11/30/2023 0 Comments Graphpad prism volcano plotIf you chose the False Discovery Rate approach, you need to choose a value for Q, which is the acceptable percentage of discoveries that will prove to be false. Prism offers three methods to control the FDR. Of all the rows of data flagged as "discoveries", the goal is that no more than Q% of them will be false discoveries (due to random scatter of data) while at least 100%-Q% of the discoveries are true differences between population means. In other words, it is the maximum desired FDR. You set Q, which is the desired maximum percent of "discoveries" that are false discoveries. This method doesn't use the term "significant" but rather the term "discovery". The whole idea of controlling the FDR is quite different than that of declaring certain comparisons to be "statistically significant". ![]() The other choice is to base the decision on the False Discovery Rate (FDR recommended). One approach is based on the familiar idea of statistical significance. Prism offers two approaches to decide when a two-tailed P value is small enough to make that comparison worthy of further study. When performing a whole bunch of t tests at once, the goal is usually to come up with a subset of comparisons where the difference seems substantial enough to be worth investigating further. How to decide which P values are small enough to investigate further If the different rows represent different conditions, or perhaps different brain regions, and all the data are measurements of the same outcome, then it might make sense to assume equal standard deviation and choose the "more power" option. So if the different rows represent different gene products, or different measures of educational achievement (to pick two very different examples), then choose the "few assumptions" choice. Certainly if the data in the different rows represent different quantities, perhaps measured in different units, then there would be no reason to assume that the scatter is the same in all. So the data in each row will influence the P value not only for that row, but also for every other row.Ĭhoosing between these options is not always straightforward. Note the pooled SD is for both data set columns for all rows. This gives you more degrees of freedom and thus more power. Prism therefore computes one pooled SD, as it would by doing two-way ANOVA. This is the assumption of homoscedasticity. But the assumption is that this variation is random, and really all the data from all rows comes from populations with the same SD. ![]() You assume that all the data from both columns and all the rows are sampled from populations with identical standard deviations. ![]() This is the standard assumption of an unpaired test - that the two samples being compared are sampled from populations with identical standard deviations. Note that while you are not assuming that data on different rows are sampled from populations with identical standard deviations, you are assuming that data from the two columns on each row are sampled from populations with the same standard deviation. There are fewer df, so less power, but you are making fewer assumptions. The values in the other rows have nothing at all to do with how the values in a particular row are analyze. With this choice, each row is analyzed individually. There are two ways it can do this calculation. Prism computes an unpaired t test for each row, and reports the corresponding two-tailed P value.
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