparation itself should alter absolute concentrations in a nontrivial way. So maybe we should forget about absolute concentration, and focus instead on *relative* mRNA concentration. One way to estimate this might be via

This approximation makes the assumption that , and is a well-known quantity. Indeed, the quantity above is

where RPKM is the well known Reads per Kilobase per Million Reads. However, Wagner, Kin, and Lynch point out that there are better estimates for the total abundance of transcripts, and introduce a related measure, TPM, in place of RPKM.

This all goes out the window with array data. Instead of counts, one has two-channel intensities for competitively-bound cDNA, and these intensities are some (hopefully monotonic) function of post-library-construction absolute concentration. While you’d like to have a measure of relative concentration (relative to the total mRNA concentration), you’re stuck with relative intensity (relative to whatever reference mRNA sample you used) per gene. While there is literature in fitting Langmuir or stoichiometric models to titration experiments, which enables a direct estimate of absolute mRNA concentration from probe intensity, these models cannot be directly applied to expression microarrays. First, the models in these papers were fit to different arrays; second, in order to train the parameters, tens of thousands of titration experiments would need to be performed to fit calibration curves for each appropriate (mRNA, probe) pair; and, even should these be fit to a single chip, synthesis efficiency during manufacturing is poor enough that the resulting estimates would be very noisy.

But even stuck with relative intensities, there’s still a question about the correct axes. Dudoit 2002 makes a great deal about the (M, A) = (log R/G, log R*G) versus (logR, logG) axes, even though these coordinate systems are linearly related. From a pure dimensional analysis perspective, log R/G is most appealing, as the quantity R/G is dimensionless, and so the log doesn’t screw up any units.

For splicing, again, ideally we would want isoform-level absolute concentrations. We could settle for isoform-level relative concentrations (TPM as for expression), but breaking down total mRNA abundance for a gene (viewed as the union of all of its isoforms) into abundance for each isoform separately turns out to be a fairly difficult inference problem. Instead, differential splicing (and spliceQTLs) tend to focus on the *exon inclusion fraction, *: the proportion of mRNA, for a given gene, which *contain* the *i*th exon. There are many, many models for estimating . DEXSeq uses a generalized linear model (negative binomial), the mean parameter of which is , SpliceTrap takes a Bayesian approach (combinations of normals and betas) to estimate , MATS uses a similar approach but with the binomial as opposed to normal distribution, and Xiong et al use a beta-binomial model with a *positional bootstrap* to account for mapping biases (see SM of that paper).

How about for differential histone modification? The important biological detail here is, for a given locus, what proportion of chromosomes have a histone with the given modification sitting at that locus. For non-histone ChIP-seq, such as for transcription factor binding, the story becomes more complicated, as the protein may directly bind DNA, or *indirectly* bind (i.e. as a part of a complex where some other TF binds). Here, the unit is the *affinity-frequency* *distribution*; that is, the frequency of direct and indirect binding, where “degree of directness” is itself a real-valued parameter to be inferred from the data. ChIPDiff uses a beta-binomial model to perform an initial, local estimate of chromatin modification frequency within groups ( and ). It combines this with an HMM to aggregate information over multiple small bins, to estimate the indicator function

and .

Many other methods (F-Seq, dCaP, and Wu’s Nonparametric) utilize the naive estimator

not as an estimate of , but rather as one piece of the final test statistic (for instance ).

**Part 2: Normalization**

Surely, having as best as possible placed your data in the appropriate units, you are ready to proceed to differential expression! Let’s say you’ve done basic high-throughput exome sequencing on two flowcells of HiSeq, genotyped each separately, and have some 50-100 samples’ worth of data. You take your genotypes, and perform PCA. Much to your chagrin, one of the top PCs is the flowcell. You go online (say to seqanswers) and they suggest that you put the raw reads together and jointly genotype all the samples — and maybe add some 1000 Genomes samples for good measure. You do so, repeat the PCA, and find that none of the top PCs correlate with flowcell. You may not think this is normalization, but it really is. In particular you normalized out coverage and artefactual variation that are due to differences in library preparation and Illumina’s manufacturing process. These artifacts, whether due to small variations in reagent concentration, temperature, cycling time, the accuracy of manufacturing, the initial quantity of your sample, the generation number of cell lines, (&c &c &c) distort your measurements. They’re noise. Some effects are subtle, and can be controlled for as covariates (see part 3); others are the dominant source of variance, and somehow they need to be removed before you can even start. For oligonucleotide arrays, the major distortion is due to manufacturing differences; for *seq efforts, it’s total read depth. Removing these particular effects is referred to as *normalization*.

