You are currently browsing the tag archive for the ‘RNA-Seq’ tag.

The development of microarray technology two decades ago heralded genome-wide comparative studies of gene expression in human, but it was the widespread adoption of RNA-Seq that has led to differential expression analysis becoming a staple of molecular biology studies. RNA-Seq provides measurements of transcript abundance, making possible not only gene-level analyses, but also differential analysis of isoforms of genes. As such, its use has necessitated refinements of the term “differential expression”, and new terms such as “differential transcript expression” have emerged alongside “differential gene expression”. A difficulty with these concepts is that they are used to describe biology, statistical hypotheses, and sometimes to describe types of methods. The aims of this post are to provide a unifying framework for thinking about the various concepts, to clarify their meaning, and to describe connections between them.

To illustrate the different concepts associated to differential expression, I’ll use the following example, consisting of a comparison of a single two-isoform gene in two conditions (the figure is Supplementary Figure 1 in Ntranos, Yi *et al.* Identification of transcriptional signatures for cell types from single-cell RNA-Seq, 2018):

The isoforms are labeled *primary* and *secondary*, and the two conditions are called “A” and “B”. The black dots labeled conditions A and B have x-coordinates and corresponding to the abundances of the primary isoform in the respective conditions, and y-coordinates and corresponding to the abundance of the secondary isoforms. In data from an experiment the black dots will represent the mean level of expression of the constituent isoforms as derived from replicates, and there will be uncertainty as to their exact location. In this example I’ll assume they represent the true abundances.

**Biology**

Below is a list of terms used to characterize changes in expression:

**Differential transcript expression (DTE) **is change in one of the isoforms. In the figure, this is represented (conceptually) by the two red lines along the x- and y-axes respectively. Algebraically, one might compute the change in the primary isoform by and the change in the secondary isoform by . However the term DTE is used to denote not only the extent of change, but also the event that a single isoform of a gene changes between conditions, i.e. when the two points lie on a horizontal or vertical line. DTE can be understood to occur as a result of transcriptional regulation if an isoform has a unique transcription start site, or post-transcriptional regulation if it is determined by a unique splicing event.

**Differential gene expression (DGE) **is the change in the overall output of the gene. Change in the overall output of a gene is change in the direction of the line , and the extent of change can be understood geometrically to be the distance between the projections of the two points onto the line (blue line labeled DGE). The distance will depend on the metric used. For example, the change in expression could be defined to be the total expression in condition B () minus the change in expression in condition A (), which is . This is just the length of the blue line labeled “DGE” given by the norm. Alternatively, one could consider “DGE” to be the length of the blue line in the norm. As with DTE, DGE can also refer to a specific type of change in gene expression between conditions, one in which every isoform changes (relatively) by the same amount so that the line joining the two points has a slope of 1 (i.e. is angled at 45°). DGE can be understood to be the result of transcriptional regulation, driving overall gene expression up or down.

**Differential transcript usage (DTU) **is the change in *relative* expression between the primary and secondary isoforms. This can be interpreted geometrically as the angle between the two points, or alternatively as the length (as given by some norm) of the green line labeled DTU. As with DTE and DGE, DTU is also a term used to describe a certain kind of difference in expression between two conditions, one in which the line joining the two points has a slope of -1. DTU events are most likely controlled by post-transcriptional regulation.

**Gene differential expression ****(GDE)** is represented by the red line. It is the amount of change in expression along in the direction of line joining the two points. GDE is a notion that, for reasons explained below, is not typically tested for, and there are few methods that consider it. However GDE is biologically meaningful, in that it generalizes the notions of DGE, DTU and DTE, allowing for change in *any *direction. A gene that exhibits *some* change in expression between conditions is GDE regardless of the direction of change. GDE can represent complex changes in expression driven by a combination of transcriptional and post-transcriptional regulation. Note that DGE, DTU and DTE are all special cases of GDE.

If the norm is used to measure length and denote DTE in the primary and secondary isoforms respectively, then it is clear that DGE, DTU, DTE and GDE satisfy the relationship

**Statistics**

The terms DTE, DGE, DTU and GDE have an intuitive biological meaning, but they are also used in genomics as descriptors of certain null hypotheses for statistical testing of differential expression.

The **differential transcript expression (DTE)** null hypothesis for an isoform is that it did not change between conditions, i.e. for the primary isoform, or for the secondary isoform. In other words, in this example there are two DTE null hypotheses one could consider.

The **differential gene expresión (DGE)** null hypothesis is that there is no change in overall expression of the gene, i.e. .

The **differential transcript usage ****(DTU)** null hypothesis is that there is no change in the difference in expression of isoforms, i.e. .

The **gene differential expression (GDE)** null hypothesis is that there is no change in expression in *any* direction, i.e. for all constants , .

The **union differential transcript expression (UDTE) **null hypothesis is that there is no change in expression of *any* *isoform. *That is, that *and* (this null hypothesis is sometimes called DTE+G). The terminology is motivated by .

Not that , because if we assume GDE, and set we obtain DTE for the primary isoform and setting we obtain DTE for the secondary isoform. To be clear, by GDE or DTE in this case we mean the GDE (respectively DTE)* null hypothesis. *Furthermore, we have that

.

This is clear because if and then both DTE null hypotheses are satisfied by definition, and both DGE and DTU are trivially satisfied. However no other implications hold, i.e. , similarly , and .

**Methods**

The terms DGE, DTE, DTU and GDE also used to describe methods for differential analysis.

A **differential gene expression method** is one whose goal is to identify changes in overall gene expression. Because DGE depends on the projection of the points (representing gene abundances) to the line y=x, DGE methods typically take as input gene counts or abundances computed by summing transcript abundances and . Examples of early DGE methods for RNA-Seq were DESeq (now DESeq2) and edgeR. One problem with DGE methods is that it is problematic to estimate gene abundance by adding up counts of the constituent isoforms. This issue was discussed extensively in Trapnell *et al.* 2013. On the other hand, if the biology of a gene is DGE, i.e. changes in expression are the same (relatively) in all isoforms, then DGE methods will be optimal, and the issue of summed counts not representing gene abundances accurately is moot.

A **differential transcript expression method **is one whose goal is to identify individual transcripts that have undergone DTE. Early methods for DTE were Cufflinks (now Cuffdiff2) and MISO, and more recently sleuth, which improves DTE accuracy by modeling uncertainty in transcript quantifications. A key issue with DTE is that there are many more transcripts than genes, so that rejecting DTE null hypotheses is harder than rejecting DGE null hypotheses. On the other hand, DTE provides differential analysis at the highest resolution possible, pinpointing specific isoforms that change and opening a window to study post-transcriptional regulation. A number of recent examples highlight the importance of DTE in biomedicine (see, e.g., Vitting-Seerup and Sandelin 2017). Unfortunately DTE results do not always translate to testable hypotheses, as it is difficult to knock out individual isoforms of genes.

A **differential transcript usage **method is one whose goal is to identify genes whose overall expression is constant, but where isoform switching leads to changes in relative isoform abundances. Cufflinks implemented a DTU test using Jensen-Shannon divergence, and more recently RATs is a method specialized for DTU.

As discussed in the previous section, none of null hypotheses DGE, DTE and DTU imply any other, so users have to choose, prior to performing an analysis, which type of test they will perform. There are differing opinions on the “right” approach to choosing between DGE, DTU and DTE. Sonseson *et al.* 2016 suggest that while DTE and DTU may be appropriate in certain niche applications, generally it’s better to choose DGE, and they therefore advise not to bother with transcript-level analysis. In Trapnell *et al.* 2010, an argument was made for focusing on DTE and DTU, with the conclusion to the paper speculating that “differential RNA level isoform regulation…suggests functional specialization of the isoforms in many genes.” Van den Berge *et al. *2017 advocate for a middle ground: performing a gene-level analysis but saving some “FDR budget” for identifying DTE in genes for which the UDTE null hypothesis has been rejected.

There are two alternatives that have been proposed to get around the difficulty of having to choose, prior to analysis, whether to perform DGE, DTU or DTE:

A **differential transcript expression aggregation (DTE->G) **method is a method that first performs DTE on all isoforms of every gene, and then aggregates the resulting p-values (by gene) to obtain gene-level p-values. The “aggregation” relies on the observation that under the null hypothesis, p-values are uniformly distributed. There are a number of different tests (e.g. Fisher’s method) for testing whether (independent) p-values are uniformly distributed. Applying such tests to isoform p-values per gene provides gene-level p-values and the ability to reject UDTE. A DTE->G method was tested in Soneson *et al.* 2016 (based on Šidák aggregation) and the stageR method (Van den Berge *et al. *2017) uses the same method as a first step. Unfortunately, naïve DTE->G methods perform poorly when genes change by DGE, as shown in Yi *et al.* 2017. The same paper shows that Lancaster aggregation is a DTE->G method that achieves the best of both the DGE and DTU worlds. One major drawback of DTE->G methods is that they are non-constructive, i.e. the rejection of UDTE by a DTE->G method provides no information about *which* transcripts were differential and how. The stageR method averts this problem but requires sacrificing some power to reject UDTE in favor of the interpretability provided by subsequent DTE.

A **gene differential expression method **is a method for gene-level analysis that tests for differences *in the direction of change *identified between conditions. For a GDE method to be successful, it must be able to identify the direction of change, and that is not possible with bulk RNA-Seq data. This is because of the one in ten rule that states that approximately one predictive variable can be estimated from ten events. In bulk RNA-Seq, the number of replicates in standard experiments is three, and the number of isoforms in multi-isoform genes is at least two, and sometimes much more than that.

In Ntranos, Yi *et al.* 2018, it is shown that single-cell RNA-Seq provides enough “replicates” in the form of cells, that logistic regression can be used to predict condition based on expression, effectively identifying the direction of change. As such, it provides an alternative to DTE->G for rejecting UDTE. The Ntranos and Yi GDE methods is extremely powerful: by identifying the direction of change it is a DGE methods when the change is DGE, it is a DTU method when the change is DTU, and it is a DTE method when the change is DTE. Interpretability is provided in the prediction step: it is the estimated direction of change.

### Remarks

The discussion in this post is based on an example consisting of a gene with two isoforms, however the concepts discussed are easy to generalize to multi-isoform genes with more than two transcripts. I have not discussed differential exon usage (DEU), which is the focus of the DEXSeq method because of the complexities arising in genes which don’t have well-defined shared exons. Nevertheless, the DEXSeq approach to rejecting UDTE is similar to DTE->G, with DTE replaced by DEU. There are many programs for DTE, DTU and (especially) DGE that I haven’t mentioned; the ones cited are intended merely to serve as illustrative examples. This is not a comprehensive review of RNA-Seq differential expression methods.

**Acknowledgments**

The blog post was motivated by questions of Charlotte Soneson and Mark Robinson arising from an initial draft of the Ntranos, Yi *et al.* 2018 paper. The exposition was developed with Vasilis Ntranos and Lynn Yi. Valentine Svensson provided valuable comments and feedback.

[September 2, 2017: A response to this post has been posted by the authors of Patro *et al.* 2017, and I have replied to them with a rebuttal]

**Spot the difference**

One of the maxims of computational biology is that “no two programs ever give the same result.” This is perhaps not so surprising; after all, most journals seek papers that report a significant improvement to an existing method. As a result, when developing new methods, computational biologists ensure that the results of their tools are different, specifically better (by some metric), than those of previous methods. The maxim certainly holds for RNA-Seq tools. For example, the large symmetric differences displayed in the Venn diagram below (from Zhang *et al.* 2014) are typical for differential expression tool benchmarks:

In a comparison of RNA-Seq quantification methods, Hayer *et al. *2015 showed that methods differ even at the level of summary statistics (in Figure 7 from the paper, shown below, Pearson correlation was calculated using ground truth from a simulation):

These sort of of results are the norm in computational genomics. Finding a pair of software programs that produce identical results is about as likely as finding someone who has won the lottery… twice…. in one week. Well, it turns out there has been such a person, and here I describe the computational genomics analog of that unlikely event. Below are a pair of plots made using two different RNA-Seq quantification programs:

The two volcano plots show the log-fold change in abundance estimated for samples sequenced by Boj *et al.* 2015, plotted against *p*-values obtained with the program limma-voom. I repeat: **the plots were made with quantifications from two different RNA-Seq programs**. Details are described in the next section, but before reading it first try playing spot the difference.

