Package: MicrobiomeSurv 0.1.0

MicrobiomeSurv: A Biomarker Validation Approach for Classification and Predicting Survival Using Microbiome Data

An approach to identify microbiome biomarker for time to event data by discovering microbiome for predicting survival and classifying subjects into risk groups. Classifiers are constructed as a linear combination of important microbiome and treatment effects if necessary. Several methods were implemented to estimate the microbiome risk score such as majority voting technique, LASSO, Elastic net, supervised principle component analysis (SPCA), and supervised partial least squares analysis (SPLS). Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified. Large scale cross validation can be performed in order to investigate the mostly selected microbiome and for internal validation. During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique.

Authors:Thi Huyen Nguyen [aut, cre], Olajumoke Evangelina Owokotomo [aut], Ziv Shkedy [aut]

MicrobiomeSurv_0.1.0.tar.gz
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MicrobiomeSurv.pdf |MicrobiomeSurv.html✨
MicrobiomeSurv/json (API)

# Install 'MicrobiomeSurv' in R:
install.packages('MicrobiomeSurv', repos = c('https://n-t-huyen.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/n-t-huyen/microbiomesurv/issues

Datasets:

On CRAN:

28 exports 0.83 score 145 dependencies 2 scripts 152 downloads

Last updated 12 months agofrom:ae3e545487. Checks:OK: 1 ERROR: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 01 2024
R-4.5-winERRORSep 01 2024
R-4.5-linuxERRORSep 01 2024
R-4.4-winERRORSep 01 2024
R-4.4-macERRORSep 01 2024
R-4.3-winERRORSep 01 2024
R-4.3-macERRORSep 01 2024

Exports:CoxPHUniCVLasoelascoxCVMajorityvotesCVMSpecificCoxPhCVPcaPlsCVSITaxaDistHRElasticNetVaryAlphaEstimateHRf.pcaFirstFilterGetRAIntermediatePCAIntermediatePLSLasoelascoxMajorityvotesMiFreqMSpecificCoxPhplotQuantileAnalysisSecondFilterSITaxasummarySummaryDataSurvPcaClassSurvPlsClassTop1UniZerosPerGroup

Dependencies:abindade4apeaskpassbackportsbayesmBiobaseBiocGenericsbiomformatBiostringsbitopsbootbroomcarcarDatacaToolscliclustercodetoolscolorspacecommonmarkcompositionscorrplotcowplotcpp11crayoncurldata.tableDEoptimRDerivdigestdoBydplyrevaluateexactRankTestsfansifarverforeachgenericsGenomeInfoDbGenomeInfoDbDataggplot2ggpubrggrepelggsciggsignifggtextglmnetgluegplotsgridExtragridtextgtablegtoolshighrhttrigraphIRangesisobanditeratorsjpegjsonliteKernSmoothkm.ciKMsurvknitrlabelinglatticelifecyclelme4lmtestmagrittrmarkdownMASSMatrixMatrixModelsmaxstatmgcvmicrobenchmarkmicrobiomemimeminqamodelrmulttestmunsellmvtnormnlmenloptrnnetnumDerivopensslpbkrtestpermutephyloseqpillarpixmappkgconfigplsplyrpngpolynompurrrquantregR6RColorBrewerRcppRcppArmadilloRcppEigenreshape2rhdf5rhdf5filtersRhdf5librlangrobustbaserstatixRtsneS4VectorsscalesshapespSparseMstringistringrsuperpcsurvivalsurvminersurvMiscsystensorAtibbletidyrtidyselectUCSC.utilsutf8vctrsveganviridisLitewithrxfunxml2xtableXVectoryamlzlibbioczoo

Introduction to MicrobiomeSurv

Rendered fromIntroduction_to_MicrobiomeSurv.Rmdusingknitr::rmarkdownon Sep 01 2024.

Last update: 2023-09-25
Started: 2023-09-25

Readme and manuals

Help Manual

Help pageTopics
This function will fit the full and reduced models and calculate LRT raw p-value and adjusted p-value based on BH MethodCoxPHUni
Cross Validations for Lasso Elastic Net Survival predictive models and ClassificationCVLasoelascox
The cvle Class.cvle cvle-class cvle-method plot,cvle,missing-method show,cvle-method summary,cvle-method
Cross validation for majority votesCVMajorityvotes
The cvmm Class.cvmm cvmm-class cvmm-method plot,cvmm,ANY-method show,cvmm-method summary,cvmm-method
Cross validation for the Taxon specific analysisCVMSpecificCoxPh
The cvmv Class.cvmv cvmv-class cvmv-method plot,cvmv,ANY-method show,cvmv-method summary,cvmv-method
Cross Validations for PCA and PLS based methodsCVPcaPls
The cvpp Class.cvpp cvpp-class cvpp-method plot,cvpp,missing-method show,cvpp-method summary,cvpp-method
The cvsit Class.cvsit cvsit-class cvsit-method plot,cvsit,missing-method show,cvsit-method summary,cvsit-method
Cross validation for sequentially increases taxaCVSITaxa
Zero per treatment groups.data_zero_per_group_otu_w3
Null Distribution of the Estimated HRDistHR
Classification, Survival Estimation and VisualizationEstimateHR
Information at family level.fam_info_w3
Dataset at family level.fam_shan_trim_w3
This function is used for the first step of filtering which removes OTUs having all zeros (inactive OTUs). The input is an OTU matrix with rows are OTUs and columns are subjects.FirstFilter
This function convert OTU matrix to RA matrix.GetRA
Hello, World!hello
Wapper function for glmnetLasoelascox
Classifiction for Majority VotesMajorityvotes
Metadata taxonomy.metadata_taxonomy
Frequency of Selected Taxa from the LASSO, Elastic-net Cross-ValidationMiFreq
The ms Class.ms ms,ANY ms-class ms-method plot,ms,ANY-method show,ms-method summary,ms-method
Taxon by taxon Cox proportional analysisMSpecificCoxPh
The perm Class.perm perm-class perm-method plot,perm,ANY-method show,perm-method summary,perm-method
Quantile sensitivity analysisQuantileAnalysis
This function is used for the second step of filtering which removes OTUs based on a threshold.SecondFilter
Sequential Increase in Taxa for the PCA or PLS classifierSITaxa
This function gives indices such as Observed richness, Shannon index, Inverse Simpson, ... of higher level such as levelily, order, phylum, ...SummaryData
Survival PCA and Classification for microbiome dataSurvPcaClass
Survival PLS and Classification for microbiome dataSurvPlsClass
This function finds out the taxon has the smallest p-value, then calculate risk score of patients based on that taxon. Categorized subjects into high or low risk groups based on the mean of the risk score as a cutoff point Checking whether the two groups are significant difference in the probability to be survival.Top1Uni
OTU table at week 3.Week3_otu
Response datase.Week3_response
This function returns a matrix with rows are Micros and 9 columns containing number and the proportion of zeros per groups of treatments and in total.ZerosPerGroup