Dudoit 2002 references nonparametric regression (Loess) as a means of correcting for printhead-specific biases. This is still the standard approach for analyzing (or re-analyzing) microarray data — and the Loess is performed within printhead and within sample; this enables one to compare across multiple chips. An example is shown above, where the multiple lines show how different print heads lead to different smoothed M-A curves. The goal would be to shift each of these curves to some “reference” curve. One advantage of Loess is that the smoothing model can be semiparametric and include other kinds of error covariates (such as sequence GC%, gene length, and so on). Another approach introduced in Bolstad 2003 is quantile normalization. It should really be called “rank normalization”. Consider having performed four RNA-seq experiments and calculated RPKMs (or equivalent) for these samples. Something typical to see looks like: Here there are slight distortions making the cumulative (rank) distributions not equal. Something very common is zero-inflation: distortion about genes with zero or very few reads.This can be due to inefficient ribosomal RNA depletion — ribosomal RNA so dominates that even a small variance can render RPKM calculations not directly comparable across experiments.

Quantile normalization (“rank normalization”), in it’s most aggressive form, replaces each curve in the above plot by the average curve. Suppose, for instance, that in sample *i*, gene *j* is the 5000th gene (when sorted by RPKM). Then the expression value is replaced by the average expression of the 5000th gene *across all samples*. Note that *which* gene is gene 5000 will differ between samples. That is, we directly force the cumulative distributions of all samples to match, while constraining the transformation to be rank-preserving within each sample, so that there is no variance within ranks, but there is still variance within genes. While this approach was developed particularly for competitive hybridization (where the R and G channels would be *separately* transformed, thereby indirectly normalizing the M and A plots), it is also regularly applied to RNA-seq — particularly in data sets with widely varying library complexities or RNA integrity numbers.

This degree of tampering is entirely unphysical; so there are less heavy-handed approaches which, rather than forcing every rank to match exactly, choose a set of “plausibly invariant” measurements (things whose expression *should* match — e.g. housekeeping genes or spike-ins), and fit a smooth function which forces (as much as possible) the expression of the invariant genes to match. A very nice explication of these approaches can be found here.

Quantile normalization has been extended to regress away technical covariates as well, resulting in *conditional* quantile normalization, which regresses out covariates using cubic splines, and then applies quantile normalization to the *residuals* to attempt to match a target distribution; this normalization is directly applied to highly-expressed genes, and extended (via an approximation) to genes with lower expression. In practice, full quantile normalization does not appear to add additional power to detect differentially-expressed genes over simply dividing the entire expression distribution by a single summary such as the 1st quartile (Bullard, 2010). Nevertheless, for some work I have done in the past, we felt the warping of gene expression distributions between samples was mainly due to experimental artifacts, and so full rank normalized (with CQN) to a carefully-constructed and replicated reference distribution (similar to the use of Brain atlases in MRIs).

The ERCC proposed a set of RNA spike-in standards to aid in normalization (the idea is the *absolute* concentration of these unique RNA sequences is known up-front, and can be used to calibrate across experiments). However, the resulting reads are still too unstable to be sufficient for normalization purposes – I remember hearing frustration about this from a number of labs.

Empirically, DESeq’s approach (each gene is separately normalized by the geometric mean) appears to perform the best, but not much better than other approaches. Notably, this paper does not include CQN in the comparison. The modern standards are dChip and IRON.

