1. False Discovery Rate (FDR) = proportion of false positive results out of all positive results (positive result = statistically significant result)
Ladislav Pecen

2. Positive False Discovery Rate Comparison of approaches
Outline
Introduction
Familywise Error Rate
False Discovery Rate
Positive False Discovery Rate
Comparison of approaches
Example – microarray study
Estimate of FDR and pFDR
Bayes approach to FDR and pFDR

3. The aim is to find an approach to Problem of multiple testing
Background
The aim is to find an approach to Problem of multiple testing
Problem, which occur by multiple testing
each single test of one null hypothesis has probability of type I error equal to α
calculation of several tests, where each has probability of type I error equal to α, the probability of overall type I error increases
this leads to high value of false positive results
in the worst situation (tests are independent) – the probabilities of type I error are summing
assume 100 of independent tests
each test has probability of type I error equal to α = 0.05
in about 5% of tests ( = 5 tests) you will make type I error, i.e. you will reject the null hypothesis although it is true
Typical areas where you can meet multiple testing
microarray studies
genetics and all lab testing

4. Theory of hypothesis testing
We test the null hypothesis H0 against the alternative hypothesis H1
One of the following four possibilities will happen
Usually we require restrictions on the two errors:
probability of type I error, i.e. false positive rate, should be limited by α
P(rejection of H0 | H0 is true) = P(type I error) ≤ α
power of the test should not be lower than 1 – β
P(rejection of H0 | H0 is not true) = 1 – P(acceptation of H0 | H0 is not true)
= 1 – P(type II error) ≥ 1 - β
Testing result
True
reject null hypothesis
accept null hypothesis
null hypothesis
type I error
alternative hypothesis
type II error

5. Multiple hypotheses testing
Testing of multiple hypotheses
Also here we have restrictions on the false results,
these could be similar as in one test of hypothesis :
control false positive rate: (# of incorrectly rejected H0)/(# of true H0) = S/m0
this is a classical approach, also called “Familywise error rate approach” (FWER)
or can be approached from different point of view
control false discovery rate (# of incorrectly rejected H0)/(# of rejected H0) = S/R0
Connected characteristics
sensitivity – proportion of correctly identified DE genes: U/m1
specificity – proportion of correctly identified non-DE genes: S/m0
Testing result
True
# of rejected null hypotheses
# of accepted null hypotheses
Total
# of true null hypotheses S T m0
# of true alternatives U W
m1=m-m0 R0
R1=m-R0 m

6. Determined as non-DE genes
Example
Example from genetics, microarray studies
genes examined
search for differentially expressed genes
type I error rate (false positive rate) = 475 / = 5%
type II error rate (false negative rate) = 100 / 500 = 20%
sensitivity of the test (power) = 400 / 500 = 80%
false discovery rate = 475 / 875 = 54%
more than a half of discovered DE genes are faults
false non-discovery rate = 100 / 9125 = 1%
Results of testing
Determined as DE genes
Determined as non-DE genes
Total
Reality
Truly DE genes
400
100
500
Truly non-DE genes
475
9 025
9 500
875
9 125
10 000

7. Common approach Control of familywise error rate (FWER)
probability of making one or more false discoveries (type I error)
estimate: number of false discoveries out of all tests done
assume, we provide k independent tests, each at significance level α = 0.05
i.e. in each test we have 5% probability of making false positive decision
using Bonferroni inequality, we can estimate the overall probability of type I error, i.e. the probability of making at least one false positive decision as k * α = k * 0.05
already for 10 tests we have the upper bound for probability of at least one false positive result 50%
to keep the overall significance level controlled (e.g. equal to 5%), one have to decrease the significance level in each particular test
to be sure that overall significance level is α = 0.05, each test has to be provided at significance level α = 0.05 / k
for 10 test, each has to have its significance level 0.005; for thousand of tests, ...
Such approach leads to highly conservative results
for thousands of test it is highly difficult to prove truly positive result
the more tests, the lower power

