diff --git a/lbm_phyloseq_test.R b/lbm_phyloseq_test.R
index 918d16b..68ba345 100644
--- a/lbm_phyloseq_test.R
+++ b/lbm_phyloseq_test.R
@@ -3,23 +3,26 @@ library(ggplot2)
 library(sbm)
 library(biomformat)
 
+source("utils.R")
+
 # data("enterotype")
+# the_data <- enterotype
 # data("mach")
-data <- import_biom("data/chaillou/chaillou.biom")
-net_table <- otu_table(data)
+the_data <- import_biom("data/chaillou/chaillou.biom")
+# the_data <- import_biom("data/mach/kinetic.biom")
+# the_data <- import_biom("data/ravel/ravel.biom")
+net_tables <- collapse_otu_at_taxo(the_data)
+net_table <- as.matrix(net_tables[[3]])
 dim(net_table)
 
 nb_inter <- sum(net_table > 0)
 all_possible_inter <- nrow(net_table) * ncol(net_table)
 (density <- nb_inter / all_possible_inter)
 
-res <- estimateBipartiteSBM(net_table, dimLabels = c("OTU", "Sample"), model = "poisson")
-res_t <- estimateBipartiteSBM(t(net_table), dimLabels = rev(c("OTU", "Sample")))
+res <- estimateBipartiteSBM(net_table, dimLabels = c("OTU", "Sample"), model = "poisson", estimOptions = list(plot = 0))
 
 plot(res, type = "data")
 
-plot(res_t, type = "data")
-
 sdCol <- function(matrix) {
     sapply(seq_len(ncol(matrix)), function(col) sd(matrix[, col]))
 }
@@ -37,17 +40,3 @@ ggplot(stats_df, aes(x = mean, y = var)) +
     scale_y_log10() +
     scale_x_log10() +
     ggtitle("OTU for log(mean) = log(var)")
-
-ggplot(stats_df, aes(x = mean, y = var / mean)) +
-    geom_line() +
-    geom_point() +
-    geom_hline(yintercept = 1, color = "red") +
-    ggtitle("OTU for f(mean) = var/mean = 1")
-
-ggplot(stats_df, aes(x = 1 / mean, y = var / mean^2)) +
-    geom_line() +
-    geom_point() +
-    geom_abline(slope = 1, color = "red") +
-    scale_y_log10() +
-    scale_x_log10() +
-    ggtitle("OTU for log(1/mean) = log(var/mean^2)")