**The reveal**

The top plot is reproduced from Supplementary Figure 6 in Beaulieu-Jones and Greene, 2017. The quantification program used in that paper was kallisto, an RNA-Seq quantification program based on pseudoalignment that was published in

- Near-optimal probabilistic RNA-Seq quantification by Nicolas Bray, Harold Pimentel, Páll Melsted and Lior Pachter, Nature Biotechnology 34 (
**2016**), 525–527.

The bottom plot was made using the quantification program Salmon, and is reproduced from a GitHub repository belonging to the lead author of

- Salmon provides fast and bias-aware quantification of transcript expression by Rob Patro, Geet Duggal, Michael I. Love, Rafael A. Irizarry and Carl Kingsford, Nature Methods 14 (
**2017**), 417–419.

Patro *et al.* 2017 claim that “[Salmon] achieves the same order-of-magnitude benefits in speed as kallisto and Sailfish but with greater accuracy”, however after being unable to spot *any* differences myself in the volcano plots shown above, I decided, with mixed feelings of amusement and annoyance, to check for myself whether the similarity between the programs was some sort of fluke. Or maybe I’d overlooked something obvious, e.g. the fact that programs may tend to give more similar results at the *gene* level than at the *transcript *level. Thus began this blog post.

In the figure below, made by quantifying RNA-Seq sample ERR188140 with the latest versions of the two programs, each point is a *transcript* and its coordinates are the estimated counts produced by kallisto and salmon respectively.

**Strikingly, the Pearson correlation coefficient is 0.9996026. **However astute readers will recognize a possible sleight of hand on my part. The correlation may be inflated by similar results for the very abundant transcripts, and the plot hides thousands of points in the lower left-hand corner. RNA-Seq analyses are notorious for such plots that appear sounds but can be misleading. However in this case I’m not hiding anything. The Pearson correlation computed with is still extremely high (0.9955965) and the Spearman correlation, which gives equal balance to transcripts irrespective of the magnitude of their counts is 0.991206. My observation is confirmed in Table 3 of Sarkar *et al.* 2017 (note that in this table “quasi-mapping” corresponds to Salmon):

For context, the Spearman correlation between kallisto and a truly different RNA-Seq quantification program, RSEM, is 0.8944941. At this point I have to say… **I’ve been doing computational biology for more than 20 years and I have never seen a situation where two ostensibly different programs output such similar results**.

Patro and I are not alone in finding that Salmon kallisto (if kallisto and Salmon gave identical results I would write that Salmon = kallisto but in lieu of the missing 0.004 in correlation I use the symbol to denote the very *very *strong similarity). Examples in the literature abound, e.g. Supplementary Figure 5 from Majoros *et al.* 2017 (shown later in the post), Figure 1 from Everaert *et al.* 2017

or Figure 3A from Jin *et al.* 2017:

Just a few weeks ago, Sahraeian *et al*. 2017 published a comprehensive analysis of 39 RNA-Seq analysis tools and performed hierarchical clusterings of methods according to the similarity of their output. Here is one example (their Supplementary Figure 24a):

Amazingly, kallisto and Salmon-Quasi (the latest version of Salmon) are the two closest programs to each other in the entire comparison, producing output even more similar than the *same* program, e.g. Cufflinks or StringTie run with different alignments!

This raises the question of how, with kallisto published in May 2016 and Salmon kallisto, **Patro et al. 2017 was published in**

**one of the most respected scientific publications**that advertises first and foremost that it “is a forum for the publication of

*and*

**novel methods***to tried-and-tested basic research techniques in the life sciences.” ?*

**significant improvements****How not to perform a differential expression analysis**

The Patro *et al.* 2017 paper presents a number of comparisons between kallisto and Salmon in which Salmon appears to dramatically improve on the performance of kallisto. For example Figure 1c from Patro *et al. *2017 is a table showing an enormous performance difference between kallisto and Salmon:

At a false discovery rate of 0.01, the authors claim that in a simulation study where ground truth is known Salmon identifies 4.5 times more truly differential transcripts than kallisto!

This can explain how Salmon was published, namely the reviewers and editor believed Patro *et al.’s* claims that Salmon *significantly *improves on previous work. In one analysis Patro *et al. *provide a *p*-value to help the “significance” stick. They write that “we found that Salmon’s distribution of mean absolute relative differences was significantly smaller (Mann-Whitney U test, P=0.00017) than those of kallisto. But how can the result Salmon >> kallisto, be reconciled with the fact that everybody repeatedly finds that Salmon kallisto?

A closer look reveals three things:

- In a differential expression analysis billed as “a typical downstream analysis” Patro
*et al.*did not examine differential expression results for a typical biological experiment with a handful of replicates. Instead they examined a simulation of two conditions with*eight*replicates in each. - The large number of replicates allowed them to apply the log-ratio
*t*-test directly to abundance estimates based on transcript per million (TPM) units, rather than estimated counts which are required for methods such as their own DESeq2. - The simulation involved generation of GC bias in an approach compatible with the inference model, with one batch of eight samples exhibiting “weak GC content dependence” while the other batch of eight exhibiting “more severe fragment-level GC bias.” Salmon was run in a GC bias correction mode.

These were unusual choices by Patro *et al*. What they did was allow Patro *et al.* to showcase the performance of their method in a way that leveraged the match between one of their inference models and the procedure for simulating the reads. The showcasing was enabled by **having**** a confounding variable (bias) that exactly matches their condition variable, **the use of TPM units to magnify the impact of that effect on their inference, simulation with a large number of replicates to enable the use of TPM, which was possible because with many replicates one could directly apply the log *t*-test. This complex chain of dependencies is unraveled below:

There is a reason why log-fold changes are not directly tested in standard RNA-Seq differential expression analyses. Variance estimation is challenging with few replicates and RNA-Seq methods developers understood this early on. That is why *all*** **competitive methods for differential expression analysis such as DESeq/DESeq2, edgeR, limma-voom, Cuffdiff, BitSeq, sleuth, etc. regularize variance estimates (i.e., perform shrinkage) by sharing information across transcripts/genes of similar abundance. In a careful benchmarking of differential expression tools, Shurch *et al.* 2016 show that log-ratio *t*-test is the *worst* method. See, e.g., their Figure 2:

The log-ratio *t-*test performs poorly not only when the number of replicates is small and regularization of variance estimates is essential. **Schurch et al. specifically**

**recommend DESeq2 (or edgeR) when up to 12 replicates are performed**. In fact, the log-ratio

*t*-test was so bad that it didn’t even make it into their Table 2 “summary of recommendations”.

The authors of Patro *et al.* 2017 are certainly well-aware of the poor performance of the log-ratio *t-*test. After all, one of them was specifically thanked in the Schurch *et al.* 2016 paper “for his assistance in identifying and correcting a bug”. Moreover, the recommended program by Schurch *et*. *al. *(DESeq2) was authored by one of the coauthors on the Patro* et al.* paper, who regularly and publicly advocates for the use of his programs (and not the log-ratio *t*-test):

This recommendation has been codified in a detailed RNA-Seq tutorial where M. Love *et al.* write that “This [Salmon + tximport] is our current recommended pipeline for users”.

In Soneson and Delorenzi, 2013, the authors wrote that “there is no general consensus regarding which [RNA-Seq differential expression] method performs best in a given situation” and despite the development of many methods and benchmarks since this influential review, the question of how to perform differential expression analysis continues to be debated. While it’s true that “best practices” are difficult to agree on, one thing I hope everyone can agree on is that in a “typical downstream analysis” with a handful of replicates

do not perform differential expression with a log-ratiot-test.

Turning to Patro* **et al.*‘s choice of units, it is important to note that the requirement of shrinkage for RNA-Seq differential analysis is the reason most differential expression tools require abundances measured in counts as input, and do not use length normalized units such as **T**ranscripts **P**er **M**illion (TPM). In TPM units the abundance for a transcript *t* is where are the estimated counts for transcript *t,* is the (effective) length of *t* and *N* the number of total reads.* *Whereas counts are approximately Poisson distributed (albeit with some over-dispersion), variance estimates of abundances in TPM units depend on the lengths used in normalization and therefore cannot be used directly for regularization of variance estimation. Furthermore, the dependency of TPM on effective lengths means that abundances reported in TPM are very sensitive to the estimates of effective length.

This is why, when comparing the quantification accuracy of different programs, it is important to compare abundances using estimated counts. This was highlighted in Bray *et al.* 2016: “Estimated counts were used rather than transcripts per million (TPM) because the latter is based on both the assignment of ambiguous reads and the estimation of effective lengths of transcripts, so a program might be penalized for having a differing notion of effective length despite accurately assigning reads.” Yet Patro *et al.* perform no comparisons of programs in terms of estimated counts.

**A typical analysis**

The choices of Patro *et al.* in designing their benchmarks are demystified when one examines what would have happened had they compared Salmon to kallisto on typical data with standard downstream differential analysis tools such as their own tximport and DESeq2. I took the definition of “typical” from one of the Patro *et al.* coauthors’ own papers (Soneson *et al.* 2016): “Currently, one of the most common approaches is to define a set of non-overlapping targets (typically, genes) and use the number of reads overlapping a target as a measure of its abundance, or expression level.”

The Venn diagram below shows the differences in transcripts detected as differentially expressed when kallisto and Salmon are compared using the workflow the authors recommend publicly (quantifications -> tximport -> DESeq2) on a typical biological dataset with three replicates in each of two conditions. The number of overlapping genes is shown for a false discovery rate of 0.05 on RNA-Seq data from Trapnell *et al.* 2014:

This example provides Salmon the benefit of the doubt- the dataset was chosen to be older (when bias was more prevalent) and Salmon was not run in default mode but rather with GC bias correction turned on (option –gcBias).

**When I saw these numbers for the first time I gasped. **Of course I shouldn’t have been surprised; they are consistent with repeated published experiments in which comparisons of kallisto and Salmon have revealed near identical results. And while I think it’s valuable to publish confirmation of previous work, I did wonder whether *Nature Methods* would have accepted the Patro *et al.* paper had the authors conducted an actual “typical downstream analysis”.

**What about the TPM?**

Patro *et al.* utilized TPM based comparisons for *all* the results in their paper, presumably to highlight the improvements in accuracy resulting from better effective length estimates. Numerous results in the paper suggest that Salmon is *much* more accurate than kallisto. However I had seen a figure in Majoros *et al.* 2017 that examined the (cumulative) distribution of both kallisto and Salmon abundances in TPM units (their Supplementary Figure 5) in which the curves literally overlapped at almost all thresholds:

The plot above was made with Salmon v0.7.2 so in fairness to Patro *et al.* I remade it using the ERR188140 dataset mentioned above with Salmon v0.8.2:

The blue circles correspond to kallisto and the red stars inside to Salmon. With the latest version of Salmon the similarity is even higher than what Majoros *et al.* observed! The Spearman correlation between kallisto and Salmon with TPM units is 0.9899896.

It’s interesting to examine what this means for a (truly) typical TPM analysis. One way that TPMs are used is to filter transcripts (or genes) by some threshold, typically TPM > 1 (in another deviation from “typical”, a key table in Patro *et al. *2017 – Figure 1d – is made by thresholding with TPM > 0.1). The Venn diagram below shows the differences between the programs at the *typical* TPM > 1 threshold:

The figures above were made with Salmon 0.8.2 run in default mode. The correlation between kallisto and Salmon (in TPM) units decreases a tiny amount, from 0.9989224 to 0.9974325 with the –gcBias option and even the Spearman correlation decreases by only 0.011 from 0.9899896 to 0.9786092.

I think it’s perfectly fine for authors to present their work in the best light possible. What is not ok is to deliberately hide important and relevant truth, which in this case is that Salmon kallisto.

**A note on speed**

One of the claims in Patro *et al.* 2017 is that “[the speed of Salmon] roughly matches the speed of the recently introduced kallisto.” The Salmon claim is based on a benchmark of an experiment (details unknown) with 600 million 75bp paired-end reads using 30 threads. Below are the results of a similar benchmark of Salmon showing time to process 19 samples from Boj *et al. *2015 with variable numbers of threads:

First, Salmon with –gcBias is considerably slower than default Salmon. Furthermore, there is a rapid decrease in performance gain with increasing number of threads, something that should come as no surprise. It is well known that quantification can be I/O bound which means that at some point, extra threads don’t provide any gain as the disk starts grinding limiting access from the CPUs. So why did Patro *et al.* choose to benchmark runtime with **30 threads**?

