Package: NeighborFinder 1.0.1

Mathilde Sola

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

Implementation of the local approach described in Sola et al., 2026 <doi:10.64898/2025.12.05.692507> to identify companion species of a bacteria of interest. From several abundance tables of metagenomic data, 'NeighborFinder' suggests a shortlist of companion species based on the integration of results. A visualization via a network is proposed.

Authors:Mathilde Sola [aut, cre], Mahendra Mariadassou [aut], Magali Berland [aut]

NeighborFinder_1.0.1.tar.gz
NeighborFinder_1.0.1.tar.gz(r-4.7-any)NeighborFinder_1.0.1.tar.gz(r-4.6-any)
NeighborFinder_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
NeighborFinder/json (API)

# Install 'NeighborFinder' in R:
install.packages('NeighborFinder', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.00 score 25 exports 53 dependencies

Last updated from:d2527c165e. Checks:4 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK159
source / vignettesOK267
linux-release-x86_64OK158
wasm-releaseOK171

Exports:%>%apply_NeighborFinderapply_NF_simplechoose_params_valuescompute_precisioncompute_recallcvglm_to_coeffs_by_objectfinal_stepfind_all_module_neighborsfind_module_neighborsget_count_tablegraph_stepidentify_moduleintersections_networkintersections_tablemclrmodule_to_nodenew_synth_datanorm_dataprev_for_selected_nodessimulate_by_prevalencesimulate_from_ecdftest_filtertruth_by_prevalencevisualize_network

Dependencies:clicodacodetoolscpp11crayondplyrfarverforcatsforeachgenericsGGallyggplot2ggstatsglmnetgluegtablehmsigraphisobanditeratorslabelinglatticelifecyclemagrittrMatrixmvtnormnetworkpatchworkpillarpkgconfigprettyunitsprogresspurrrR6RColorBrewerRcppRcppEigenrlangS7scalesshapesnastatnet.commonstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithr

Detailed example
Presentation of dataset: CRC Example | Preview of the data | 1) The abundance table | 2) The metadata | 3) The taxonomy | 4) The graph | Aim of this use case | Test if the default parameters of NeighborFinder are suitable for your species of interest & dataset | Apply NeighborFinder & look for Escherichia coli neighbors in CRC patients | Visualize the corresponding network | Apply NeighborFinder with covariate option | Look at the intersection of neighbors found in the 3 subgroups
Rendered fromNeighborFinder_vignette.Rmdusingknitr::rmarkdown

Last update: 2026-06-30
Started: 2026-06-30

Technical Report
Why use NeighborFinder? | How to use it? | Input dataframe format | What is behind apply_NeighborFinder() ? | 1) Pre-processing: Counts & Normalization | a) Prevalence filter & shotgun pre-treatment | b) Normalization | 2) Regularized linear regressions | a) Simple case: no covariates | b) Handling covariates | 3) Post-processing | a) Filtering the results | b) Increasing robusteness | How to calibrate the parameters values ?
Rendered fromNeighborFinder_technical_report.Rmdusingknitr::rmarkdown

Last update: 2026-06-30
Started: 2026-06-30

Readme and manuals

Help Manual

Help pageTopics
Apply NeighborFinder on raw dataapply_NeighborFinder
Apply NeighborFinder simplest version on raw dataapply_NF_simple
Render a table to give an indication of the values to choose for the prevalence level and the top filtering percentagechoose_params_values
Compute precision ratecompute_precision
Compute recall ratecompute_recall
Apply cv.glmnet() for a list of module IDs and for each prevalence levelcvglm_to_coeffs_by_object
datadata
Gather lists of neighbors of true ones from the graph and detected ones from cv.glmnet()final_step
Apply cv.glmnet() for a list of module IDsfind_all_module_neighbors
Apply cv.glmnet() for a given mmodule IDfind_module_neighbors
Conversion to count table function with prevalence filterget_count_table
Generate a graph with a "cluster-like" structure, only needed for simulation purposesgraph_step
graphsgraphs
List the modules corresponding to a given object of interestidentify_module
Display the intersection network from 2 or more datasetsintersections_network
Display the intersection table summarizing the results from 2 or more datasetsintersections_table
Modified central log ratio (mclr) transformation extracted from the SPRING packagemclr
metadatametadata
Correspondence between the module ID (msp or functional module) and its name (bacteria or function)module_to_node
Simulate data from some empirical count dataset with a "cluster-like" structurenew_synth_data
Normalize data and filters it by prevalence levelnorm_data
Extract edges in graph involving any module in object_of_interest setprev_for_selected_nodes
result_exampleresult_example
List the simulated count tables by level of prevalencesimulate_by_prevalence
Simulate data Generates synthetic count data based on empirical cumulative distribution (ecdf) of real count datasimulate_from_ecdf
taxotaxo
Render a table gathering precision and recall rates before and after filtering on coefficient valuestest_filter
Give true neighbors by level of prevalencetruth_by_prevalence
Display network after applying NeighborFindervisualize_network