Hypothesis testing is one of the workhorses of science. It is how we can draw conclusions or make decisions based on finite samples of data. For instance, new treatments for a disease are usually approved on the basis of clinical trials that aim to decide whether the treatment has better efficacy compared to the other available options, and an acceptable trade-off of side effects. Such trials are expensive and can take a long time. Therefore, the number of patients we can enroll is limited, and we need to base our inference on a limited sample of observed patient responses. The data are noisy, since a patient’s response depends not only on the treatment, but on many other factors outside of our control. The sample size needs to be large enough to enable us to make a reliable conclusion. On the other hand, it also must not be too large, so that we do not waste precious resources or time, e.g., by making drugs more expensive than necessary, or by denying patients that would benefit from the new drug access to it. The machinery of hypothesis testing was developed largely with such applications in mind, although today it is used much more widely.

In biological data analysis (and in many other fields85 Detecting credit card fraud, email spam detection, \(...\)) we see hypothesis testing applied to screen thousands or millions of possible hypotheses to find the ones that are worth following up. For instance, researchers screen genetic variants for associations with a phenotype, or gene expression levels for associations with disease. Here, “worthwhile” is often interpreted as “statistically significant”, although the two concepts are clearly not the same. It is probably fair to say that statistical significance is a necessary condition for making a data-driven decision to find something interesting, but it’s clearly not sufficient. In any case, such large-scale association screening is closely related to multiple hypothesis testing.

In this chapter we will:

Familiarize ourselves with the statistical machinery of hypothesis testing, its vocabulary, its purpose, and its strengths and limitations.

Understand what multiple testing means.

See that multiple testing is not a problem – but rather, an opportunity, as it overcomes many of the limitations of single testing.

Understand the false discovery rate.

Learn how to make diagnostic plots.

Use hypothesis weighting to increase the power of our analyses.

If statistical testing – decision making with uncertainty – seems a hard task when making a single decision, then brace yourself: in genomics, or more generally with “big data”, we need to accomplish it not once, but thousands or millions of times. In Chapter 2, we saw the example of epitope detection and the challenges from considering not only one, but several positions. Similarly, in whole genome sequencing, we scan every position in the genome for a difference between the DNA library at hand and a reference (or, another library): that’s on the order of three billion tests if we are looking at human data! In genetic or chemical compound screening, we test each of the reagents for an effect in the assay, compared to a control: that’s again tens of thousands, if not millions of tests. In Chapter 8, we will analyse RNA-Seq data for differential expression by applying a hypothesis test to each of the thousands of genes assayed.

Figure 6.1: High-throughput data in modern biology are being screened for associations with millions of hypothesis tests. (Source: Bayer)

Yet, in many ways, multiplicity makes the task simpler, not harder. Since we have so much data, and so many tests, we can ask questions like: are the requirements of the tests actually met by the data? What are the prior probabilities that we should assign to the possible outcomes of the tests? Answers to these questions can be incredibly helpful, and we can address them *because* of the multiplicity. So we should think about it not as a multiple testing “problem”, but as an opportunity!

There is a powerful premise in large-scale association screening: we usually expect that most tests will not be rejected. Out of the thousands or millions of tests, we expect that only a small fraction will be interesting. In fact, if that is not the case, if the hits are not rare, then arguably our analysis method – serially univariate screening each variable for association with the outcome – is not suitable for the dataset. Either we need better data (a more specific assay), or a different analysis method, e.g., a multivariate model.

So, since we can assume that most of our many null hypotheses are true, we can use the behaviour of their test statistics and p-values to empirically understand the null distributions, their correlations, and so on. Rather than having to rely on abstract assumptions we can check the requirements empirically.

Suppose we measured the expression level of a marker gene to decide whether the cells we are studying are from cell type A or B. First, let’s consider that we have no prior assumption, and it’s equally important to us to get the assignment right no matter whether the true cell type is A or B. This is a *classification* task. We’ll cover classification in Chapter 12. In this chapter, we consider the asymmetric case: based on what we already know (we could call this our *prior*), we lean towards calling it an A, and would need strong enough evidence to be convinced otherwise. We can think of this as an application of **Occam’s razor**86 See also https://en.wikipedia.org/wiki/Occam%27s_razor: do not use a complicated explanation if a simpler one does the job. Or maybe the consequences and costs of calling A and B are very different, for instance, A could be healthy and B diseased. In these cases, the machinery of hypothesis testing is right for us.

Formally, there are many similarities between hypothesis testing and classification. In both cases, we aim to use data to choose between several possible decisions. It is even possible to think of hypothesis testing as a special case of classification. However, these two approaches are geared towards different objectives and underlying assumptions, and when you encounter a statistical decision problem, it is good to keep that in mind in your choice of methodology.

To understand multiple tests, let’s first review the mechanics of single hypothesis testing. For example, suppose we are flipping a coin to see if it is fair87 We don’t look at coin tossing because it’s inherently important, but because it is an easy “model system” (just as we use model systems in biology): everything can be calculated easily, and you do not need a lot of domain knowledge to understand what coin tossing is. All the important concepts come up, and we can apply them, only with more additional details, to other applications.. We flip the coin 100 times and each time record whether it came up heads or tails. So, we have a record that could look something like this:

`H H T T H T H T T ...`

which we can simulate in `R`

. Let’s assume we are flipping a biased coin, so we set `probHead`

different from 1/2:

```
set.seed(0xdada)
numFlips = 100
probHead = 0.6
coinFlips = sample(c("H", "T"), size = numFlips,
replace = TRUE, prob = c(probHead, 1 - probHead))
head(coinFlips)
```

`## [1] "T" "T" "H" "T" "H" "H"`

Now, if the coin were fair, we would expect half of the time to get heads. Let’s see.

`table(coinFlips)`

```
## coinFlips
## H T
## 59 41
```

So that is different from 50/50. Suppose we showed the data to a friend without telling them whether the coin is fair, and their prior assumption, i.e., their null hypothesis, is that coins are, by and large, fair. Would the data be strong enough to make them conclude that this coin isn’t fair? They know that random sampling differences are to be expected. To decide, let’s look at the sampling distribution of our test statistic – the total number of heads seen in 100 coin tosses – for a fair coin88 We haven’t really defined what we mean be fair – a reasonable definition would be that head and tail are equally likely, and that the outcome of each coin toss does not depend on the previous ones. For more complex applications, nailing down the most suitable null hypothesis can take some thought.. As we saw in Chapter 1, the number, \(k\), of heads, in \(n\) independent tosses of a coin is

\[\begin{equation} P(K=k\,|\,n, p) = \left(\begin{array}{c}n\\k\end{array}\right) p^k\;(1-p)^{n-k}, \tag{6.1} \end{equation}\]

where \(p\) is the probability of heads (0.5 if we assume a fair coin). We read the left hand side of the above equation as “the probability that the observed value for \(K\) is \(k\), given the values of \(n\) and \(p\)”. Statisticians like to make a difference between all the possible values of a statistic and the one that was observed89 In other words, \(K\) is the abstract random variable in our probabilistic model, whereas \(k\) is its realization, that is, a specific data point., and we use the upper case \(K\) for the possible values (so \(K\) can be anything between 0 and 100), and the lower case \(k\) for the observed value.

