# Find Neighbor Species of a Bacteria of Interest in the Human Gut Microbiota

## Documentation for package ‘NeighborFinder’ version 1.0.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package README

## Help Pages

apply_NeighborFinder | Apply NeighborFinder on raw data
apply_NF_simple | Apply NeighborFinder simplest version on raw data
choose_params_values | Render a table to give an indication of the values to choose for the prevalence level and the top filtering percentage
compute_precision | Compute precision rate
compute_recall | Compute recall rate
cvglm_to_coeffs_by_object | Apply cv.glmnet() for a list of module IDs and for each prevalence level
data | data
final_step | Gather lists of neighbors of true ones from the graph and detected ones from cv.glmnet()
find_all_module_neighbors | Apply cv.glmnet() for a list of module IDs
find_module_neighbors | Apply cv.glmnet() for a given mmodule ID
get_count_table | Conversion to count table function with prevalence filter
graphs | graphs
graph_step | Generate a graph with a "cluster-like" structure, only needed for simulation purposes
identify_module | List the modules corresponding to a given object of interest
intersections_network | Display the intersection network from 2 or more datasets
intersections_table | Display the intersection table summarizing the results from 2 or more datasets
mclr | Modified central log ratio (mclr) transformation extracted from the SPRING package
metadata | metadata
module_to_node | Correspondence between the module ID (msp or functional module) and its name (bacteria or function)
new_synth_data | Simulate data from some empirical count dataset with a "cluster-like" structure
norm_data | Normalize data and filters it by prevalence level
prev_for_selected_nodes | Extract edges in graph involving any module in object_of_interest set
result_example | result_example
simulate_by_prevalence | List the simulated count tables by level of prevalence
simulate_from_ecdf | Simulate data Generates synthetic count data based on empirical cumulative distribution (ecdf) of real count data
taxo | taxo
test_filter | Render a table gathering precision and recall rates before and after filtering on coefficient values
truth_by_prevalence | Give true neighbors by level of prevalence
visualize_network | Display network after applying NeighborFinder