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filteredDataLung.Rdata
imageFron2019_01_28.RData
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preprocessing.R
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preprocessing.R
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#Preprocessing Data #### 1.1 MetaData metadata <- RJSONIO::fromJSON("P:/TCGA/clinical.cases_selection.2019-01-18.json", nullValue = NA, simplify = FALSE) metadata <- plyr::ldply(metadata, data.frame) #flatten the list into a data frame DataExplorer::plot_intro(metadata) DataExplorer::plot_missing(metadata) metadata <- filter.columns.as.na(metadata) #15 colums were dropped because of the NA values. From here I'd suggest to extract colums of interest because many arent necessary for the evaluation. metadata <- rename.columns(metadata) #Now you can select your colums of interest. For this example 11 colums are selected. Note that you may have duplicated column names. metadataSelect <- subset(metadata, select = c(case_id, tumor_stage, primary_diagnosis, site_of_resection_or_biopsy, vital_status, days_to_death, age_at_diagnosis, gender, race, ethnicity)) #the last thing you have to do is to change your column with the patient/case id from a factor to characters. metadataSelect$case_id <- as.character(metadataSelect$case_id) ####1.2 Organ data | Primary cancer Data | Expression data etc. ### Now you are set to work with your objects created by multimodalR #2.1 Load your file (filteredOrgan.Rdata or .rds file) #2.2 Match meta data case_id with filteredOrgan case_id lungMeta <- filter.metadata.uni(fSelect, lungXY, key = "case_id")
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