[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290575-105":59,"doc-detail-290575-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","supplementary-text-s2-r-codes-to-run-faime","Supplementary Text S2 - R codes to run FAIME","","Supplementary Text S2 provides R code for running FAIME, an algorithm designed to predict Functional Analysis of Individual Microarray Expression. It includes a wrapper function runFAIME and a core scoring function that computes weighted ranks of gene expression, then derives pathway scores using members versus non-members. Inputs include expression data, gene/probe mappings, geneset-to-gene relationships, and options such as na.last, logCheck, and weightRank (including a mild weighting mode).",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":30,"@type":76,"position":81},"https://docshare.wps.com/document/technology/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/supplementary-text-s2-r-codes-to-run-faime/290575/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/supplementary-text-s2-r-codes-to-run-faime/290575.png","ImageObject",300,407,{"name":92,"@type":93},"Cart","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-16","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is FAIME in this supplementary material?","Question",{"text":112,"@type":113},"FAIME is introduced as a new algorithm to predict Functional Analysis of Individual Microarray Expression. The provided code implements how pathway scores are computed from microarray expression.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which main functions are included to run FAIME?",{"text":117,"@type":113},"The material defines one main function runFAIME as an R wrapper for FAIME, and it also includes the key function FAIME that computes a score for each gene set per sample.",{"name":119,"@type":110,"acceptedAnswer":120},"How does weightRank affect the FAIME scoring?",{"text":121,"@type":113},"weightRank controls whether a weighted rank is used. When weightRank=\"mild\", an exponential weighting exp((rank-N)/N) is applied to scale weights between 0 and 1; otherwise weighted rank uses a different exponential form or a negative unweighted rank when weightRank is FALSE.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290575,1790711405,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":29,"category_name":30,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":81,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":39},18829141979164,"https://eur-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Supplementary Text S2: R codes to run FAIME  \n\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#  \n\\# \"FAIME\" is a new algorithm to predict Functional Analysis of Individual Microarray Expression.\\#  \n\\# This R code is composed one main function \"runFAIME\", which is a R wrap for FAIME. \\#  \n\\# Programmer: Xinan Yang (xyang2 [at uchicago.edu](at uchicago.edu)) \\#  \n\\# Last updated on Feb 22th, 2010. \\#  \n\\# Version 2 \\#\\# \\#  \n\\# Update recorder: \\#  \n\\# Added a parameter weightRank=TRUE; if FALSE, no weighted rank will be used instead \\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#  \n\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\# Pre-required R package \\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#  \n\\#\\# To install Bioconductor three packages, include the following in your R code.  \n\\#\\# source(\"[http://bioconductor.org/biocLite.R](http://bioconductor.org/biocLite.R)\")  \n\\#\\# biocLite(\"Biobase\")  \nlibrary(\"Biobase\")  \n\\# Declaration of instants which need to be locally modified by user.  \nArrayInput \\# mRNA-expression input file name.  \nGeneset2gene \\# a 2 column matrix mapping between GO to genes  \nFDR.T = 0.05 \\# Threshold to call a gene-set as significant  \ngenewprobe \\# array of microarray probe IDs with annotated gene symbol  \n\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\# The key function to implement FAIME algorithm \\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\# parameters (inputs):  \n\\#\\# sampleExp: A vector of the normalized exp value with probe Name  \n\\#\\# genemembers: A vector of the probe Name of the gene members ofa geneset of interest \\#\\# weightRank: A logical to decide whether weighted rank is to be used, default is TRUE.\\#\\# if weightRank=\"mild\", the exp((rank-N)/N) is applied which controls the weights \\#\\# between 0 and 1  \n\\#\\# output:  \n\\#\\# y: A score assigned to each pathway  \nFAIME \u003C-function(sampleExp, genemembers, na.last=TRUE, weightRank, logCheck=FALSE)  \n{  \n\\# check if exp values are log transformed, if not, log transformed \\#  \nif (logCheck) { \\# by this transformation, negative value will be scaled to NA \\# if (max(sampleExp) > 20) sampleExp \u003C-log2(SampleExp)  \n}  \nif (any([is.na](is.na)(names(sampleExp)))) stop(\"Please input sampleExp with probe IDs\")  \nallGenes \u003C-names(sampleExp)  \nN \u003C-length(allGenes)  \nnongenemembers \u003C-allGenes[-which(allGenes %in% genemembers)]  \n\\# Step 1: Calculation of weighted rank of gene expression \\#  \n\\# Rank from the lowest to highest, thus the leading up-regulated genes get the higher weighted score \\#  \nrankedExp \u003C-rank(sampleExp, na.last=na.last)  \nif(weightRank==\"mild\") {  \nrankscore \u003C-rankedExp*exp((rankedExp-N)/N)  \n} else {  \nif (weightRank) {  \nrankscore \u003C-rankedExp*exp(rankedExp/N)} else {  \nrankscore \u003C -rankedExp}  \n}  \n\\# Step 2: Calculate F-score for each individual gene-set per sample using mRNA expression of their gene members and that of its non-members \\#  \nST \u003C-sum(rankscore[genemembers])/length(genemembers)  \nSN \u003C-sum(rankscore[nongenemembers])/length(nongenemembers)  \ny \u003C -ST-SN  \nreturn(y)}  \n\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\# The main function to run FAIME \\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\# parameters (inputs):  \n\\#\\# dat: An expression matrix, row for a probe Name and column for a sample  \n\\#\\# genewprobe: A vector of gene Symbols for rows of dat, the names of which are probe Names \\#\\# geneset2gene: An one-to-one mapping matrix with two columns,  \n\\#\\# the 1st column is geneset ID/name, and the 2nd is its gene members \\#\\# weightRank: A logical to decide whether weighted rank is to be used, default is TRUE.\\#\\# if we","cbCaiq2GycrNaYy7","https://ap.wps.com/l/cbCaiq2GycrNaYy7","pdf",60789,"English","# Pre-required R package\n## Main function to implement FAIME algorithm\n## The main function to run FAIME","[{\"question\":\"What is FAIME in this supplementary material?\",\"answer\":\"FAIME is introduced as a new algorithm to predict Functional Analysis of Individual Microarray Expression. The provided code implements how pathway scores are computed from microarray expression.\"},{\"question\":\"Which main functions are included to run FAIME?\",\"answer\":\"The material defines one main function runFAIME as an R wrapper for FAIME, and it also includes the key function FAIME that computes a score for each gene set per sample.\"},{\"question\":\"How does weightRank affect the FAIME scoring?\",\"answer\":\"weightRank controls whether a weighted rank is used. When weightRank=\\\"mild\\\", an exponential weighting exp((rank-N)/N) is applied to scale weights between 0 and 1; otherwise weighted rank uses a different exponential form or a negative unweighted rank when weightRank is FALSE.\"}]","Supplementary Text S2 - R codes to run FAIME | PDF",1789642114]