inst/scripts/make-data_19Q3.R
In UCLouvain-CBIO/depmap: Cancer Dependency Map Data Package
### =========================================================================
### Make 19Q3 depmap data
### -------------------------------------------------------------------------
###
## This scripts documents how to download and generate the data files.
## Note 1: This scipt assumes (!!!) that it is run in ./depmap/inst/scripts/ and
## saves the resulting .rda files in ./depmap/inst/data
## (e.g. *setwd(./depmap/inst/scripts/)*)
## Note 2: the Broad Institute may change the download urls for these datasets.
## If the link to one of these datasets is broken, please contact the package
## maintainer. All Depmap data can be downloaded from the API at
## https://depmap.org/portal/download/ even if the specific links may change
library("readr")
library("dplyr")
library("tidyr")
library("ExperimentHub")
##########################################
## EH3086 depmap `metadata_19Q3` dataset
##########################################
## data cleaning of `metadata` dataset
### loading data (downloading .csv file from online source)
url_15 <- "https://ndownloader.figshare.com/files/16757723"
sample_info <- read_csv(url_15)
### DepMap-2019q1-celllines.csv data renamed to `metadata`
metadata_19Q3 <- sample_info
# names(metadata_19Q3)
# [1] "DepMap_ID" "stripped_cell_line_name" "CCLE Name"
# [4] "alias" "COSMIC_ID" "lineage"
# [7] "lineage_subtype" "lineage_sub_subtype" "sex"
# [10] "source" "Achilles_n_replicates" "cell_line_NNMD"
# [13] "culture_type" "culture_medium" "cas9_activity"
# [16] "RRID" "sample_collection_site" "primary_or_metastasis"
# [19] "disease" "disease_subtype" "age"
# [22] "Sanger_model_ID" "additional_info"
#### Rename `metadata` columns to contain underscores and be in camel case
## note: "metadata_19Q3" has different columns than "metadata_19Q3"
names(metadata_19Q3)[1:23] <-c("depmap_id", "stripped_cell_line_name",
"cell_line", "aliases", "cosmic_id", "lineage",
"lineage_subtype", "lineage_sub_subtype", "sex",
"source", "Achilles_n_replicates",
"cell_line_NNMD", "culture_type",
"culture_medium", "cas9_activity", "RRID",
"sample_collection_site", "primary_or_metastasis",
"primary_disease", "subtype_disease", "age",
"sanger_id", "additional_info")
### visual check
# View(metadata_19Q3)
### saving cleaned and converted `metadata` data as .rda file
save(metadata_19Q3, file = "../eh_data/metadata_19Q3.rda",
compress = "xz", compression_level = 9)