The figure below provides a possible answer:

In other words, not only is Salmon kallisto in accuracy, but contrary to the claims in Patro *et al.* 2017, **kallisto is faster**. This result is confirmed in Table 1 of Sarkar *et al.* 2017 who find that Salmon is slower by roughly the same factor as seen above (in the table “quasi-mapping” is Salmon).

Having said that, the speed differences between kallisto and Salmon should not matter much in practice and large scale projects made possible with kallisto (e.g. Vivian *et al.* 2017) are possible with Salmon as well. Why then did the authors not report their running time benchmarks honestly?

**The first common notion**

The Patro *et al.* 2017 paper uses the term “quasi-mapping” to describe an algorithm, published in Srivastava *et al.* 2016, for obtaining their (what turned out to be near identical to kallisto) results. I have written previously how “quasi-mapping” is the same as pseudoalignment as an alignment concept, even though Srivastava *et al.* 2016 initially implemented pseudoalignment differently than the way we described it originally in our preprint in Bray *et al.* 2015. However the reviewers of Patro *et al.* 2017 may be forgiven for assuming that “quasi-mapping” is a technical advance over pseudoalignment. The Srivastava *et al.* paper is dense and filled with complex technical detail. Even for an expert in alignment/RNA-Seq it is not easy to see from a superficial reading of the paper that “quasi-mapping” is an equivalent concept to kallisto’s pseudoalignment (albeit implemented with suffix arrays instead of a de Bruijn graph). Nevertheless, the key to the paper is a simple sentence: “Specifically, the algorithm [RapMap, which is now used in Salmon] reports the intersection of transcripts appearing in all hits” in the section 2.1 of the paper. That’s the essence of pseudoalignment right there. The paper acknowledges as much, “This lightweight consensus mechanism is inspired by Kallisto ( Bray *et al.* , 2016 ), though certain differences exist”. Well, as shown above, those differences appear to have made no difference in standard practice, except insofar as the Salmon implementation of pseudoalignment being slower than the one in Bray *et al.* 2016.

Srivastava *et al. *2016 and Patro *et al.* 2017 make a fuss about the fact that their “quasi-mappings” take into account the starting positions of reads in transcripts, thereby including more information than a “pure” pseudoalignment. This is a pedantic distinction Patro *et al.* are trying to create. Already in the kallisto preprint (May 11, 2015), it was made clear that this information was trivially accessible via a reasonable approach to pseudoalignment: “Once the graph and contigs have been constructed, kallisto stores a hash table mapping each k-mer to the contig it is contained in, **along with the position** within the contig.”

In other words, Salmon is not producing near identical results to kallisto due to an unprecedented cosmic coincidence. The underlying method is the same. I leave it to the reader to apply Euclid’s first common notion:

Things which equal the same thing are also equal to each other.

**Convergence**

While Salmon is now producing almost identical output to kallisto and is based on the same principles and methods, this was not the case when the program was first released. The history of the Salmon program is accessible via the GitHub repository, which recorded changes to the code, and also via the bioRxiv preprint server where the authors published three versions of the Salmon preprint prior to its publication in Nature Methods.

The first preprint was published on the BioRxiv on June 27, 2015. It followed shortly on the heels of the kallisto preprint which was published on May 11, 2015. However the first Salmon preprint described a program very different from kallisto. Instead of pseudoalignment, Salmon relied on chaining SMEMs (super-maximal exact matches) between reads and transcripts to identifying what the authors called “approximately consistent co-linear chains” as proxies for alignments of reads to transcripts. The authors then compared Salmon to kallisto writing that “We also compare with the recently released method of Kallisto which employs an idea similar in some respects to (but significantly different than) our lightweight-alignment algorithm and again find that Salmon tends to produce more accurate estimates in general, and in particular is better able [to] estimate abundances for multi-isoform genes.” In other words, in 2015 Patro *et al.* claimed that Salmon was “better” than kallisto. If so, why did the authors of Salmon later change the underlying method of their program to pseudoalignment from SMEM alignment?

Inspired by temporal ordering analysis of expression data and single-cell pseudotime analysis, I ran *all* the versions of kallisto and Salmon on ERR188140, and performed PCA on the resulting *transcript* abundance table to be able to visualize the progression of the programs over time. The figure below shows all the points with the exception of three: Sailfish 0.6.3, kallisto 0.42.0 and Salmon 0.32.0. I removed Sailfish 0.6.3 because it is such an outlier that it caused all the remaining points to cluster together on one side of the plot (the figure is below in the next section). In fairness I also removed one Salmon point (version 0.32.0) because it differed substantially from version 0.4.0 that was released a few weeks after 0.32.0 and fixed some bugs. Similarly, I removed kallisto 0.42.0, the first release of kallisto which had some bugs that were fixed 6 days later in version 0.42.1.

Evidently kallisto output has changed little since May 12, 2015. Although some small bugs were fixed and features added, the quantifications have been very similar. The quantifications have been stable because the algorithm has been the same.

On the other hand the Salmon trajectory shows a steady convergence towards kallisto. The result everyone is finding, namely that currently Salmon kallisto is revealed by the clustering of recent versions of Salmon near kallisto. However the first releases of Salmon are very different from kallisto. This is also clear from the heatmap/hierarchical clustering of Sahraeian *et al. *in which Salmon-SMEM was included (Salmon used SMEMs until version 0.5.1, sometimes labeled fmd, until “quasi-mapping” became the default). **A question: if Salmon ca. 2015 was truly better than kallisto then is Salmon ca. 2017 worse than Salmon ca. 2015?**

**Prestamping**

The bioRxiv preprint server provides a feature by which a preprint can be linked to its final form in a journal. This feature is useful to readers of the bioRxiv, as final published papers are generally improved after preprint reader, reviewer, and editor comments have been addressed. Journal linking is also a mechanism for authors to time stamp their published work using the bioRxiv. However I’m sure the bioRxiv founders did not intend the linking feature to be abused as a “prestamping” mechanism, i.e. a way for authors to ex post facto receive a priority date for a published paper that had very little, or nothing, in common with the original preprint.

A comparison of the June 2015 preprint mentioning the Salmon program and the current Patro *et al. *paper reveals almost nothing in common. The initial method changed drastically in tandem with an update to the preprint on October 3, 2015 at which point the Salmon program was using “quasi mapping”, later published in Srivastava *et al.* 2016. Last year I met with Carl Kingsford (co-corresponding author of Patro *et al.* 2017) to discuss my concern that Salmon was changing from a method distinct from that of kallisto (SMEMs of May 2015) to one that was replicating all the innovations in kallisto, without properly disclosing that it was essentially a clone. Yet despite a promise that he would raise my concerns with the Salmon team, I never received a response.

At this point, the Salmon core algorithms have changed completely, the software program has changed completely, and the benchmarking has changed completely. The Salmon project of 2015 and the Salmon project of 2017 are two very different projects although the name of the program is the same. While some features have remained, for example the Salmon mode that processes transcriptome alignments (similar to eXpress) was present in 2015, and the approach to likelihood maximization has persisted, considering the programs the same is to descend into Theseus’ paradox.

Interestingly, Patro specifically asked to have the *S*almon preprint linked to the journal:

The linking of preprints to journal articles is a feature that arXiv does not automate, and perhaps wisely so. If bioRxiv is to continue to automatically link preprints to journals it needs to focus not only on eliminating false negatives but also false positives, so that journal linking cannot be abused by authors seeking to use the preprint server to prestamp their work after the fact.

**The fish always win?**

The Sailfish program was the precursor of Salmon, and was published in Patro *et al.* 2014. At the time, a few students and postdocs in my group read the paper and then discussed it in our weekly journal club. It advocated a philosophy of “lightweight algorithms, which make frugal use of data, respect constant factors and effectively use concurrent hardware by working with small units of data where possible”. Indeed, two themes emerged in the journal club discussion:

1. Sailfish was much faster than other methods by virtue of being* simpler.*

2. The simplicity was to replace *approximate* alignment of *reads *with *exact *alignment of *k-*mers. When reads are shredded into their constituent *k*-mer “mini-reads”, the difficult read -> reference alignment problem in the presence of errors becomes an exact matching problem efficiently solvable with a hash table.

Despite the claim in the Sailfish abstract that “Sailfish provides quantification time…without loss of accuracy” and Figure 1 from the paper showing Sailfish to be more accurate than RSEM, we felt that the shredding of reads must lead to reduced accuracy, and we quickly checked and found that to be the case; this was later noted by others, e.g. Hensman *et al.* 2015, Lee *et al.* 2015).

After reflecting on the Sailfish paper and results, Nicolas Bray had the key idea of abandoning alignments as a requirement for RNA-Seq quantification, developed pseudoalignment, and later created kallisto (with Harold Pimentel and Páll Melsted).

I mention this because after the publication of kallisto, Sailfish started changing along with Salmon, and is now frequently discussed in the context of kallisto and Salmon as an equal. Indeed, the PCA plot above shows that (in its current form, v0.10.0) Sailfish is also nearly identical to kallisto. This is because with the release of Sailfish 0.7.0 in September 2015, Patro *et al.* started changing the Sailfish approach to use pseudoalignment in parallel with the conversion of Salmon to use pseudoalignment. To clarify the changes in Sailfish, I made the PCA plot below which shows where the original version of Sailfish that coincided with the publication of Patro *et al.* 2014 (version 0.6.3 March 2014) lies relative to the more recent versions and to Salmon:

In other words, despite a series of confusing statements on the Sailfish GitHub page and an out-of-date description of the program on its homepage, Sailfish in its published form was substantially less accurate and slower than kallisto, and in its current form Sailfish is kallisto.

In retrospect, the results in Figure 1 of Patro *et al.* 2014 seem to be as problematic as the results in Figure 1 of Patro *et al.* 2017. Apparently crafting computational experiments via biased simulations and benchmarks to paint a distorted picture of performance is a habit of Patro *et al.*

**Addendum [August 5, 2017]**

In the post I wrote that “The history of the Salmon program is accessible via the GitHub repository, which recorded changes to the code, and also via the bioRxiv preprint server where the authors published three versions of the Salmon preprint prior to its publication in Nature Methods” Here are the details of how these support the claims I make (tl;dr https://twitter.com/yarbsalocin/status/893886707564662784):

**Sailfish (current version) and Salmon implemented kallisto’s pseudoalignment algorithm using suffix arrays**

First, both Sailfish and Salmon use RapMap (via `SACollector`) and call `mergeLeftRightHits()`:

Sailfish:

https://github.com/kingsfordgroup/sailfish/blob/352f9001a442549370eb39924b06fa3140666a9e/src/SailfishQuantify.cpp#L192

Salmon:

https://github.com/COMBINE-lab/salmon/commit/234cb13d67a9a1b995c86c8669d4cefc919fbc87#diff-594b6c23e3bdd02a14cc1b861c812b10R2205

The RapMap code for “quasi mapping” executes an algorithm identical to psuedoalignment, down to the detail of what happens to the k-mers in a single read:

First, `hitCollector()` calls `getSAHits_()`:

https://github.com/COMBINE-lab/RapMap/blob/bd76ec5c37bc178fd93c4d28b3dd029885dbe598/include/SACollector.hpp#L249

Here kmers are used hashed to SAintervals (Suffix Array intervals), that are then extended to see how far ahead to jump. This is the one of two key ideas in the kallisto paper, namely that not all the k-mers in a read need to be examined to pseudoalign the read. It’s much more than that though, it’s the actual exact same algorithm to the level of exactly the k-mers that are examined. kallisto performs this “skipping” using contig jumping with a different data structure (the transcriptome de Bruijn graph) but aside from data structure used what happens is identical:

https://github.com/COMBINE-lab/RapMap/blob/c1e3132a2e136615edbb91348781cb71ba4c22bd/include/SACollector.hpp#L652

makes a call to jumping and the code to compute MMP (skipping) is

https://github.com/COMBINE-lab/RapMap/blob/c1e3132a2e136615edbb91348781cb71ba4c22bd/include/SASearcher.hpp#L77

There is a different detail in the Sailfish/Salmon code which is that when skipping forward the suffix array is checked for exact matching on the skipped sequence. kallisto does not have this requirement (although it could). On error-free data these will obviously be identical; on error prone data this may make Salmon/Sailfish a bit more conservative and kallisto a bit more robust to error. Also due to the structure of suffix arrays there is a possible difference in behavior when a transcript contains a repeated k-mer. These differences affect a tiny proportion of reads, as is evident from the result that kallisto and Salmon produce near identical results.