We plot Equation (6.1) in Figure 6.2; for good measure, we also mark the observed value `numHeads`

with a vertical blue line.

```
library("dplyr")
k = 0:numFlips
numHeads = sum(coinFlips == "H")
binomDensity = tibble(k = k,
p = dbinom(k, size = numFlips, prob = 0.5))
```

Figure 6.2: The binomial distribution for the parameters \(n=100\) and \(p=0.5\), according to Equation (6.1).

```
library("ggplot2")
ggplot(binomDensity) +
geom_bar(aes(x = k, y = p), stat = "identity") +
geom_vline(xintercept = numHeads, col = "blue")
```

Suppose we didn’t know about Equation (6.1). We can still use Monte Carlo simulation to give us something to compare with:

Figure 6.3: An approximation of the binomial distribution from \(10^{4}\) simulations (same parameters as Figure 6.2).

```
numSimulations = 10000
outcome = replicate(numSimulations, {
coinFlips = sample(c("H", "T"), size = numFlips,
replace = TRUE, prob = c(0.5, 0.5))
sum(coinFlips == "H")
})
ggplot(tibble(outcome)) + xlim(0, 100) +
geom_histogram(aes(x = outcome), binwidth = 1, center = 0) +
geom_vline(xintercept = numHeads, col = "blue")
```

`## Warning: Removed 2 rows containing missing values (geom_bar).`

As expected, the most likely number of heads is 50, that is, half the number of coin flips. But we see that other numbers near 50 are also quite likely. How do we quantify whether the observed value, 59, is among those values that we are likely to see from a fair coin, or whether its deviation from the expected value is already large enough for us to conclude with enough confidence that the coin is biased? We divide the set of all possible \(k\) (0 to 100) in two complementary subsets, the **rejection region** and the region of no rejection. Our choice here90 More on this in Section 6.3.1. is to fill up the rejection region with as many \(k\) as possible while keeping their total probability, assuming the null hypothesis, below some threshold \(\alpha\) (say, 0.05).

Figure 6.4: As Figure 6.2, with rejection region (red) that has been chosen such that it contains the maximum number of bins whose total area is at most \(\alpha=0.05\).

```
library("dplyr")
alpha = 0.05
binomDensity = arrange(binomDensity, p) %>%
mutate(reject = (cumsum(p) <= alpha))
ggplot(binomDensity) +
geom_bar(aes(x = k, y = p, col = reject), stat = "identity") +
scale_colour_manual(
values = c(`TRUE` = "red", `FALSE` = "darkgrey")) +
geom_vline(xintercept = numHeads, col = "blue") +
theme(legend.position = "none")
```

In the code above, we use the function `arrange`

from the **dplyr** package to sort the p-values from lowest to highest, then pass the result to `mutate`

, which adds another dataframe column `reject`

that is defined by computing the cumulative sum (`cumsum`

) of the p-values and thresholding it against `alpha`

. The logical vector `reject`

therefore marks with `TRUE`

a set of `k`

s whose total probability is less than `alpha`

. These are marked in Figure 6.4, and we can see that our rejection region is not contiguous – it comprises both the very large and the very small values of `k`

.

The explicit summation over the probabilities is clumsy, we did it here for pedagogic value. For one-dimensional distributions, R provides not only functions for the densities (e.g., `dbinom`

) but also for the cumulative distribution functions (`pbinom`

), which are more precise and faster than `cumsum`

over the probabilities. These should be used in practice.

► Task

Do the computations for the rejection region and produce a plot like Figure 6.4 without using `dbinom`

and `cumsum`

, and with using `pbinom`

instead.

We see in Figure 6.4 that the observed value, 59, lies in the grey shaded area, so we would *not* reject the null hypothesis of a fair coin from these data at a significance level of \(\alpha=0.05\).

► Question 6.1

Does the fact that we don’t reject the null hypothesis mean that the coin is fair?

► Question 6.2

Would we have a better chance of detecting that the coin is not fair if we did more coin tosses? How many?

► Question 6.3

If we repeated the whole procedure and again tossed the coin 100 times, might we *then* reject the null hypothesis?

► Question 6.4

The rejection region in Figure 6.4 is asymmetric – its left part ends with \(k=40\), while its right part starts with \(k=61\). Why is that? Which other ways of defining the rejection region might be useful?

We have just gone through the steps of a binomial test. In fact, this is such a frequent activity in R that it has been wrapped into a single function, and we can compare its output to our results.

`binom.test(x = numHeads, n = numFlips, p = 0.5)`

```
##
## Exact binomial test
##
## data: numHeads and numFlips
## number of successes = 59, number of trials = 100,
## p-value = 0.08863
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.4871442 0.6873800
## sample estimates:
## probability of success
## 0.59
```

Let’s summarise the general principles of hypothesis testing:

Decide on the effect that you are interested in, design a suitable experiment or study, pick a data summary function and

**test statistic**.Set up a

**null hypothesis**, which is a simple, computationally tractable model of reality that lets you compute the**null distribution**, i.e., the possible outcomes of the test statistic and their probabilities under the assumption that the null hypothesis is true.Decide on the

**rejection region**, i.e., a subset of possible outcomes whose total probability is small91 More on this in Section 6.3.1..Do the experiment and collect the data92 Or if someone else has already done it, download their data.; compute the test statistic.

Make a decision: reject the null hypothesis93 that is, conclude that it is unlikely to be true if the test statistic is in the rejection region.

Note how in this idealized workflow, we make all the important decisions in Steps 1–3 before we have even seen the data. As we already alluded to in the Introduction (Figures 0.1 and 0.2), this is often not realistic. We will also come back to this question in Section 6.6.

