serve data as MultiAssayExperiment? about depmap

[lgatto]

Thank you for your interest, Allison. An MAE would be very much appreciated indeed. If you have time to send a PR, that would be great. Note that the data are disseminated through ExperimentHub, so I assume we would use the same mechanism for the MAE.

[allisonvuong]

Hi,

Okay great, I will do that!

Best,
Allison

[lgatto]

See also this comment

[tfkillian]

I have created a script to convert most of the DepMap datasets to a MultiAssayExperiment object. However, I'm a bit stuck coercing the mutationCalls dataset to a RangedSummarizedExperiment Does anyone have any suggested solutions?

## Create MultiAssayExperiment object out of the most current Depmap datasets

# TODO:
# 1) convert the mutationCalls to a RangedSummarizedExperiment
# 2) make MAE out of all datesets

## load libraries
# BiocManager::install(
#   c("MultiAssayExperiment", "SummarizedExperiment", "S4Vectors", "dplyr",
#     "tidyr", "tibble", "readr", "ExperimentHub", "depmap"))

library("MultiAssayExperiment")
library("SummarizedExperiment")
library("S4Vectors")
library("dplyr")
library("tidyr")
library("tibble")
library("readr")
library("ExperimentHub")
library("depmap")

Sys.setenv(VROOM_CONNECTION_SIZE = 500072)

## output file path
file_path <- "media/seq-srv-05/vrc/Project/Project_Theo" ## server
#file_path <- "~/tmp/multiomics/data/" ## local

#################### depmap `metadata_21Q4` dataset ############################
## "metadata" dataset will serve as MAE colData
readr::read_csv("https://ndownloader.figshare.com/files/31316011") %>% 
  as.data.frame() -> metadata_21Q4
names(metadata_21Q4)[1:26] <- c(
    "depmap_id", "cell_line_name", "stripped_cell_line_name", "cell_line",
    "aliases", "cosmic_id", "sex", "source", "Achilles_n_replicates",
    "cell_line_NNMD", "culture_type", "culture_medium", "cas9_activity",
    "RRID", "WTSI_master_cell_ID", "sample_collection_site",
    "primary_or_metastasis", "primary_disease", "subtype_disease", "age",
    "sanger_id", "additional_info", "lineage", "lineage_subtype",
    "lineage_sub_subtype", "lineage_molecular_subtype")
rownames(metadata_21Q4) <- metadata_21Q4$depmap_id

######################## depmap dep_2_name  ###################################
### `dep_2_name` to add `depmap_id` or `cell_line` to other datasets
metadata_21Q4 %>% dplyr::select(depmap_id, cell_line) -> dep_2_name_21Q4

###################### depmap `mutationCalls_21Q4` dataset #####################
mutationCalls_21Q4 <- readr::read_csv("https://ndownloader.figshare.com/files/31315930")
names(mutationCalls_21Q4)[1:32] <- c(
    "gene_name", "entrez_id", "ncbi_build", "chromosome", "start_pos",
    "end_pos", "strand", "var_class", "var_type", "ref_allele",
    "tumor_seq_allele1", "dbSNP_RS", "dbSNP_val_status", "genome_change",
    "annotation_trans", "depmap_id", "cDNA_change", "codon_change",
    "protein_change", "is_deleterious", "is_tcga_hotspot", "tcga_hsCnt",
    "is_cosmic_hotspot", "cosmic_hsCnt", "ExAC_AF", "var_annotation",
    "CGA_WES_AC", "HC_AC", "RD_AC", "RNAseq_AC", "sanger_WES_AC", "WGS_AC")
mutationCalls_21Q4 %>% dplyr::select(depmap_id, everything()) -> mutationCalls_21Q4

######################## depmap `copyNumber_21Q4` dataset ######################
readr::read_csv("https://ndownloader.figshare.com/files/31315924") %>% 
  as.data.frame() %>% dplyr::rename(depmap_id = names(.)[1]) -> copyNumber_21Q4
names(copyNumber_21Q4) <- gsub("&", ";", sub(" \\(.+\\)$", "", names(copyNumber_21Q4)))
copyNumber_21Q4 %>%
  tibble::column_to_rownames(var = "depmap_id") %>%
  t() %>% as.data.frame() -> copyNumber_21Q4_mat

## build SE object
data.frame(Samples = names(copyNumber_21Q4_mat)) -> copyNumber_colData
rownames(copyNumber_colData) <- copyNumber_colData$Samples
SummarizedExperiment(assays = copyNumber_21Q4_mat,
                     colData = copyNumber_colData) -> copyNumber_se

######################## depmap `crispr_21Q4` dataset ##########################
readr::read_csv("https://ndownloader.figshare.com/files/31315996") %>%
    as.data.frame() %>% dplyr::rename(depmap_id = names(.)[1]) -> crispr_21Q4
names(crispr_21Q4) <- gsub("&", ";", sub(" \\(.+\\)$", "", names(crispr_21Q4)))
crispr_21Q4 %>%
  tibble::column_to_rownames(var = "depmap_id") %>%
  t() %>% as.data.frame() -> crispr_21Q4_mat