## access the data on ExperimentHub
# hub <- ExperimentHub()
# x <- query(hub, "depmap")
# EH3086 <- x[["EH3086"]]
##########################################
## `depmap_id_to_name_19Q3 map `depmap_id` to `cell_line`
##########################################
## generation of `metadata` subset `depmap_id_to_name_19Q3`
## The subset of `metadata`, `depmap_id_to_name_Q2` is used to map `cell_line`
## and depmap_id via left_join in other depmap datasets that do not contain
## both variables. If you are generating the depmap data from scratch, you will
## need to run the following code to generate the data correctly.
### `depmap_id_to_name` to add `depmap_id` or `cell_line` to other datasets
depmap_id_to_name_19Q3 <- metadata_19Q3 %>% dplyr::select(depmap_id, cell_line)
### visual check
# head(depmap_id_to_name_19Q3)
##########################################
## EH3085 depmap `mutationCalls_19Q3` dataset
##########################################
## data cleaning of `mutationCalls` dataset
### loading data (downloading .csv file from online source)
url_16 <- "https://ndownloader.figshare.com/files/16757702"
CCLE_mutations_19Q3 <- read_csv(url_16)
### depmap_19Q1_mutation_calls data renamed to `mutationCalls`
mutationCalls_19Q3 <- CCLE_mutations_19Q3
# head(mutationCalls_19Q3)
#### rename last column to depmap_id
# names(mutationCalls_19Q3)
# [1] "Hugo_Symbol" "Entrez_Gene_Id" "NCBI_Build"
# [4] "Chromosome" "Start_position" "End_position"
# [7] "Strand" "Variant_Classification" "Variant_Type"
# [10] "Reference_Allele" "Tumor_Seq_Allele1" "dbSNP_RS"
# [13] "dbSNP_Val_Status" "Genome_Change" "Annotation_Transcript"
# [16] "Tumor_Sample_Barcode" "cDNA_Change" "Codon_Change"
# [19] "Protein_Change" "isDeleterious" "isTCGAhotspot"
# [22] "TCGAhsCnt" "isCOSMIChotspot" "COSMIChsCnt"
# [25] "ExAC_AF" "CGA_WES_AC" "SangerWES_AC"
# [28] "SangerRecalibWES_AC" "RNAseq_AC" "HC_AC"
# [31] "RD_AC" "WGS_AC" "Variant_annotation"
# [34] "DepMap_ID"
## note: "mutationCalls_19Q3" has different columns than "mutationCalls_19Q1"
## the variable "VA_WES_AC" is no longer present in this dataset, unlike
## previous releases (e.g. 19Q1)!
names(mutationCalls_19Q3)[1:34] <- 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_transcript", "tumor_sample_barcode",
"cDNA_change", "codon_change", "protein_change",
"is_deleterious", "is_tcga_hotspot",
"tcga_hsCnt", "is_cosmic_hotspot",
"cosmic_hsCnt", "ExAC_AF", "CGA_WES_AC",
"sanger_WES_AC", "sanger_recalib_WES_AC",
"RNAseq_AC", "HC_AC", "RD_AC", "WGS_AC",
"var_annotation","depmap_id")
### rearrange columns into same column format as other datasets
mutationCalls_19Q3 <- mutationCalls_19Q3 %>%
dplyr::select(depmap_id, 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_transcript, tumor_sample_barcode, cDNA_change,
codon_change, protein_change, is_deleterious, is_tcga_hotspot,
tcga_hsCnt, is_cosmic_hotspot, cosmic_hsCnt, ExAC_AF,
CGA_WES_AC, sanger_WES_AC, sanger_recalib_WES_AC, RNAseq_AC,
HC_AC, RD_AC, WGS_AC, var_annotation)
### visual check
# head(mutationCalls_19Q3)
### saving cleaned and converted `mutationCalls` data as .rda file
save(mutationCalls_19Q3, file = "../eh_data/mutationCalls_19Q3.rda",
compress = "xz", compression_level = 9)
## access the data on ExperimentHub
# hub <- ExperimentHub()
# x <- query(hub, "depmap")
# EH3085 <- x[["EH3085"]]
##########################################
## EH3082 depmap `copyNumber_19Q3` dataset
##########################################
## data cleaning of `copyNumber` dataset
### loading data (downloading .csv file from online source)
url_17 <- "https://ndownloader.figshare.com/files/16757699"
CCLE_gene_cn_19Q3 <- read_csv(url_17)
