Before revision:  First, we propose statistical tests to evaluate the quality of p-value  and the cross-batch comparability of any other statistic. Second, we propose a lasso based variable selection statistic, based on when the predictor variable first becomes active, and compute its p-value to achieve unified FDR control across multiple selections. In the end, we apply our tests on covTest, selectiveInference, and our statistic, based on real and null datasets for network inference in normal and high-dimensional settings. Results demonstrate higher p-value quality from our statistic and reveal p-value errors from others hidden before. We implement our statistic as lassopv in R .
After revision:  First, we propose statistical tests to evaluate the quality of p-value and computational biology. Lassopv is implemented in R and freely available at URL and URL