The second key idea in kallisto of intersecting equivalence classes for a read. This exact procedure is in:

https://github.com/COMBINE-lab/RapMap/blob/bd76ec5c37bc178fd93c4d28b3dd029885dbe598/include/SACollector.hpp#L363

which calls:

https://github.com/COMBINE-lab/RapMap/blob/bd76ec5c37bc178fd93c4d28b3dd029885dbe598/src/HitManager.cpp#L599

There was a choice we had to make in kallisto of how to handle information from paired end reads (does one require consistent pseudoalignment in both? Just one suffices to pseudoalign a read?)

The code for intersection between left and right reads making the identical choices as kallisto is:

https://github.com/COMBINE-lab/RapMap/blob/bd76ec5c37bc178fd93c4d28b3dd029885dbe598/include/RapMapUtils.hpp#L810

In other words, stepping through what happens to the k-mers in a read shows that Sailfish/Salmon copied the algorithms of kallisto and implemented it with the only difference being a different data structure used to hash the kmers. This is why, when I did my run of Salmon vs. kallisto that led to this blog post I found that

kallisto pseudoaligned 69,780,930 reads

vs

salmon 69,701,169.

That’s a difference of 79,000 out of ~70 million = 0.1%.

Two additional points:

- Until the kallisto program and preprint was published Salmon used SMEMs. Only after kallisto does Salmon change to using kmer cached suffix array intervals.
- The kallisto preprint did not discuss outputting position as part of pseudoalignment because it was not central to the idea. It’s trivial to report pseudoalignment positions with either data structure and in fact both kallisto and Salmon do.

I want to make very clear here that I think there can be great value in implementing an algorithm with a different data structure. It’s a form of reproducibility that one can learn from: how to optimize, where performance gains can be made, etc. Unfortunately most funding agencies don’t give grants for projects whose goal is solely to reproduce someone else’s work. Neither do most journal publish papers that set out to do that. That’s too bad. If Patro et al. had presented their work honestly, and explained that they were implementing pseudoalignment with a different data structure to see if it’s better, I’d be a champion of their work. That’s not how they presented their work.

**Salmon copied details in the quantification**

The idea of using the EM algorithm for quantification with RNA-Seq goes back to Jiang and Wong, 2009, arguably even to Xing et al. 2006. I wrote up the details of the history in a review in 2011 that is on the arXiv. kallisto runs the EM algorithm on equivalence classes, an idea that originates with Nicolae et al. 2011 (or perhaps even Jiang and Wong 2009) but whose significance we understood from the Sailfish paper (Patro et al. 2014). Therefore the fact that Salmon (now) and kallisto both use the EM algorithm, in the same way, makes sense.

However Salmon did not use the EM algorithm before the kallisto preprint and program were published. It used an online variational Bayes algorithm instead. In the May 18, 2015 release of Salmon there is no mention of EM. Then, with the version 0.4 release date Salmon suddenly switches to the EM. In implementing the EM algorithm there are details that must be addressed, for example setting thresholds for when to terminate rounds of inference based on changes in the (log) likelihood (i.e. determine convergence).

For example, kallisto sets parameters

const double alpha_limit = 1e-7;

const double alpha_change_limit = 1e-2;

const double alpha_change = 1e-2;

in EMalgorithm.h

https://github.com/pachterlab/kallisto/blob/90db56ee8e37a703c368e22d08b692275126900e/src/EMAlgorithm.h

The link above shows that these kallisto parameters were set and have not changed since the release of kallisto

Also they were not always this way, see e.g. the version of April 6, 2015:

https://github.com/pachterlab/kallisto/blob/2651317188330f7199db7989b6a4dc472f5d1669/src/EMAlgorithm.h

This is because one of the things we did is explore the effects of these thresholds, and understand how setting them affects performance. This can be seen also in a legacy redundancy, we have both alpha_change and alpha_change_limit which ended up being unnecessary because they are equal in the program and used on one line.

The first versions of Salmon post-kallisto switched to the EM, but didn’t even terminate it the same way as kallisto, adopting instead a maximum iteration of 1,000. See

https://github.com/COMBINE-lab/salmon/blob/59bb9b2e45c76137abce15222509e74424629662/include/CollapsedEMOptimizer.hpp

from May 30, 2015.

This changed later first with the introduction of minAlpha (= kallisto’s alpha_limit)

https://github.com/COMBINE-lab/salmon/blob/56120af782a126c673e68c8880926f1e59cf1427/src/CollapsedEMOptimizer.cpp

and then alphaCheckCutoff (kallisto’s alpha_change_limit)

https://github.com/COMBINE-lab/salmon/blob/a3bfcf72e85ebf8b10053767b8b506280a814d9e/src/CollapsedEMOptimizer.cpp

Here are the salmon thresholds:

double minAlpha = 1e-8;

double alphaCheckCutoff = 1e-2;

double cutoff = minAlpha;

Notice that they are identical except that minAlpha = 1e-8 and not kallisto’s alpha_limit = 1e-7. However in kallisto, from the outset, the way that alpha_limit has been used is:

if (alpha_[ec] < alpha_limit/10.0) {

alpha_[ec] = 0.0;

}

In other words, alpha_limit in kallisto is really 1e-8, and has been all along.

The copying of all the details of our program have consequences for performance. In the sample I ran kallisto performed 1216 EM rounds of EM vs. 1214 EM rounds in Salmon.

**Sailfish (current version) copied our sequence specific bias method**

One of the things we did in kallisto is implement a sequence specific bias correction along the lines of what was done previously in Roberts et al. 2011, and later in Roberts et al. 2013. Implementing sequence specific bias correction in kallisto required working things out from scratch because of the way equivalence classes were being used with the EM algorithm, and not reads. I worked this out together with Páll Melsted during conversations that lasted about a month in the Spring of 2015. We implemented it in the code although did not release details of how it worked with the initial preprint because it was an option and not default, and we thought we might want to still change it before submitting the journal paper.

Here Rob is stating that Salmon can account for biases that kallisto cannot:

https://www.biostars.org/p/143458/#143639

This was a random forest bias correction method different from kallisto’s.

Shortly thereafter, here is the source code in Sailfish deprecating the Salmon bias correction and switching to kallisto’s method:

https://github.com/kingsfordgroup/sailfish/commit/377f6d65fe5201f7816213097e82df69e4786714#diff-fe8a1774cd7c858907112e6c9fda1e9dR76

This is the update to effective length in kallisto:

https://github.com/pachterlab/kallisto/blob/e5957cf96f029be4e899e5746edcf2f63e390609/src/weights.cpp#L184

Here is the Sailfish code:

https://github.com/kingsfordgroup/sailfish/commit/be0760edce11f95377088baabf72112f920874f9#diff-8341ac749ad4ac5cfcc8bfef0d6f1efaR796

Notice that there has been a literal copying down to the variable names:

https://github.com/kingsfordgroup/sailfish/commit/be0760edce11f95377088baabf72112f920874f9#diff-8341ac749ad4ac5cfcc8bfef0d6f1efaR796

The code written by the student of Rob was:

effLength *=alphaNormFactor/readNormFactor;

The code written by us is

efflen *= 0.5*biasAlphaNorm/biasDataNorm;

The code rewritten by Rob (editing that of the student):

effLength *= 0.5 * (txomeNormFactor / readNormFactor);

Note that since our bias correction method was not reported in our preprint, this had to have been copied directly from our codebase and was done so without any attribution.

I raised this specific issue with Carl Kingsford by email prior to our meeting in April 13 2016. We then discussed it in person. The conversation and email were prompted by a change to the Sailfish README on April 7, 2016 specifically accusing us of comparing kallisto to a “ ****very old**** version of Sailfish”:

https://github.com/kingsfordgroup/sailfish/commit/550cd19f7de0ea526f512a5266f77bfe07148266

What was stated is “The benchmarks in the kallisto paper *are* made against a very old version of Sailfish” not “were made against”. By the time that was written, it might well have been true. But kallisto was published in May 2015, it benchmarked with the Sailfish program described in Patro et al. 2014, and by 2016 Sailfish had changed completely implementing the pseudoalignment of kallisto.

**Token attribution**

Another aspect of an RNA-Seq quantification program is effective length estimation. There is an attribution to kallisto in the Sailfish code now explaining that this is from kallisto:

“Computes (and returns) new effective lengths for the transcripts based on the current abundance estimates (alphas) and the current effective lengths (effLensIn). This approach is based on the one taken in Kallisto

https://github.com/kingsfordgroup/sailfish/blob/b1657b3e8929584b13ad82aa06060ce1d5b52aed/src/SailfishUtils.cpp

This is from January 23rd, 2016, almost 9 months after kallisto was released, and 3 months before the Sailfish README accused us of not testing the latest version of Sailfish in May 2015.

The attribution for effective lengths is also in the Salmon code, from 6 months later June 2016:

https://github.com/COMBINE-lab/salmon/blob/335c34b196205c6aebe4ddcc12c380eb47f5043a/include/DistributionUtils.hpp

There is also an acknowledgement in the Salmon code that a machine floating point tolerance we use

https://github.com/pachterlab/kallisto/blob/master/src/EMAlgorithm.h#L19

was copied.

The acknowledgment in Salmon is here

https://github.com/COMBINE-lab/salmon/blob/a3bfcf72e85ebf8b10053767b8b506280a814d9e/src/CollapsedEMOptimizer.cpp

This is the same file where the kallisto thresholds for the EM were copied to.

So after copying our entire method, our core algorithm, many of our ideas, specific parameters, and numerous features… really just about everything that goes into an RNA-Seq quantification project, there is an acknowledgment that our machine tolerance threshold was “intelligently chosen”.

[**Update July 15, 2016**: A preprint describing sleuth is available on BioRxiv]

Today my student Harold Pimentel released the beta version of his new RNA-Seq analysis method and software program called **sleuth**. A sleuth for RNA-Seq begins with the quantification of samples with **kallisto**, and together a sleuth of kallistos can be used to analyze RNA-Seq data rigorously and rapidly.

**Why do we need another RNA-Seq ****program?**

A major challenge in transcriptome analysis is to determine the transcripts that have changed in their abundance across conditions. This challenge is not entirely trivial because the stochasticity in transcription both within and between cells (*biological variation*), and the randomness in the experiment (RNA-Seq) that is used to determine transcript abundances (*technical variation*), make it difficult to determine what constitutes “significant” change.

Technical variation can be assessed by performing *technical replicates* of experiments. In the case of RNA-Seq, this can be done by repeatedly sequencing from one cDNA library. Such replicates are fundamentally distinct from *biological replicates *designed to assess biological variation. Biological replicates are performed by sequencing different cDNA libraries that have been constructed from repeated biological experiments performed under the same (or in practice near-same) conditions. Because biological replicates require sequencing different cDNA libraries, **a key point is that biological replicates include technical variation**.

In the early days of RNA-Seq a few papers (e.g. Marioni *et al*. 2008, Bullard *et al.* 2010) described analyses of technical replicates and concluded that they were not really needed in practice, because technical variation could be predicted statistically from the properties of the Poisson distribution. The point is that in an idealized RNA-Seq experiment counts of reads are multinomial (according to abundances of the transcripts they originate from), and therefore approximately Poisson distributed. Their variance is therefore approximately equal to the mean, so that it is possible to predict the variance in counts across technical replicates based on the abundance of the transcripts they originate from. There is, however, one important subtlety here: “counts of reads” as used above refers to the number of reads originating from a transcript, but in many cases, especially in higher eukaryotes, **reads are frequently ambiguous as to their transcript of origin** because of the presence of multi-isoform genes and genes families. In other words, ** transcript counts cannot be precisely measured**. However, the statement about the Poisson distribution of counts in technical replicates remain true when considering counts of reads by

*genomic*features because then reads are no longer ambiguous.