### public_19Q1_gene_cn.csv data renamed to `copyNumber`
copyNumber_19Q3 <- CCLE_gene_cn_19Q3
# names(copyNumber_19Q3)
### rename column first column to "depmap_id"
names(copyNumber_19Q3)[1] <- "depmap_id"
### gather into long form on columns: `depmap_id`, `gene`, `logCopyNumber`
copyNumber_19Q3_long <- gather(copyNumber_19Q3, gene, log_copy_number,
-depmap_id)
### mutate gene column into `gene_name` and `entrez_id`
copyNumber_19Q3_long <- copyNumber_19Q3_long %>%
mutate(entrez_id = gsub("&", ";", sub("\\)", "", sub("^.+ \\(", "", gene))),
gene_name = gsub("&", ";", sub(" \\(.+\\)$", "", gene)))
### left_join `copyNumber` & `depmap_id_to_name_19Q3` on `depmap_id`,
## `cell_line`
copyNumber_19Q3 <- copyNumber_19Q3_long %>%
left_join(depmap_id_to_name_19Q3, by = c("depmap_id" = "depmap_id"))
### rearrange columns into same column format as other datasets
copyNumber_19Q3 <- copyNumber_19Q3 %>%
dplyr::select(depmap_id, gene, log_copy_number,
entrez_id, gene_name, cell_line) %>%
type_convert(cols(entrez_id = "i"))
### visual check
# head(copyNumber_19Q3)
### saving cleaned and converted `copyNumber` data as .rda file
save(copyNumber_19Q3, file = "../eh_data/copyNumber_19Q3.rda",
compress = "xz", compression_level = 9)
## access the data on ExperimentHub
# hub <- ExperimentHub()
# x <- query(hub, "depmap")
# EH3082 <- x[["EH3082"]]
##########################################
## EH3081 depmap `crispr_19Q3` dataset
##########################################
## data cleaning of `crispr` dataset`
### loading data (downloading .csv file from online source)
url_18 <- "https://ndownloader.figshare.com/files/16757666"
Achilles_gene_effect_19Q3 <- read_csv(url_18)
### gene_effect_corrected.csv data renamed to `crispr`
crispr_19Q3 <- Achilles_gene_effect_19Q3
### visual check
# head(crispr_19Q3)
### rename column first column to "depmap_id"
names(crispr_19Q3)[1] <-"depmap_id"
### gather cripsr into long form with columns: `depmap_id`, `gene`, `dependency`
crispr_19Q3_long <- gather(crispr_19Q3, gene, dependency, -depmap_id)
### mutate gene into `gene_name` and `entrez_id`
crispr_19Q3_long <- crispr_19Q3_long %>%
mutate(entrez_id = gsub("&", ";", sub("\\)", "", sub("^.+ \\(", "", gene))),
gene_name = gsub("&", ";", sub(" \\(.+\\)$", "", gene)))
### left_join `crispr_long` and `depmap_id_to_name` to add `cell_line` column
crispr_19Q3 <- crispr_19Q3_long %>% left_join(depmap_id_to_name_19Q3,
by = c("depmap_id" = "depmap_id"))
### rearrange columns into same column format as other datasets
crispr_19Q3 <- crispr_19Q3 %>% dplyr::select(depmap_id, gene,
dependency, entrez_id,
gene_name, cell_line) %>%
type_convert(cols(entrez_id = "i"))
### visual check
# head(crispr_19Q3)
### saving cleaned and converted `crispr` data as .rda file
save(crispr_19Q3, file = "../eh_data/crispr_19Q3.rda",
compress = "xz", compression_level = 9)
## access the data on ExperimentHub
# hub <- ExperimentHub()
# x <- query(hub, "depmap")
# EH3081 <- x[["EH3081"]]
##########################################
## EH3084 depmap `TPM_19Q3` dataset
##########################################
## data cleaning of `TPM` dataset
### loading data (downloading .csv file from online source)
url_19 <- "https://ndownloader.figshare.com/files/16757690"
CCLE_expression_full_19Q3 <- read_csv(url_19)
### CCLE_depMap_19Q3_TPM.csv data renamed to `TPM`
TPM_19Q3 <- CCLE_expression_full_19Q3
# names(TPM_19Q3)
### rename column first column to "depmap_id"
names(TPM_19Q3)[1] <-"depmap_id"
### gather `TPM` into long form on columns: `depmap_id`, `gene`, `expression`
TPM_19Q3_long <- gather(TPM_19Q3, gene, expression, -depmap_id)
### mutate gene into gene_name and ensembl_id
TPM_19Q3_long <- TPM_19Q3_long %>%
mutate(ensembl_id = gsub("&", ";", sub("\\)", "", sub("^.+ \\(", "",gene))),
