CoreMicrobiomeR (version 0.1.0)

visualize: Visualizing the effect of minimum count on the core size

Description

The visualize function generates interactive line plots that allow users to explore the impact of different min_count values on the number of core OTUs. Users can interact with the plots to examine the relationship between filtering criteria and core OTU identification visually.

Usage

visualize(filtered_otu, min_count_val, max_count_val, count_val_interval,
prop, min_total_count, method, top_percentage)

Value

This function gives a line plot which shows change in number of core OTUs with minimum count

Arguments

filtered_otu

A dataframe of OTUs obtained before filtering which is retrieved from CoreMicrobiome function where the first row is the OTU ID and column names refer to sites/sample names

min_count_val

A numeric value of Minimum count for each OTU to be present in each to be included after the filtering

max_count_val

A numeric value of Maximum count for each OTU to be present in each to be included after the filtering

count_val_interval

Count value interval for each OTU to be present in each to be included after the filtering

prop

Minimum proportion of samples in which an OTU must be present

min_total_count

Minimum total count for each OTU to be included after the filtering

method

Different normalization methods, includes "rrarefy", "srs", "css", "tmm", or "none"

top_percentage

Percentage used for obtaining the Core OTUs

Examples

#To run input data
core_1 <- CoreMicrobiome(
 otu_table = demo_otu,
 tax_table = demo_tax,
 metadata_table = demo_md,
 filter_type = "occupancy_fun_filter", #Or "abundance_fun_filter", Or "combined_filter"
 percent = 0.5,
 method = "css",  # Or "srs", "rrarefy", "tmm", "tmmwsp", "rle", "upperquartile", "none"
 beta_diversity_method = "jaccard",
 top_percentage = 10  # Adjust the percentage as needed for core/non-core OTUs
)
#To view the line plot
visualize(filtered_otu = core_1[["final_otu_table_bef_filter"]],
         min_count_val = 5,
         max_count_val = 25,
         count_val_interval = 5,
         prop = 0.1,
         min_total_count = 10,
         method = "srs",
         top_percentage =10)