This is why, in so-called “count-based methods” for RNA-Seq analysis, there is an analysis only at the gene level. Programs such as DESeq/DESeq2, edgeR and a literally dozens of other count-based methods first require counting reads across genome features using tools such as HTSeq or featureCounts. By utilizing read counts to genomic features, technical replicates are unnecessary in lieu of the statistical assumption that they would reveal Poisson distributed data, and instead the methods focus on modeling biological variation. The issue of how to model biological variation is non-trivial because typically very few biological replicates are performed in experiments. Thus, there is a need for pooling information across genes to obtain reliable variance estimates via a statistical process called shrinkage. How and what to shrink is a matter of extensive debate among statisticians engaged in the development of count-based RNA-Seq methods, but one theme that has emerged is that shrinkage approaches can be compatible with general and generalized linear models, thus allowing for the analysis of complex experimental designs.

Despite these accomplishments, count-based methods for RNA-Seq have two major (related) drawbacks: first, the use of counts to gene features prevents inference about the transcription of isoforms, and therefore with most count-based methods it is impossible to identify splicing switches and other isoform changes between conditions. Some methods have tried to address this issue by restricting genomic features to specific exons or splice junctions (e.g. DEXSeq) but this requires throwing out a lot of data, thereby reducing power for identifying statistically significant differences between conditions. Second, because of the fact that in general it is mathematically incorrect to estimate gene abundances by adding up counts to their genomic region. One consequence of this, is that it is not possible to accurately measure fold change between conditions by using counts to gene features. In other words, count-based methods are problematic even at the gene-level and it is necessary to estimate transcript-level counts.

While reads might be ambiguous as to exactly which transcripts they originated from, it is possible to statistically infer an estimate of the number of reads from each transcript in an experiment. This kind of quantification has its origin in papers of Jiang and Wong, 2009 and Trapnell *et al. *2010. **However the process of estimating transcript-level counts introduces technical variation**. That is to say, if multiple technical replicates were performed on a cDNA library and then transcript-level counts were to be inferred, **those inferred counts would no longer be Poisson distributed.** Thus, there appears to be a need for performing technical replicates after all. Furthermore, it becomes unclear how to work within the shrinkage frameworks of count-based methods.

There have been a handful of attempts to develop methods that combine the uncertainty of count estimates at the transcript level with biological variation in the assessment of statistically significant changes in transcript abundances between conditions. For example, the Cuffdiff2 method generalizes DESeq while the bitSeq method relies on a Bayesian framework to simultaneously quantify abundances at the transcript level while modeling biological variability. Despite showing improved performance over count-based methods, they also have significant shortcomings. For example the methods are not as flexible as those of general(ized) linear models, and bitSeq is slow partly because it requires MCMC sampling.

Thus, despite intensive research on both statistical and computational methods for RNA-Seq over the past years, there has been no solution for analysis of experiments that allows biologists to take full advantage of the power and resolution of RNA-Seq.

**The sleuth model**

The main contribution of** sleuth **is an intuitive yet powerful model for RNA-Seq that bridges the gap between count-based methods and quantification algorithms in a way that fully exploits the advantages of both.

To understand **sleuth**, it is helpful to start with the *general linear model*:

.

Here the subscript *t* refers to a specific transcript, is a vector describing transcript abundances (of length equal to the number of samples), is a design matrix (of size number of samples x number of confounders), is a parameter vector (of size the number of confounders) and is a noise vector (of size the number of samples). In this model the abundances are normally distributed. For the purposes of RNA-Seq data, the may be assumed to be the logarithm of the counts (or normalized counts per million) from a transcript, and indeed this is the approach taken in a number of approaches to RNA-Seq modeling, e.g. in limma-voom. A common alternative to the general linear model is the *generalized* *linear model*, which postulates that some function of has a distribution with mean equal to where *g* is a link function, such as log, thereby allowing for distributions other than the normal to be used for the observed data. In the RNA-Seq context, where the negative binomial distribution may make sense because it is frequently a good distribution for modeling count data, the *generalized* model is sometimes preferred to the standard general model (e.g. by DESeq2). There is much debate about which approach is “better”.

In the **sleuth** model the in the general linear model are modeled as *unobserved*. They can be thought of us the unobserved logarithms of true counts for each transcript across samples and are assumed to be normally distributed. The *observed data** * is the logarithm of estimated counts for each transcript across samples, and is modeled as

where the vector parameterizes a perturbation to the unobserved . This can be understood as the technical noise due to the random sequencing of fragments from a cDNA library and the uncertainty introduced in estimating transcript counts.

The sleuth model incorporates the assumptions that the response error is *additive*, i.e. if the variance of transcript *t *in sample *i *is then where the variance and the variance . Intuitively, **sleuth** teases apart the two sources of variance by examining both technical and biological replicates, and in doing so directly estimates “true” biological variance, i.e. the variance in biological replicates that is *not* technical. In lieu of actual technical replicates, **sleuth** takes advantage of the bootstraps of **kallisto **which serve as accurate proxies.

In a test of **sleuth** on *data simulated according to the DESeq2 model* we* *found that **sleuth **significantly outperforms other methods:

In this simulation transcript counts were simulated according to a negative binomial distribution, following closely the protocol of the DESeq2 paper simulations. Reference parameters for the simulation were first estimated by running DESeq2 on a the female Finnish population from the GEUVADIS dataset (59 individuals). In the simulation above size factors were set to be equal in accordance with typical experiments being performed, but we also tested **sleuth** with size factors drawn at random with geometric mean of 1 in accordance with the DESeq2 protocol (yielding factors of 1, 0.33, 3, 3, 0.33 and 1) and **sleuth** still outperformed other methods.

There are many details in the implementation of **sleuth** that are crucial to its performance, e.g. the approach to shrinkage to estimate the biological variance . A forthcoming preprint, together with Nicolas Bray and Páll Melsted that also contributed to the project along with myself, will provide the details.

**Exploratory data analysis with sleuth**

One of the design goals of **sleuth** was to create a simple and efficient workflow in line with the principles of **kallisto**. Working with the Shiny web application framework we have designed an html interface that allows users to interact with **sleuth **plots allowing for real time exploratory data analysis.

The **sleuth** Shiny interface is much more than just a GUI for making plots of **kallisto **processed data. First, it allows for the exploration of the **sleuth** fitted models; users can explore the technical variation of each transcript, see where statistically significant differential transcripts appear in relationship to others in terms of abundance and variance and much more. Particularly useful are interactive features in the plots. For example, when examining an MA plot, users can highlight a region of points (dynamically created box in upper panel) and see their variance breakdown of the transcripts the points correspond to, and the list of the transcripts in a table below:

The web interface contains diagnostics, summaries of the data, “maps” showing low-dimensional representations of the data and tools for analysis of differential transcripts. The interactivity via Shiny can be especially useful for diagnostics; for example, in the diagnostics users can examine scatterplots of any two samples, and then select outliers to examine their variance, including the breakdown of technical variance. This allows for a determination of whether outliers represent high variance transcripts, or specific samples gone awry. Users can of course make figures showing transcript abundances in all samples, including boxplots displaying the extent of technical variation. Interested in the differential transcribed isoform ENST00000349155 of the TBX3 gene shown in Figure 5d of the Cuffdiff2 paper? It’s trivial to examine using the transcript viewer:

One can immediately see visually that differences between conditions completely dominate both the technical and biological variation within conditions. The **sleuth** q-value for this transcript is 3*10^(-68).

Among the maps, users can examine PCA projections onto any pair of components, allowing for rapid exploration of the structure of the data. Thus, with **kallisto **and **sleuth** raw RNA-Seq reads can be converted into a complete analysis in a matter of minutes. Experts will be able to generate plots and analyses in R using the **sleuth** library as they would with any R** **package. We plan numerous improvements and developments to the **sleuth **interface in the near future that will further facilitate data exploration; in the meantime we welcome feedback from users.

**How to try out sleuth**

Since **sleuth** requires the bootstraps and quantifications output by **kallisto** we recommend starting by running **kallisto **on your samples. The **kallisto **program is very fast, processing 30 million reads on a laptop in a matter of minutes. You will have to run **kallisto **with bootstraps- we have been using 100 bootstraps per sample but it should be possible to work with many fewer. We have yet to fully investigate the minimum number of bootstraps required for **sleuth **to be accurate.

To learn how to use **kallisto **start here. If you have already run **kallisto **you can proceed to the tutorial for **sleuth**. If you’re really eager to see **sleuth** without first learning **kallisto**, you can skip ahead and try it out using pre-computed **kallisto** runs of the Cuffdiff2 data- the tutorial explains where to obtain the data for trying out **sleuth**.

For questions, suggestions or help see the program websites and also the kallisto-sleuth user group. We hope you enjoy the tools!

Today I posted the preprint N. Bray, H. Pimentel, P. Melsted and L. Pachter, Near-optimal RNA-Seq quantification with kallisto to the arXiv. It describes the RNA-Seq quantification program **kallisto**. [Update April 5, 2016: a revised version of the preprint has been published: Nicolas L. Bray, Harold Pimentel, Páll Melsted and Lior Pachter, Near-optimal probabilistic RNA-Seq quantification, Nature Biotechnology (2016), doi:10.1038/nbt.3519 published online April 4, 2016.]

The project began in August 2013 when I wrote my second blog post, about another arXiv preprint describing a program for RNA-Seq quantification called Sailfish (now a published paper). At the time, a few students and postdocs in my group read the paper and then discussed it in our weekly journal club. It advocated a philosophy of “lightweight algorithms, which make frugal use of data, respect constant factors and effectively use concurrent hardware by working with small units of data where possible”. Indeed, two themes emerged in the journal club discussion:

1. Sailfish was much faster than other methods by virtue of being* simpler.*

2. The simplicity was to replace *approximate* alignment of *reads *with *exact* alignment of *k-mers*. When reads are shredded into their constituent *k*-mer “mini-reads”, the difficult read -> reference alignment problem in the presence of errors becomes an exact matching problem efficiently solvable with a hash table.

We felt that the shredding of reads must lead to reduced accuracy, and we quickly checked and found that to be the case. In fact, in our simulations, we saw that Sailfish significantly underperformed methods such as RSEM. However the fact that simpler was ** so** much faster led us to wonder whether the prevailing wisdom of seeking to improve RNA-Seq analysis by looking at increasingly complex models was ill-founded. Perhaps simpler could be not only fast, but also accurate, or at least close enough to best-in-class for practical purposes.

After thinking about the problem carefully, my (now former) student Nicolas Bray realized that the key is to abandon the idea that alignments are necessary for RNA-Seq quantification. Even Sailfish makes use of alignments (of *k*-mers rather than reads, but alignments nonetheless). In fact, thinking about all the tools available, Nick realized that every RNA-Seq analysis program was being developed in the context of a “pipeline” of first aligning reads or parts of them to a reference genome or transcriptome. Nick had the insight to ask: what can be gained if we let go of that paradigm?

By April 2014 we had formalized the notion of “pseudoalignment” and Nick had written, in Python, a prototype of a pseudoaligner. He called the program kallisto. The basic idea was to determine, for each read, not *where* in each transcript it aligns, but rather which transcripts it is *compatible* with. That is asking for a lot less, and as it turns out, pseudoalignment can be *much* faster than alignment. At the same time, the information in pseudoalignments is enough to quantify abundances using a simple model for RNA-Seq, a point made in the isoEM paper, and an idea that Sailfish made use of as well.

Just how fast is pseudoalignment? In January of this year Páll Melsted from the University of Iceland came to visit my group for a semester sabbatical. Páll had experience in exactly the kinds of computer science we needed to optimize kallisto; he has written about efficient *k*-mer counting using the bloom filter and de Bruijn graph construction. He translated the Python kallisto to C++, incorporating numerous clever optimizations and a few new ideas along the way. His work was done in collaboration with my student Harold Pimentel, Nick (now a postdoc with Jacob Corn and Jennifer Doudna at the Innovative Genomics Initiative) and myself.

The screenshot below shows kallisto being used on my 2012 iMac desktop to build an index of the human transcriptome (5 min 8 sec), and then quantify 78.6 million GEUVADIS human RNA-Seq reads (14 min). When we first saw these results we thought they were simply too good to be true. Let me repeat: **The quantification of 78.6 million reads takes 14 minutes on a standard desktop using a single CPU core. **In some tests we’ve gotten even faster running times, up to 15 million reads quantified per minute.