There was also idealization in our null hypothesis that we used in the example above: we postulated that a fair coin should have a probability of exactly 0.5 (not, say, 0.500001) and that there should be absolutely no dependence between tosses. We did not worry about any possible effects of air drag, elasticity of the material on which the coin falls, and so on. This gave us the advantage that the null hypothesis was computationally tractable, namely, with the binomial distribution. Here, these idealizations may not seem very controversial, but in other situations the trade-off between how tractable and how realistic a null hypothesis is can be more substantial. The problem is that if a null hypothesis is too idealized to start with, rejecting it is not all that interesting. The result may be misleading, and certainly we are wasting our time.

The test statistic in our example was the total number of heads. Suppose we observed 50 tails in a row, and then 50 heads in a row. Our test statistic ignores the order of the outcomes, and we would conclude that this is a perfectly fair coin. However, if we used a different test statistic, say, the number of times we see two tails in a row, we might notice that there is something funny about this coin.

► Question 6.5

What is the null distribution of this different test statistic?

► Question 6.6

Would a test based on that statistic be generally preferable?

► Solution

What we have just done is look at two different classes of **alternative hypotheses**. he first class of alternatives was that subsequent coin tosses are still independent of each other, but that the probability of heads differed from 0.5. The second one was that the overall probability of heads may still be 0.5, but that subsequent coin tosses were correlated.

► Question 6.7

Recall the concept of sufficient statistics from Chapter 1. Is the total number of heads a sufficient statistic for the binomial distribution? Why might it be a good test statistic for our first class of alternatives, but not for the second?

So let’s remember that we typically have multiple possible choices of test statistic (in principle it could be any numerical summary of the data). Making the right choice is important for getting a test with good power94 See Sections 1.4.1 and 6.4.. What the right choice is will depend on what kind of alternatives we expect. This is not always easy to know in advance.

Once we have chosen the test statistic we need to compute its null distribution. You can do this either with pencil and paper or by computer simulations. A pencil and paper solution is parametric and leads to a closed form mathematical expression (like Equation (6.1)), which has the advantage that it holds for a range of model parameters of the null hypothesis (such as \(n\), \(p\)). It can also be quickly computed for any specific set of parameters. But it is not always as easy as in the coin tossing example. Sometimes a pencil and paper solution is impossibly difficult to compute. At other times, it may require simplifying assumptions. An example is a null distribution for the \(t\)-statistic (which we will see later in this chapter). We can compute this if we assume that the data are independent and normally distributed: the result is called the \(t\)-distribution. Such modelling assumptions may be more or less realistic. Simulating the null distribution offers a potentially more accurate, more realistic and perhaps even more intuitive approach. The drawback of simulating is that it can take a rather long time, and we need extra work to get a systematic understanding of how varying parameters influence the result. Generally, it is more elegant to use the parametric theory when it applies95 The assumptions don’t need to be *exactly* true – it is sufficient that the theory’s predictions are an acceptable approximation of the truth.. When you are in doubt, simulate – or do both.

How to choose the right rejection region for your test? First, what should its size be? That is your choice of the **significance level** or false positive rate \(\alpha\), which is the total probability of the test statistic falling into this region even if the null hypothesis is true96 Some people at some point in time for a particular set of questions colluded on \(\alpha=0.05\) as being “small”. But there is nothing special about this number, and in any particular case the best choice for a decision threshold may very much depend on context (Wasserstein and Lazar 2016; Altman and Krzywinski 2017)..

Given the size, the next question is about its shape. For any given size, there are usually multiple possible shapes. It makes sense to require that the probability of the test statistic falling into the rejection region is as large possible if the alternative hypothesis is true. In other words, we want our test to have high **power**, or true positive rate.

The criterion that we used in the code for computing the rejection region for Figure 6.4 was to make the region contain as many `k`

as possible. That is because in absence of any information about the alternative distribution, one `k`

is as good as any other, and we maximize their total number.

A consequence of this is that in Figure 6.4 the rejection region is split between the two tails of the distribution. This is because we anticipate that unfair coins could have a bias either towards head or toward tail; we don’t know. If we did know, we would instead concentrate our rejection region all on the appropriate side, e.g., the right tail if we think the bias would be towards head. Such choices are also referred to as *two-sided* and *one-sided* tests. More generally, if we have assumptions about the alternative distribution, this can influence our choice of the shape of the rejection region.

Having set out the mechanics of testing, we can assess how well we are doing. Table 6.1 compares reality (whether or not the null hypothesis is in fact true) with our decision whether or not to reject the null hypothesis after we have seen the data.

Table 6.1: Types of error in a statistical test.

Test vs reality | Null hypothesis is true | \(...\) is false |
---|---|---|

Reject null hypothesis |
Type I error (false positive) | True positive |

Do not reject |
True negative | Type II error (false negative) |

Figure 6.5: The trade-off between type I and II errors. The densities represent the distributions of a hypothetical test statistic under either the null or the alternative hypothesis. The peak on the left (light and dark blue plus dark red) represents the test statistic’s distribution under the null. It integrates to 1. Suppose the decision boundary is the black line and the hypothesis is rejected if the statistic falls to the right. The probability of a false positive (the FPR) is then simply the dark red area. Similarly, if the peak on the right (light and dark red plus dark blue area) is the test statistic’s distribution under the alternative, the probability of a false negative (the FNR) is the dark blue area.

It’s always possible to reduce one of the two error types at the cost of increasing the other one. The real challenge is to find an acceptable trade-off between both of them. This is exemplified in Figure 6.5. We can always decrease the **false positive rate** (FPR) by shifting the threshold to the right. We can become more “conservative”. But this happens at the price of higher **false negative rate** (FNR). Analogously, we can decrease the FNR by shifting the threshold to the left. But then again, this happens at the price of higher FPR. A bit on terminology: the FPR is the same as the probability \(\alpha\) that we mentioned above. \(1 - \alpha\) is also called the **specificity** of a test. The FNR is sometimes also called \(\beta\), and \(1 - \beta\) the **power**, **sensitivity** or **true positive rate** of a test.

► Question 6.8

At the end of Section 6.3 we learned about one- and two-sided tests. Why does this distinction exist – why don’t we alway just use the two-sided test, which is sensitive to a larger class of alternatives?

