Hi, @taowenmicro @YongxinLiu
Thank you for this package!
I found out that each time the networks looked different using the same data and script. Why is that and how can we ensure the same result each time we run the same code? I used the network.pip pipeline.
CB.net = network.pip( ps = CB, N = 80, big = T, select_layout = F, layout_net = "model_maptree2", r.threshold = 0.6, p.threshold = 0.05, maxnode = 2, method = "sparcc", label = FALSE, lab = "elements", group = "Passage_forAnal", fill = "Phylum", size = "igraph.degree", zipi = TRUE, ram.net = T, clu_method = "cluster_fast_greedy", step = 100, R=10, ncpus = 2 )
I assume that some randomization is implemented in the process. My question is how do you infer the biological meaning, i.e., zi-pi plot for identifying hub/keystone taxa if the results are different from each run.
I look forward to hearing back from you.
Thanks!
Hi, @taowenmicro @YongxinLiu
Thank you for this package!
I found out that each time the networks looked different using the same data and script. Why is that and how can we ensure the same result each time we run the same code? I used the network.pip pipeline.
CB.net = network.pip( ps = CB, N = 80, big = T, select_layout = F, layout_net = "model_maptree2", r.threshold = 0.6, p.threshold = 0.05, maxnode = 2, method = "sparcc", label = FALSE, lab = "elements", group = "Passage_forAnal", fill = "Phylum", size = "igraph.degree", zipi = TRUE, ram.net = T, clu_method = "cluster_fast_greedy", step = 100, R=10, ncpus = 2 )I assume that some randomization is implemented in the process. My question is how do you infer the biological meaning, i.e., zi-pi plot for identifying hub/keystone taxa if the results are different from each run.
I look forward to hearing back from you.
Thanks!