The results in our paper indicate that kallisto is not just fast, but also very accurate. This is not surprising: underlying RNA-Seq analysis are the alignments, and although kallisto is pseudoaligning instead, it is almost always only the compatibility information that is used in actual applications. As we show in our paper, from the point of view of compatibility, the pseudoalignments and alignments are almost the same.

Although accuracy is a primary concern with analysis, we realized in the course of working on kallisto that speed is also paramount, and not just as a matter of convenience. The speed of kallisto has three major implications:

1. It allows for efficient bootstrapping. All that is required for the bootstrap are reruns of the EM algorithm, and those are particularly fast within kallisto. The result is that we can accurately estimate the uncertainty in abundance estimates. One of my favorite figures from our paper, made by Harold, is this one:

It is based on an analysis of 40 samples of 30 million reads subsampled from 275 million rat RNA-Seq reads. Each dot corresponds to a transcript and is colored by its abundance. The *x*-axis shows the variance estimated from kallisto bootstraps on a single subsample while the *y*-axis shows the variance computed from the different subsamples of the data. We see that the bootstrap recapitulates the empirical variance. This result is non-trivial: the standard dogma, that the technical variance in RNA-Seq is “Poisson” (i.e. proportional to the mean) is false, as shown in Supplementary Figure 3 of our paper (the correlation becomes 0.64). Thus, the bootstrap will be invaluable when incorporated in downstream application and we are already working on some ideas.

2. It is not just the kallisto quantification that is fast; the index building, and even compilation of the program are also easy and quick. The implication for biologists is that RNA-Seq analysis now becomes interactive. Instead of “freezing” an analysis that might take weeks or even months, data can be explored dynamically, e.g. easily quantified against different transcriptomes, or re-quantified as transcriptomes are updated. The ability to analyze data locally instead of requiring cloud computation means that analysis is portable, and also easily secure.

3. We have found the fast turnaround of analysis helpful in improving the program itself. With kallisto we can quickly check the effect of changes in the algorithms. This allows for much faster debugging of problems, and also better optimization. It also allows us to release improvements knowing that users will be able to test them without resorting to a major computation that might take months. For this reason we’re not afraid to say that some improvements to kallisto will be coming soon.

As someone who has worked on RNA-Seq since the time of 32bp reads, I have to say that kallisto has personally been extremely liberating. It offers freedom from the bioinformatics core facility, freedom from the cloud, freedom from the multi-core server, and in my case freedom from my graduate students– for the first time in years I’m analyzing tons of data on my own; because of the simplicity and speed I find I have the time for it. Enjoy!

When I was an undergraduate at Caltech I took a combinatorics course from Rick Wilson who taught from his then just published textbook A Course in Combinatorics (co-authored with J.H. van Lint). The course and the book emphasized design theory, a subject that is beautiful and fundamental to combinatorics, coding theory, and statistics, but that has sadly been in decline for some time. It was a fantastic course taught by a brilliant professor- an experience that had a profound impact on me. Though to be honest, I haven’t thought much about designs in recent years. Having kids changed that.

A few weeks ago I was playing the card game Colori with my three year old daughter. It’s one of her favorites.

The game consists of 15 cards, each displaying drawings of the same 15 items (beach ball, boat, butterfly, cap, car, drum, duck, fish, flower, kite, pencil, jersey, plane, teapot, teddy bear), with each item colored using two of the colors red, green, yellow and blue. Every pair of cards contains exactly one item that is colored *exactly* the same. For example, the two cards the teddy bear is holding in the picture above are shown below:

The only pair of items colored exactly the same are the two beach balls. The gameplay consists of shuffling the deck and then placing a pair of cards face-up. Players must find the matching pair, and the first player to do so keeps the cards. This is repeated seven times until there is only one card left in the deck, at which point the player with the most cards wins. When I play with my daughter “winning” consists of enjoying her laughter as she figures out the matching pair, and then proceeds to try to eat one of the cards.

An inspection of all 15 cards provided with the game reveals some interesting structure:

Every card contains exactly one of each type of item. Each item therefore occurs 15 times among the cards, with fourteen of the occurrences consisting of seven matched pairs, plus one extra. This is a type of partially balanced incomplete block design. Ignoring for a moment the extra item placed on each card, what we have is 15 items, each colored one of seven ways (v=15*7=105). The 105 items have been divided into 15 blocks (the cards), so that b=15. Each block contains 14 elements (the items) so that k=14, and each element appears in two blocks (r=2). If every pair of different (colored) items occurred in the same number of cards, we would have a balanced incomplete block design, but this is not the case in Colori. Each item occurs in the same block as 26 (=2*13) other items (we are ignoring the extra item that makes for 15 on each card), and therefore it is not the case that every pair of items occurs in the same number of blocks as would be the case in a balanced incomplete block design. Instead, there is an association scheme that provides extra structure among the 105 items, and in turn describes the way in which items do or do not appear together on cards. The association scheme can be understood as a graph whose nodes consist of the 105 items, with edges between items labeled either 0,1 or 2. An edge between two items of the same type is labeled 0, edges between different items that appear on the same card are labeled 1, and edges between different items that do not appear on the same card are labeled 2. This edge labeling is called an “association scheme” because it has a special property, namely the number of triangles with a base edge labeled *k*, and other two edges labeled *i* and *j *respectively is dependent only on *i,j* and *k* and not on the specific base edge selected. In other words, there is a special symmetry to the graph. Returning to the deck of cards, we see that every pair of items appears in the same card exactly 0 or 1 times, and the number depends only on the association class of the pair of objects. This is called a partially balanced incomplete block design.

The author of the game, Reinhard Staupe, made it a bit more difficult by adding an extra item to each card making the identification of the matching pair harder. The addition also ensures that each of the 15 items appears on each card. Moreover, the items are permuted in location on the cards, in an arrangement similar to a latin square, making it hard to pair up the items. And instead of using 8 different colors, he used only four, producing the eight different “colors” of each item on the cards by using pairwise combinations of the four. The yellow-green two-colored beach balls are particularly difficult to tell apart from the green-yellow ones. Of course, much of this is exactly the kind of thing you would want to do **if you were designing an RNA-Seq experiment**!

Instead of 15 types of items, think of 15 different strains of mice. Instead of colors for the items, think of different cellular conditions to be assayed. Instead of one pair for each of seven color combinations, think of one pair of replicates for each of seven cellular conditions. Instead of cards, think of different sequencing centers that will prepare the libraries and sequence the reads. An ideal experimental setup would involve distributing the replicates and different cellular conditions across the different sequencing centers so as to reduce batch effect. This is the essence of part of the paper Statistical Design and Analysis of RNA Sequencing Data by Paul Auer and Rebecca Doerge. For example, in their Figure 4 (shown below) they illustrate the advantage of balanced block designs to ameliorate lane effects:

Figure 4 from P. Auer and R.W. Doerge’s paper Statistical Design and Analysis of RNA Sequencing Data.

Of course the use of experimental designs for constructing controlled gene expression experiments is not new. Kerr and Churchill wrote about the use of combinatorial designs in Experimental Design for gene expression microarrays, and one can trace back a long chain of ideas originating with R.A. Fisher. But design theory seems to me to be a waning art insofar as molecular biology experiments are concerned, and it is frequently being replaced with biological intuition of what makes for a good control. The design of good controls is sometimes obvious, but not always. So next time you design an experiment, if you have young kids, first play a round of Colori. If the kids are older, play Set instead. And if you don’t have any kids, plan for an extra research project, because what else would you do with your time?

“An entertaining freshness… Tic Tac!” This is Ferrero‘s tag line for its most successful product, the ubiquitous Tic Tac. And the line has stuck. As WikiHow points out in how to make your breath fresh: *first* buy some mints,* then *brush your teeth.

One of the amazing things about Tic Tacs is that they are sugar free. Well… almost not. As the label explains, a single serving (one single Tic Tac) contains 0g of sugar (to be precise, less than 0.5g, as explained in a footnote). In what could initially be assumed to be a mere coincidence, the weight of a single serving is 0.49g. It did not escape my attention that 0.50-0.49=0.01. Why?

To understand it helps to look at the labeling rules of the FDA. I’ve reproduced the relevant section (Title 21) below, and boldfaced the relevant parts:

TITLE 21–FOOD AND DRUGS

CHAPTER I–FOOD AND DRUG ADMINISTRATION

DEPARTMENT OF HEALTH AND HUMAN SERVICES

SUBCHAPTER B–FOOD FOR HUMAN CONSUMPTION (c)

Sugar content claims–(1)Use of terms such as “Consumers may reasonably be expected to regard terms that represent that the food contains no sugars or sweeteners e.g., “sugar free,” or “no sugar,” as indicating a product which is low in calories or significantly reduced in calories. Consequently, except as provided in paragraph (c)(2) of this section,sugar free,” “free of sugar,” “no sugar,” “zero sugar,” “without sugar,” “sugarless,” “trivial source of sugar,” “negligible source of sugar,” or “dietarily insignificant source of sugar.”a food may not be labeled with such terms unless:(i)

The food contains less than 0.5 g of sugars, as defined in 101.9(c)(6)(ii), per reference amount customarily consumed and per labeled serving or, in the case of a meal product or main dish product, less than 0.5 g of sugars per labeled serving;and(ii) The food contains no ingredient that is a sugar or that is generally understood by consumers to contain sugars

unless the listing of the ingredient in the ingredient statement is followed by an asterisk that refers to the statement below the list of ingredients, which states “adds a trivial amount of sugar,” “adds a negligible amount of sugar,” or “adds a dietarily insignificant amount of sugar;” and(iii)(A) It is labeled “low calorie” or “reduced calorie” or bears a relative claim of special dietary usefulness labeled in compliance with paragraphs (b)(2), (b)(3), (b)(4), or (b)(5) of this section, or, if a dietary supplement, it meets the definition in paragraph (b)(2) of this section for “low calorie” but is prohibited by 101.13(b)(5) and 101.60(a)(4) from bearing the claim; or

(B) Such term is immediately accompanied, each time it is used, by either the statement “not a reduced calorie food,” “not a low calorie food,” or “not for weight control.”

It turns out that **Tic Tacs are in fact almost pure sugar**. Its easy to figure this out by looking at the number of calories per serving (1.9) and multiplying the number of calories per gram of sugar (3.8) by 0.49 => 1.862 calories. 98% sugar! Ferrero basically admits this in their FAQ. Acting completely within the bounds of the law, they have simply exploited an **arbitrary threshold of the FDA. **Arbitrary thresholds are always problematic; not only can they have unintended consequences, but they can be manipulated to engineer desired outcomes. In computational biology they have become ubiquitous, frequently being described as “filters” or “pre-processing steps”. Regardless of how they are justified, thresholds are thresholds are thresholds. They can sometimes be beneficial, but they are dangerous when wielded indiscriminately.

There is one type of thresholding/filtering in used in RNA-Seq that my postdoc Bo Li and I have been thinking about a bit this year. It consists of removing duplicate reads, i.e. reads that map to the same position in a transcriptome. The motivation behind such filtering is to reduce or eliminate amplification bias, and it is based on the intuition that it is unlikely that lightning strikes the same spot multiple times. That is, it is improbable that many reads would map to the exact same location assuming a model for sequencing that posits selecting fragments from transcripts uniformly. The idea is also called de-duplication or digital normalization.

Digital normalization is obviously problematic for high abundance transcripts. Consider, for example, a transcripts that is so abundant that it is extremely likely that at least one read will start at *every* site (ignoring the ends, which for the purposes of the thought experiment are not relevant). This would also be the case if the transcript was twice as abundant, and so digital normalization would prevent the possibility for estimating the difference. This issue was noted in a paper published earlier this year by Zhou *et al. * The authors investigate in some detail the implications of this problem, and quantify the bias it introduces in a number of data sets. But a key question not answered in the paper is what does digital normalization actually do?

To answer the question, it is helpful to consider how one might estimate the abundance of a transcript after digital normalization. One naive approach is to just count the number of reads after de-duplication, followed by normalization for the length of the transcript and the number of reads sequenced. Specifically if there are *n *sites where a read might start, and *k *of the sites had at least one read, then the naive approach would be to use the estimate suitably normalized for the total number of reads in the experiment. This is exactly what is done in standard de-duplication pipelines, or in digital normalization as described in the preprint by Brown *et al.* However assuming a simple model for sequencing, namely that every read is selected by first choosing a transcript according to a multinomial distribution and then choosing a location on it uniformly at random from among the sites, a different formula emerges.

