Don’t believe the anti-hype. They are saying that RNA-Seq promises the discovery of new expression events, but it doesn’t deliver:

rna_hta_table

Is this true? There have been a few papers comparing microarrays to RNA-Seq technology (including one of my own) that I’ll discuss below, but first a break-down of the Affymetrix “evidence”. The first is this figure (the poor quality of the images is exactly what Affymetrix provides, and not due to a reduction in quality on this site; they are slightly enlarged when clicked on):

figure_3_large

The content of this figure is an illustration of the gene LMNB1 (Lamin protein of type B), used to argue that microarrays can provide transcript level resolution whereas RNA-Seq can’t!! Really? Affymetrix is saying that RNA-Seq users would likely use the RefSeq annotation which only has three isoforms. But this is a ridiculous claim. It is well known that RefSeq is a conservative annotation and certainly RNA-Seq users have the same access to the multiple databases Affymetrix used to build their annotation (presumably, e.g. Ensembl). It therefore seems that what Affymetrix is saying with this figure is that RNA-Seq users are dumb.

The next figure is showing the variability in abundance estimates as a function of expression level for RNA-SEq and the HTA 2.0, with the intended message being that microarrays are less noisy:

figure_1_large

But there is a subtle trick going on here. And its in the units. The x-axis is showing RPM, which is an abbreviation for Reads Per Million. This is not a commonly used unit, and there is a reason. First, its helpful to review what is used. In his landmark paper on RNA-Seq, Ali Mortazavi introduced the units RPKM (note the extra K) that stands for reads per kilobase of transcript per million mapped. Why the extra kilobase term? In my review on RNA-Seq quantification I explain that RPKM is proportional to a maximum likelihood estimate of transcript abundance (obtained from a simple RNA-Seq model). The complete derivation is on page 6 ending in Equation 13; I include a summary here:

The maximum likelihood (ML) abundances \hat{\rho}_t are  given by

\hat{\rho}_t = \frac{\frac{\hat{\alpha}_t}{l_t}}{\sum_{r \in T} \frac{\hat{\alpha}_r}{l_r}} \propto \frac{X_t}{\left( \frac{l_t}{10^3}\right) \left( \frac{N}{10^6}\right) }

In these equations l_t is the length of transcript (if reads are long it is necessary to modify the length due to edge effects, hence the tilde in the paper), the \hat{\alpha}_t are the maximum likelihood estimates for the probabilities of selecting reads from transcripts (unnormalized by their length) and finally X_t is the number of reads mapping to transcript t while N is the total number of mapped reads. The point is that RPKM (the rightmost formula for abundance) is really a unit for describing the maximum likelihood relative abundances (\hat{\rho}) scaled by some factors.

RPKM as a unit has two problems. The first is that in current RNA-Seq experiments reads are paired so that the actual units being counted (in X_t) are fragments. For this reason we modified RPKM to FPKM in the Cufflinks paper (the “F” replaced “R” for fragment instead of read). A more serious problem, noted by Bo Li and Colin Dewey in their paper on RSEM, is that while FPKM is proportional to ML estimates of abundance, the proportionality constants may vary between experiments. For this reason they proposed TPM (transcripts per million) which is also proportional to the ML abundance estimates but with a proportionality constant (a million) that is the same between experiments. All of these units are used simply to avoid writing down the \hat{\rho}_t which are in many cases tiny numbers since they must all sum to 1.

Returning to the Affymetrix figure, we see the strange RPM units. In essence, this is the rightmost term in the equation above, with the l_t length terms removed from the denominators. Therefore RPM is proportional to the \hat{\alpha}_t. If a transcript is short, even if it is equally abundant to a longer transcript ,it will produce less RNA-Seq reads and therefore its \hat{\alpha}_t will be (possibly considerably) smaller. The effect of displaying RPM for RNA-Seq vs. expression level for the HTA 2.0 arrays is therefore to mix apples and oranges. Since what is being displayed is a coefficient of variation, there is a bias caused by the relationship between length and expression (e.g. many highly expressed housekeeping genes are short).

To be fair to Affymetrix the conversion between the \hat{\alpha} and the \hat{\rho} can be confusing (its explained in Lemma 14 in the Supplement of the Cufflinks paper). So maybe the discordant x-axes were unintentional…but then there is the third figure:

figure_2_large

Here its a bit hard to tell what is going on because not all the information needed to decipher the figure is provided. For example, its not clear how the “expression of exons” was computed or measured for the RNA-Seq experiment. I suspect that as with the previous figure, read numbers were not normalized by length of exons, and moreover spliced reads (and other possibly informative reads from transcripts) were ignored. In other words, I don’t really believe the result.

Having said this, it is true that expression arrays can have an advantage in measuring exon expression, because an array measurement is absolute (as opposed to the relative quantification that is all that is possible with RNA-Seq). Array signal is based on hybridization, and it is probably a reasonable assumption that some minimum amount of RNA triggers a signal, and that this amount is independent of the remainder of the RNA in an experiment. So arrays can (and in many cases probably do) have advantages over RNA-Seq.

There are a few papers that have looked into this, for example the paper “A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae ” by Nookaew et al., Nucleic Acids Research 40 (2012) who find high reproducibility in RNA-Seq and consistency between arrays and RNA-Seq.  Xu et al., in “Human transcriptome array for high-throughput clinical studies“, PNAS 108 (2011), 3707–3712 are more critical, agreeing with Affymetrix that arrays are more sensitive at the exon level. For disease studies, they recommend using RNA-Seq to identify transcripts relevant to the disease, and then screening for those transcripts on patients using arrays.

For the Cuffdiff2 paper describing our new statistical procedures for differential analysis of transcripts and genes, the Rinn lab performed deep RNA-Seq and array expression measurement on the same samples from a HOXA1 knowdown (the experiments included multiple replicates of both the RNA-Seq and the arrays). To my knowledge, it is the deepest and most comprehensive data currently available for comparing arrays and RNA-Seq. Admittedly, the arrays used were not Affymetrix but Agilent SurePrint G3, and the RNA-Seq coverage was deep, however we had two main findings very different from the Affymetrix claims. First, we found overall strong correlation between array expression values and RNA-Seq abundance estimates. The correlation remained strong for large regimes of expression even with very few reads (tested by sequencing fewer reads from a MiSeq). Second, we found that arrays were missing differentially expressed transcripts, especially at low abundance levels. In other words, we found RNA-Seq to have higher resolution. The following figure from our paper made the case (note the overall Spearman Correlation was 0.86):

fig2aarray

There are definitely continued applications for arrays. Both in high-throughput screening applications (as suggested in the Xu et al. paper), and also in the development of novel assays. For example Mercer et al. “Targeted rNA sequencing reveals the deep complexity of the human transcriptome“, Nature Biotechnology 30 (2012) 99–104  show how to couple capture (with arrays) with RNA-Seq to provide ultra deep sequencing in subsets of the transcriptome. So its not yet the time to write off arrays. But RNA-Seq has many applications of its own. For example the ability to better detect allele-specific expression, the opportunity to identify RNA-DNA differences (and thereby study RNA editing), and the ability to study expression in non-model organisms where genomes sequences are incomplete and annotations poor. For all these reasons I’m betting on RNA-Seq.