Merge pull request #3 from dyaus-dev/transcriptome

Add R script for integrating TCGA and CCLE data

.gitignore

@@ -27,6 +27,8 @@ errout/
 
 # transcriptomic data
 transcriptomic_data/*.csv
+GDCdata
+*.rda
 
 # Python ---
 
@@ -58,30 +60,9 @@ share/python-wheels/
 *.egg
 MANIFEST
 
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
 # Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
 .cache
-nosetests.xml
-coverage.xml
-*.cover
-*.py,cover
-.hypothesis/
 .pytest_cache/
-cover/
 
 # Translations
 *.mo
@@ -131,12 +112,12 @@ vignettes/*.pdf
 .httr-oauth
 
 # knitr and R markdown default cache directories
-*_cache/
-/cache/
+ *_cache/
+ /cache/
 
 # Temporary files created by R markdown
-*.utf8.md
-*.knit.md
+ *.utf8.md
+ *.knit.md
 
 # R Environment Variables
 .Renviron

README.md

@@ -106,7 +106,10 @@ Workflow for classifying breast cancer subtypes based on intracellular signaling
 
 ## Integration of TCGA and CCLE data
 
-[TODO] Write analysis procedure here.
+```bash
+$ cd transcriptomic_data
+$ Rscript transcriptomic_data_integration.R
+```
 
