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01-load_bulk_data.R
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01-load_bulk_data.R
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################################################################################
################################################################################
################################################################################
################################################################################
########################## BULK DATA / METADATA SETUP #########################
# Plan:
# - Download metadata from TCGA biolinks (for DLBCL, Burkitt, and Follicular)
# - Build metadata and sample files for all.
# - Set up count matrices (all, rtx, and hervs only)
# - Set up annotations
#################################### SETUP #####################################
library(tidyverse)
library(readxl)
library(GenomicDataCommons)
library(TCGAbiolinks)
library(dplyr)
library(rtracklayer)
library(data.table)
library(scopetools)
library(sva)
############################# LOAD TE ANNOTATIONS ##############################
## load annotation
retro.hg38.v1 <-
readr::read_tsv(
"https://github.com/mlbendall/telescope_annotation_db/raw/master/builds/retro.hg38.v1/genes.tsv.gz",
na=c('.'))
retro.hg38.v1 <- retro.hg38.v1 %>%
tidyr::separate(locus, c("family"), sep='_', remove=F, extra='drop') %>%
dplyr::mutate(
te_class = factor(ifelse(is.na(l1base_id), 'LTR', 'LINE'), levels=c('LTR','LINE')),
)
retro.annot.v2 <- read.csv("/efs/projects/hematological_malignancies_te_analysis/refs/TE_annotation.v2.0.tsv",
sep = "\t")
rownames(retro.annot.v2) <- retro.annot.v2$Locus
# Annotation directory for scopetools
ddir <- system.file("extdata", package="scopetools")
# Remove the confounding LINE element (L1FLnI_Xq21.1db) that has a poly A tail
# in the middle of it:
retro.hg38.v1<-
retro.hg38.v1[!(retro.hg38.v1$locus=="L1FLnI_Xq21.1db"),]
retro.annot <- retro.hg38.v1
row.names(retro.annot) <- retro.annot$locus
row.names(retro.annot) <- gsub("_", "-", row.names(retro.annot))
############################ LOAD GENE ANNOTATIONS #############################
gtf <- rtracklayer::import("refs/gencode.v38.annotation.gtf")
gtf_df=as.data.frame(gtf)
gtf_df <-
gtf_df[,
c("gene_id", "seqnames", "start", "end", "strand", "width", "gene_name",
"gene_type")]
colnames(gtf_df) <- c("gene_id", "chrom", "start", "end", "strand", "length",
"gene_name", "gene_type")
gene_table <-
gtf_df[!duplicated(gtf_df[,c(1,7)]), ] %>%
dplyr::select('gene_id', 'gene_name', 'gene_type')
gene_table <-
rbind(gene_table, data.frame(gene_id=retro.annot$locus,
gene_name=retro.annot$locus,
gene_type=retro.annot$te_class))
rownames(gene_table) <- gene_table$gene_id
gene_table$display <- gene_table$gene_name
gene_table[duplicated(gene_table$gene_name), 'display'] <-
paste(gene_table[duplicated(gene_table$gene_name), 'display'],
gene_table[duplicated(gene_table$gene_name), 'gene_id'], sep='|')
############################## CLINICAL METADATA ###############################
# Download DLBCL NCICCR metadata
NCICCR_DLBCL_clinical_metadata <-
GDCquery_clinic("NCICCR-DLBCL", type = "clinical", save.csv = FALSE)
# Download DLBCL TCGA metadata
TCGA_DLBCL_clinical_metadata <-
GDCquery_clinic("TCGA-DLBC", type = "clinical", save.csv = FALSE)
