10 Hypothesis Testing

Statistical hypothesis testing The expression level of a gene in a given condition is measured several times. A mean x of these measurements is calculated. From many previous experiments, it is known that the mean expression level of the given gene in normal conditions is m. How can you decide which genes are significantly regulated in a microarray experiment? For instance, one can apply an arbitrary cutoff such as a threshold of at least twofold up or down regulation. One can formulate the following hypotheses: The gene is up-regulated in the condition under study: x>m The gene is down-regulated in the condition under study: x<m The gene is unchanged in the condition under study: x=m Something has gone awry during the lab experiments and the genes measurements are completely off; the mean of the measurements may be higher or lower than the normal: x≠m.

Statistical hypothesis testing When a hypothesis test is viewed as a decision procedure, two types of error are possible, depending on which hypothesis, H0 or H1, is actually true. If a test rejects H0 (and accept H1) when H0 is true, it is called a type I error, a, (rejection error). a = P (reject H0 | H0 is true) If a test fails to reject H0 when H1 is true, it is called a type II error (acceptance error). b = P (do not reject H0 | H0 is false) The following shows the results of the different decisions. H0 Decision Do not reject H0 Reject H0 H0 is True Correct decision Type I error H0 is False Type II error

Statistical hypothesis testing The next step is to generate two hypotheses. The two hypotheses must be mutually exclusive and all inclusive. Mutually exclusive – the two hypotheses cannot be true both at the same time All inclusive means that their union has to cover all possibilities Expression ratios are converted into probability values to test the hypothesis that particular genes are significantly regulated Null hypothesis H0 that there is no difference in signal intensity across the conditions being tested The other hypothesis (called alternate or research hypothesis) named H1. If we believe that the gene is up-regulated, the research hypothesis will be H1: x ＞ m, The null hypothesis has to be mutually exclusive and also has to include all other possibilities, therefore, the null hypothesis will be H0: x≦ m. One assigns a p-value for testing the hypothesis. The p-value is the probability of a measurement more extreme than a certain threshold occurring just by chance. The probability of rejecting the null hypothesis when it is true is the significance level a , which is typically set at p<0.05, in other words we accept that 1 in 20 cases our conclusion can be wrong.

Statistical hypothesis testing Single sample, test of hypothesis (TOH) x = sample mean, s = sample standard deviation Type of test TOH Known parameters Unknown parameters Statistics Normal distribution test x m, s, x, s t-test m, x, s s Chi-square s, x, s

Statistical hypothesis testing One-tail testing The alternative hypothesis specifies that the parameter is greater than the values specified under H0, e.g. H1: m>15. such a hypothesis is called upper one-tail testing. Example The expression level of a gene is measured 4 times in a given condition. The 4 measurements are used to calculate a mean expression level of x=90. it is known from the literature that the mean expression level of the given gene, measured with the same technology in normal conditions is m=100 and the standard deviation is s=10. We expect the gene to be down-regulated in the condition under study and we would like to test whether the data support this assumption. The alternative hypothesis H1 is “the gene is down-regulated” or H0: x≧m, therefore, H1 x<m This is an example of a one-tail hypothesis in which we expect the values to be in one particular tail of the distribution.

Statistical hypothesis testing From the sampling theorem, the means of samples are distributed approximately as a normal distribution. Sample size = 4, Mean x = 90 Standard deviation s = 10 Assuming a significance level of 5% The null hypothesis is rejected if the computed p-value is lower than the critical value (0.05) We can calculate the value of Z as The probability of having such a value just by chance, i.e. the p-value, is : p(Z < -2) = 0.02275 The computed p-value is lower than our significance threshold 0.02275 < 0.05, therefore we reject the null hypothesis. In other words, we accept the alternate hypothesis. We stated that “the gene is down-regulated at 5% significance level”. This will be understood by the knowledgeable reader as a conclusion that is wrong in 5% of the cases or fewer.

Normal distribution table

Normal distribution table NORMDIST - Area under the curve start from left hand side Z=0 Z=2

Standard normal distribution NORMSDIST

Statistical hypothesis testing Two-tail testing A novel gene has just been discovered. A large number of expression experiments measured the mean expression level of this gene as 100 with a standard deviation of 10. Subsequently, the same gene is measured 4 times in 4 cancer patients. The mean of these 4 measurements is 109. Can we conclude that this gene is differential expressed in cancer? We do not whether the gene will be up-regulated or down-regulated. Null hypothesis H0: = 100, Alternative hypothesis H1: ≠ 100 At a significant level of 5%  2.5% for the left tail and 2.5% for the right tail Z = (109 – 100)/(10/√4) = 9/(10)*2 = 1.8 p-value, p(Z≧1.8) = 1 – p(Z≦1.8) = 1 – 0.9641 = 0.0359 > 0.025  that is the p-value is higher than the significant level, so we cannot reject the null hypothesis 2.5% 2.5%

Tests involving the mean – the t distribution Hypothesis testing Parametric testing – where the data are known or assumed to follow a certain probability distribution (e.g. normal distribution) Non-parametric testing – where no a priori knowledge is available and no such assumptions are made. The t distribution test or student’s t distribution test is a parametric test, it was discovered by William S. Gossett, a 32-year old research chemist employed by the famous Irish brewery (釀造，如啤酒) Guinness.