gene_name = gsub("&", ";", sub(" \\(.+\\)$", "", gene)))
### left_join join `TPM` and `depmap_id_to_name_19Q3` to add `cell_line` column
TPM_19Q3 <- TPM_19Q3_long %>% left_join(depmap_id_to_name_19Q3,
by = c("depmap_id" = "depmap_id"))
### rearrange columns into same column format as other datasets
TPM_19Q3 %>%
dplyr::select(depmap_id, gene, expression, entrez_id, gene_name,
cell_line) %>%
type_convert(cols(entrez_id = "i")) -> TPM_19Q3
### saving cleaned and converted `TPM` data as .rda file
save(TPM_19Q3, file = "../eh_data/TPM_19Q3.rda", compress="xz",
compression_level=9)
## access the data on ExperimentHub
# hub <- ExperimentHub()
# x <- query(hub, "depmap")
# EH3084 <- x[["EH3084"]]
##########################################
## EH3083 depmap `RPPA_19Q3` dataset
##########################################
## data cleaning of `RPPA` dataset
### loading data (downloading .csv file from online source)
url_20 <- "https://depmap.org/portal/download/api/download/external?file_name=ccle%2Fccle_2019%2FCCLE_RPPA_20181003.csv"
CCLE_RPPA_20180123_19Q3 <- read_csv(url_20)
### CCLE_RPPA_20180123.csv data renamed to `RPPA`
RPPA_19Q3 <- CCLE_RPPA_20180123_19Q3
# head(RPPA_19Q3)
### rename column first column to "cell_line"
names(RPPA_19Q3)[1] <-"cell_line"
### gather cell line columns and split gene name into gene_name and entrez_id
RPPA_19Q3_long <- RPPA_19Q3 %>% gather(antibody, expression, -cell_line)
### join cell_line name
RPPA_19Q3 <- RPPA_19Q3_long %>% left_join(depmap_id_to_name_19Q3,
by = c("cell_line" = "cell_line"))
### rearrange columns into same column format as other datasets
RPPA_19Q3 <- RPPA_19Q3 %>% dplyr::select(cell_line, antibody, expression,
depmap_id)
### visual check
# head(RPPA_19Q3)
### saving cleaned and converted `RPPA` data as .rda file
save(RPPA_19Q3, file="../eh_data/RPPA_19Q3.rda", compress="xz",
compression_level=9)
## access the data on ExperimentHub
# hub <- ExperimentHub()
# x <- query(hub, "depmap")
# EH3083 <- x[["EH3083"]]
##########################################
## EH3080 depmap `rnai_19Q3` dataset
##########################################
## data cleaning of `rnai` dataset
### loading data (downloading .csv file from online source)
url_21 <- "https://ndownloader.figshare.com/files/13515395"
D2_combined_gene_dep_scores_19Q3 <- read_csv(url_21)
### D2_combined_genetic_dependency_scores.csv data renamed to `rnai`
rnai_19Q3 <- D2_combined_gene_dep_scores_19Q3
# head(rnai_19Q3)
### rename column first column to "gene"
names(rnai_19Q3)[1] <- "gene"
### gather rnai into long form with columns: depmap_id, gene and dependency
rnai_19Q3 <- gather(rnai_19Q3, key = cell_line, value = dependency, -gene) %>%
mutate(entrez_id = gsub("&", ";", sub("\\)", "", sub("^.+ \\(", "", gene))),
gene_name = gsub("&", ";", sub(" \\(.+\\)$", "", gene))) %>%
left_join(depmap_id_to_name_19Q3, by = c("cell_line" = "cell_line"))
### rearrange columns into same column format as other datasets
rnai_19Q3 <- rnai_19Q3 %>% dplyr::select(gene, cell_line, dependency, entrez_id,
gene_name, depmap_id) %>%
type_convert(cols(entrez_id = "i"))
### visual check
# head(rnai_19Q3)
### saving cleaned and converted `rnai` data as .rda file
save(rnai_19Q3, file="../eh_data/rnai_19Q3.rda", compress="xz",
compression_level=9)
## access the data on ExperimentHub
# hub <- ExperimentHub()
# x <- query(hub, "depmap")
# EH3080 <- x[["EH3080"]]
##########################################
## EH3087 depmap `drug_sensativity_19Q3` dataset
##########################################
## data cleaning of `drug_sensativity` dataset
### loading data (downloading .csv file from online source)
url_22 <- "https://depmap.org/portal/download/api/download/external?file_name=drug%2Fprimary-screen-e5c7.7%2Fprimary_replicate_collapsed_logfold_change.csv"
