If you aren't sure a non-parametric test like Wilcoxon is better. t-test assumes your data are normally distributed, if they aren't you're going to get spurious p-values. The difference between these formulas is in the mean calculation. However, the R programming language provides a function called scale, which makes the computation of z-scores easier and more efficient. You can't calculate a p-value on the fold-change values, you need to use the concentrations in triplicate thus giving a measure of the variance for the t-test to use. Yes, you can use the second one for volcano plots, but it might help to understand what it's implying. The previous example shows how to calculate z-scores manually based on its formula. I want to convert expression level value to z-score (mean-x/sd). In case your data would contain missing values, those values would be removed for the computation of z-scores.Įxample 2: Standardize Values Using scale() Function Can I use LogFC values for co-expression analysis. Note that we have specified the na.rm argument to be equal to TRUE. The previous output of the RStudio console shows the standardized values that correspond to our input vector. In statistics, the task is to standardize variables which are called valuating z-scores. Likewise, Let v ( x) 1/ (1+exp (-crossprod (coefD, x))) be the expected value of the discrete component. The following code shows a simple example of some standardised positively skewed data. For each gene, let u ( x) be the expected value of the continuous component, given a covariate x and the estimated coefficients coefC, ie, u ( x) crossprod (x, coefC). A simple strategy of logs on z-scores A simple strategy for log transforming a variable is to first add a constant to the variable such that the minimum value is one. X_stand1 <- (x - mean(x, na.rm = TRUE)) / sd(x, na.rm = TRUE) # Standardize manually Details The log-fold change is defined as follows.
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