Dear all,
My data consists of 8000 rows and 14 columns, as follows:
The rows correspond to quadruplicate measurements of 2000 variables and columns represent different non time-related conditions.
I want to identify variables with statistically significant size difference in any pairwise comparison between conditions.
I would like to find the maximum difference for a variable among two different conditions of it, but not comparing the variable’s values of two different rows.
A two-way between subjects Anova and Tukey test calculates the mean of each column and not the mean of each variable in every column. On the other hand, one-way Anova post-hoc analysis with Tukey must be done 2000 times. In that case, I am not sure if FDR is the appropriate way for multiple testing correction.
I would only like a comparison among all pairwise column combinations within each variable with an average difference and a p-value for each comparison.
Thank you in andvance
My data consists of 8000 rows and 14 columns, as follows:
The rows correspond to quadruplicate measurements of 2000 variables and columns represent different non time-related conditions.
I want to identify variables with statistically significant size difference in any pairwise comparison between conditions.
I would like to find the maximum difference for a variable among two different conditions of it, but not comparing the variable’s values of two different rows.
A two-way between subjects Anova and Tukey test calculates the mean of each column and not the mean of each variable in every column. On the other hand, one-way Anova post-hoc analysis with Tukey must be done 2000 times. In that case, I am not sure if FDR is the appropriate way for multiple testing correction.
I would only like a comparison among all pairwise column combinations within each variable with an average difference and a p-value for each comparison.
Thank you in andvance
a bit more information on the subjects would be nice. It sounds almost like a random regression analysis which can tease out the fixed effects of the individual 2000 against the random effects of itself and against the covariates (the measurements).