 ## Individualization of the mechanistic model
 

transcriptomic_data/transcriptomic_data_integration.R

@@ -0,0 +1,162 @@
+library(dplyr)
+library(stringr)
+library(tibble)
+
+# TCGA ---------------------------------------------------------------------------------------
+
+# download TCGA data from TCGA biolinks -----------------------------------
+# https://bioconductor.org/packages/release/bioc/manuals/TCGAbiolinks/man/TCGAbiolinks.pdf
+
+library(TCGAbiolinks)
+
+# download counts file
+query <- GDCquery(project = "TCGA-BRCA",
+                  data.category = "Transcriptome Profiling",
+                  data.type = "Gene Expression Quantification",
+                  workflow.type = "HTSeq - Counts")
+results <- getResults(query)
+
+GDCdownload(query)
+df <- GDCprepare(query, save = T, save.filename = "TCGABRCA_counts.rda",
+                 summarizedExperiment = F)
+
+# download clinical data
+BRCA_path_subtypes <- TCGAquery_subtype(tumor = "brca")
+BRCA_path_subtypes_age_stage <- data.frame(patient = BRCA_path_subtypes$patient,
+                                           stage = BRCA_path_subtypes$pathologic_stage,
+                                           subtype = BRCA_path_subtypes$BRCA_Subtype_PAM50,
+                                           age = BRCA_path_subtypes$age_at_initial_pathologic_diagnosis)
+
+
+
+# exclude columns other than counts data ----------------------------------
+df_counts <- df[grep("ENSG", df$X1),]
+
+
+# exclude some samples ----------------------------------------------------
+# exclude normal(non-cancerous) and duplicated samples
+# https://docs.gdc.cancer.gov/Encyclopedia/pages/TCGA_Barcode/
+# https://gdc.cancer.gov/resources-tcga-users/tcga-code-tables/sample-type-codes
+pattern <- c("-11A-", "-11B-", "-01B-", "-01C-")
+TCGA_counts <- df_counts[,-grep(paste(pattern, collapse = "|"), colnames(df_counts))]
+colnames(TCGA_counts) <- str_sub(colnames(TCGA_counts), start = 1, end = 16)
+TCGA_counts <- TCGA_counts[,!duplicated(colnames(TCGA_counts))] #exclude duplicated samples
+TCGA_counts$X1 <- str_sub(TCGA_counts$X1, start = 1, end = 15) 
+
+
+# screen samples with patient's age and pathologic stage ------------------
+# patients's age <= 59 & pathological stage <- stage I and II
+BRCA_path_subtypes_age_stage_filtered <- BRCA_path_subtypes_age_stage[BRCA_path_subtypes_age_stage$age <= 59 &
+                                                                        BRCA_path_subtypes_age_stage$stage %in% c("Stage_I", "Stage_II"),]
+BRCA_path_subtypes_age_stage_filtered$patient <- gsub("-", ".", BRCA_path_subtypes_age_stage_filtered$patient)
+
+# make counts file with filtered patients ---------------------------------
+TCGA_counts_filtered <- data.frame(Name = TCGA_counts$X1,
+                                   TCGA_counts[,grep(paste(BRCA_path_subtypes_age_stage_filtered$patient, collapse = "|"),
+                                                     colnames(TCGA_counts))])
+
+
+# CCLE ---------------------------------------------------------------------------------------
+
+# download counts file
+# https://data.broadinstitute.org/ccle/CCLE_RNAseq_genes_counts_20180929.gct.gz
+url <- "https://data.broadinstitute.org/ccle/CCLE_RNAseq_genes_counts_20180929.gct.gz"
+tmp <- tempfile()
+download.file(url, tmp)
+CCLE_counts <- read.table(gzfile(tmp), skip = 2, header = T)
+
+# get only cell lines of breast cancer from CCLE datasets
+CCLE_BRCA <- data.frame(Name = CCLE_counts$Name,
+                        Description = CCLE_counts$Description,
+                        CCLE_counts[,grep("BREAST", colnames(CCLE_counts))])
+CCLE_BRCA[,1] <- str_sub(CCLE_BRCA[,1], start = 1, end = 15)
+
+
+
+
+# Merge TCGA and CCLE ------------------------------------------------------------------------
+
+TCGA_CCLE_counts <- inner_join(CCLE_BRCA, TCGA_counts_filtered, 
+                               by = "Name")
+TCGA_CCLE_counts <- TCGA_CCLE_counts %>%
+  dplyr::distinct(Description, .keep_all=T) %>% column_to_rownames(var = "Name")
+TCGA_CCLE_counts_numeric <- sapply(TCGA_CCLE_counts[,-1], as.numeric)
+rownames(TCGA_CCLE_counts_numeric) <- rownames(TCGA_CCLE_counts)
+
+
+
+
+# ComBat-seq ---------------------------------------------------------------------------------
+# https://academic.oup.com/nargab/article/2/3/lqaa078/5909519
+# https://rdrr.io/bioc/sva/man/ComBat_seq.html
+# make batch file for ComBat-seq
+
+library(sva)
+
+batch <- data.frame(sample = colnames(TCGA_CCLE_counts_numeric),
+                    batch = c(rep(1,length(grep("BREAST", colnames(TCGA_CCLE_counts_numeric)))), 
+                              rep(2,length(grep("TCGA", colnames(TCGA_CCLE_counts_numeric))))))
+TCGA_CCLE_counts_matrix <- data.matrix(TCGA_CCLE_counts_numeric)
+postComBat <- ComBat_seq(TCGA_CCLE_counts_matrix, batch = batch$batch, group = NULL)
+postComBat_ensembl <- data.frame(Name = rownames(postComBat),
+                                 Description = TCGA_CCLE_counts$Description,
+                                 postComBat)
+
+
+
+# Normalization ------------------------------------------------------------------------------
+
+# get gene length for calculating TPM
+library(biomaRt)
+
+mart <- biomaRt::useMart("ENSEMBL_MART_ENSEMBL", dataset = "hsapiens_gene_ensembl")
+listAttributes(mart)
+
+gene.annotations <- biomaRt::getBM(mart = mart, attributes=c("ensembl_gene_id", "start_position", "end_position"))
+gene.annotations <- dplyr::transmute(gene.annotations, ensembl_gene_id, length = end_position - start_position)   
+
+# excluding samples with total read counts below 40000000 and above 140000000
+allread <- apply (postComBat_ensembl[,-c(1:2)],2,sum)
+allread_sub <- allread[allread >= 40000000 & allread <= 140000000]
+
+
+postComBat_sub_ensembl <- data.frame(ensembl_gene_id = postComBat_ensembl$Name,
+                                     Description = postComBat_ensembl$Description,
+                                     postComBat_ensembl[,colnames(postComBat_ensembl) %in% names(allread_sub)])
+
+countfile_length <- inner_join(gene.annotations,
+                               postComBat_sub_ensembl,
+                               by = "ensembl_gene_id")
+
+
+
+# Count TPM ---------------------------------------------------------------
+
+library(edgeR)
+
+counts_tpm <- (countfile_length[,4:ncol(countfile_length)]/countfile_length$length) *1000
+counts_tpm <- cbind(ensembl_gene_id = countfile_length$ensembl_gene_id, counts_tpm) %>% as_tibble()
+
+normfactor <- DGEList(counts = counts_tpm[,2:ncol(counts_tpm)],
+                      group = colnames(counts_tpm[,2:ncol(counts_tpm)]))
+normfactor <- calcNormFactors(normfactor, method="RLE")
+normfactor_samples <- normfactor$samples
+normfactor_samples$normlib <- normfactor_samples$lib.size*normfactor_samples$norm.factors
+
+
+for(i in 1:(dim(counts_tpm)[2]-1)){
+  counts_tpm[,i+1] <- (counts_tpm[,i+1]/normfactor_samples$normlib[i])*1000000
+}
+ensembl_symbol <- countfile_length[,c(1,3)]
+counts_tpm_symbol <- inner_join(ensembl_symbol,
+                                counts_tpm,
+                                by = "ensembl_gene_id")
+
+write.csv(counts_tpm_symbol, "TPM_RLE_postComBat.csv")
+
+
+# counts_tpm_symbol_log2 <- data.frame(ensembl_gene_id = counts_tpm_symbol$ensembl_gene_id,
+#                                      Description = counts_tpm_symbol$Description,
+#                                      log2(counts_tpm_symbol[,-c(1,2)] + 1))
+
+# write.csv(counts_tpm_symbol_log2, "TPM_RLE_log2_postComBat.csv")