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

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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