Many experimental measurements are reported as rational numbers, and the simplest comparison we can make is between two groups, say, cells treated with a substance compared to cells that are not. The basic test for such situations is the \(t\)-test. The test statistic is defined as

\[\begin{equation} t = c \; \frac{m_1-m_2}{s}, \tag{6.2} \end{equation}\]

where \(m_1\) and \(m_2\) are the mean of the values in the two groups, \(s\) is the pooled standard deviation and \(c\) is a constant that depends on the sample sizes, i.e., the numbers of observations \(n_1\) and \(n_2\) in the two groups. In formulas97 Everyone should try to remember Equation (6.2), whereas many people get by with looking up (6.3) when they need it.,

\[\begin{align} m_g &= \frac{1}{n_g} \sum_{i=1}^{n_g} x_{g, i} \quad\quad\quad g=1,2\nonumber\\ s^2 &= \frac{1}{n_1+n_2-2} \left( \sum_{i=1}^{n_1} \left(x_{1,i} - m_1\right)^2 + \sum_{j=1}^{n_2} \left(x_{2,j} - m_2\right)^2 \right) \nonumber\\ c &= \sqrt{\frac{n_1n_2}{n_1+n_2}} \tag{6.3} \end{align}\]

where \(x_{g, i}\) is the \(i^{\text{th}}\) data point in the \(g^{\text{th}}\) group. Let’s try this out with the `PlantGrowth`

data from R’s **datasets** package.

Figure 6.6: The `PlantGrowth`

data.

```
library("ggbeeswarm")
data("PlantGrowth")
ggplot(PlantGrowth, aes(y = weight, x = group, col = group)) +
geom_beeswarm() + theme(legend.position = "none")
tt = with(PlantGrowth,
t.test(weight[group =="ctrl"],
weight[group =="trt2"],
var.equal = TRUE))
tt
```

```
##
## Two Sample t-test
##
## data: weight[group == "ctrl"] and weight[group == "trt2"]
## t = -2.134, df = 18, p-value = 0.04685
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.980338117 -0.007661883
## sample estimates:
## mean of x mean of y
## 5.032 5.526
```

► Question 6.9

What do you get from the comparison with `trt1`

? What for `trt1`

versus `trt2`

?

► Question 6.10

What is the significance of the `var.equal = TRUE`

in the above call to `t.test`

?

► Solution

► Question 6.11

Rewrite the above call to `t.test`

using the formula interface, i.e., by using the notation `weight`

\(\sim\) `group`

.

To compute the p-value, the `t.test`

function uses the asymptotic theory for the \(t\)-statistic (6.2); this theory states that under the null hypothesis of equal means in both groups, the statistic follows a known, mathematical distribution, the so-called \(t\)-distribution with \(n_1+n_2\) degrees of freedom. The theory uses additional technical assumptions, namely that the data are independent and come from a normal distribution with the same standard deviation. We could be worried about these assumptions. Clearly they do not hold: weights are always positive, while the normal distribution extends over the whole real axis. The question is whether this deviation from the theoretical assumption makes a real difference. We can use a permutation test to figure this out (we will discuss the idea behind permutation tests in a bit more detail in Section 6.5.1).

Figure 6.7: The null distribution of the (absolute) \(t\)-statistic determined by simulations – namely, by random permutations of the group labels.

```
abs_t_null = with(
dplyr::filter(PlantGrowth, group %in% c("ctrl", "trt2")),
replicate(10000,
abs(t.test(weight ~ sample(group))$statistic)))
ggplot(tibble(`|t|` = abs_t_null), aes(x = `|t|`)) +
geom_histogram(binwidth = 0.1, boundary = 0) +
geom_vline(xintercept = abs(tt$statistic), col = "red")
mean(abs(tt$statistic) <= abs_t_null)
```

`## [1] 0.0489`

► Question 6.12

Why did we use the absolute value function (`abs`

) in the above code?

► Task

Plot the (parametric) \(t\)-distribution with the appropriate degrees of freedom.

The \(t\)-test comes in multiple flavors, all of which can be chosen through parameters of the `t.test`

function. What we did above is called a two-sided two-sample unpaired test with equal variance. *Two-sided* refers to the fact that we were open to reject the null hypothesis if the weight of the treated plants was either larger or smaller than that of the untreated ones.

*Two-sample*98 It can be confusing that the term *sample* has a different meaning in statistics than in biology. In biology, a sample is a single specimen on which an assay is performed; in statistics, it is a set of measurements, e.g., the \(n_1\)-tuple \(\left(x_{1,1},...,x_{1,n_1}\right)\) in Equation (6.3), which can comprise several biological samples. In contexts where this double meaning might create confusion, we refer to the data from a single biological sample as an *observation*. indicates that we compared the means of two groups to each other; another option is to compare the mean of one group against a given, fixed number.

*Unpaired* means that there was no direct 1:1 mapping between the measurements in the two groups. If, on the other hand, the data had been measured on the same plants before and after treatment, then a paired test would be more appropriate, as it looks at the change of weight within each plant, rather than their absolute weights.

*Equal variance* refers to the way the statistic (6.2) is calculated. That expression is most appropriate if the variances within each group are about the same. If they are very different, an alternative form99 Welch’s \(t\)-test and associated asymptotic theory exist.

**The independence assumption**. Now let’s try something peculiar: duplicate the data.

```
with(rbind(PlantGrowth, PlantGrowth),
t.test(weight[group == "ctrl"],
weight[group == "trt2"],
var.equal = TRUE))
```

```
##
## Two Sample t-test
##
## data: weight[group == "ctrl"] and weight[group == "trt2"]
## t = -3.1007, df = 38, p-value = 0.003629
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.8165284 -0.1714716
## sample estimates:
## mean of x mean of y
## 5.032 5.526
```

Note how the estimates of the group means (and thus, of the difference) are unchanged, but the p-value is now much smaller! We can conclude two things from this:

The power of the \(t\)-test depends on the sample size. Even if the underlying biological differences are the same, a dataset with more observations tends to give more significant results100 You can also see this from the way the numbers \(n_1\) and \(n_2\) appear in Equation (6.3)..

The assumption of independence between the measurements is really important. Blatant duplication of the same data is an extreme form of dependence, but to some extent the same thing happens if you mix up different levels of replication. For instance, suppose you had data from 8 plants, but measured the same thing twice on each plant (technical replicates), then pretending that these are now 16 independent measurements is wrong.

What happened above when we contrasted the outcome of the parametric \(t\)-test with that of the permutation test applied to the \(t\)-statistic? It’s important to realize that these are two different tests, and the similarity of their outcomes is desirable, but coincidental. In the parametric test, the null distribution of the \(t\)-statistic follows from the assumed null distribution of the data, a multivariate normal distribution with unit covariance in the \((n_1+n_2)\)-dimensional space \(\mathbb{R}^{n_1+n_2}\), and is continuous: the \(t\)-distribution. In contrast, the permutation distribution of our test statistic is discrete, as it is obtained from the finite set of \((n_1+n_2)!\) permutations101 Or a random subset, in case we want to save computation time. of the observation labels, from a single instance of the data (the \(n_1+n_2\) observations). All we assume here is that under the null hypothesis, the variables \(X_{1,1},...,X_{1,n_1},X_{2,1},...,X_{2,n_2}\) are exchangeable. Logically, this assumption is implied by that of the parametric test, but is weaker. The permutation test employs the \(t\)-statistic, but not the \(t\)-distribution (nor the normal distribution). The fact that the two tests gave us a very similar result is a consequence of the Central Limit Theorem.

