Package 'SAFEMCN'

Analyze Network Topology Parameters with Rarefaction

Description

Calculate network topology parameters from OTU tables with customizable correlation thresholds, parallel processing options, and visualization capabilities including topology parameter plots, exponential formula fitting, prediction of future sample sizes, and AR1 analysis.

Usage

analyze_network_topology(
  otu_file,
  r_threshold = 0.6,
  p_threshold = 0.05,
  cor_method = "spearman",
  start_size = 5,
  end_size = NULL,
  step_size = 1,
  replicates = 50,
  use_parallel = FALSE,
  output_prefix = "network_parameters",
  plot_topology = FALSE,
  plot_start_size = 11,
  fit_formula = FALSE,
  fit_start_size = 11,
  predict_end_size = NULL,
  run_ar1 = FALSE,
  ar1_input_file = NULL,
  ar1_windows = c(5, 10, 15)
)

Arguments

otu_file	OTU table file name (full path)
r_threshold	Correlation coefficient threshold (default: 0.6)
p_threshold	P-value threshold (default: 0.05)
cor_method	Correlation method: "spearman" or "pearson" (default: "spearman")
start_size	Starting sample size (default: 5)
end_size	Ending sample size (default: NULL, uses total samples)
step_size	Step size for sample sizes (default: 1)
replicates	Number of replicates (default: 50)
use_parallel	Use parallel processing (default: FALSE)
output_prefix	Output file prefix (default: "network_parameters")
plot_topology	Whether to plot topology parameters vs sample size (default: FALSE)
plot_start_size	Starting sample size for topology plots (default: 11)
fit_formula	Whether to perform exponential formula fitting (default: FALSE)
fit_start_size	Starting sample size for formula fitting (default: 11)
predict_end_size	End sample size for prediction (default: NULL, no prediction)
run_ar1	Whether to run AR1 analysis (default: FALSE)
ar1_input_file	Input file name for AR1 analysis (default: NULL, auto-select)
ar1_windows	Sliding window sizes for AR1 (default: c(5, 10, 15))

Value

A list containing raw results, summary statistics, fit parameters, and predictions

Examples

# Create a small toy dataset (8 OTUs x 10 samples)
set.seed(42)
otu_data <- data.frame(
  matrix(rpois(80, lambda = 10), nrow = 8, ncol = 10)
)
rownames(otu_data) <- paste0("OTU", 1:8)
colnames(otu_data) <- paste0("Sample", 1:10)
tmp_file <- tempfile(fileext = ".csv")
write.csv(otu_data, tmp_file)

results <- analyze_network_topology(
  otu_file = tmp_file,
  r_threshold = 0.4,
  p_threshold = 0.05,
  start_size = 5,
  end_size = 7,
  step_size = 1,
  replicates = 2,
  use_parallel = FALSE
)
# Check structure of returned list
names(results)
head(results$summary)


# Larger run with fitting and prediction (takes longer)
set.seed(100)
otu_big <- data.frame(
  matrix(rpois(200, lambda = 10), nrow = 10, ncol = 20)
)
rownames(otu_big) <- paste0("OTU", 1:10)
colnames(otu_big) <- paste0("Sample", 1:20)
tmp_file2 <- tempfile(fileext = ".csv")
write.csv(otu_big, tmp_file2)

results2 <- analyze_network_topology(
  otu_file = tmp_file2,
  r_threshold = 0.4,
  start_size = 5,
  end_size = 18,
  step_size = 1,
  replicates = 5,
  fit_formula = TRUE,
  fit_start_size = 5,
  predict_end_size = 25
)
str(results2$fit_parameters)

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