I need to calculate the survival rate of TCGA databases
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tirordep • 0
@a2675c82
Last seen 9 weeks ago
Brazil

I'm doing a study of two types of cancer using TCGA database. I have already used some scripts in R to find the upregulated and downregulated genes of each cancer and the both intersection.

Now I need to calculate de survival rate, and I'll use SPSS software. Then I need to do a table with the individual code, normalized dosage of genes, survival time and death.

But I don't know how I'll obtain this table.

Somebody could help me, please?
TCGAbiolinks Survival • 280 views
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Welcome to the site. This might not be the best place to get SPSS help, as the site is dedicated to support Bioconductor R pacakges. In any case it would help if you provide what have you tried: code attempted, pages visited that helped you, ...

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Hello, I want to use R packages to create the table that I will use on SPSS. I found this code on Youtube, but there are some things that I didn't understand.

library(DESeq2)
library(TCGAbiolinks)
library(survminer)
library(survival)
library(SummarizedExperiment)
library(tidyverse)


clinical_HNSC <- GDCquery_clinic("TCGA-HNSC")
any(colnames(clinical_HNSC) %in% c("vital_status", "days_to_last_follow_up", "days_to_death"))
which(colnames(clinical_HNSC) %in% c("vital_status", "days_to_last_follow_up", "days_to_death"))
clinical_HNSC[,c(9,43,48)]

table (clinical_HNSC$vital_status)

clinical_HNSC$deceased <- ifelse(clinical_HNSC$vital_status =="Alive",FALSE,TRUE)

clinical_HNSC$overall_survival <- ifelse(clinical_HNSC$vital_status == "Alive",
                                         clinical_HNSC$days_to_last_follow_up,
                                         clinical_HNSC$days_to_death)
write.csv(clinical_HNSC, "tabela.csv", row.names = FALSE)

query_HNSC_all <- GDCquery(
                      project = "TCGA-HNSC", 
                      data.category = "Transcriptome Profiling",
                      experimental.strategy = "RNA-Seq",
                      workflow.type = "STAR - Counts",
                      data.type = "Gene Expression Quantification",
                      sample.type = "Primary Tumor",
                      access="open"
                      )

output_HNSC <- getResults(query_HNSC_all)
tumor <- output_HNSC$cases

query_HNSC_all <- GDCquery(
  project = "TCGA-HNSC", 
  data.category = "Transcriptome Profiling",
  experimental.strategy = "RNA-Seq",
  workflow.type = "STAR - Counts",
  data.type = "Gene Expression Quantification",
  sample.type = "Primary Tumor",
  access="open",
  barcode=tumor
)

GDCdownload (query_HNSC_all)
tcga_hnsc_data <- GDCprepare(query_HNSC_all,summarizedExperiment = TRUE)
hnsc_matrix <- assay(tcga_hnsc_data, "unstranded")
hnsc_matrix[1:10,1:10]

gene_metadata <- as.data.frame(rowData(tcga_hnsc_data))
coldata <- as.data.frame (colData(tcga_hnsc_data))

dds <- DESeqDataSetFromMatrix(countData=hnsc_matrix, colData = coldata, design = ~1)

keep <- rowSums(counts(dds)) >=10
dds <- dds[keep,]

vsd <- vst(dds,blind=FALSE)
hnsc_matrix_vst <- assay (vsd)

hnsc_tp53 <- hnsc_matrix_vst %>% 
  as.data.frame() %>% 
  rownames_to_column(var = 'gene_id') %>% 
  gather(key = 'case_id', value = 'counts', -gene_id) %>% 
  left_join(., gene_metadata, by = "gene_id") %>% 
  filter(gene_name == "TP53")

median_value <- median(hnsc_tp53$counts)

hnsc_tp53$strata <- ifelse(hnsc_tp53$counts >= median_value, "HIGH", "LOW")

hnsc_tp53$case_id <- gsub('-01.*', '', hnsc_tp53$case_id)
hnsc_tp53 <- merge(hnsc_tp53, clinical_HNSC, by.x = 'case_id', by.y = 'submitter_id')


fit <- survfit(Surv(overall_survival, deceased) ~ strata, data = hnsc_tp53)
fit
ggsurvplot(fit,
           data = hnsc_tp53,
           pval = T,
           risk.table = T)


fit2 <- survdiff(Surv(overall_survival, deceased) ~ strata, data = hnsc_tp53)
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Edit the question to add the relevant code and specify what do you not understand. Good luck!