Let’s go back to the coin tossing example. We did not reject the null hypothesis (that the coin is fair) at a level of 5% – even though we “knew” that it is unfair. After all, `probHead`

was chosen as 0.6 in Section 6.2. Let’s suppose we now start looking at different test statistics. Perhaps the number of consecutive series of 3 or more heads. Or the number of heads in the first 50 coin flips. And so on. At some point we will find a test that happens to result in a small p-value, even if just by chance (after all, the probability for the p-value to be less than 5% under the null is 0.05, not an infinitesimally small number). We just did what is called **p-value hacking**102 http://fivethirtyeight.com/features/science-isnt-broken (Head et al. 2015). You see what the problem is: in our zeal to prove our point we tortured the data until some statistic did what we wanted. A related tactic is **hypothesis switching** or **HARKing** – hypothesizing after the results are known: we have a dataset, maybe we have invested a lot of time and money into assembling it, so we need results. We come up with lots of different null hypotheses and test statistics, test them, and iterate, until we can report something.

These tactics violate the rules of hypothesis testing, as described in Section 6.3, where we laid out one sequential procedure of choosing the hypothesis and the test, and then collecting the data. But, as we saw in Chapter 2, such tactics can be tempting in reality. With biological data, we tend to have so many different choices for “normalising” the data, transforming the data, correcting for apparent batch effects, removing outliers, …. The topic is complex and open-ended. Wasserstein and Lazar (2016) give a readable short summary of the problems with how p-values are used in science, and of some of the misconceptions. They also highlight how p-values can be fruitfully used. The essential message is: be completely transparent about your data, what analyses were tried, and how they were done. Provide the analysis code. Only with such contextual information can a p-value be useful.

**Avoid fallacy**. Keep in mind that our statistical test is never attempting to prove our null hypothesis is true - we are simply saying whether or not there is evidence for it to be false. If a high p-value *were* indicative of the truth of the null hypothesis, we could formulate a completely crazy null hypothesis, do an utterly irrelevant experiment, collect a small amount of inconclusive data, find a p-value that would just be a random number between 0 and 1 (and so with some high probability above our threshold \(\alpha\)) and, whoosh, our hypothesis would be demonstrated!

► Question 6.13

Look up xkcd cartoon 882. Why didn’t the newspaper report the results for the other colors?

The quandary illustrated in the cartoon occurs with high-throughput data in biology. And with force! You will be dealing not only with 20 colors of jellybeans, but, say, with 20,000 genes that were tested for differential expression between two conditions, or with 3 billion positions in the genome where a DNA mutation might have happened. So how do we deal with this? Let’s look again at our table relating statistical test results with reality (Table 6.1), this time framing everything in terms of many hypotheses.

Table 6.2: Types of error in multiple testing. The letters designate the number oftimes each type of error occurs.

Test vs reality | Null hypothesis is true | \(...\) is false | Total |
---|---|---|---|

Rejected |
\(V\) | \(S\) | \(R\) |

Not rejected |
\(U\) | \(T\) | \(m-R\) |

Total |
\(m_0\) | \(m-m_0\) | \(m\) |

\(m\): total number of hypotheses

\(m_0\): number of null hypotheses

\(V\): number of false positives (a measure of type I error)

\(T\): number of false negatives (a measure of type II error)

\(S\), \(U\): number of true positives and true negatives

\(R\): number of rejections

In the rest of this chapter, we look at different ways of taking care of the type I and II errors.

The **family wise error rate** (FWER) is the probability that \(V>0\), i.e., that we make one or more false positive errors. We can compute it as the complement of making no false positive errors at all103 Assuming independence..

\[\begin{equation} P(V>0) = 1 - P(\text{no rejection of any of $m_0$ nulls}) = 1 - (1 - \alpha)^{m_0} \to 1 \quad\text{as } m_0\to\infty. \tag{6.4} \end{equation}\]

For any fixed \(\alpha\), this probability is appreciable as soon as \(m_0\) is in the order of \(1/\alpha\), and it tends towards 1 as \(m_0\) becomes larger. This relationship can have serious consequences for experiments like DNA matching, where a large database of potential matches is searched. For example, if there is a one in a million chance that the DNA profiles of two people match by random error, and your DNA is tested against a database of 800000 profiles, then the probability of a random hit with the database (i.e., without you being in it) is:

`1 - (1 - 1/1e6)^8e5`

`## [1] 0.5506712`

That’s pretty high. And once the database contains a few million profiles more, a false hit is virtually unavoidable.

► Question 6.14

Prove that the probability (6.4) does indeed become very close to 1 when \(m_0\) is large.

How are we to choose the per-hypothesis \(\alpha\) if we want FWER control? The above computations suggest that the product of \(\alpha\) with \(m_0\) may be a reasonable ballpark estimate. Usually we don’t know \(m_0\), but we know \(m\), which is an upper limit for \(m_0\), since \(m_0\le m\). The Bonferroni correction is simply that if we want FWER control at level \(\alpha_{\text{FWER}}\), we should choose the per hypothesis threshold \(\alpha = \alpha_{\text{FWER}}/m\). Let’s check this out on an example.

Figure 6.8: Bonferroni correction. The plot shows the graph of (6.4) for \(m=10^{4}\) as a function of \(\alpha\).

```
m = 10000
ggplot(tibble(
alpha = seq(0, 7e-6, length.out = 100),
p = 1 - (1 - alpha)^m),
aes(x = alpha, y = p)) + geom_line() +
xlab(expression(alpha)) +
ylab("Prob( no false rejection )") +
geom_hline(yintercept = 0.05, col = "red")
```

In Figure 6.8, the black line intersects the red line (which corresponds to a value of 0.05) at \(\alpha=5.13\times 10^{-6}\), which is just a little bit more than the value of \(0.05/m\) implied by the Bonferroni correction.

► Question 6.15

Why are the two values not exactly the same?

A potential drawback of this method, however, is that if \(m_0\) is large, the rejection threshold is very small. This means that the individual tests need to be very powerful if we want to have any chance of detecting something. Often this is not possible, or would not be an effective use of our time and money. We’ll see that there are more nuanced methods of controlling our type I error.

