{ "_id": "6ac75cecde46216d5edcee76", "Package": "MicrobiomeSurv", "Type": "Package", "Title": "A Biomarker Validation Approach for Classification and\nPredicting Survival Using Microbiome Data", "Version": "0.1.0", "Authors@R": "c(person(\"Thi Huyen\",\"Nguyen\", email=\"thihuyen.nguyen@uhasselt.be\", role=c(\"aut\",\"cre\")),\nperson(\"Olajumoke Evangelina\",\"Owokotomo\", email=\"olajumoke.x.owokotomo@gsk.com\", role=\"aut\"),\nperson(\"Ziv\",\"Shkedy\", email=\"ziv.shkedy@uhasselt.be\", role=\"aut\"))", "Maintainer": "Thi Huyen Nguyen ", "Description": "An approach to identify microbiome biomarker for time to\nevent data by discovering microbiome for predicting survival\nand classifying subjects into risk groups. Classifiers are\nconstructed as a linear combination of important microbiome and\ntreatment effects if necessary. Several methods were\nimplemented to estimate the microbiome risk score such as\nmajority voting technique, LASSO, Elastic net, supervised\nprinciple component analysis (SPCA), and supervised partial\nleast squares analysis (SPLS). Sensitivity analysis on the\nquantile used for the classification can also be accessed to\ncheck the deviation of the classification group based on the\nquantile specified. Large scale cross validation can be\nperformed in order to investigate the mostly selected\nmicrobiome and for internal validation. During the evaluation\nprocess, validation is accessed using the hazard ratios (HR)\ndistribution of the test set and inference is mainly based on\nresampling and permutations technique.", "URL": "https://github.com/N-T-Huyen/MicrobiomeSurv", "BugReports": "https://github.com/N-T-Huyen/MicrobiomeSurv/issues/new", "License": "GPL-3", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.2.3", "VignetteBuilder": "knitr", "Config/pak/sysreqs": "cmake libglpk-dev make libicu-dev libjpeg-dev\nlibpng-dev libxml2-dev libssl-dev zlib1g-dev", "Repository": "https://n-t-huyen.r-universe.dev", "Date/Publication": "2023-10-04 10:34:30 UTC", "RemoteUrl": "https://github.com/n-t-huyen/microbiomesurv", "RemoteRef": "HEAD", "RemoteSha": "ae3e5454878c291fe2cb39e44e6e65a65d961e42", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-08 08:52:08 UTC", "User": "root" }, "Author": "Thi Huyen Nguyen [aut, cre],\nOlajumoke Evangelina Owokotomo [aut],\nZiv Shkedy [aut]", "_user": "n-t-huyen", 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"title": "Dataset at family level.", "object": "fam_shan_trim_w3", "file": "fam_shan_trim_w3.xlsx", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "X.", "Run1031", "Run1032", "Run1033", "Run1034", "Run1035", "Run1036", "Run1037", "Run1038", "Run1039", "Run1040", "Run1041", "Run1042", "Run1043", "Run1044", "Run1045", "Run1047", "Run1048", "Run1049", "Run1050", "Run1051", "Run1052", "Run1053", "Run1054", "Run1055", "Run1101", "Run1102", "Run1103", "Run1104", "Run1105", "Run1106", "Run1107", "Run1108", "Run1109", "Run1110", "Run1111", "Run1112", "Run1114", "Run1115", "Run1116", "Run1117", "Run1118", "Run1119", "Run1121", "Run1122", "Run1123", "Run1124", "Run1125", "Run1126", "Run1127", "Run1128", "Run1129", "Run1130", "Run1131", "Run1133", "Run1193", "Run1194", "Run1195", "Run1196", "Run1197", "Run1198", "Run1335", "Run1336", "Run1337", "Run1338", "Run1339", "Run1340", "Run1341", "Run1403", "Run1404", "Run1405", "Run1406", "Run1407", "Run1408", "Run1409", "Run1418", "Run1419", "Run1420", "Run1421", "Run3424", "Run3425", "Run3426" ], "rows": 6, "table": true, "tojson": true }, { "name": "metadata_taxonomy", "title": "Metadata taxonomy.", "object": "metadata_taxonomy", "class": [ "data.frame" ], "fields": [ "OTUID", "Taxon", "Confidence" ], "rows": 2720, "table": true, "tojson": true }, { "name": "Week3_otu", "title": "OTU table at week 3.", "object": "Week3_otu", "file": "Week3_otu.xlsx", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [], "rows": 81, "table": true, "tojson": true }, { "name": "Week3_response", "title": "Response datase.", "object": "Week3_response", "file": "Week3_response.xlsx", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "SampleID", "Treatment", "T1Dweek", "T1D", "Treatment_new" ], "rows": 81, "table": true, "tojson": true } ], "_help": [ { "page": "CoxPHUni", "title": "This function will fit the full and reduced models and calculate LRT raw p-value and adjusted p-value based on BH Method", "topics": [ "CoxPHUni" ] }, { "page": "CVLasoelascox", "title": "Cross Validations for Lasso Elastic Net Survival predictive models and Classification", "topics": [ "CVLasoelascox" ] }, { "page": "cvle-class", "title": "The cvle Class.", "topics": [ "cvle", "cvle-class", "cvle-method", "plot,cvle,missing-method", "show,cvle-method", "summary,cvle-method" ] }, { "page": "CVMajorityvotes", "title": "Cross validation for majority votes", "topics": [ "CVMajorityvotes" ] }, { "page": "cvmm-class", "title": "The cvmm Class.", "topics": [ "cvmm", "cvmm-class", "cvmm-method", "plot,cvmm,ANY-method", "show,cvmm-method", "summary,cvmm-method" ] }, { "page": "CVMSpecificCoxPh", "title": "Cross validation for the Taxon specific analysis", "topics": [ "CVMSpecificCoxPh" ] }, { "page": "cvmv-class", "title": "The cvmv Class.", "topics": [ "cvmv", "cvmv-class", "cvmv-method", "plot,cvmv,ANY-method", "show,cvmv-method", "summary,cvmv-method" ] }, { "page": "CVPcaPls", "title": "Cross Validations for PCA and PLS based methods", "topics": [ "CVPcaPls" ] }, { "page": "cvpp-class", "title": "The cvpp Class.", "topics": [ "cvpp", "cvpp-class", "cvpp-method", "plot,cvpp,missing-method", "show,cvpp-method", "summary,cvpp-method" ] }, { "page": "cvsit-class", "title": "The cvsit Class.", "topics": [ "cvsit", "cvsit-class", "cvsit-method", "plot,cvsit,missing-method", "show,cvsit-method", "summary,cvsit-method" ] }, { "page": "CVSITaxa", "title": "Cross validation for sequentially increases taxa", "topics": [ "CVSITaxa" ] }, { "page": "data_zero_per_group_otu_w3", "title": "Zero per treatment groups.", "topics": [ "data_zero_per_group_otu_w3" ] }, { "page": "DistHR", "title": "Null Distribution of the Estimated HR", "topics": [ "DistHR" ] }, { "page": "EstimateHR", "title": "Classification, Survival Estimation and Visualization", "topics": [ "EstimateHR" ] }, { "page": "fam_info_w3", "title": "Information at family level.", "topics": [ "fam_info_w3" ] }, { "page": "fam_shan_trim_w3", "title": "Dataset at family level.", "topics": [ "fam_shan_trim_w3" ] }, { "page": "FirstFilter", "title": "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.", "topics": [ "FirstFilter" ] }, { "page": "GetRA", "title": "This function convert OTU matrix to RA matrix.", "topics": [ "GetRA" ] }, { "page": "hello", "title": "Hello, World!", "topics": [ "hello" ] }, { "page": "Lasoelascox", "title": "Wapper function for glmnet", "topics": [ "Lasoelascox" ] }, { "page": "Majorityvotes", "title": "Classifiction for Majority Votes", "topics": [ "Majorityvotes" ] }, { "page": "metadata_taxonomy", "title": "Metadata taxonomy.", "topics": [ "metadata_taxonomy" ] }, { "page": "MiFreq", "title": "Frequency of Selected Taxa from the LASSO, Elastic-net Cross-Validation", "topics": [ "MiFreq" ] }, { "page": "ms-class", "title": "The ms Class.", "topics": [ "ms", "ms,ANY", "ms-class", "ms-method", "plot,ms,ANY-method", "show,ms-method", "summary,ms-method" ] }, { "page": "MSpecificCoxPh", "title": "Taxon by taxon Cox proportional analysis", "topics": [ "MSpecificCoxPh" ] }, { "page": "perm-class", "title": "The perm Class.", "topics": [ "perm", "perm-class", "perm-method", "plot,perm,ANY-method", "show,perm-method", "summary,perm-method" ] }, { "page": "QuantileAnalysis", "title": "Quantile sensitivity analysis", "topics": [ "QuantileAnalysis" ] }, { "page": "SecondFilter", "title": "This function is used for the second step of filtering which removes OTUs based on a threshold.", "topics": [ "SecondFilter" ] }, { "page": "SITaxa", "title": "Sequential Increase in Taxa for the PCA or PLS classifier", "topics": [ "SITaxa" ] }, { "page": "SummaryData", "title": "This function gives indices such as Observed richness, Shannon index, Inverse Simpson, ... of higher level such as levelily, order, phylum, ...", "topics": [ "SummaryData" ] }, { "page": "SurvPcaClass", "title": "Survival PCA and Classification for microbiome data", "topics": [ "SurvPcaClass" ] }, { "page": "SurvPlsClass", "title": "Survival PLS and Classification for microbiome data", "topics": [ "SurvPlsClass" ] }, { "page": "Top1Uni", "title": "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.", "topics": [ "Top1Uni" ] }, { "page": "Week3_otu", "title": "OTU table at week 3.", "topics": [ "Week3_otu" ] }, { "page": "Week3_response", "title": "Response datase.", "topics": [ "Week3_response" ] }, { "page": "ZerosPerGroup", "title": "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.", "topics": [ "ZerosPerGroup" ] } ], "_readme": "https://github.com/n-t-huyen/microbiomesurv/raw/HEAD/README.md", "_rundeps": [ "abind", "ade4", "ape", "backports", "bayesm", "Biobase", "BiocGenerics", "biomformat", "Biostrings", "bitops", "boot", "broom", "car", "carData", "caTools", "cli", "cluster", "codetools", "colorspace", "commonmark", "compositions", "corrplot", "cowplot", "cpp11", "crayon", 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