## build SE object
data.frame(Samples = names(crispr_21Q4_mat)) -> crispr_colData
rownames(crispr_colData) <- crispr_colData$Samples
SummarizedExperiment(assays = crispr_21Q4_mat,
                     colData = crispr_colData) -> crispr_se

######################## depmap `TPM_21Q4` dataset #############################
readr::read_csv("https://ndownloader.figshare.com/files/31315882") %>%
  as.data.frame() %>% dplyr::rename(depmap_id = names(.)[1]) -> TPM_21Q4
names(TPM_21Q4) <- gsub("&", ";", sub(" \\(.+\\)$", "", names(TPM_21Q4)))
TPM_21Q4 %>%
  tibble::column_to_rownames(var = "depmap_id") %>%
  t() %>% as.data.frame() -> TPM_21Q4_mat

## build SE object
data.frame(Samples = names(TPM_21Q4_mat)) -> TPM_colData
rownames(TPM_colData) <- TPM_colData$Samples
SummarizedExperiment(assays = TPM_21Q4_mat,
                     colData = TPM_colData) -> TPM_se

##################### depmap `RPPA_19Q3` dataset ###############################
## download the 19Q3 metadata
eh <- ExperimentHub()
query(eh, "depmap")
eh[["EH3086"]] -> metadata_19Q3
metadata_19Q3 %>% dplyr::select(depmap_id, cell_line) -> dep_2_name_19Q3

### loading data (downloading .csv file from online source)
readr::read_csv(
  paste0("https://depmap.org/portal/download/api/download?file_name=ccle%2Fccle",
         "_2019%2FCCLE_RPPA_20181003.csv&bucket=depmap-external-downloads")) %>% 
  dplyr::rename(cell_line = names(.)[1]) %>% 
  dplyr::left_join(dep_2_name_19Q3, by = "cell_line") %>% 
  dplyr::select(depmap_id, cell_line, everything()) -> RPPA_19Q3

## fill in NA depmap ID value (source):
## https://web.expasy.org/cellosaurus/CVCL_9980 # NCIH684_LIVER
## https://web.expasy.org/cellosaurus/CVCL_3386 # KE97_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE
RPPA_19Q3$depmap_id[RPPA_19Q3$cell_line == "NCIH684_LIVER"] <- "ACH-000089"
RPPA_19Q3$depmap_id[RPPA_19Q3$cell_line == "KE97_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE"] <- "ACH-000167"
RPPA_19Q3 %>% dplyr::select(-cell_line) %>% 
  tibble::column_to_rownames(var = "depmap_id") %>%
  t() %>% as.data.frame() -> RPPA_19Q3_mat

## build SE object
data.frame(Samples = names(RPPA_19Q3_mat)) -> RPPA_colData
rownames(RPPA_colData) <- RPPA_colData$Samples
SummarizedExperiment(assays = RPPA_19Q3_mat,
                     colData = RPPA_colData) -> RPPA_se

##################### depmap `proteomic_20Q2` dataset ##########################
read_csv("https://gygi.hms.harvard.edu/data/ccle/protein_quant_current_normalized.csv.gz") %>% 
  dplyr::select(Gene_Symbol, Uniprot_Acc, MDAMB468_BREAST_TenPx01:last_col()) -> proteomic_20Q2

## manually add gene names that are NA (these were taken from Uniprot)
proteomic_20Q2$Gene_Symbol[proteomic_20Q2$Uniprot_Acc == "H7BZ55"] <- "CROCC2" # 270
proteomic_20Q2$Gene_Symbol[proteomic_20Q2$Uniprot_Acc == "A6NL28"] <- "LOC101929943" # 576
proteomic_20Q2$Gene_Symbol[proteomic_20Q2$Uniprot_Acc == "Q9H552"] <- "KRT8P11" # 1054
proteomic_20Q2$Gene_Symbol[proteomic_20Q2$Uniprot_Acc == "S4R362"] <- "SEPTIN1" #3233
proteomic_20Q2$Gene_Symbol[proteomic_20Q2$Uniprot_Acc == "Q96FF7"] <- "MISP3" # 4833
proteomic_20Q2$Gene_Symbol[proteomic_20Q2$Uniprot_Acc == "Q2M2H8"] <- "MGAM2" # 7933
proteomic_20Q2$Gene_Symbol[proteomic_20Q2$Uniprot_Acc == "P01614"] <- "IGKV2D-40" # 8523
proteomic_20Q2$Gene_Symbol[proteomic_20Q2$Uniprot_Acc == "P01619"] <- "IGKV3-20" # 8623
proteomic_20Q2$Gene_Symbol[proteomic_20Q2$Uniprot_Acc == "Q69YL0"] <- "NCBP2AS2" # 10005
proteomic_20Q2$Gene_Symbol[proteomic_20Q2$Uniprot_Acc == "I3L1I5"] <- "ARHGEF18" # 10487
proteomic_20Q2$Gene_Symbol[proteomic_20Q2$Uniprot_Acc == "E9PS84"] <- "AP000783.1" # 10676
proteomic_20Q2$Gene_Symbol[proteomic_20Q2$Uniprot_Acc == "P60507"] <- "ERVFC1" # 10822
proteomic_20Q2$Gene_Symbol[proteomic_20Q2$Uniprot_Acc == "H3BQF6"] <- "RP11-12J10.3" # 11313
proteomic_20Q2$Gene_Symbol[proteomic_20Q2$Uniprot_Acc == "C9J7I0"] <- "UMAD1" # 11632
proteomic_20Q2$Gene_Symbol[proteomic_20Q2$Uniprot_Acc == "Q902F9"] <- "HERVK_113" # 11691
proteomic_20Q2$Gene_Symbol[proteomic_20Q2$Uniprot_Acc == "P63135"] <- "ERVK-7" # 11692
proteomic_20Q2$Gene_Symbol[proteomic_20Q2$Uniprot_Acc == "H3BMH7"] <- "RP11-178L8.4" # 11735
proteomic_20Q2$Gene_Symbol[proteomic_20Q2$Uniprot_Acc == "P01780"] <- "IGHV3-7" # 12070