# Import CCGI Burkitt Lymphoma metadata
BL_clinical_metadata <-
read.csv("metadata/BL/BL_samples_metadata.tsv", sep="\t")
BL_other_metadata <-
read_excel("metadata/BL/blood.2022016534-s02.xlsx")
BL_subgroup <-
read_excel("metadata/BL/blood.2022016534-s02.xlsx", sheet = 12)
# Import Follicular Lymphoma metadata
FL_clinical_metadata <-
read_excel("metadata/FL/CGCI_NHL_ClinicalDataSet_20110710.xlsx")
FL_SRA_run_table <-
read.csv("metadata/FL/FL_SRA.txt")
########################## DLBCL SUB-CLASSIFICATIONS ###########################
# Import paper-specific metadata for DLBCL classifications
LymphGen <-
read_excel("metadata/DLBCL/Full_Wright_et_al_2021_LymphGen.xlsx", skip = 1)
EcoTyper <-
read_excel("metadata/DLBCL/Full_Steen_et_al_2021_EcoTyper.xlsx", sheet = "S2F", skip=1)
Holmes_scCOO <-
read_excel("metadata/DLBCL/Full_Holmes_et_al_2020.xlsx", skip=1)
########################## CREATE DLBCL METADATA FILES #########################
# Put TCGA and NCICCR metadata together
# Add the following metadata columns: ann_arbor_clinical_stage, age_at_diagnosis,
# gender, vital_status, days_to_last_follow_up, prior_malignancy, prior_treatment,
# international_prognostic_index, last_known_disease_status,
# progression_or_recurrence,year_of_diagnosis, year_of_death, tissue_or_organ_of_origin
DLBCL_clinical_metadata <-
plyr::rbind.fill(TCGA_DLBCL_clinical_metadata, NCICCR_DLBCL_clinical_metadata)
DLBCL_clinical_metadata <-
DLBCL_clinical_metadata[,
c("submitter_id",
"gender", "vital_status",
"ann_arbor_clinical_stage",
"age_at_diagnosis",
"international_prognostic_index",
"days_to_last_follow_up",
"tissue_or_organ_of_origin")]
# Filter metadata from all other studies so that we only have the NCI and/or
# TCGA datasets together
EcoTyper <- EcoTyper[EcoTyper$Cohort == "Schmitz et al.",]
Holmes_scCOO <- Holmes_scCOO[Holmes_scCOO$Dataset == "NCI-DLBCL",]
LymphGen <- LymphGen[LymphGen$Cohort == "NCI",]
LymphGen <- LymphGen[!grepl("CTSP",LymphGen$Cohort),]
# Subset our original sample metadata sheet to only contain essentials
# We'll pull out updated metadata from the other files
DLBCL_metadata <- read.table("metadata/DLBCL/DLBCL_samples.tsv", sep = "\t", header = TRUE)
DLBCL_metadata <- DLBCL_metadata[, c("sample", "project", "case")]
# Add essential metadata from other files
DLBCL_metadata <- merge(DLBCL_metadata,
DLBCL_clinical_metadata,
by.x = "case", by.y = "submitter_id")
DLBCL_metadata$IPI_score <- LymphGen$`IPI\r\nScore`[match(DLBCL_metadata$case,
LymphGen$`Donor name`)]
DLBCL_metadata$RCHOP_like_chemo <- LymphGen$`R-CHOp-like\r\nChemo`[match(DLBCL_metadata$case,
LymphGen$`Donor name`)]
DLBCL_metadata$COO_class <- LymphGen$`COO\r\nClass`[match(DLBCL_metadata$case,
LymphGen$`Donor name`)]
DLBCL_metadata$LymphGen_call <- LymphGen$`LymphGen\r\ncall`[match(DLBCL_metadata$case,
LymphGen$`Donor name`)]
DLBCL_metadata$DblHit_call <- LymphGen$`Dbl.Hit\r\nCall`[match(DLBCL_metadata$case,
LymphGen$`Donor name`)]
DLBCL_metadata$EcoTyper_call <-
EcoTyper$`B cell state`[match(DLBCL_metadata$case, EcoTyper$`Sample ID`)]
DLBCL_metadata$Schmitz_call <-
Holmes_scCOO$`Genetic Subtype (Schmitz et al., NEJM 2018)`[match(DLBCL_metadata$case,
Holmes_scCOO$`Sample ID`)]
DLBCL_metadata$Chapuay_call <-
