I need help interpreting a kernal density plot and a histogram of this data. I have provided some context below let me know if you need more information
A co-worker that I am collaborating with has a list of genes ranked by expression fold change. He says most of the genes in one dataset are noise with respect to his analysis so he is trying to filter this out. He has sent me a document of his methods for generating a new list of genes which he think are relevant. he has run a permutation test on the ranked genes I have provided a quote here:
"To determine which genes contributed more strongly in real data, as compared by permuted data, the rankings achieved for each gene in real data were compared to the shuffled rankings. For each gene, and each permutation type, a p-value was calculated by counting how many times that gene was ranked higher in shuffled data than in real data, and dividing it by the number of comparisons (16 * 256 = 4096). To achieve p<0.05 a gene’s real ranking would have to be higher than 95% of its shuffled rankings."
Following this he provides kernel density plots of the genes under different rankings.
"Below are kernel density estimation (kde) plots, overlaid on histograms, of the rankings of two genes randomly selected from the few genes with p<0.05 vs all three permutations."
"Below are kde plots of the rankings of two genes randomly selected from the many genes with p>0.05 vs all three permutations."
What am I looking at? What does this tell me about the genes ranking statistics.