Adding LBM sequential first draft
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lbm_at_diff_taxo_seq.R
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72
lbm_at_diff_taxo_seq.R
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source("utils.R")
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library(biomformat)
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library(phyloseq)
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library(R.utils)
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library(stringr)
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library(sbm)
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library(blockmodels)
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the_data <- import_biom("data/chaillou/chaillou.biom")
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per_taxa_network <- collapse_otu_at_taxo(the_data)
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# Detect if first rank (higher) is fully collapsed
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if (nrow(per_taxa_network[[1]]) <= 1L) {
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message("The first network has only one row. Removing it.")
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per_taxa_network <- per_taxa_network[-1]
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}
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# fit_res <- lapply(per_taxa_network, function(network) {
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# withTimeout(
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# expr = {
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# fit <- estimateBipartiteSBM(network, model = "poisson", estimOptions = list(plot = 0))
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# }, timeout = 300,
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# onTimeout = "warning"
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# )
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# if (exists("fit")) {
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# out <- fit
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# } else {
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# out <- NULL
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# }
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# return(fit)
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# })
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model <- BM_poisson(
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membership_type = "LBM",
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adj = per_taxa_network[[1]],
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verbosity = 0,
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plotting = "",
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ncores = 1L
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)
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model$estimate()
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# Here we extract the memberships
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best_model_memberships <- model$memberships[[which.max(model$ICL)]]
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next_cc <- list(Z1 = best_model_memberships$Z1, Z2 = best_model_memberships$Z2)
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# Here we dispatch the memberships for the OTU
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## Propagate the rownames
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rank_id <- 3
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rownames(next_cc$Z1) <- rownames(per_taxa_network[[rank_id - 2]])
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next_Z1 <- propagate_taus(tau_matrix = next_cc$Z1, physeq = the_data, taxrank = phyloseq::rank_names(the_data)[rank_id])
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## Noising of the next_Z1
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dZ1 <- dim(next_Z1)
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nZ1 <- dZ1[1] * dZ1[2]
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next_Z1 <- next_Z1 + matrix(rnorm(n = nZ1, mean = 0, sd = 0.001), nrow = dZ1[1], ncol = dZ1[2])
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## Normalizing by rows
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C_Z1 <- rowSums(next_Z1)
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next_Z1 <- next_Z1 / C_Z1
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next_cc$Z1 <- next_Z1
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next_res <- blockmodels:::dispatcher(
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membership_name = "LBM",
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membership_init = next_cc,
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model_name = "poisson",
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network = list(adjacency = per_taxa_network[[2]]), real_EM = TRUE
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)
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next_cc <- next_res[["membership"]][c("Z1", "Z2")]
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rank_id <- rank_id + 1
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