Holmes_scCOO$`Genetic Subtype (Chapuy et al., Nat. Medicine 2018)`[match(DLBCL_metadata$case,
Holmes_scCOO$`Sample ID`)]
DLBCL_metadata$scCOO_class_call <-
Holmes_scCOO$`sc-COO Class`[match(DLBCL_metadata$case,
Holmes_scCOO$`Sample ID`)]
DLBCL_metadata$scCOO_group_call <-
Holmes_scCOO$`sc-COO Group`[match(DLBCL_metadata$case,
Holmes_scCOO$`Sample ID`)]
remove(EcoTyper, Holmes_scCOO, LymphGen, NCICCR_DLBCL_clinical_metadata,
TCGA_DLBCL_clinical_metadata, DLBCL_clinical_metadata)
############################ CREATE FL METADATA FILES ##########################
# Import Follicular Lymphoma metadata
FL_clinical_metadata <-
FL_clinical_metadata[FL_clinical_metadata$`WHO diagnosis` %like% "FOLLICULAR",]
FL_clinical_metadata$sample_id <- FL_SRA_run_table$BioSample[match(FL_clinical_metadata$Patient_ID,
FL_SRA_run_table$submitted_subject_id)]
FL_metadata <- FL_clinical_metadata
remove(FL_clinical_metadata, FL_SRA_run_table)
############################ CREATE BL METADATA FILES ##########################
# Import Follicular Lymphoma metadata
#BL_metadata <- BL_clinical_metadata[BL_clinical_metadata$pilot %like% "True",]
BL_metadata <- BL_clinical_metadata
BL_metadata <- BL_metadata[, c("case", "project_id", "submitter_id",
"sample_type", "sample", "tissue_type",
"tumor_descriptor", "cohort", "clinical_variant",
"ebv_status", "ebv_genome_type", "sex",
"age_at_diagnosis", "anatomic_site_classification",
"tissue_source_site")]
BL_metadata$tumor_biopsy <- BL_other_metadata$`Tumor biopsy`[match(BL_metadata$case,
BL_other_metadata$`Patient barcode`)]
BL_metadata$MYC_SV <- BL_other_metadata$`MYC SV`[match(BL_metadata$case,
BL_other_metadata$`Patient barcode`)]
BL_metadata$MYC_SV_Partner <- BL_other_metadata$`MYC SV partner`[match(BL_metadata$case,
BL_other_metadata$`Patient barcode`)]
BL_metadata$Total_N_SSM <- BL_other_metadata$`total N of SSM`[match(BL_metadata$case,
BL_other_metadata$`Patient barcode`)]
BL_subgroup$patient_barcode <- substr(BL_subgroup$`Patient barcode`, 1, 21)
BL_metadata$subgroup <- BL_subgroup$Subgroup[match(BL_metadata$sample,
BL_subgroup$patient_barcode)]
remove(BL_clinical_metadata)
######################## RENAME COLUMNS & MERGE METADATA #######################
colnames(BL_metadata) <- c("case", "project_id", "submitter_id",
"sample_type", "sample", "tissue_type",
"tumor_descriptor", "cohort", "clinical_variant",
"ebv_status", "ebv_genome_type", "gender",
"age_at_diagnosis", "anatomic_site_classification",
"tissue_source_site", "tumor_biopsy", "MYC_SV", "MYC_SV_partner",
"Total_N_SSM", "subgroup")
colnames(FL_metadata) <- c("patient_id", "who_diagnosis",
"days_to_birth_from_date_of_diagnosis",
"gender", "stage", "stage_group", "performance_status",
"LDH_ratio", "extranodal_sites", "tumor_size",
"IPI_score", "primary_treatment",
"days_to_primary_treatment_start_from_date_of_diagnosis",
"response", "days_to_response_assessment_from_date_of_diagnosis",
"days_to_progression_from_date_of_diagnosis",
"site_progression", "secondary_treatment", "HSCT",
"days_to_HSCT_from_date_of_diagnosis",
"days_to_transformed_lymphoma_diagnosed_from_date_of_diagnosis",
"days_to_last_follow_up_from_date_of_diagnosis",
"status_lasrt_follow_up", "cause_of_death",
"cause_of_death_ICD10", "sample")
DLBCL_metadata <- DLBCL_metadata %>% remove_rownames %>% column_to_rownames(var="sample")
