filtering for the network

scripts/isd_crete_biodiversity.R

@@ -496,7 +496,9 @@ total_reads <- sum(crete_biodiversity$abundance)
 # filtering taxa that have more than 100 reads and appear to more than 5% of the
 # samples (n=7)
 network_taxa_metadata <- asv_metadata |>
-    filter(total_reads_srs>=50) |>
+    mutate(prevalence_class=ifelse(n_samples>1 & mean_rel_abundance>(-0.01*proportion_samples+0.001),
+                                   "prevalent", "no prevalent")) |>
+    filter(prevalence_class=="prevalent") |>
     ungroup()
 
 filtered_reads <- sum(network_taxa_metadata$total_reads)

scripts/isd_crete_network.jl

@@ -2,9 +2,12 @@ using Graphs
 using FlashWeave
 
 data_path = string("results/network_community_matrix.tsv")
+metadata_path = string("results/sample_metadata.tsv")
 
 #meta_data_path = "results/sample_metadata.tsv"
-netw_results = learn_network(data_path, sensitive=true, heterogeneous=false)
+netw_results = learn_network(data_path, sensitive=true, heterogeneous=false, verbose=true, max_k=3, alpha=0.05, FDR=true)
+
+#netw_results_heterogenous = learn_network(data_path, metadata_path, sensitive=false, heterogeneous=true, verbose=true, max_k=6, alpha=0.01, FDR=true)
 
 save_network("results/network_output.gml", netw_results, detailed=true)