8. False Discovery Rate Definition
False Discovery Rate (FDR) = proportion of false positive results out of
all positive (= statistically significant) results
Advantages
if null hypothesis is rejected, we know the probability, that it is correctly rejected
FDR = 5% means that out of 100 positive tests circa 5 are false positive and remaining 95 are truly positive
usually it is more powerful than the traditional FWER approach
convenient especially when testing large amount of null hypotheses
Disadvantages
do not need to keep the probability of at least one wrongly rejected null hypothesis lower than α
one has to care about situation, when number of rejected H0 is zero
Factors determining False Discovery Rate (FDR)
proportion of truly DE genes: m1/m
distribution of true differences
variability
sample size

9. False Discovery Rate
The FDR as defined above works nicely, when at least one of the null hypotheses is rejected, i.e. when R0 > 0
In case, when P(R0 = 0) > 0, three possibilities are available
FDR1 = E(S / R0 | R0 > 0) * P(R0 > 0)
FDR2 = E(S / R0 | R0 > 0)
FDR3 = E(S) / E(R0)
The second and third alternatives are equal to 1, if m0 = m
FDR2 and FDR3 cannot be controlled (limited by α) whenever m0 = m, hence Benjamini and Hochberg decided to work with FDR1
in further, it is called False Discovery Rate (FDR)
when controlling FDR1 by α, it means that we control FDR1 = E(S/R0 |R0> 0) only with α / P(R0 > 0), hence Storey decided to work with FDR2
the fact that FDR2 = 1 when m0 = m is not a problem, since this result is obvious
in further, it is called positive False Discovery Rate (pFDR)

10. Benjamini-Hochberg - FDR
Procedure controlling false discovery rate
consider testing of m null hypotheses H1, H2, H3, ..., Hm
order the respective p-values such that p(1) ≤ p(2) ≤ p(3) ≤ ... ≤ p(m) and denote the null hypothesis corresponding to p(i) as H(i)
choose k* such that p(k*) is the largest p-value less than α * k / m, i.e k* = argmax{k: p(k) ≤ α * k / m; 1 ≤ k ≤ m}
then reject all hypotheses H0(i) for which p(i) ≤ p(k*)
Properties
for independent test statistics and any configuration of false null hypotheses, the procedure controls FDR at value α
E(S/k0) = E(falsely rejected / number of rejected) ≤ α *m0/m
Benjamini, Y. and Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. (1995) J. R. Stat. Soc. Ser. B 57289–300

11. Positive False Discovery Rate
Definition
false discovery rate given that at least one test has positive result
i.e. proportion of false positive results between all the positive results given at least one positive result occur
pFDR = E(V / R0 | R0 > 0)
Additional characteristics can be defined
q-value: a natural pFDR counterpart to common p-value
p-value is defined as test statistic greater than equal to observed value given null hypothesis P (T ≥ t | H0)
q-value is Bayesian analogue to p-value
q-value is posterior probability of null hypothesis given test statistic is greater than or equal to the observed value P(H0 | T ≥ t)
for more hypotheses: q-value(t) = inf {pFDR(Γα); t ϵ Γα}

12. Positive False Discovery Rate
for more hypotheses: q-value(t) = inf {pFDR(Γα); t ϵ Γα}
the minimum pFDR that can occur, when rejecting a statistic with value t
minimum posterior probability of null hypothesis over all significance regions containing the statistics
q-value minimizes the ratio of the type I error to the power over all significance regions that contain the statistic
pFNR (positive False Negatives hypothesis Rate): a natural counterpart to pFDR
rate of false negatives hypothesis between all negative results
first we define False Non-discovery rate (FNR)
FNR = E(T / (m – R0) | (m – R0) > 0) P((m – R0) > 0)
the positive False Non-discovery rate (FNR) is defined as
pFNR = E(T / (m – R0) | (m – R0) > 0)

13. Comparison of FWER, FDR and pFDR
FWER – control for multiple error rate
we fix the error rate and estimate the rejection area
FDR – control of false positive between all positive
we fix the rejection area and estimate the error rate
Interpretation of FDR and pFDR
FDR - rate that false discoveries occur
pFDR - rate that discoveries are false
when all the null hypotheses are true (i.e. when all genes are non-DE and m0 = m), the pFDR will be always equal to 1 (and hence cannot be controlled by some prespecified value of α
when controlling FDR at level α, and positive findings have occurred, then FDR has really only been controlled at level α / P(R0 > 0)