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I have already used this code to find what genes I will focus in

library(TCGAbiolinks)
library(SummarizedExperiment)
library(plyr)
library(dplyr)
library(biomaRt)
library(DT)
library(limma)
library(biomaRt)


listSamples <- c("TCGA-2W-A8YY", "TCGA-BI-A0VR", "TCGA-BI-A0VS", "TCGA-C5-A1BE", "TCGA-C5-A1BF", "TCGA-C5-A1BI", "TCGA-C5-A1BJ", "TCGA-C5-A1BL", "TCGA-C5-A1BM", "TCGA-C5-A1BN", "TCGA-C5-A1BQ", "TCGA-C5-A1M7", "TCGA-C5-A1ME", "TCGA-C5-A1MF", "TCGA-C5-A1MH", "TCGA-C5-A1MI", "TCGA-C5-A1ML", "TCGA-C5-A1MN", "TCGA-C5-A2LS", "TCGA-C5-A2LT", "TCGA-C5-A2LV", "TCGA-C5-A2LX", "TCGA-C5-A2LZ", "TCGA-C5-A2M2", "TCGA-C5-A3HD", "TCGA-C5-A3HF", "TCGA-C5-A7CJ", "TCGA-C5-A7CK", "TCGA-C5-A7CL", "TCGA-C5-A7CM", "TCGA-C5-A7CO", "TCGA-C5-A7UE", "TCGA-C5-A7UI", "TCGA-C5-A7X8", "TCGA-C5-A7XC", "TCGA-C5-A8XH", "TCGA-C5-A8XI", "TCGA-C5-A8YQ", "TCGA-C5-A8YT", "TCGA-C5-A8ZZ", "TCGA-C5-A901", "TCGA-C5-A902", "TCGA-C5-A907", "TCGA-DG-A2KH", "TCGA-DG-A2KK", "TCGA-DG-A2KL", "TCGA-DR-A0ZM", "TCGA-DS-A0VK", "TCGA-DS-A1O9", "TCGA-DS-A1OC", 
                 "TCGA-DS-A1OD", "TCGA-DS-A3LQ", "TCGA-DS-A5RQ", "TCGA-DS-A7WF", "TCGA-EA-A556", "TCGA-EK-A2RA", "TCGA-EK-A2RB", "TCGA-EX-A1H5", "TCGA-EX-A69M", "TCGA-FU-A2QG", "TCGA-FU-A3EO", "TCGA-FU-A3HY", "TCGA-FU-A3NI", "TCGA-FU-A3TQ", "TCGA-FU-A3TX", "TCGA-FU-A3WB", "TCGA-FU-A3YQ", "TCGA-FU-A5XV", "TCGA-FU-A770", "TCGA-GH-A9DA", "TCGA-HG-A2PA", "TCGA-HM-A3JK", "TCGA-HM-A4S6", "TCGA-HM-A6W2", "TCGA-HM-A6W2", "TCGA-IR-A3L7", "TCGA-IR-A3LB", "TCGA-IR-A3LH", "TCGA-JW-A5VI", "TCGA-JW-A5VK", "TCGA-JW-AAVH", "TCGA-JX-A3Q0", "TCGA-MA-AA3W", "TCGA-MA-AA3X", "TCGA-MA-AA41", "TCGA-MA-AA43", "TCGA-MU-A5YI", "TCGA-MU-A8JM", "TCGA-MY-A913", "TCGA-Q1-A6DW", "TCGA-Q1-A73Q", "TCGA-Q1-A73S", "TCGA-RA-A741", "TCGA-UC-A7PD", "TCGA-UC-A7PF", "TCGA-UC-A7PG", "TCGA-UC-A7PG", "TCGA-VS-A8EB", "TCGA-VS-A8EH", "TCGA-VS-A8QC", "TCGA-VS-A8QF", "TCGA-VS-A8QM", "TCGA-VS-A94Y", "TCGA-VS-A94Z", "TCGA-VS-A9UP", "TCGA-VS-A9UU", "TCGA-VS-A9V0", "TCGA-VS-A9V1", "TCGA-VS-A9V4", "TCGA-WL-A834", "TCGA-XS-A8TJ", "TCGA-ZJ-A8QQ", "TCGA-ZJ-A8QR", "TCGA-ZJ-AAX4", "TCGA-ZJ-AAXA", "TCGA-ZJ-AAXN", "TCGA-ZJ-AAXT", "TCGA-ZJ-AAXU", "TCGA-ZJ-AB0I", "TCGA-4J-AA1J", "TCGA-C5-A0TN", "TCGA-C5-A1BK", "TCGA-C5-A1M5", "TCGA-C5-A1M6", "TCGA-C5-A1M8", "TCGA-C5-A1M9", "TCGA-C5-A1MJ", "TCGA-C5-A1MK", "TCGA-C5-A1MP", "TCGA-C5-A1MQ", "TCGA-C5-A2LY", "TCGA-C5-A2M1", "TCGA-C5-A3HE", "TCGA-C5-A3HL", "TCGA-C5-A7CG", "TCGA-C5-A7CH", "TCGA-C5-A7UH", "TCGA-C5-A7X3", "TCGA-C5-A8XJ", "TCGA-C5-A8YR", "TCGA-DG-A2KM", "TCGA-DR-A0ZL", "TCGA-DS-A0VL", "TCGA-DS-A0VM", "TCGA-DS-A0VN", "TCGA-DS-A1OA", "TCGA-DS-A7WH", "TCGA-DS-A7WI", "TCGA-EA-A1QS", "TCGA-EA-A1QT", "TCGA-EA-A3HQ", "TCGA-EA-A3HR", "TCGA-EA-A3HS", "TCGA-EA-A3HT", "TCGA-EA-A3HU", "TCGA-EA-A3QD", "TCGA-EA-A3QE", "TCGA-EA-A3Y4", "TCGA-EA-A410", "TCGA-EA-A411", "TCGA-EA-A439", "TCGA-EA-A43B", "TCGA-EA-A44S", "TCGA-EA-A4BA", "TCGA-EA-A50E", "TCGA-EA-A5FO", "TCGA-EA-A5O9", "TCGA-EA-A5ZD", "TCGA-EA-A5ZE", "TCGA-EA-A5ZF", "TCGA-EA-A6QX", "TCGA-EA-A78R", "TCGA-EA-A97N", "TCGA-EK-A3GM", "TCGA-EX-A1H6", "TCGA-EX-A3L1", "TCGA-EX-A69L", "TCGA-EX-A8YF", "TCGA-FU-A23K", "TCGA-FU-A23L", "TCGA-FU-A3HZ", "TCGA-FU-A40J", "TCGA-FU-A57G", "TCGA-HG-A9SC", "TCGA-IR-A3LA", "TCGA-IR-A3LC", "TCGA-IR-A3LF", "TCGA-IR-A3LI", "TCGA-IR-A3LK", "TCGA-IR-A3LL", "TCGA-JW-A5VG", "TCGA-JW-A5VH", "TCGA-JW-A5VJ", "TCGA-JW-A5VL", "TCGA-JW-A69B", "TCGA-JW-A852", "TCGA-JX-A3PZ", "TCGA-JX-A3Q8", "TCGA-JX-A5QV", "TCGA-LP-A4AU", "TCGA-LP-A4AV", "TCGA-LP-A4AW", "TCGA-LP-A4AX", "TCGA-LP-A5U2", "TCGA-LP-A5U3", "TCGA-LP-A7HU", "TCGA-MA-AA3Y", "TCGA-MA-AA3Z", "TCGA-MA-AA42", "TCGA-MU-A51Y", "TCGA-MY-A5BD", "TCGA-MY-A5BE", "TCGA-MY-A5BF", "TCGA-Q1-A5R1", "TCGA-Q1-A5R2", "TCGA-Q1-A5R3", "TCGA-Q1-A6DT", "TCGA-Q1-A6DV", "TCGA-Q1-A73O", "TCGA-Q1-A73P", "TCGA-Q1-A73R", "TCGA-R2-A69V", "TCGA-UC-A7PI", "TCGA-VS-A8EG", "TCGA-VS-A8EI", "TCGA-VS-A8EJ", "TCGA-VS-A8EL", "TCGA-VS-A8Q8", "TCGA-VS-A8Q9", "TCGA-VS-A8QA", "TCGA-VS-A8QH", "TCGA-VS-A94W", "TCGA-VS-A94X", "TCGA-VS-A950", "TCGA-VS-A952", "TCGA-VS-A953", "TCGA-VS-A954", "TCGA-VS-A957", "TCGA-VS-A958", "TCGA-VS-A959", "TCGA-VS-A9U5", "TCGA-VS-A9U6", "TCGA-VS-A9U7", "TCGA-VS-A9UA", "TCGA-VS-A9UB", "TCGA-VS-A9UC", "TCGA-VS-A9UD", "TCGA-VS-A9UH", "TCGA-VS-A9UI", "TCGA-VS-A9UJ", "TCGA-VS-A9UL", "TCGA-VS-A9UM", "TCGA-VS-A9UO", "TCGA-VS-A9UQ", "TCGA-VS-A9UR", "TCGA-VS-A9UT", "TCGA-VS-A9UV", "TCGA-VS-A9UY", "TCGA-VS-A9UZ", "TCGA-VS-A9V2", "TCGA-VS-A9V3", "TCGA-VS-A9V5", "TCGA-VS-AA62", "TCGA-ZJ-AAXB", "TCGA-ZX-AA5X")