Tests involving the mean – the t distribution Tests involving a single sample may focus on the mean of the sample (t-test, where variance of the population is not known) and the variance (c2-test). The following hypotheses may be formulated if the testing regards the mean of the sample: H0: m = c, H1: m≠c H0: m≧c, H1: m＜c H0: m≦c, H1: m＞c The first hypotheses corresponds to a two-tail testing in which no a prior knowledge is available, while the second and the third correspond to a one-tail testing in which the measured value c is expected to be higher and lower than the population mean m, respectively.

Tests involving the mean – the t distribution The expression level of a gene is known to have a mean expression level of 18 in the normal human population. The following expression values have been obtained in five measurements: 21, 18, 23, 20, 18. Is this data consistent with the published mean of 18 at a 5% significant level? Population s.d. s is not known  t-test, calculate sample s.d. s to estimate s H0 : = m = 18, H1 : ≠ m = 18  two-tail test Calculate the t-test statistics Remember using n-1 when calculating standard deviation s.

Tests involving the mean – the t distribution is symmetric Degree of freedom, n, n=5-1=4. Using a table of the t-distribution with four degree of freedom, the p-value associated with this test statistic is found to be between 0.05 and 0.1. The 5% two-tail test corresponds to a critical value of 2.776. Since the p-value is greater than 0.05 (t-value=2.11 < critical value=2.776), the evidence is not strong enough to reject the null hypothesis of mean 18  accept H0.

The t-distribution table - cumulative probability starting from left hand side Two-tails a=0.10, 0.05

The t-distribution table – Excel – TINV gives the two-tails critical value

Excel – TINV, p=0.05, degree of freedom is 3, tinv = 3.182

Tests involving the mean – the t distribution The expression level of a gene is known to have a mean expression level of 225 in the normal human population. The expression values have been obtained in sixteen measurements, in which the sample mean and s.d. are found to be 241.5 and 98.7259 respectively. Is this data higher than the published mean at a 5% significant level? This is a left-hand one-tail test Null hypothesis H0: x≦m=225 alternative hypothesis H1: x>m=225 t-score = (241.5-225)/[98.7259/sqrt(16)] = 0.6685 Degree of freedom = 15 The 5% level corresponds to a critical value (t0.05(15)) of 1.753 The t-score is less than the critical value, i.e. 0.6685 < 1.753. Based on the critical value, we can accept the null hypothesis. The gene expression data set is not higher than the published mean of 225 at a 5% significant level

Evaluate the significance of the following gene expression differences – t test pgs2e-exer-04-03-0.jpg Expected that (Exp./ref.) =1, is gene A or B or C up-regulated ?

Evaluate the significance of the following gene expression differences – t test Expect average ratio = 1, H0 : measured mean ≦1, H1: measured mean >1 left-hand one-tail test t-score = (average -1)/(s/n0.5) The p-values (for 16.37 and 6.71) are less than 0.05 (t0.05(4)=2.132) for genes 1 and 3 (reject H0), but not for 2. It is conclude that the level of expression is increased only in genes 1 and 3.

Tests involving the variance – the chi-square distribution The expression level of a gene is known to have a variance s2 = 5000 in the normal human population. The same gene is measured 26 times and found to have a s2 = 9200 . Is there evidence that the new measurement different from the population at a 2% significant level? Unknown population mean, c2 test Null hypotheses H0: s2 = s2 = 5000, that is the new measured variance is not different from the population s The alternative hypotheses H1: s2 ≠ s2 = 5000 (two-tail test) The new variable of score is This variable with the interesting that if all possible samples of size n are drawn from a normal population with a variance s2 and for each such sample the quantity is computed, these value will always form the same distribution. This distribution will be a sample distribution called a c2 (chi-square) distribution. For right hand tail, p=0.01 p=0.99 two-tail test reject H0 accept H0 reject H0

Tests involving the variance – the chi-square distribution If the sample standard deviation s is close to the population standard deviation s, the value of c2 will be close to n-1 (degree of freedom) If the sample standard deviation s is very different to the population standard deviation s, the value of c2 will be very different from n-1 Use the c2 distribution to solve the above problem. http://commons.bcit.ca/math/faculty/david_sabo/apples/math2441/section8/onevariance/chisqtable/chisqtable.htm Assuming a 2% significant level, the critical values for c20.01(25) = 44.314 and c20.99(25) = 11.524 (right-hand tail) Reject areas are c2 ≦ 11.524 or c2≧ 44.313 Since 46 > 44.313  reject null hypothesis The measurement is different from the population at a 2% significant level probability, a

The chi-square distribution Excel - CHIINV, uses right hand tail

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