14. Comparison of FWER, FDR and pFDR
Two approaches to false discovery rate
to fix the acceptable rate α and estimate the corresponding significance threshold
is available only when using FDR, since the pFDR cannot be controlled
to fix the significance threshold and to estimate the corresponding rate α
both FDR as well as pFDR can be used ... the later one leads to “stronger” results

15. Example Back to the genetics and the microarray study
the problems come from high percentage
of truly non-DE genes
lowering of significance level decreases also FDR
Assume the following situation
evaluate circa genes to decide whether they are DE or non-DE
the genes are independent or slightly dependent
for each gene compare two independent groups with equal variance
n arrays per group
usage of standard t-statistics with pooled variance

16. Example Any formal statistical testing procedure
denote α the significance level for each one test, not an overall significance level for all multiple tests together
Any formal statistical testing procedure
compute relevant test statistic for each gene
sort the statistics (or p-values) by order
determine the cut-off point dividing the genes into DE and non-DE
In such situation it makes sense to care about
FDR – how many of the rejected null hypotheses are rejected wrongly
FNR – proportion of true alternative missed by the test

17. Genetics, microarray studies, particular situations
Example
Genetics, microarray studies, particular situations
FDR varies depending on sample size per group (n), significance level (α) and proportion of true null hypotheses (π0 = m0 / m)
n = 5 microarrays per group
at significance level α = 5% we get
sensitivity of the test about 35%
FDR at π0 = 0.9 is greater than 60% and at π0 = it is around 95%
at sensitivity equal to 80% we get
significance level around 0.45
FDR at π0 = 0.9 is around 82% and at π0 = 0.99 it is almost 99%
hence, n = 5 leads to underpowered study

18. Example n = 20 microarrays per group n = 30 microarrays per group
at significance level α = 5% we get
sensitivity of the test around 90%
FDR at π0 = 0.9 is around 35% and at π0 = 0.99 it is more than 80%
n = 30 microarrays per group
at significance level α = 5% the results are still poor
at significance level α = 0.4% we get
sensitivity of the test around 80%
FDR at π0 = 0.9 is slightly above 20% and at π0 = 0.99 it is around 72%
for any n, the best results associated with significance level α = 5% can be
minimal FDR at π0 = 0.9 is 18% and at π0 = 0.99 it is 71%

19. Example Genetics, microarray study
FDR enables another approach to sample size estimation
the sample size depends on number and distribution of truly DE genes and on the tolerated value of FDR
if n = 5, we have to have about π0 = 80% of truly DE genes to obtain reasonable FDR
another possibility is to hope for large differences
in both cases we use really small significance level
if π0 = 0.9 and we desire FDR less than 10%, then we should have at least n = 30 observations per group
if π0 = 0.99 and we classify 1% of top genes as DE, we need sample size of n = 45 to observe FDR around 10% (sample size n = 35 is necessary for FDR less than 20%)
if we can estimate π0, then it makes sense to apply the rule to reject top (1 – π0) * 100% of hypotheses, since then FDR = FNR = 1 – sensitivity
we control both these statistics together

20. Typical examples
Situations, where controlling of FWER (i.e. probability of one false rejection of H0) is not needed and then controlling of FDR is meaningful
multiple endpoints
typical goal: to recommend a new test or treatment over the standard one
the aim is to find as many endpoints in which the new treatment can exceed the standard one
the limitation on false positive result is not so strict, but too many false discoveries are also bad
multiple separate decisions without an overall decision
multiple subgroups problem, where two treatments are compared in various subgroups of patients
we want to find as many subgroups with potentially different reaction on the two treatments as possible
but we want to control the rate of false discoveries
screening of multiple potential effects
multiple potential effect is screened to weed out the null effect
screening of various chemicals when screening of potential drug development
again, we want as many discoveries as possible, while controlling for FDR

21. Thank you

25. Sometimes I would like to exchange all my knowledge
for a bottle of Whisky