Let’s look at some real data. We load up the RNA-Seq dataset `airway`

, which contains gene expression measurements (gene-level counts) of four primary human airway smooth muscle cell lines with and without treatment with dexamethasone, a synthetic glucocorticoid. We’ll use the **DESeq2** method that we’ll discuss in more detail in Chapter 8. For now it suffices to say that it performs a test for differential expression for each gene. Conceptually, the tested null hypothesis is very similar to that of the \(t\)-test, although the details are slightly more involved since we are dealing with count data.

```
library("DESeq2")
library("airway")
data("airway")
aw = DESeqDataSet(se = airway, design = ~ cell + dex)
aw = DESeq(aw)
awde = as.data.frame(results(aw)) %>% dplyr::filter(!is.na(pvalue))
```

► Task

Have a look at the content of `awde`

.

► Task

The **p-value histogram** is an important sanity check for any analysis that involves multiple tests. It is a mixture composed of two components:

the p-values resulting from the tests for which the null hypothesis is true.

the p-values resulting from the tests for which the null hypothesis is not true.

The relative size of these two components depends on the fraction of true nulls and true alternatives, and can often be visually estimated from the histogram. If our analysis has high statistical power, then component 1 consists all of small p-values, i.e., appears as a peak near 0 in the histogram; if the power is not high for some of the alternatives, we expect that this peak extends towards the right, i.e., has a “shoulder”. For component 0, we expect (by definition of the p-value for continuous data and test statistics) a uniform distribution in \([0,1]\). Let’s plot the histogram of p-values for the airway data.

Figure 6.9: p-value histogram of for the `airway`

data.

```
ggplot(awde, aes(x = pvalue)) +
geom_histogram(binwidth = 0.025, boundary = 0)
```

In Figure 6.9 we see the expected mixture. We also see that the null component is not exactly flat (uniform): this is because the data are counts. While these appear quasi-continuous when high, for the tests with low counts the discreteness of the data and the resulting p-values shows up in the spikes towards the right of the histogram.

Now suppose we reject all tests with a p-value less than \(\alpha\). We can visually determine an estimate of the false discovery proportion with a plot such as in Figure 6.10, generated by the following code.

Figure 6.10: Visual estimation of the FDR with the p-value histogram.

```
alpha = binw = 0.025
pi0 = 2 * mean(awde$pvalue > 0.5)
ggplot(awde,
aes(x = pvalue)) + geom_histogram(binwidth = binw, boundary = 0) +
geom_hline(yintercept = pi0 * binw * nrow(awde), col = "blue") +
geom_vline(xintercept = alpha, col = "red")
```

We see that there are 4772 p-values in the first bin \([0,\alpha]\), among which we expect around 945 to be nulls (as indicated by the blue line). Thus we can estimate the fraction of false rejections as

`pi0 * alpha / mean(awde$pvalue <= alpha)`

`## [1] 0.1980092`

The **false discovery rate** (FDR) is defined as

\[\begin{equation} \text{FDR} = \text{E}\!\left [\frac{V}{\max(R, 1)}\right ], \tag{6.5} \end{equation}\]

where \(R\) and \(V\) are as in Table 6.2. The expression in the denominator makes sure that the FDR is well-defined even if \(R=0\) (in that case, \(V=0\) by implication). Note that the FDR becomes identical to the FWER if all null hypotheses are true, i.e., if \(V=R\). \(\text{E[ ]}\) stands for the **expected value**. That means that the FDR is not a quantity associated with a specific outcome of \(V\) and \(R\) for one particular experiment. Rather, given our choice of tests and associated rejection rules for them, it is the average104 Since the FDR is an expectation value, it does not provide worst case control: in any single experiment, the so-called false discovery proportion (FDP), that is the realized value \(v/r\) (without the \(\text{E[ ]}\)), could be much higher or lower. proportion of type I errors out of the rejections made, where the average is taken (at least conceptually) over many replicate instances of the experiment.

There is a more elegant alternative to the “visual FDR” method of the last section. The procedure, introduced by Benjamini and Hochberg (1995) has these steps:

First, order the p-values in increasing order, \(p_{(1)} ... p_{(m)}\)

Then for some choice of \(\varphi\) (our target FDR), find the largest value of \(k\) that satisfies: \(p_{(k)} \leq \varphi \, k / m\)

Finally reject the hypotheses \(1, ..., k\)

We can see how this procedure works when applied to our RNA-Seq p-values through a simple graphical illustration:

Figure 6.11: Visualization of the Benjamini-Hochberg procedure. Shown is a zoom-in to the 7000 lowest p-values.

```
phi = 0.10
awde = mutate(awde, rank = rank(pvalue))
m = nrow(awde)
ggplot(dplyr::filter(awde, rank <= 7000), aes(x = rank, y = pvalue)) +
geom_line() + geom_abline(slope = phi / m, col = "red")
```

The method finds the rightmost point where the black (our p-values) and red lines (slope \(\varphi / m\)) intersect. Then it rejects all tests to the left.

```
kmax = with(arrange(awde, rank),
last(which(pvalue <= phi * rank / m)))
kmax
```

`## [1] 4099`

► Question 6.16

Compare the value of `kmax`

with the number of 4772 from above (Figure 6.10). Why are they different?

► Question 6.17

Look at the code associated with the option `method="BH"`

of the `p.adjust`

function that comes with R. How does it compare to what we did above?

While the xkcd cartoon in the chapter’s opening figure ends with a rather sinister intepretation of the multiple testing problem as a way to accumulate errors, Figure 6.12 highlights the multiple testing opportunity: when we do many tests, we can use the multiplicity to increase our understanding beyond what’s possible with a single test.

Figure 6.13: Local false discovery rate and the two-group model, with some choice of \(f_{\text{alt}}(p)\), and \(\pi_0=0.6\). Top: densities. Bottom: distribution functions.

