t-test
Theory
A t-test evaluates if there is a significant difference in mean between two groups of data. The parametric t-tests are often preferred over non-parametric tests because the t-tests are more powerful. However, the t-tests are based on the assumption that the data population conforms to a normal distribution. If the data do not conform to a normal distribution, the t-test will not produce reliable results. Therefore, it is important that you have made a good estimate of your data's underlying distribution, e.g. with the Kolomogorov-Smirnov test, before you proceed with a t-test. If the normality test does not indicate a normal distribution, non-parametric tests are preferred. The tests also assume that the two groups share a common variance. There are tests for this such as Levene's test or Bartlett's test, but they are not implemented in GenEx at the moment.
How to
Open the Statistics tab among the analyses tabs in the top of the main window, and press the t-test button to load the analysis into the Control panel. There must be defined groups of samples in the data set to be able to load the analysis. If the data has been transposed, make sure that there are groups of genes. The t-test compare the difference of means of two groups, so if you have more than two groups to compare you must use an ANOVA test. The groups are defined in the Data manager under the Groups tab.
There is a check box list with all defined groups to the left in the Control panel. Select exactly two of these, the groups which means will be compared in the t-test. Also select the genes that you are interested in in the right check box. If you are interested whether the two group means are equal or not, select a 2-tail test with the radio buttons. Select a 1-tail test if you are only interested in if one group has a higher expression than the other group.
Indicate whether the experimental design was paired or not with the radio buttons. An unpaired t-test is when the samples are independent of each other, e.g. if 100 subjects are distributed at random in two groups before they are treated (drug/placebo), one treatment per group, an tested once. A paired t-test is when there are pairs of samples that depend on each other, e.g. if 100 subjects are distributed at random in two groups, and they are tester both before and after they have received treatment (drug/placebo). This way, you have two paired measures for each subject and should therefore use a paired t-test, which usually is more powerful than the unpaired t-test. Press the Run button the run the analysis.
The result table includes the results of a Kolomogorov-Smirnov test which test if the data population is normally distributed. KS is the Kolomogorov-Smirnov test statistic and KS P-Value its corresponding p-value. A high KS p-value indicates that the samples are indeed normally distributed, while a low p-value indicate that they are not. This is summarized in the row Norm. dist. (Normally distributed) which is green and states TRUE if Kolomogorov-Smirnov's test indicates that the data is normally distributed, or is red saying FALSE if not. The t-test assumes that the data is normally distributed, so it is important to have an indication of whether this is true for your data or not. Kolomogorov-Smirnov's test gives such an indication. If the test comes out as FALSE, non-parametric tests should be used instead.
The report further displays the number of measurements (Count), the average measurement (Mean), the standard deviation from the mean (STDEV), the degrees of freedom (df), and the test statistic (t). The most important row for practical purposes is probably the p-value, P (1-tail) or P (2-tail) depending on whether a 1-tail or 2-tail test was selected in the Control panel. The p-value is the probability that, given that the null hypothesis is true, you would obtain data at least as extreme as the data that was actually observed. A low p-value indicates that the null hypothesis should be rejected, and that there is indeed a difference of means between the two groups. If more than one gene is tested at the same time, you are performing multiple testing with an increased risk of finding differences between groups purely by chance. Remember to use the corrected threshold p-value given in the message dialog.
