## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE) ## ----eval = FALSE------------------------------------------------------------- # if(!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # BiocManager::install("rnaEditr") ## ----message=FALSE------------------------------------------------------------ library(rnaEditr) ## ----------------------------------------------------------------------------- data(rnaedit_df) ## ----------------------------------------------------------------------------- rnaedit_df[1:3, 1:3] ## ----------------------------------------------------------------------------- pheno_df <- readRDS( system.file( "extdata", "pheno_df.RDS", package = 'rnaEditr', mustWork = TRUE ) ) ## ----------------------------------------------------------------------------- pheno_df[1:3, 1:3] ## ----------------------------------------------------------------------------- identical(pheno_df$sample, colnames(rnaedit_df)) ## ----------------------------------------------------------------------------- rnaedit2_df <- CreateEditingTable( rnaEditMatrix = rnaedit_df ) ## ----------------------------------------------------------------------------- table(pheno_df$sample_type) ## ----results='hide'----------------------------------------------------------- tumor_single_df <- TestAssociations( # an RNA editing dataframe with special class "rnaEdit_df" from function # CreateEditingTable() if site-specific analysis, from function # SummarizeAllRegions() if region-based analysis. rnaEdit_df = rnaedit2_df, # a phenotype dataset that must have variable "sample" whose values are a # exact match to the colnames of "rnaEdit_df". pheno_df = pheno_df, # name of outcome variable in phenotype dataset "pheno_df" that you want to # test. responses_char = "sample_type", # names of covariate variables in phenotype dataset "pheno_df" that you want # to add into the model. covariates_char = NULL, # type of outcome variable that you input in argument "responses_char". respType = "binary", # order the final results by p-values or not. orderByPval = TRUE ) ## ----------------------------------------------------------------------------- tumor_single_df[1:3, ] ## ----message=FALSE------------------------------------------------------------ tumor_annot_df <- AnnotateResults( # the output dataset from function TestAssociations(). results_df = tumor_single_df, # close-by regions, since this is site-specific analysis, set to NULL. closeByRegions_gr = NULL, # input regions, since this is site-specific analysis, set to NULL. inputRegions_gr = NULL, genome = "hg19", # the type of analysis result from function TestAssociations(), since we are # running site-specific analysis, set to "site-specific". analysis = "site-specific" ) ## ----------------------------------------------------------------------------- tumor_annot_df[1:3, ] ## ----------------------------------------------------------------------------- allGenes_gr <- readRDS( system.file( "extdata", "hg19_annoGene_gr.RDS", package = 'rnaEditr', mustWork = TRUE ) ) ## ----------------------------------------------------------------------------- allGenes_gr[1:3] ## ----------------------------------------------------------------------------- # If input is gene symbol inputGenes_gr <- TransformToGR( # input a character vector of gene symbols genes_char = c("PHACTR4", "CCR5", "METTL7A"), # the type of "gene_char". As we input gene symbols above, set to "symbol" type = "symbol", genome = "hg19" ) ## ----------------------------------------------------------------------------- inputGenes_gr ## ----------------------------------------------------------------------------- # If input is region ranges inputRegions_gr <- TransformToGR( # input a character vector of region ranges. genes_char = c("chr22:18555686-18573797", "chr22:36883233-36908148"), # the type of "gene_char". As we input region ranges above, set to "region". type = "region", genome = "hg19" ) # Here we use AddMetaData() to find the gene symbols for inputRegions_gr. AddMetaData(target_gr = inputRegions_gr, genome = "hg19") ## ----results="hide"----------------------------------------------------------- closeByRegions_gr <- AllCloseByRegions( # a GRanges object of genomic regions retrieved or created in section 4.1. regions_gr = inputGenes_gr, # an RNA editing matrix. rnaEditMatrix = rnaedit_df, maxGap = 50, minSites = 3 ) ## ----------------------------------------------------------------------------- closeByRegions_gr ## ----results="hide"----------------------------------------------------------- closeByCoeditedRegions_gr <- AllCoeditedRegions( # a GRanges object of close-by regions created by AllCloseByRegions(). regions_gr = closeByRegions_gr, # an RNA editing matrix. rnaEditMatrix = rnaedit_df, # type of output data. output = "GRanges", rDropThresh_num = 0.4, minPairCorr = 0.1, minSites = 3, # the method for computing correlations. method = "spearman", # When no co-edited regions are found in an input genomic region, you want to # output the whole region (when set to TRUE) or NULL (when set to FALSE). returnAllSites = FALSE ) ## ----------------------------------------------------------------------------- closeByCoeditedRegions_gr ## ----fig.height=6, fig.width=6------------------------------------------------ PlotEditingCorrelations( region_gr = closeByCoeditedRegions_gr[1], rnaEditMatrix = rnaedit_df ) ## ----results='hide'----------------------------------------------------------- summarizedRegions_df <- SummarizeAllRegions( # a GRanges object of close-by regions created by AllCoeditedRegions(). regions_gr = closeByCoeditedRegions_gr, # an RNA editing matrix. rnaEditMatrix = rnaedit_df, # available methods: "MaxSites", "MeanSites", "MedianSites", and "PC1Sites". selectMethod = MedianSites ) ## ----------------------------------------------------------------------------- summarizedRegions_df[1:3, 1:5] ## ----results="hide"----------------------------------------------------------- tumor_region_df <- TestAssociations( # an RNA editing dataframe with special class "rnaEdit_df" from function # CreateEditingTable() if site-specific analysis, from function # SummarizeAllRegions() if region-based analysis. rnaEdit_df = summarizedRegions_df, # a phenotype dataset that must have variable "sample" whose values are a # exact match to the colnames of "rnaEdit_df". pheno_df = pheno_df, # name of outcome variable in phenotype dataset "pheno_df" that you want to # test. responses_char = "sample_type", # names of covariate variables in phenotype dataset "pheno_df" that you want # to add into the model. covariates_char = NULL, # type of outcome variable that you input in argument "responses_char". respType = "binary", # order the final results by p-values or not. orderByPval = TRUE ) ## ----------------------------------------------------------------------------- tumor_region_df[1:3, ] ## ----------------------------------------------------------------------------- tumor_annot_df <- AnnotateResults( # the output dataset from function TestAssociations(). results_df = tumor_region_df, # close-by regions which is a output of AllCloseByRegions(). closeByRegions_gr = closeByRegions_gr, # input regions, which are created in section 4.1. inputRegions_gr = inputGenes_gr, genome = "hg19", # the type of analysis result from function TestAssociations(), since we are # doing region-based analysis, use default here. analysis = "region-based" ) ## ----------------------------------------------------------------------------- tumor_annot_df[1:3, ] ## ----results="hide"----------------------------------------------------------- tumor_region_df <- TestAssociations( # an RNA editing dataframe with special class "rnaEdit_df" from function # CreateEditingTable() if site-specific analysis, from function # SummarizeAllRegions() if region-based analysis. rnaEdit_df = summarizedRegions_df, # a phenotype dataset that must have variable "sample" whose values are a # exact match to the colnames of "rnaEdit_df". pheno_df = pheno_df, # name of outcome variable in phenotype dataset "pheno_df" that you want to # test. responses_char = "age_at_diagnosis", # names of covariate variables in phenotype dataset "pheno_df" that you want # to add into the model. covariates_char = NULL, # type of outcome variable that you input in argument "responses_char". respType = "continuous", # order the final results by p-values or not. orderByPval = TRUE ) ## ----------------------------------------------------------------------------- tumor_region_df[1:3, ] ## ----results="hide"----------------------------------------------------------- tumor_region_df <- TestAssociations( # an RNA editing dataframe with special class "rnaEdit_df" from function # CreateEditingTable() if site-specific analysis, from function # SummarizeAllRegions() if region-based analysis. rnaEdit_df = summarizedRegions_df, # a phenotype dataset that must have variable "sample" whose values are a # exact match to the colnames of "rnaEdit_df". pheno_df = pheno_df, # name of outcome variable in phenotype dataset "pheno_df" that you want to # test. responses_char = c("OS.time", "OS"), # names of covariate variables in phenotype dataset "pheno_df" that you want # to add into the model. covariates_char = NULL, # type of outcome variable that you input in argument "responses_char". respType = "survival", # order the final results by p-values or not. orderByPval = TRUE ) ## ----------------------------------------------------------------------------- tumor_region_df[1:3, ] ## ----size = 'tiny'------------------------------------------------------------ sessionInfo()