BL_metadata <- BL_metadata %>% remove_rownames %>% column_to_rownames(var="sample")
FL_metadata <- FL_metadata %>% remove_rownames %>% column_to_rownames(var="sample")
FL_metadata <- FL_metadata[!(row.names(FL_metadata) %in% "SAMN05182469"),]
# Add cancer type & COO
DLBCL_metadata$cancer_type <- "DLBCL"
DLBCL_metadata$subtype <- DLBCL_metadata$COO_class
FL_metadata$cancer_type <- "FL"
FL_metadata$subtype <- FL_metadata$who_diagnosis
BL_metadata$cancer_type <- "BL"
BL_metadata$subtype <- paste0(BL_metadata$clinical_variant, " ", BL_metadata$ebv_status)
all_metadata <- rbind(
DLBCL_metadata[, c("cancer_type", "subtype")],
BL_metadata[, c("cancer_type", "subtype")],
FL_metadata[, c("cancer_type", "subtype")]
)
######################## LOAD HEALTHY GCB BULK METADATA ########################
bulk_metadata <- read.csv("metadata/GCB/GCB_Bulk.csv",
header = TRUE)
rownames(bulk_metadata) <- bulk_metadata$BioSample
bulk_metadata$cancer_type <- "GCB_Bulk"
agirre_metadata <- read.csv("metadata/GCB_Agirre/SraRunTable_Agirre.csv",
header = TRUE)
rownames(agirre_metadata) <- agirre_metadata$BioSample
agirre_metadata$cancer_type <- "GCB_Agirre"
################################ LOAD TELESCOPE ################################
# Old telescope reports
load_all_lymphoma_old <- function(df) {
sample_names <- rownames(df)
t_files <- file.path(paste("results/", df$cancer_type[1], "/telescope",
sep = ""),
paste0(sample_names, '/', sample_names,
'_telescope.report.tsv'))
names(t_files) <- df$bulk_RNAseq
counts.rtx <- load_telescope_reports(t_files,
all_locs=retro.hg38.v1$locus,
count_column = "count")
assign(paste(df$cancer_type[1], "counts", "rtx", sep="."),
counts.rtx,
envir=.GlobalEnv)
}
load_all_lymphoma_old(DLBCL_metadata)
# New telescope reports
load_all_lymphoma_new <- function(df) {
sample_names <- rownames(df)
t_files <- file.path(paste("results/", df$cancer_type[1], "/telescope",
sep = ""),
paste0(sample_names, '/', sample_names,
'-telescope_report.tsv'))
names(t_files) <- df$bulk_RNAseq
counts.rtx <- load_telescope_reports(t_files,
all_locs=retro.hg38.v1$locus,
count_column = "final_count")
assign(paste(df$cancer_type[1], "counts", "rtx", sep="."),
counts.rtx,
envir=.GlobalEnv)
}
load_all_lymphoma_new(BL_metadata)
load_all_lymphoma_new(FL_metadata)
load_all_lymphoma_new(bulk_metadata)
load_all_lymphoma_new(agirre_metadata)
################################## LOAD STAR ###################################
DLBCL_files <- Sys.glob(file.path("results/DLBCL/star_alignment/*", '*.ReadsPerGene.out.tab'))
BL_files <- Sys.glob(file.path("results/BL/star_alignment/*", '*.ReadsPerGene.out.tab'))
FL_files <- Sys.glob(file.path("results/FL/star_alignment/*", '*.ReadsPerGene.out.tab'))
GCB_Buk_files <- Sys.glob(file.path("results/GCB_Bulk/star_alignment/*", '*.ReadsPerGene.out.tab'))
GCB_Agirre_fules <- Sys.glob(file.path("results/GCB_Agirre/star_alignment/*", '*.ReadsPerGene.out.tab'))
DLBCL.counts.tx <- load_star_counts(DLBCL_files)
BL.counts.tx <- load_star_counts(BL_files)
FL.counts.tx <- load_star_counts(FL_files)
GCB_Bulk.counts.tx <- load_star_counts(GCB_Buk_files)
GCB_Agirre.counts.tx <- load_star_counts(GCB_Agirre_fules)
################################# SANITY CHECK #################################
stopifnot(all(rownames(DLBCL.counts.rtx) == retro.hg38.v1$locus))