proteomic_20Q2 %>%
  dplyr::filter(!is.na(Gene_Symbol)) %>%
  dplyr::filter(!duplicated(Gene_Symbol)) %>%
  tibble::column_to_rownames(var = "Gene_Symbol") %>% 
  dplyr::select(-Uniprot_Acc) -> proteomic_20Q2
names(proteomic_20Q2) <- gsub('(.*)_\\w+', '\\1', names(proteomic_20Q2))

proteomic_20Q2 %>% 
  t() %>% as.data.frame() %>%
  tibble::rownames_to_column() %>%
  dplyr::rename(cell_line = rowname) %>% 
  dplyr::left_join(dep_2_name_21Q4, by = "cell_line") %>% 
  dplyr:: select(cell_line, depmap_id, everything()) -> proteomic_20Q2

## manually add depmap_ids that are NA (these were taken from CCLE)
proteomic_20Q2$depmap_id[proteomic_20Q2$cell_line == "SW948_LARGE_INTESTINE.1"] <- "ACH-000680" 
proteomic_20Q2$depmap_id[proteomic_20Q2$cell_line == "CAL120_BREAST.1"] <- "ACH-000212" 
proteomic_20Q2$depmap_id[proteomic_20Q2$cell_line == "X8505C_THYROID"] <- "ACH-001307" 
proteomic_20Q2$depmap_id[proteomic_20Q2$cell_line == "HCT15_LARGE_INTESTINE.1"] <- "ACH-000997"
proteomic_20Q2$depmap_id[proteomic_20Q2$cell_line == "SW948_LARGE_INTESTINE"] <- "ACH-000680"

proteomic_20Q2 %>%
  dplyr::filter(!is.na(depmap_id)) %>%
  dplyr::filter(!duplicated(depmap_id)) %>%
  dplyr::select(-cell_line) %>%
  tibble::column_to_rownames(var = "depmap_id") %>%
  t() %>% as.data.frame() -> proteomic_20Q2_mat

data.frame(Samples = names(proteomic_20Q2_mat),
           row.names = proteomic_colData$Samples) -> proteomic_colData

## build SE object
SummarizedExperiment(assays = proteomic_20Q2_mat,
                     colData = proteomic_colData) -> proteomic_se

#################### depmap `drug_dependency_21Q4` dataset #####################
# read_csv("https://ndownloader.figshare.com/files/17741420") %>% 
#   as.data.frame() %>% dplyr::rename(depmap_id = names(.)[1]) %>% 
#   dplyr::mutate(depmap_id = gsub("_FAILED_STR", "", depmap_id)) %>% 
#   tibble::column_to_rownames(var = "depmap_id") %>% 
#   as.matrix() %>% t() -> drug_dep_19Q3_w

#################### depmap `drug_dependency_21Q4` metadata ####################
## data cleaning of `drug_sensativity` dataset and screen info
# read_csv("https://ndownloader.figshare.com/files/20237715") %>% 
#   rename(compound = column_name) -> screen_info
# eh[["EH3087"]] -> drug_dep_19Q3

############################# build depmap MAE #################################
## Combine to a named list and call the ExperimentList constructor function
list(crispr_21Q4 = crispr_21Q4_se,
     copyNumber_21Q4 = copyNumber_21Q4_se,
     TPM_21Q4 = TPM_21Q4_se,
     proteomic_20Q2 = proteomic_20Q2_se,
     # mutationCalls_21Q4 = mutationCalls_21Q4_mat,
     # drug_dep_19Q3 = drug_dep_19Q3,
     RPPA_19Q3 = RPPA_19Q3_se) -> assayList

## Use the ExperimentList constructor
exp_list <- ExperimentList(assayList)

## build colData, a DataFrame describing the characteristics of biological units
mae_coldata <- DataFrame(metadata_21Q4)

## build MAE
MultiAssayExperiment::MultiAssayExperiment(
    experiments = exp_list,
    colData = mae_coldata #,
    #sampleMap = sample_map
    ) -> depmap_mae

# saveRDS(depmap_mae, file = paste0(file_path, "depmap_mae.rds"))