Let *X *be a random variable that denotes the number of sites on a transcript of length *n *that are covered in a random sequencing experiment, where the number of reads starting at each site of the transcript is Poisson distributed with parameter *c *(i.e., the average coverage of the transcript is *c*). Note that

.

The maximum likelihood estimate for *c *can also be obtained by the method of moments, which is to set

from which it is easy to see that

.

This is the same as the (derivation of the) Jukes-Cantor correction in phylogenetics where the method of moments equation is replaced by yielding , but I’ll leave an extended discussion of the Jukes-Cantor model and correction for a future post.

The point here, as noticed by Bo Li, is that since by Taylor approximation, it follows that the average coverage can be estimated by . This is exactly the naive estimate of de-duplication or digital normalization, and the fact that as means that blows up, at high coverage hence the results of Zhou *et al.*

Digital normalization as proposed by Brown *et al.* involves possibly thresholding at more than one read per site (for example choosing a threshold *C* and removing all but at most *C* reads at every site). But even this modified heuristic fails to adequately relate to a probabilistic model of sequencing. One interesting and easy exercise is to consider the second or higher order Taylor approximations. But a more interesting approach to dealing with amplification bias is to avoid thresholding *per se*, and to instead identify outliers among duplicate reads and to adjust them according to an estimated distribution of coverage. This is the approach of Hashimoto *et al.* in a the paper “Universal count correction for high-throughput sequencing” published in March in PLoS One. There are undoubtedly other approaches as well, and in my opinion the issue will received renewed attention in the coming year as the removal of amplification biases in single-cell transcriptome experiments becomes a priority.

As mentioned above, digital normalization/de-duplication is just one of many thresholds applied in a typical RNA-Seq “pipeline”. To get a sense of the extent of thresholding, one need only scan the (supplementary?) methods section of any genomics paper. For example, the GEUVADIS RNA-Seq consortium describe their analysis pipeline as follows:

“We employed the JIP pipeline (T.G. & M.S., data not shown) to map mRNA-seq reads and to quantify mRNA transcripts. For alignment to the human reference genome sequence (GRCh37, autosomes + X + Y + M), we used the GEM mapping suite24 (v1.349 which corresponds to publicly available pre-release 2) to first map (max. mismatches = 4%, max. edit distance = 20%, min. decoded strata = 2 and strata after best = 1) and subsequently to split-map (max.mismatches = 4%, Gencode v12 and de novo junctions) all reads that did not map entirely. Both mapping steps are repeated for reads trimmed 20 nucleotides from their 3′-end, and then for reads trimmed 5 nucleotides from their 5′-end in addition to earlier 3′-trimming—each time considering exclusively reads that have not been mapped in earlier iterations. Finally, all read mappings were assessed with respect to the mate pair information: valid mapping pairs are formed up to a maximum insert size of 100,000 bp, extension trigger = 0.999 and minimum decoded strata = 1. The mapping pipeline and settings are described below and can also be found in https://github.com/gemtools, where the code as well as an example pipeline are hosted.”

This is not a bad pipeline- the paper shows it was carefully evaluated– and it may have been a practical approach to dealing with the large amount of RNA-Seq data in the project. But even the first and seemingly innocuous thresholding to trim low quality bases from the ends of reads is controversial and potentially problematic. In a careful analysis published earlier this year, Matthew MacManes looked carefully at the effect of trimming in RNA-Seq, and concluded that aggressive trimming of bases below Q20, a standard that is frequently employed in pipelines, is problematic. I think his Figure 3, which I’ve reproduced below, is very convincing:

It certainly appears that some mild trimming can be beneficial, but a threshold that is optimal (and more importantly *not detrimental) *depends on the specifics of the dataset and is difficult* *or impossible to determine *a priori.* MacManes’ view (for more see his blog post on the topic) is consistent with another paper by Del Fabbro *et al*. that while seemingly positive about trimming in the abstract, actually concludes that “In the specific case of RNA-Seq, the tradeoff between sensitivity (number of aligned reads) and specificity (number of correctly aligned reads) seems to be always detrimental when trimming the datasets (Figure S2); in such a case, the modern aligners, like Tophat, seem to be able to overcome low quality issues, therefore making trimming unnecessary.”

Alas, Tic Tac thresholds are everywhere. My advice is: brush your teeth *first*.

I was recently reading the latest ENCODE paper published in PNAS when a sentence in the caption of Figure 2 caught my attention:

“Depending on the total amount of RNA in a cell, one transcript copy per cell corresponds to between 0.5 and 5 FPKM in PolyA+ whole-cell samples according to current estimates (with the upper end of that range corresponding to small cells with little RNA and vice versa).”

Although very few people actually care about ENCODE, many people do care about the interpretation of RNA-Seq FPKM measurements and to them this is likely to be a sentence of interest. In fact, there have been a number of attempts to provide intuitive meaning for RPKM (and FPKM) in terms of copy numbers of transcripts per cell. Even though the ENCODE PNAS paper provides no citation for the statement (or methods section explaining the derivation), I believe its source is the* *RNA-Seq paper by Mortazavi *et al.* In that paper, the authors write that

“…absolute transcript levels per cell can also be calculated. For example, on the basis of literature values for the mRNA content of a liver cell [Galau

et al.1977] and the RNA standards, we estimated that 3 RPKM corresponds to about one transcript per liver cell. For C2C12 tissue culture cells, for which we know the starting cell number and RNA preparation yields needed to make the calculation, a transcript of 1 RPKM corresponds to approximately one transcript per cell. “

This statement has been picked up on in a number of publications (e.g., Hebenstreit *et al.*, 2011, van Bakel *et al.*, 2011). However the inference of transcript copies per cell directly from RPKM or FPKM estimates is not possible and conversion factors such as 1 RPKM = 1 transcript per cell are **incoherent.** At the same time, the estimates of Mortazavi *et al.* and the range provided in the ENCODE PNAS paper are** informative. **The “incoherence” stems from a subtle issue in the normalization of RPKM/FPKM that I have discussed in a talk I gave at CSHL, and is the reason why TPM is a better unit for RNA abundance. Still, the estimates turn out to be “informative”, in the sense that the effect of (lack of) normalization appears to be smaller than variability in the amount of RNA per cell. I explain these issues below:

**Why is the sentence incoherent?**

RNA-Seq can be used to estimate transcript abundances in an RNA sample. Formally, a sample consists of *n* distinct types of transcripts, and each occurs with different multiplicity (copy number), so that transcript *i *appears times in the sample. By “abundance” we mean the relative amounts where . Note that and . Suppose that for some *j. *The corresponding is therefore where . The question is what does this value correspond to in RPKM (or FPKM).

RPKM stands for “reads per kilobase of transcript per million reads mapped” and FPKM is the same except with “fragment” replacing read (initially reads were not paired-end, but with the advent of paired-end sequencing it makes more sense to speak of fragments, and hence FPKM). As a unit of measurement for an estimate, what FPKM really refers to is the *expected* number of fragments per kilboase of transcript per million reads. Formally, if we let be the length of transcript *i *and define then abundance in FPKM for transcript *i *is abundance measured as . In terms of , we obtain that

.

The term in the denominator can be considered a kind of normalization factor, that while identical for each transcript, depends on the abundances of each transcript (unless all lengths are equal). It is in essence an average of lengths of transcripts weighted by abundance. Moreover, the length of each transcript should be taken to be taken to be its “effective” length, i.e. the length with respect to fragment lengths, or equivalently, the number of positions where fragments can start.

The implication for finding a relationship between FPKM and relative abundance constituting one transcript copy per cell is that one cannot. Mathematically, the latter is equivalent to setting in the formula above and then trying to determine . Unfortunately, all the remaining are still in the formula, and must be known in order to calculate the corresponding FPKM value.

The argument above makes clear that it does not make sense to estimate transcript copy counts per cell in terms of RPKM or FPKM. Measurements in RPKM or FPKM units depend on the abundances of transcripts in the specific sample being considered, and therefore the connection to copy counts is incoherent. The obvious and correct solution is to work directly with the . This is the rationale of TPM (transcripts per million) used by Bo Li and Colin Dewey in the RSEM paper (the argument for TPM is also made in Wagner *et al.* 2012).

**Why is the sentence informative?**

Even though incoherent, it turns out there is some truth to the ranges and estimates of copy count per cell in terms of RPKM and FPKM that have been circulated. To understand why requires noting that there are in fact two factors that come into play in estimating the FPKM corresponding to abundance of one transcript copy per cell. First, is *M *as defined above to be the total number of transcripts in a cell. This depends on the amount of RNA in a cell. Second are the relative abundances of all transcripts and their contribution to the denominator in the formula.

The best paper to date on the connection between transcript copy numbers and RNA-Seq measurements is the careful work of Marinov *et al.* in “From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing” published in Genome Research earlier this year. First of all, the paper describes careful estimates of RNA quantities in different cells, and concludes that (at least for the cells studied in the paper) amounts vary by approximately one order of magnitude. Incidentally, the estimates in Marinov *et al.* confirm and are consistent with rough estimates of Galau *et al.* from 1977, of 300,000 transcripts per cell. Marinov *et al. *also use spike-in measurements are used to conclude that in “GM12878 single cells, one transcript copy corresponds to ∼10 FPKM on average.”. The main value of the paper lies in its confirmation that RNA quantities can vary by an order of magnitude, and I am guessing this factor of 10 is the basis for the range provided in the ENCODE PNAS paper (0.5 to 5 FPKM).

In order to determine the relative importance of the denominator in I looked at a few RNA-Seq datasets we are currently examining. In the GEUVADIS data, the weighted average can vary by as much as 20% between samples. In a rat RNA-Seq dataset we are analyzing, the difference is a factor of two (and interestingly very dependent on the exact annotation used for quantification). The point here is that even the denominator in *does* vary, but less, it seems, than the variability in RNA quantity. In other words, the estimate of 0.5 to 5 FPKM corresponding to one transcript per cell is incoherent albeit probably not too far off.

One consequence of all of the above discussion is that while differential analysis of experiments can be performed based on FPKM units (as done for example in Cufflinks, where the normalization factors are appropriately accounted for), it does *not* make sense to compare raw FPKM values across experiments. This is precisely what is done in Figure 2 of the ENCODE PNAS paper. What the analysis above shows, is that actual abundances may be off by amounts much larger than the differences shown in the figure. In other words, while the caption turns out to contain an interesting comment the overall figure doesn’t really make sense. Specifically, I’m not sure the relative RPKM values shown in the figure deliver the correct relative amounts, an issue that ENCODE can and should check. Which brings me to the last part of this post…

**What is ENCODE doing?**

Having realized the possible issue with RPKM comparisons in Figure 2, I took a look at Figure 3 to try to understand whether there were potential implications for it as well. That exercise took me to a whole other level of ENCODEness. To begin with, I was trying to make sense of the *x-*axis, which is labeled “biochemical signal strength (log10)” when I realized that the different curves on the plot all come from different, completely unrelated *x-*axes. If this sounds confusing, it is. The green curves are showing graphs of functions whose domain is in log 10 RPKM units. However the histone modification curves are in *log (-10 log p), *where *p* is a *p*-value that has been computed. I’ve never seen anyone plot log(log(p-values)); what does it mean?! Nor do I understand how such graphs can be placed on a common x-axis (?!). What is “biochemical signal strength” (?) Why in the bottom panel is the grey H3K9me3 showing %nucleotides conserved *decreasing *as “biochemical strength” is increasing (?!) Why is the green RNA curves showing conservation *below* genome average for low expressed transcripts (?!) and why in the top panel is the red H3K4me3 an “M” shape (?!) What does any of this mean (?!) What I’m supposed to understand from it, or frankly, what is going on at all ??? I know many of the authors of this ENCODE PNAS paper and I simply cannot believe they saw and approved this figure. It is truly beyond belief… see below:

All of these figures are of course to support the main point of the paper. Which is that even though 80% of the genome is functional it is also true that this is not what was meant to be said , and that what is true is that “survey of biochemical activity led to a significant increase in genome coverage and thus accentuated the discrepancy between biochemical and evolutionary estimates… where function is ascertained independently of cellular state but is dependent on environment and evolutionary niche therefore resulting in estimates that differ widely in their false-positive and false-negative rates and the resolution with which elements can be defined… [unlike] genetic approaches that rely on sequence alterations to establish the biological relevance of a DNA segment and are often considered a gold standard for defining function.”