Let’s get back to the histogram in Figure 6.10. Conceptually, we can think of it in terms of the so-called two-groups model (Efron 2010):

\[\begin{equation} f(p)= \pi_0 + (1-\pi_0) f_{\text{alt}}(p), \tag{6.6} \end{equation}\]

Here, \(f(p)\) is the density of the distribution (what the histogram would look like with an infinite amount of data and infinitely small bins), \(\pi_0\) is a number between 0 and 1 that represents the size of the uniform component, and \(f_{\text{alt}}\) is the alternative component. This is a mixture model, as we already saw in Chapter 4. The mixture densities and the marginal density \(f(p)\) are visualized in the upper panel of Figure 6.13: the blue areas together correspond to the graph of \(f_{\text{alt}}(p)\), the grey areas to that of \(f_{\text{null}}(p) = \pi_0\). If we now consider one particular cutoff \(p\) (say, \(p=0.1\) as in Figure 6.13), then we can compute the probability that a hypothesis that we reject at this cutoff is a false positive, as follows. We decompose the value of \(f\) at the cutoff (red line) into the contribution from the nulls (light red, \(\pi_0\)) and from the alternatives (darker red, \((1-\pi_0) f_{\text{alt}}(p)\)). The **local false disovery rate** is then

\[\begin{equation} \text{fdr}(p) = \frac{\pi_0}{f(p)}. \tag{6.7} \end{equation}\]

By definition this quantity is between 0 and 1. Note how the \(\text{fdr}\) in Figure 6.13 is a monotonically increasing function of \(p\), and this goes with our intuition that the fdr should be lowest for the smallest \(p\) and then gradually get larger, until it reaches 1 at the very right end. We can make a similar decomposition not only for the red line, but also for the area under the curve. This is

\[\begin{equation} F(p) = \int_0^p f(t)\,dt, \tag{6.8} \end{equation}\]

and the ratio of the dark grey area (that is, \(\pi_0\) times \(p\)) to the overall area \(F(p)\) is the **tail area false disovery rate** (Fdr105 The convention is to use the lower case abbreviation fdr for the local, and the abbreviation Fdr for the tail-area false discovery rate in the context of the two-groups model (6.6). The abbreviation FDR is used for the original definition (6.5), which is a bit more general.).

\[\begin{equation} \text{Fdr}(p) = \frac{\pi_0\,p}{F(p)}, \tag{6.9} \end{equation}\]

We’ll use the data version of \(F\) for diagnostics in Figure 6.17.

The packages **qvalue** and **fdrtool** offer facilities to fit these models to data.

```
library("fdrtool")
ft = fdrtool(awde$pvalue, statistic = "pvalue")
```

In **fdrtool**, what we called \(\pi_0\) above is called `eta0`

:

`ft$param[,"eta0"]`

```
## eta0
## 0.8822922
```

► Question 6.19

What do the plots that are produced by the above call to `fdrtool`

show?

► Task

Explore the other elements of the *list* `ft`

.

► Question 6.20

What does the *empirical* in empirical Bayes methods stand for?

The FDR (or the Fdr) is a set property - it is a single number that applies to a whole set of rejections made in the course of a multiple testing analysis. In contrast, the fdr is a local property - it applies to an individual additional hypothesis. Recall Figure 6.13, where the fdr was computed for each point along the \(x\)-axis of the density plot, whereas the Fdr depends on the areas to the left of the red line.

► Question 6.21

Check out the concepts of *total cost* and *marginal cost* in economics. Can you see an analogy with Fdr and fdr?

The Benjamini-Hochberg method and the two-groups model, as we have seen them so far, implicitly assume *exchangeability* of the hypotheses: all we use are the p-values. Beyond these, we do not take into account any additional information. This is not always optimal, and here we’ll study ways of how to improve on this.

Let’s look at an example. Intuitively, the signal-to-noise ratio for genes with larger numbers of reads mapped to them should be better than for genes with few reads, and that should affect the power of our tests. We look at the mean of normalized counts across observations. In the **DESeq2** package this quantity is called the `baseMean`

.

`awde$baseMean[1]`

`## [1] 708.6022`

```
cts = counts(aw, normalized = TRUE)[1, ]
cts
```

```
## SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516
## 663.3142 499.9070 740.1528 608.9063 966.3137
## SRR1039517 SRR1039520 SRR1039521
## 748.3722 836.2487 605.6024
```

`mean(cts)`

`## [1] 708.6022`

Next we produce the histogram of this quantity across genes, and plot it against the p-values (Figures 6.14 and 6.15).

Figure 6.14: Histogram of `baseMean`

. We see that it covers a large dynamic range, from close to 0 to around 3.310^{5}.

```
ggplot(awde, aes(x = asinh(baseMean))) +
geom_histogram(bins = 60)
```

Figure 6.15: Scatterplot of the rank of `baseMean`

versus the negative logarithm of the p-value. For small values of `baseMean`

, no small p-values occur. Only for genes whose read counts across all observations have a certain size, the test for differential expression has power to come out with a small p-value.

```
ggplot(awde, aes(x = rank(baseMean), y = -log10(pvalue))) +
geom_hex(bins = 60) +
theme(legend.position = "none")
```

► Question 6.22

Why did we use the \(\text{asinh}\) transformation for the histogram? How does it look like with no transformation, the logarithm, the shifted logarithm, i.e., \(\log(x+\text{const.})\)?

► Question 6.23

In the scatterplot, why did we use \(-\log_{10}\) for the p-values? Why the rank transformation for the `baseMean`

?

For convenience, we discretize `baseMean`

into a factor variable `group`

, which corresponds to six equal-sized groups.

```
awde = mutate(awde, stratum = cut(baseMean, include.lowest = TRUE,
breaks = signif(quantile(baseMean,probs=seq(0,1,length.out=7)),2)))
```

In Figures 6.16 and 6.17 we see the histograms of p-values and the ECDFs stratified by `stratum`

.

Figure 6.16: p-value histograms of the airway data, stratified into equally sized groups defined by increasing value of `baseMean`

.

```
ggplot(awde, aes(x = pvalue)) + facet_wrap( ~ stratum, nrow = 4) +
geom_histogram(binwidth = 0.025, boundary = 0)
```

Figure 6.17: Same data as in Figure 6.16, shown with ECDFs.

```
ggplot(awde, aes(x = pvalue, col = stratum)) +
stat_ecdf(geom = "step") + theme(legend.position = "bottom")
```

If we were to fit the two-group model to these strata separately, we would get quite different estimayes for \(\pi_0\) and \(f_{\text{alt}}\). For the most lowly expressed genes, the power of the **DESeq2**-test is low, and the p-values essentially all come from the null component. As we go higher in average expression, the height of the small-p-values peak in the histograms increases, reflecting the increasing power of the test.