primary_replicate_collapsed_logfold_change_19Q3 <- read_csv(url_22)
### primary_replicate_collapsed_logfold_change_19Q3.csv data renamed to
### `drug_sensativity_19Q3`
drug_sensativity_19Q3 <- primary_replicate_collapsed_logfold_change_19Q3
# head(drug_sensativity_19Q3)
### rename column first column to "depmap_id"
names(drug_sensativity_19Q3)[1] <- "depmap_id"
### gather drug_sensativity into long form with columns: depmap_id, compound and
### dependency
drug_sensativity_19Q3 <- gather(drug_sensativity_19Q3, key = compound,
value = dependency, -depmap_id) %>%
left_join(depmap_id_to_name_19Q3,
by = c("depmap_id" = "depmap_id"))
### rearrange columns into same column format as other datasets
drug_sensativity_19Q3 <- drug_sensativity_19Q3 %>%
dplyr::select(depmap_id, cell_line, compound, dependency)
### visual check
head(drug_sensativity_19Q3)
### saving cleaned and converted `drug_sensativity` data as .rda file
save(drug_sensativity_19Q3, file="../eh_data/drug_sensativity_19Q3.rda",
compress="xz", compression_level=9)
## access the data on ExperimentHub
# hub <- ExperimentHub()
# x <- query(hub, "depmap")
# EH3087 <- x[["EH3087"]]
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### =========================================================================
### Make 19Q3 depmap data
### -------------------------------------------------------------------------
###
## This scripts documents how to download and generate the data files.
## Note 1: This scipt assumes (!!!) that it is run in ./depmap/inst/scripts/ and
## saves the resulting .rda files in ./depmap/inst/data
## (e.g. *setwd(./depmap/inst/scripts/)*)
## Note 2: the Broad Institute may change the download urls for these datasets.
## If the link to one of these datasets is broken, please contact the package
## maintainer. All Depmap data can be downloaded from the API at
## https://depmap.org/portal/download/ even if the specific links may change
library("readr")
library("dplyr")
library("tidyr")
library("ExperimentHub")
##########################################
## EH3086 depmap `metadata_19Q3` dataset
##########################################
## data cleaning of `metadata` dataset
### loading data (downloading .csv file from online source)
url_15 <- "https://ndownloader.figshare.com/files/16757723"
sample_info <- read_csv(url_15)
### DepMap-2019q1-celllines.csv data renamed to `metadata`
metadata_19Q3 <- sample_info
# names(metadata_19Q3)
# [1] "DepMap_ID" "stripped_cell_line_name" "CCLE Name"
# [4] "alias" "COSMIC_ID" "lineage"
# [7] "lineage_subtype" "lineage_sub_subtype" "sex"
# [10] "source" "Achilles_n_replicates" "cell_line_NNMD"
# [13] "culture_type" "culture_medium" "cas9_activity"
# [16] "RRID" "sample_collection_site" "primary_or_metastasis"
# [19] "disease" "disease_subtype" "age"
# [22] "Sanger_model_ID" "additional_info"
#### Rename `metadata` columns to contain underscores and be in camel case
## note: "metadata_19Q3" has different columns than "metadata_19Q3"
names(metadata_19Q3)[1:23] <-c("depmap_id", "stripped_cell_line_name",
"cell_line", "aliases", "cosmic_id", "lineage",
"lineage_subtype", "lineage_sub_subtype", "sex",
"source", "Achilles_n_replicates",
"cell_line_NNMD", "culture_type",
"culture_medium", "cas9_activity", "RRID",
"sample_collection_site", "primary_or_metastasis",
"primary_disease", "subtype_disease", "age",
"sanger_id", "additional_info")
### visual check
# View(metadata_19Q3)
### saving cleaned and converted `metadata` data as .rda file
save(metadata_19Q3, file = "../eh_data/metadata_19Q3.rda",
compress = "xz", compression_level = 9)