The ENCODE PNAS paper was first published behind a paywall. However after some public criticism, the authors relented and paid for it to be open access. This was a mistake. Had it remained behind a paywall not only would the consortium have saved money, I and others might have been spared the experience of reading the paper. I hope the consortium will afford me the courtesy of paywall next time.

Last Monday some biostatisticians/epidemiologists from Australia published a paper about a “visualization tool which may allow greater understanding of medical and epidemiological data”:

H. Wand et al., “Quilt Plots: A Simple Tool for the Visualisation of Large Epidemiological Data“, PLoS ONE 9(1): e85047.

A brief look at the “paper” reveals that the quilt plot they propose is a special case of what is commonly known as a heat map, a point the authors acknowledge, with the caveat that they claim that

” ‘heat maps’ require the specification of 21 arguments including hierarchical clustering, weights for reordering the row and columns dendrogram, which are not always easily understood unless one has an extensive programming knowledge and skills. One of the aims of our paper is to present ‘‘quilt plots’’ as a useful tool with simply formulated R-functions that can be easily understood by researchers from different scientific backgrounds without high-level programming skills.”

In other words, the quilt plot is a simplified heat map and the authors think it should be used because specifying parameters for a heat map (in R) would require a terrifying skill known as programming. This is of course all completely ridiculous. Not only does usage of R not require programming skill, there are also simplified heat map functions in many programming languages/computation environments that are as simple as the quilt plot function.

The fact that a paper like this was published in a journal is preposterous, and indeed the authors and editor of the paper have been ridiculed on social media, blogs and in comments to their paper on the PLoS One website.

**BUT…**

Wand *et al.* do have one point… those 21 parameters are not an entirely trivial matter. In fact, the majority of computational biologists (including many who have been ridiculing Wand) appear not to understand heat maps themselves, despite repeatedly (ab)using them in their own work.

**What are heat maps?**

In the simplest case, heat maps are just the conversion of a table of numbers into a grid with colored squares, where the colors represent the magnitude of the numbers. In the quilt plot paper that is the type of heat map considered. However in applications such as gene expression analysis, heat maps are used to visualize distances between experiments.

Heat maps have been popular for visualizing multiple gene expression datasets since the publication of the “Eisengram” (or the guilt plot?). So when my student Lorian Schaeffer and I recently needed to create a heat map from RNA-Seq abundance estimates in multiple samples we are analyzing with Ryan Forster and Dirk Hockemeyer, we assumed there would be a standard method (and software) we could use. However when starting to look at the literature we quickly found 3 papers with 4 different opinions about which similarity measure to use:

- In “The evolution of gene expression levels in mammalian organs“, Brawand et al.,
*Nature***478**(2011), 343-348, the authors use two different distance measures for evaluating the similarity between samples. They use the measure where is Spearman’s rank correlation (Supplementary figure S2) and also the Euclidean distance (Supplementary figure S3). - In “Differential expression analysis for sequence count data“, Anders and Huber,
*Genome Biology***11**(2010), R106, a heat map tool is provided that is based on Euclidean distance but differs from the method in Brawand et al. by first performing variance stabilization on the abundance estimates. - In “Evolutionary Dynamics of Gene and Isoform Regulation in Mammalian Tissues“, Merkin et al.,
*Science***338**(2012), a heat map is displayed based on the Jensen-Shannon metric.

There are also the folks who don’t worry too much and just try anything and everything (for example using the heatmap.2 function in R) hoping that some distance measure produces the figure they need for their paper. There are certainly a plethora of distance measures for them to try out. And even if none of the distance measures provide the needed figure, there is always the opportunity to play with the colors and shading to “highlight” the desired result. In other words, heat maps are great for cheating with what appears to be statistics.

**We wondered… what is the “right” way to make a heat map?**

Consider first the obvious choice for measuring similarity: Euclidean distance. Suppose that we are estimating the distance between abundance estimates from two RNA-Seq experiments, where for simplicity we assume that there are only three transcripts (A,B,C). The two abundance estimates can be represented by 3-tuples and such that both and . If and , then the Euclidean distance is given by . This obviously depends on and , a dependence that is problematic. What has changed between the two RNA-Seq experiments is that transcript has gone from being the only one transcribed, to not being transcribed at all. It is difficult to justify a distance metric that depends on the relative changes in and . Why, for example, should be closer to than to ?

The Jensen-Shannon divergence, defined for two distributions and by

where and is the Kullback-Leibler divergence, is an example of a distance measure that does not have this problem. For the example above the JSD is always (regardless of and ). However the JSD is not a metric (hence the term divergence in its name). In particular, it does not satisfy the triangle inequality (which the Euclidean distance does). Interestingly, this defect can be rectified by replacing JSD with the square root of JSD (the *JSD metric).* Formal proofs that the square root of JSD is a metric were provided in “A new Metric for Probability Distributions” by Dominik Endres and Johannes Schindelin (2003), and separately (and independently) in “A new class of metric divergences on probability spaces and its applicability in statistics” by Ferdinand Österreicher and Igor Vajda (2003). The paper “Jensen-Shannon Divergence and Hilbert space embedding” by Bent Fuglede and Flemming Topsøe (2004) makes clear the mathematical origins for this result by showing that the square root of JSD can be isometrically embedded into Hilbert space (as a logarithmic spiral)

The 2-simplex with contour lines showing points equidistant

from the probability distribution (1/3, 1/3, 1/3) for the JSD metric.

The meaning of the JSD metric is not immediately apparent based on its definition, but a number of results provide some insight. First, the JSD metric can be approximated by Pearson’s distance (Equation (7) in Endres and Schindelin). This relationship is confirmed in the numerical experiments of Sung-Hyuk Cha (see Figure 3 in “Comprehensive survey on distance/similarity measures between probability distance functions“, in particular the close relationship between JSD and the probabilistic symmetric ). There are also information theoretic and physical interpretations for the JSD metric stemming from the definition of JSD in terms of Kullback-Leibler divergence.

In “Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation“, Trapnell et al., Nature Biotechnology 28 (2010), we used the JSD metric to examine changes to relative isoform abundances in genes (see, for example, the Minard plot in Figure 2c). This application of the JSD metric makes sense, however the JSD metric is not a panacea. Consider Figure 1 in the Merkin et al. paper mentioned above. It displays a heat map generated from 7713 genes (genes with singleton orthologs in the five species studied). Some of these genes will have higher expression, and therefore higher variance, than others. The nature of the JSD metric is such that those genes will dominate the distance function, so that the heat map is effectively generated only from the highly abundant genes. Since there is typically an (approximately) exponential distribution of transcript abundance this means that, in effect, very few genes dominate the analysis.

I started thinking about this issue with my student Nicolas Bray and we began by looking at the first obvious candidate for addressing the issue of domination by high variance genes: the Mahalanobis distance. Mahalanobis distance is an option in many heat map packages (e.g. in R), but has been used only rarely in publications (although there is some history of its use in the analyses of microarray data). Intuitively, Mahalanobis distance seeks to remedy the problem of genes with high variance among the samples dominating the distance calculation by appropriate normalization. This appears to have been the aim of the method in the Anders and Huber paper cited above, where the expression values are first normalized to obtain equal variance for each gene (the variance stabilization procedure). Mahalanobis distance goes a step further and better, by normalizing using the entire covariance matrix (as opposed to just its diagonal).

Intuitively, given a set of points in some dimension, the Mahalanobis distance is the Euclidean distance between the points *after* they have been transformed via a linear transformation that maps an ellipsoid fitted to the points to a sphere. Formally, I think it is best understood in the following slightly more general terms:

Given an expression matrix (rows=transcripts, columns=experiments), let be the matrix consisting of projections of onto its principal components, and denote by the distance between projection of points *i *and *j *onto the *k*th component, i.e. . Let be the singular values. For some , define the distance

When and then the distance *D* defined above is the *Mahalanobis distance*.

The Mahalanobis ellipses. In this figure the distance shown is from every point to the center (mean of the point) rather than between pairs of points. Mahalanobis distance is sometimes defined in this way. The figure is reproduced from this website. Note that the Anders-Huber heat map produces distances looking only at the variance in each direction (in this case horizontal and vertical) which assumes that the gene expression values are independent, or equivalently that the ellipse is not rotated.

It is interesting to note that *D* is defined even when , providing a generalization of Mahalanobis distance for high-dimensional data.

The cutoff *p* involves ignoring the last few principal components. The reason one might want to do this is that the last few principal components presumably correspond to noise in the data. Amplifying this noise and treating it the same as the signal is not desirable. This is because as *p* increases the denominators get smaller, and therefore have an increasing effect on the distance. So even though it makes sense to normalize by variance thereby allowing all genes to count the same, it is important to keep in mind that the last few principal components may be desirable to toss out. One way one could choose the appropriate threshold is by examination of a scree plot.

We’re still not completely happy with Mahalanobis distance. For example, unlike the Jensen-Shannon metric, it does not provide a metric over probability distributions. In functional genomics, almost all *Seq assays produce an output which is a (discrete) probability distribution (for example in RNA-Seq the output after quantification is a probability distribution on the set of transcripts). So making heat maps for such data seems to not be entirely trivial…

**Does any of this matter?**

The landmark Michael Eisen *et al. *paper “Cluster analysis and the display of genome-wide expression patterns“, PNAS **95** (1998), 14863–14868 describing the “Eisengram” was based on correlation as the distance measure between expression vectors. This has a similar problem to the issues we discussed above, namely that abundant genes are weighted more heavily in the distance measure, and therefore they define the characteristics of the heat map. Yet the Eisengram and its variants have proven to be extremely popular and useful. It is fair to ask whether any of the issues I’ve raised matter in practice.

Depends. In many papers the heat map is a visualization tool intended for a qualitative exploration of the data. The issues discussed here touch on quantitative aspects, and in some applications changing distance measures may not change the qualitative results. Its difficult to say without reanalyzing data sets and (re)creating the heat maps with different parameters. Regardless, as expression technology continues to transition from microarrays to RNA-Seq, the demand for quantitative results is increasing. So I think it does matters how heat maps are made. Of course its easy to ridicule Handan Wand for her quilt plots, but I think those guilty of pasting ad-hoc heat maps based on arbitrary distance measures in their papers are really the ones that deserve a public spanking.

P.S. If you’re going to make your own heat map, after adhering to sound statistics, please use a colorblind-friendly palette.

P.P.S. In this post I have ignored the issue of *clustering*, namely how to order the rows and columns of heat maps so that similar expression profiles cluster together. This goes along with the problem of constructing meaningful dendograms, a visualization that has been a major factor in the popularization of the Eisengram. The choice of clustering algorithm is just as important as the choice of similarity measure, but I leave this for a future post.

Hui Jiang and Julia Salzman have posted a new paper on the arXiv proposing a novel approach to correcting for non-uniform coverage of transcripts in RNA-Seq: “A penalized likelihood approach for robust estimation of isoform expression” (October 1, 2013).

Their paper addresses the issue of non-uniformity of read coverage across transcripts in RNA-Seq, an issue that is frustrating for the challenges it presents in analysis. The non-uniformity of read coverage in RNA-Seq was first noticed in A. Mortazavi *et al*., Mapping and quantifying mammalian transcriptomes, Nature Methods **5** (2008), 621–628. Figure 1 in the paper (see below) shows an example of non-uniform coverage, and the paper discusses ideas for library preparation that can reduce bias and improve uniformity.

Figure 1b from Mortazavi et al. (2008) showing (non-uniform) coverage of Myf6.

Supplementary Figure 1a from Mortazavi et al. (2008) describing uniformity of coverage achievable with different library preparations. “Deviation from uniformity” was assessed using the Kolmogorov-Smirnov test.

The experimental approach of modifying library preparation to reduce non-uniformity has been complemented by statistical approaches to the problem. Specifically, various models have been proposed for “correcting” for experimental artefacts that induce non-uniform coverage. To understand Jiang and Salzman’s latest paper, it is helpful to review previous approaches that have been proposed. Read the rest of this entry »

## Recent Comments