The current standard in microarray expression analysis presents an odd paradox: while for a given experiment, the Loess or full quantile normalization methods are used; normalizing across experiments (for instance, analyzing multiple published microarray datasets) utilizes an array of novel methods not typically applied within a single experiment. This seems a bit strange, as cross-batch normalization should reduce to within-batch normalization if the number of batches is 1 (and let’s completely ignore the issue of what constitutes a “batch”). Lazar et al. make a distinction between three sources of error: expression heterogeneity, batch effects, and other sources of error. The origination of batch effects, they claim, is the simple fact that only a fixed set (typically 96) of samples can be processed together. In fact, the “batch” unit is just a proxy for a number of other sources of variation: library prep, experimental conditions at time of processing, and so forth. In general, within a batch there is not necessarily a good proxy for these sources of intensity (or expression) variation, but across multiple batches they can be controlled, by using the batch as a proxy. While nonparametrics like the above (LOESS or CQN against a reference distribution) still apply in this case, they are typically not used. Instead, *parametric* batch normalization typically recenter and rescale RPKM (or logR/G) values to have the same mean and variance across batches. While gene *i* and gene *j* may have different means, gene *i *in batch 1 is forced to have the same mean and variance as gene *i *in batch 2 (and so on and so forth). These normalizations can be performed in the linear or logarithmic scale. This basic approach can be extended to linear models which allow the incorporation of other covariates, but the idea is the same: estimate per-batch means and variances, and adjust the data so that these parameters are homogenous across batches for each gene. Other methods are *unsupervised* or *latent* in that they assume the primary sources of variation are nonbiological (we will see this again in statistical testing). Under this assumption, latent variables can be extracted from the expression matrix via PCA; and these are assumed to be technical sources (indeed, batch usually correlates highly with one of the top principal components). All of these methods are reviewed in Lazar et al. linked above.

The practical approach here is pretty straightforward: use the QQ-plot as a readout of how well experimental or batch differences have been controlled. A QQ-plot that is for the most part well-calibrated (we expect differential expression for a small fraction of genes only) indicates that normalization has worked effectively. Thus there’s a straightforward practical procedure: start with basic mean-variance normalization between batches, run your statistical test, and check calibration. If it’s not calibrated, pick a normalization method more or less at random and apply it; recalculate your statistics and check if the QQ-plot looks good. Lather. Rinse. Repeat.

I hope that doesn’t shatter any ideas about Very Serious Statisticians poring over a dataset and considering which model most applies to the situation, choosing appropriately based on visualization of the data and theoretical justifications, and nodding approvingly at the results. That’s not how it works for machine learning, and it’s really not how it should work for the practice of statistics. When you’ve got lots of null hypotheses, it’s really hard to argue with well-calibrated test statistics, just as it’s hard to argue with a high CVAUC. Theoretical considerations happen at the margins, but the main thing is: if you see a crappy QQ plot, you went off the rails somewhere, so it’s time to try something else.

Effectively the same approaches apply to ChIP-seq. Bailey, et al. quickly review several standard normalization procedures: depth normalization, linear normalization, reference-based normalization, LOESS, and quantile. See the paper for specific references. This can be extended to spike-ins or control samples, which is the approach taken by NCIS. At the same time, ChIP-seq (and Hi-C and clip-seq) provide additional challenges which are not entirely addressed by the above methods. These technologies have nonspecific backgrounds which are heavily dependent on DNA sequence and small variations in antibody concentration or binding efficiency during the experiment. While backgrounds are present in RNA-seq, it is generally a small enough proportion that it can be ignored. By contrast, since ChIP methods tend to rely on peak-calling (identifying bound regions by virtue of normalized/smoothed read depth exceeding a threshold), the variation of the background signal needs to be accounted for. Consider for instance two technical replicates with the same number of aligned reads; the replicate with a higher background will, because of the constraint of having the same total reads, have smaller peaks on average. A normalization approach to deal with this (as opposed to direct statistical modeling, see below) is to combine background subtraction with one of the above methods. That is, all read depths are adjusted by subtracting the background estimate (either a constant, or the result of a fitted model that maps genomic features to expected reads due to background). The resulting counts can then be normalized to a reference distribution following any of the methods described above.

More sophisticated methods do not treat the background during data normalization, but instead model it directly during statistical testing. This is the approach taken by dCaP and ChIPComp. It’s worth asking: in all these cases, seeing as the transformations are generally rank-preserving within sample, why perform normalization at all? Statistics could be calculated on the *ranks* within samples as opposed to on the direct expression/intensity/frequency estimates. The reason for this is that using ranks results in a p-value without a biologically-related effect estimate, and estimates of fold increase/decrease are important not only for interpretation, but for understanding results in context. Normalization considerations are more about retaining *effect size* than they are about finding some way of getting a calibrated *p value*.

**Part 3: Testing for differences (or: the triumph of the LMM)**

Dudoit 2002 uses a simple T statistic for testing differential expression. How have we progressed in the past decade? We’re still using (pretty much) a T statistic, although in an asymptotic guise from the LMM (or GLMM). Where we have gotten much better are in providing proxies for error covariates, and in modeling the distributions of read counts or genotyping errors. I have written about specific cases before. The general conclusion is that likelihood-based regressions result in powerful tests with well-controlled false-discovery rates. The use of a linear mixed model can even obviate the need for location-scale normalization: by including mean and variance components, batch effects can be adjusted during model fitting, without need for a separate normalization step.