Can we use that for a better multiple testing correction? It turns out that this is possible. We can use either **independent filtering** (Bourgon, Gentleman, and Huber 2010) or **independent hypothesis weighting** (IHW) (Ignatiadis et al. 2016).

```
library("IHW")
ihw_res = ihw(awde$pvalue, awde$baseMean, alpha = 0.1)
rejections(ihw_res)
```

`## [1] 4892`

Let’s compare this to what we get from the ordinary (unweighted) Benjamini-Hochberg method:

```
padj_BH = p.adjust(awde$pvalue, method = "BH")
sum(padj_BH < 0.1)
```

`## [1] 4099`

With hypothesis weighting, we get more rejections. For these data, the difference is notable though not spectacular; this is because their signal-to-noise ratio is already quite high. In other situations, where there is less power to begin with (e.g., where there are fewer replicates, the data are more noisy, or the effect of the treatment is less drastic), the difference from using IHW can be more pronounced.

We can have a look at the weights determined by the `ihw`

function (Figure 6.18).

Figure 6.18: Hypothesis weights determined by the `ihw`

function. Here the function’s default settings chose 22 strata, while in our manual exploration above (Figures 6.16, 6.17) we had used 6; in practice, this is a minor detail.

`plot(ihw_res)`

Intuitively, what happens here is that IHW chooses to put more weight on the hypothesis strata with higher `baseMean`

, and low weight on those with very low counts. The Benjamini-Hochberg method has a certain type-I error budget, and rather than spreading it equally among all hypotheses, here we take it away from those strata that have little change of small fdr anyway, and “invest” it in strata where many hypotheses can be rejected at small fdr.

► Question 6.24

Why does Figure 6.18 show 5 curves, rather than only one?

Such possibilities for stratification by an additional summary statistic besides the p-value – in our case, the `baseMean`

– exist in many multiple testing situations. Informally, we need these so-called *co-data* to be

statistically independent from our p-values under the null, but

informative of the prior probability \(\pi_0\) and/or the power of the test (the shape of the alternative density, \(f_{\text{alt}}\)) in the two-groups model.

These requirements can be assessed through diagnostic plots as in Figures 6.14–6.17.

We have explored the concepts behind *single hypothesis testing* and then moved on to *multiple testing*. We have seen how some of the limitations of interpreting a single p-value from a single test can be overcome once we are able to consider a whole distribution of outcomes from many tests. We have also seen that there are often additional summary statistics of our data, besides the p-values. We called them *co-data* and we saw how we can use them to weigh the p-values and overall get more (or better) discoveries.

The usage of hypothesis testing in the *multiple testing* scenario is quite different from that in the *single test* case: for the latter, the hypothesis test might literally be the final result, the culmination of a long and expensive data acquisition campaign (ideally, with a prespecified hypothesis and data analysis plan). In the multiple testing case, its outcome will often just be an intermediate step: a subset of most worthwhile hypotheses selected by screening a large initial set. This subset is then followed up by more careful analyses.

We have seen the concept of the *false discovery rate* (FDR). It is important to keep in mind that this is an average property, for the subset of hypotheses that were selected. Like other averages, it does not say anything about the individual hypotheses. Then there is the concept of the *local false discovery rate* (fdr), which indeed does apply to an individual hypothesis. The local false discovery rate is however quite unrelated to the p-value, as the two-group model showed us. Much of the confusion and frustration about p-values seems to come from the fact that people would like to use them for purposes that the fdr is made for. It is perhaps a historical aberration that so much of applied sciences focuses on p-values and not local false discovery rate. On the other hand, there are also practical reasons, since a p-value is readily computed, whereas a fdr is difficult to estimate or control from data without making strong modelling assumptions.

We saw the importance of diagnostic plots, in particular, to always look at the p-value histograms when encountering a multiple testing analysis.

A comprehensive text book treatment of multiple testing is given by Efron (2010).

Outcome switching in clinical trials: http://compare-trials.org

For hypothesis weighting, the

**IHW**vignette, the IHW paper (Ignatiadis et al. 2016) and the references therein.

Identify an application from your scientific field of expertise that relies on multiple testing. Find an exemplary dataset and plot the histogram of p-values. Are the hypotheses all exchangeable, or are there informative co-data? Plot the stratified histograms.

▢

Why do mathematical statisticians focus so much on the null hypothesis of a test, compared to the alternative hypothesis?

▢

How can we ever prove that the null hypothesis is true? Or that the alternative is true?

▢

Make a less extreme example of correlated test statistics than the data duplication at the end of Section 6.5. Simulate data with true null hypotheses only, and let the data morph from having completely independent replicates (columns) to highly correlated as a function of some continuous-valued control parameter. Check type-I error control (e.g., with the p-value histogram) as a function of this control parameter.

▢

Find an example in the published literature that looks as if p-value hacking, outcome switching, HARKing played a role.

▢

The FDR is an expectation value, i.e., it is used if we want to control the average behavior of a procedure. Are there methods for worst case control?

▢

What is the memory and time complexity of the Benjamini-Hochberg algorithm? How about the IHW method? Can you fit polynomial functions as a function of the number of tests \(m\)? Hint: Simulate data with increasing numbers of hypothesis tests, measure time and memory consumption with functions such as `pryr::object_size`

or `microbenchmark`

from the eponymous package, and plot these against \(m\) in a double-logarithmic plot.

▢

Altman, Naomi, and Martin Krzywinski. 2017. “Points of Significance: Interpreting P Values.” *Nature Methods* 14 (3): 213–14. https://doi.org/10.1038/nmeth.4210.

Benjamini, Yoav, and Yosef Hochberg. 1995. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” *Journal of the Royal Statistical Society B* 57: 289–300.

Bourgon, Richard, Robert Gentleman, and Wolfgang Huber. 2010. “Independent Filtering Increases Detection Power for High-Throughput Experiments.” *PNAS* 107 (21): 9546–51. http://www.pnas.org/content/107/21/9546.long.

Efron, Bradley. 2010. *Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction*. Vol. 1. Cambridge University Press.

Head, Megan L, Luke Holman, Rob Lanfear, Andrew T Kahn, and Michael D Jennions. 2015. “The Extent and Consequences of P-Hacking in Science.” *PLoS Biology* 13 (3). Public Library of Science: e1002106.

Ignatiadis, Nikolaos, Bernd Klaus, Judith Zaugg, and Wolfgang Huber. 2016. “Data-Driven Hypothesis Weighting Increases Detection Power in Genome-Scale Multiple Testing.” *Nature Methods* 13: 577–80.

Schweder, T., and E. Spjøtvoll. 1982. “Plots of P-values to Evaluate Many Tests Simultaneously.” *Biometrika* 69: 493–502. https://doi.org/10.1093/biomet/69.3.493.

Wasserstein, Ronald L, and Nicole A Lazar. 2016. “The ASA’s Statement on P-Values: Context, Process, and Purpose.” *The American Statistician*. Taylor & Francis.

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