## access the data on ExperimentHub
# hub <- ExperimentHub()
# x <- query(hub, "depmap")
# EH3086 <- x[["EH3086"]]
##########################################
## `depmap_id_to_name_19Q3 map `depmap_id` to `cell_line`
##########################################
## generation of `metadata` subset `depmap_id_to_name_19Q3`
## The subset of `metadata`, `depmap_id_to_name_Q2` is used to map `cell_line`
## and depmap_id via left_join in other depmap datasets that do not contain
## both variables. If you are generating the depmap data from scratch, you will
## need to run the following code to generate the data correctly.
### `depmap_id_to_name` to add `depmap_id` or `cell_line` to other datasets
depmap_id_to_name_19Q3 <- metadata_19Q3 %>% dplyr::select(depmap_id, cell_line)
### visual check
# head(depmap_id_to_name_19Q3)
##########################################
## EH3085 depmap `mutationCalls_19Q3` dataset
##########################################
## data cleaning of `mutationCalls` dataset
### loading data (downloading .csv file from online source)
url_16 <- "https://ndownloader.figshare.com/files/16757702"
CCLE_mutations_19Q3 <- read_csv(url_16)
### depmap_19Q1_mutation_calls data renamed to `mutationCalls`
mutationCalls_19Q3 <- CCLE_mutations_19Q3
# head(mutationCalls_19Q3)
#### rename last column to depmap_id
# names(mutationCalls_19Q3)
# [1] "Hugo_Symbol" "Entrez_Gene_Id" "NCBI_Build"
# [4] "Chromosome" "Start_position" "End_position"
# [7] "Strand" "Variant_Classification" "Variant_Type"
# [10] "Reference_Allele" "Tumor_Seq_Allele1" "dbSNP_RS"
# [13] "dbSNP_Val_Status" "Genome_Change" "Annotation_Transcript"
# [16] "Tumor_Sample_Barcode" "cDNA_Change" "Codon_Change"
# [19] "Protein_Change" "isDeleterious" "isTCGAhotspot"
# [22] "TCGAhsCnt" "isCOSMIChotspot" "COSMIChsCnt"
# [25] "ExAC_AF" "CGA_WES_AC" "SangerWES_AC"
# [28] "SangerRecalibWES_AC" "RNAseq_AC" "HC_AC"
# [31] "RD_AC" "WGS_AC" "Variant_annotation"
# [34] "DepMap_ID"
## note: "mutationCalls_19Q3" has different columns than "mutationCalls_19Q1"
## the variable "VA_WES_AC" is no longer present in this dataset, unlike
## previous releases (e.g. 19Q1)!
names(mutationCalls_19Q3)[1:34] <- 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_transcript", "tumor_sample_barcode",
"cDNA_change", "codon_change", "protein_change",
"is_deleterious", "is_tcga_hotspot",
"tcga_hsCnt", "is_cosmic_hotspot",
"cosmic_hsCnt", "ExAC_AF", "CGA_WES_AC",
"sanger_WES_AC", "sanger_recalib_WES_AC",
"RNAseq_AC", "HC_AC", "RD_AC", "WGS_AC",
"var_annotation","depmap_id")
### rearrange columns into same column format as other datasets
mutationCalls_19Q3 <- mutationCalls_19Q3 %>%
dplyr::select(depmap_id, 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_transcript, tumor_sample_barcode, cDNA_change,
codon_change, protein_change, is_deleterious, is_tcga_hotspot,
tcga_hsCnt, is_cosmic_hotspot, cosmic_hsCnt, ExAC_AF,
CGA_WES_AC, sanger_WES_AC, sanger_recalib_WES_AC, RNAseq_AC,
HC_AC, RD_AC, WGS_AC, var_annotation)
### visual check
# head(mutationCalls_19Q3)
### saving cleaned and converted `mutationCalls` data as .rda file
save(mutationCalls_19Q3, file = "../eh_data/mutationCalls_19Q3.rda",
compress = "xz", compression_level = 9)