query.exp <- GDCquery(project = "TCGA-CESC", 
                      legacy = TRUE,
                      data.category = "Gene expression",
                      data.type = "Gene expression quantification",
                      platform = "Illumina HiSeq", 
                      file.type = "results",
                      barcode = listSamples,
                      experimental.strategy = "RNA-Seq",
                      sample.type = c("Primary Tumor","Solid Tissue Normal"))
GDCdownload(query.exp)

CESChabits.exp <- GDCprepare(query = query.exp, save = TRUE,
                             save.filename = "CESC_habitsExp.rda")
dataSubt <- TCGAquery_subtype(tumor = "CESC")
# get clinical data 
dataClin <- GDCquery_clinic(project = "TCGA-CESC","clinical") 
# Which samples are Primary Tumor
dataSmTP <- TCGAquery_SampleTypes(getResults(query.exp,cols="cases"),"TP") 
# which samples are solid tissue normal
dataSmNT <- TCGAquery_SampleTypes(getResults(query.exp,cols="cases"),"NT")
dataPrep <- TCGAanalyze_Preprocessing(object = CESChabits.exp, cor.cut = 0.6)                      
dataNorm <- TCGAanalyze_Normalization(tabDF = dataPrep, geneInfo = geneInfo, method = "gcContent") 
dataFilt <- TCGAanalyze_Filtering(tabDF = dataNorm,method = "quantile", qnt.cut =  0.25)   
dataDEGs <- TCGAanalyze_DEA(mat1 = dataFilt[,dataSmNT],mat2 = dataFilt[,dataSmTP], Cond1type = "Normal", Cond2type = "Tumor", fdr.cut = 0.05 ,logFC.cut = 1,  method = "glmLRT")  
write.table(dataDEGs, "habits_CESC_selected.txt", sep="\t")
TCGAVisualize_volcano(x = dataDEGs$logFC,
                      y = dataDEGs$FDR,
                      filename = "CESC_habits_selected_volcanoexp.png",
                      x.cut = 6,
                      y.cut = 5*10^-5,
                      names = rownames(dataDEGs),
                      color = c("black","red","darkgreen"),
                      names.size = 2,
                      xlab = " Gene expression fold change (Log2)",
                      legend = "State",
                      title = "Volcano plot (CIMP-high vs CIMP-low)",
                      width = 10)
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