Differential binding from ChIP/Clip/Hi-C is the new kid on the block. Even so, there are lots of models for statistical prediction of differentially enriched regions. These have all been developed for ChIP, but almost immediately extend to other link-digest-sequence experiments. What’s surprising is the variety of models in this area. DIME uses a mixture model after Loess normalization. MAnorm fits a linear model and tests the residuals, jMOSAiCS uses a graphical model with nodes of the form

PePr normalizes ChIP peaks within sample and performs an asymptotic (Wald) test after fitting a negative binomial distribution, after extensive preprocessing and normalization. dCaP uses a LMM with a normal approximation, while dPCA compares global patterns between two conditions by decomposing the difference between multiple ChIP signals. ChIPComp uses a hierarchical model of a Poisson sampling distribution atop a linear model, and demonstrate a well-calibrated null distribution.

By contrast, differential expression, splicing, methylation, variant association and QTL studies, are almost all linear mixed models of various flavors. Wockner, et al. use LIMMA for differential methylation analysis. However, even though methylation occurs at specific CpG loci, differential methylation is typically observed in *regions*; and many methods have been developed to associate not just individual loci, but to test entire regions for differential methylation, either by aggregation or direct testing. Robinson, et al. list 14, of which at least 8 heavily involve linear models. For splicing, ARH-seq uses an entropy-based method, while rMATS is a GLM (logit) model. In practice, the only way to appropriately include covariates is through some kind of linear model. For instance, a very standard expression model is:

Where *d* is the depth, RIN is the RNA integrity, case is case status (it may also be the tissue type indicator), the *s* are individual genotypes one may want to control for, the *b* are “latent factors” (typically the top *r* principal components of the expression matrix, assumed to be nonbiological factors), *c* are clinical covariates (such as age, gender, BMI, etc), and G is the genetic relationship matrix, ideally calculated excluding the *cis*-region of the gene of interest, and excluding variants related to case/control status. The statistical test here is typically one of: a Wald test on the *case* coefficient; a likelihood ratio test (*case* included vs *case* excluded); or a score test (fit everything else, test the covariance between the *case* indicator and the residuals — care should be taken when random effects are included in this setting).

For other methods (take ARH-seq, for instance), in order to adjust for covariates, the above model would need to be fit (without the case variable) and the effects of covariates removed. NOISeq is the above model, equipped with a fixed coefficient on *d*, without any other covariates, and using the empirical distribution to estimate (or even replacing the normal with the empirical noise distribution). Soneson et al find that, empirically, LIMMA (a direct implementation of the above model) is quite robust, performing well in most situations; similarly Seyednasrollah et al find that for small sample sizes, LIMMA identifies the most number of genes at low FDR (high precision – see fig 2). The best part about the linear model here is: if you test the coefficients on *s* instead of “case”, you’ve just performed an eQTL analysis; and the models for eQTLs (ICE-EMMA, PANAMA, svaseq, Matrix eQTL, PEER, and HEFT) are all linear models, their only differences being implementation detail, and how certain covariates or latent factors are calculated.

This review is far from comprehensive, but I think it captures the practice of differential *omics as it is today. As we move forward, there’s going to be additional work particularly in crosslink-and-seq (ChIP/clip/Hi-C) and methylation statistics to better incorporate technical covariates into the detection of differentially modified or differentially bound *regions*. And, of course, there’s the big open question: how do we put it all together? (But that’s a topic for another post).

*Update Aug 18 2015*

The Pachter Lab has recently ~~released~~ announced Sleuth for testing differential expression. Professor Pachter makes an extremely good point about the necessity of estimating transcript-level abundances, and that these estimates induce technical variation due to read ambiguity. The solution is another LMM (well, what did you expect?), with boostrap-derived estimates of technical variance included in the variance component. I will be checking Kallisto/Sleuth (Доверяй, но проверяй), particularly on microRNA and mini-exons, and switch to using it in place of Bowtie+CL+Limma-VOOM. I would speculate that the Kallisto/Sleuth pipeline probably functions well (with some paramater tuning) on other feature quantifications (ChIP/Clip/Hi-C).