stopifnot(all(rownames(BL.counts.rtx) == retro.hg38.v1$locus))
stopifnot(all(rownames(FL.counts.rtx) == retro.hg38.v1$locus))
stopifnot(all(rownames(GCB_Bulk.counts.rtx) == retro.hg38.v1$locus))
stopifnot(all(rownames(GCB_Agirre.counts.rtx) == retro.hg38.v1$locus))
########################### ORDER SAMPLES / METADATA ###########################
# reorder counts.tx by metadata rowname
reorder_idx_counts.tx <- match(rownames(DLBCL_metadata), colnames(DLBCL.counts.tx))
DLBCL.counts.tx <- DLBCL.counts.tx[,reorder_idx_counts.tx]
reorder_idx_counts.tx <- match(rownames(BL_metadata), colnames(BL.counts.tx))
BL.counts.tx <- BL.counts.tx[,reorder_idx_counts.tx]
reorder_idx_counts.tx <- match(rownames(FL_metadata), colnames(FL.counts.tx))
FL.counts.tx <- FL.counts.tx[,reorder_idx_counts.tx]
reorder_idx_counts.tx <- match(rownames(bulk_metadata), colnames(GCB_Bulk.counts.tx))
GCB_Bulk.counts.tx <- GCB_Bulk.counts.tx[,reorder_idx_counts.tx]
reorder_idx_counts.tx <- match(rownames(agirre_metadata), colnames(GCB_Agirre.counts.tx))
GCB_Agirre.counts.tx <- GCB_Agirre.counts.tx[,reorder_idx_counts.tx]
# reorder counts.rtx by metadata rowname
reorder_idx_counts.rtx <- match(rownames(DLBCL_metadata), colnames(DLBCL.counts.rtx))
DLBCL.counts.rtx <- DLBCL.counts.rtx[,reorder_idx_counts.rtx]
reorder_idx_counts.rtx <- match(rownames(BL_metadata), colnames(BL.counts.rtx))
BL.counts.rtx <- BL.counts.rtx[,reorder_idx_counts.rtx]
reorder_idx_counts.rtx <- match(rownames(FL_metadata), colnames(FL.counts.rtx))
reorder_idx_counts.tx <- match(rownames(FL_metadata), colnames(FL.counts.tx))
FL.counts.rtx <- FL.counts.rtx[,reorder_idx_counts.rtx]
FL.counts.tx <- FL.counts.tx[,reorder_idx_counts.tx]
reorder_idx_counts.rtx <- match(rownames(bulk_metadata), colnames(GCB_Bulk.counts.rtx))
GCB_Bulk.counts.rtx <- GCB_Bulk.counts.rtx[,reorder_idx_counts.rtx]
reorder_idx_counts.rtx <- match(rownames(agirre_metadata), colnames(GCB_Agirre.counts.rtx))
GCB_Agirre.counts.rtx <- GCB_Agirre.counts.rtx[,reorder_idx_counts.rtx]
# sanity check
stopifnot(all(names(DLBCL.counts.tx) == names(DLBCL.counts.rtx)))
stopifnot(all(names(BL.counts.tx) == names(BL.counts.rtx)))
stopifnot(all(names(FL.counts.tx) == names(FL.counts.rtx)))
stopifnot(all(names(GCB_Bulk.counts.tx) == names(GCB_Bulk.counts.rtx)))
stopifnot(all(names(GCB_Agirre.counts.tx) == names(GCB_Agirre.counts.rtx)))
############################# BL BATCH CORRECTION ##############################
# BL.counts.comb <- rbind(BL.counts.tx, BL.counts.rtx)
# tumor_biopsy = sapply(as.character(BL_metadata$tumor_biopsy),
# switch, "frozen" = 1, "FFPE" = 2, "NA" = 3, USE.NAMES = F)
# BL_subtype = sapply(as.character(BL_metadata$subtype),
# switch, "Endemic BL EBV-negative" = 1,
# "Endemic BL EBV-positive" = 2,
# "Sporadic BL EBV-negative" = 3,
# "Sporadic BL EBV-positive" = 4,
# USE.NAMES = F)
# sex = sapply(as.character(BL_metadata$gender),
# switch, "Female" = 1,
# "Male" = 2,
# USE.NAMES = F)
#
# BL.counts.comb.corrected = ComBat_seq(counts = as.matrix(BL.counts.comb),
# batch = tumor_biopsy,
# covar_mod = cbind(BL_subtype, sex),
# full_mod = TRUE)
#
# BL.counts.tx.corrected <- as.data.frame(BL.counts.comb.corrected[rownames(BL.counts.tx),])
# BL.counts.rtx.corrected <- as.data.frame(BL.counts.comb.corrected[rownames(BL.counts.rtx),])
#
# stopifnot(all(names(BL.counts.tx.corrected) == names(BL.counts.rtx.corrected)))
# stopifnot(all(names(BL.counts.tx.corrected) == names(BL.counts.tx)))
# stopifnot(all(names(BL.counts.tx.corrected) == rownames(BL_metadata)))
#
# BL.counts.tx <- as.data.frame(BL.counts.tx.corrected)
# BL.counts.rtx <- as.data.frame(BL.counts.rtx.corrected)
################################ COMBINE SAMPLES ###############################
