42 lines
1.2 KiB
R
42 lines
1.2 KiB
R
library(phyloseq)
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library(ggplot2)
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library(sbm)
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library(biomformat)
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source("utils.R")
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# data("enterotype")
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# the_data <- enterotype
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# data("mach")
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the_data <- import_biom("data/chaillou/chaillou.biom")
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# the_data <- import_biom("data/mach/kinetic.biom")
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# the_data <- import_biom("data/ravel/ravel.biom")
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net_tables <- collapse_otu_at_taxo(the_data)
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net_table <- as.matrix(net_tables[[3]])
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dim(net_table)
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nb_inter <- sum(net_table > 0)
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all_possible_inter <- nrow(net_table) * ncol(net_table)
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(density <- nb_inter / all_possible_inter)
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res <- estimateBipartiteSBM(net_table, dimLabels = c("OTU", "Sample"), model = "poisson", estimOptions = list(plot = 0))
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plot(res, type = "data")
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sdCol <- function(matrix) {
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sapply(seq_len(ncol(matrix)), function(col) sd(matrix[, col]))
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}
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t_net_table <- t(net_table)
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std_otu <- sdCol(t_net_table)
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mean_otu <- rowMeans(net_table)
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stats_df <- data.frame(otu = rownames(net_table), mean = mean_otu, std = std_otu, var = std_otu^2)
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ggplot(stats_df, aes(x = mean, y = var)) +
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geom_line() +
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geom_point() +
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geom_abline(slope = 1, color = "red") +
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scale_y_log10() +
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scale_x_log10() +
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ggtitle("OTU for log(mean) = log(var)")
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