Hi I want to calcualte p-value for my matrix of gene expression data, based on t-tests, which are adjusted for the FDR (according to Benjamini and Hochberg 1995). In the multtest package we have mt.teststat(), which tells me how to calculate the t statistic for each of the rows in my data frame, and we have mt.rawp2adjp(), which converts raw p-values into adjusted p-values. So there is a missing step - the first function tells me how to create t. I then need to access the p-values for this t statistic, and then go on to convert them into adjusted p-values. Now, the documentation for mt.maxT() and mt.minP() *suggests* that raw p-values *can* be obtained from these functions. However, when running them and then comparing the $rawp slots to the p-values achieved by running t.test(), I find that these rawp values *do not* correspond to the equivalent p-values outputted by t.test. SO, what I now plan on doing is: 1) iterating through my matrix myself, running t.test() on each row, and storing the p-values 2) using these p-values as an input to mt.rawp2adjp() to create a list of adjusted p-values 3) mapping these adjusted p-values back onto my original data matrix So, I come to my questions: 1) can anyone tell me how to get raw p-values for the t-statistic using multtest? 2) as the documentation for mt.rawp2adjp() says "This function computes adjusted p-values for simple multiple testing procedures from a vector of raw (unadjusted) p-values", I presume plugging in p-value directly from t.test() is perfectly valid? Thanks Mick

> In the multtest package we have mt.teststat(), which tells me how to > calculate the t statistic for each of the rows in my data frame, and we > have mt.rawp2adjp(), which converts raw p-values into adjusted p-values. > > So there is a missing step - the first function tells me how to create > t. I then need to access the p-values for this t statistic, and then go > on to convert them into adjusted p-values. > > Now, the documentation for mt.maxT() and mt.minP() *suggests* that raw > p-values *can* be obtained from these functions. However, when running > them and then comparing the $rawp slots to the p-values achieved by > running t.test(), I find that these rawp values *do not* correspond to > the equivalent p-values outputted by t.test. t.test assumes the gene expression comes from a ***normal*** distribution. mt.maxT doesn't rely on such normality assumption. If the data are reasonably normally distributed, you will expect the raw p-values from mt.maxT and p-values from t.test should be close. > SO, what I now plan on doing is: > > 1) iterating through my matrix myself, running t.test() on each row, and > storing the p-values > 2) using these p-values as an input to mt.rawp2adjp() to create a list > of adjusted p-values > 3) mapping these adjusted p-values back onto my original data matrix > > So, I come to my questions: > > 1) can anyone tell me how to get raw p-values for the t-statistic using > multtest? The resampling based raw p-values can be obtained from mt.maxT. The t.test based p-values can be obtained as you mentioned by iterating t.test on each row. > 2) as the documentation for mt.rawp2adjp() says "This function computes > adjusted p-values for simple multiple testing procedures from a vector > of raw (unadjusted) p-values", I presume plugging in p-value directly > from t.test() is perfectly valid? If you are comfortable with the assumption that the gene expressions are normally distributed, then you will be OK. However, I will be hesitant to rely the normality assumptions unless some evidences supports it. You will be a little bit more safe in using permutation based raw p-values. In a future release of multtest, various bootstrap raw based p-values will be provided. Yongchao