# combine .tx and .rtx counts for all lymphoma samples
all.counts.rtx <- cbind(DLBCL.counts.rtx, BL.counts.rtx, FL.counts.rtx)
all.counts.tx <- cbind(DLBCL.counts.tx, BL.counts.tx, FL.counts.tx)
stopifnot(all(names(all.counts.tx) == names(all.counts.rtx)))
# combine .tx and .rtx counts in the same matrices
DLBCL.counts.comb <- rbind(DLBCL.counts.tx, DLBCL.counts.rtx)
BL.counts.comb <- rbind(BL.counts.tx, BL.counts.rtx)
FL.counts.comb <- rbind(FL.counts.tx, FL.counts.rtx)
all.counts.comb <- rbind(all.counts.tx, all.counts.rtx)
GCB_Bulk.counts.comb <- rbind(GCB_Bulk.counts.tx, GCB_Bulk.counts.rtx)
GCB_Agirre.counts.comb <- rbind(GCB_Agirre.counts.tx, GCB_Agirre.counts.rtx)
############################# SUBSET HERVs and L1s #############################
retro.hg38.v1 <- retro.hg38.v1 %>% remove_rownames %>% column_to_rownames(var="locus")
DLBCL.counts.herv <- DLBCL.counts.rtx[retro.hg38.v1$te_class == 'LTR',]
BL.counts.herv <- BL.counts.rtx[retro.hg38.v1$te_class == 'LTR',]
FL.counts.herv <- FL.counts.rtx[retro.hg38.v1$te_class == 'LTR',]
all.counts.herv <- all.counts.rtx[retro.hg38.v1$te_class == 'LTR',]
GCB_Bulk.counts.herv <- GCB_Bulk.counts.rtx[retro.hg38.v1$te_class == 'LTR',]
GCB_Agirre.counts.herv <- GCB_Agirre.counts.rtx[retro.hg38.v1$te_class == 'LTR',]
DLBCL.counts.l1 <- DLBCL.counts.rtx[retro.hg38.v1$te_class == 'LINE',]
BL.counts.l1 <- BL.counts.tx[retro.hg38.v1$te_class == 'LINE',]
FL.counts.l1 <- FL.counts.rtx[retro.hg38.v1$te_class == 'LINE',]
all.counts.l1 <- all.counts.rtx[retro.hg38.v1$te_class == 'LINE',]
GCB_Bulk.counts.l1 <- GCB_Bulk.counts.rtx[retro.hg38.v1$te_class == 'LINE',]
GCB_Agirre.counts.l1 <- GCB_Agirre.counts.rtx[retro.hg38.v1$te_class == 'LINE',]
################################## SAVE FILES ##################################
save(all.counts.comb, all.counts.tx, all.counts.rtx, all.counts.herv,
all.counts.l1, all_metadata, file="r_outputs/01-all_lymphoma_counts.Rdata")
save(DLBCL.counts.comb, DLBCL.counts.tx, DLBCL.counts.rtx, DLBCL.counts.herv,
DLBCL.counts.l1, DLBCL_metadata, file="r_outputs/01-DLBCL_counts.Rdata")
save(BL.counts.comb, BL.counts.tx, BL.counts.rtx, BL.counts.herv,
BL.counts.l1, BL_metadata, file="r_outputs/01-BL_counts.Rdata")
save(FL.counts.comb, FL.counts.tx, FL.counts.rtx, FL.counts.herv,
FL.counts.l1, FL_metadata, file="r_outputs/01-FL_counts.Rdata")
save(GCB_Bulk.counts.comb, GCB_Bulk.counts.tx, GCB_Bulk.counts.rtx, GCB_Bulk.counts.herv,
GCB_Bulk.counts.l1, bulk_metadata, file="r_outputs/01-GCB_Bulk_counts.Rdata")
save(GCB_Agirre.counts.comb, GCB_Agirre.counts.tx, GCB_Agirre.counts.rtx, GCB_Agirre.counts.herv,
GCB_Agirre.counts.l1, agirre_metadata, file="r_outputs/01-GCB_Agirre.Rdata")
save(all_metadata, DLBCL_metadata, BL_metadata, FL_metadata, bulk_metadata,
agirre_metadata,
file="r_outputs/01-metadata.Rdata")
save(retro.hg38.v1, retro.annot, gene_table, retro.annot.v2,
file="r_outputs/01-refs.Rdata")