## access the data on ExperimentHub
# hub <- ExperimentHub()
# x <- query(hub, "depmap")
# EH3085 <- x[["EH3085"]]
##########################################
## EH3082 depmap `copyNumber_19Q3` dataset
##########################################
## data cleaning of `copyNumber` dataset
### loading data (downloading .csv file from online source)
url_17 <- "https://ndownloader.figshare.com/files/16757699"
CCLE_gene_cn_19Q3 <- read_csv(url_17)
### public_19Q1_gene_cn.csv data renamed to `copyNumber`
copyNumber_19Q3 <- CCLE_gene_cn_19Q3
# names(copyNumber_19Q3)
### rename column first column to "depmap_id"
names(copyNumber_19Q3)[1] <- "depmap_id"
### gather into long form on columns: `depmap_id`, `gene`, `logCopyNumber`
copyNumber_19Q3_long <- gather(copyNumber_19Q3, gene, log_copy_number,
-depmap_id)
### mutate gene column into `gene_name` and `entrez_id`
copyNumber_19Q3_long <- copyNumber_19Q3_long %>%
mutate(entrez_id = gsub("&", ";", sub("\\)", "", sub("^.+ \\(", "", gene))),
gene_name = gsub("&", ";", sub(" \\(.+\\)$", "", gene)))
### left_join `copyNumber` & `depmap_id_to_name_19Q3` on `depmap_id`,
## `cell_line`
copyNumber_19Q3 <- copyNumber_19Q3_long %>%
left_join(depmap_id_to_name_19Q3, by = c("depmap_id" = "depmap_id"))
### rearrange columns into same column format as other datasets
copyNumber_19Q3 <- copyNumber_19Q3 %>%
dplyr::select(depmap_id, gene, log_copy_number,
entrez_id, gene_name, cell_line) %>%
type_convert(cols(entrez_id = "i"))
### visual check
# head(copyNumber_19Q3)
### saving cleaned and converted `copyNumber` data as .rda file
save(copyNumber_19Q3, file = "../eh_data/copyNumber_19Q3.rda",
compress = "xz", compression_level = 9)
## access the data on ExperimentHub
# hub <- ExperimentHub()
# x <- query(hub, "depmap")
# EH3082 <- x[["EH3082"]]
##########################################
## EH3081 depmap `crispr_19Q3` dataset
##########################################
## data cleaning of `crispr` dataset`
### loading data (downloading .csv file from online source)
url_18 <- "https://ndownloader.figshare.com/files/16757666"
Achilles_gene_effect_19Q3 <- read_csv(url_18)
### gene_effect_corrected.csv data renamed to `crispr`
crispr_19Q3 <- Achilles_gene_effect_19Q3
### visual check
# head(crispr_19Q3)
### rename column first column to "depmap_id"
names(crispr_19Q3)[1] <-"depmap_id"
### gather cripsr into long form with columns: `depmap_id`, `gene`, `dependency`
crispr_19Q3_long <- gather(crispr_19Q3, gene, dependency, -depmap_id)
### mutate gene into `gene_name` and `entrez_id`
crispr_19Q3_long <- crispr_19Q3_long %>%
mutate(entrez_id = gsub("&", ";", sub("\\)", "", sub("^.+ \\(", "", gene))),
gene_name = gsub("&", ";", sub(" \\(.+\\)$", "", gene)))
### left_join `crispr_long` and `depmap_id_to_name` to add `cell_line` column
crispr_19Q3 <- crispr_19Q3_long %>% left_join(depmap_id_to_name_19Q3,
by = c("depmap_id" = "depmap_id"))
### rearrange columns into same column format as other datasets
crispr_19Q3 <- crispr_19Q3 %>% dplyr::select(depmap_id, gene,
dependency, entrez_id,
gene_name, cell_line) %>%
type_convert(cols(entrez_id = "i"))
### visual check
# head(crispr_19Q3)
### saving cleaned and converted `crispr` data as .rda file
save(crispr_19Q3, file = "../eh_data/crispr_19Q3.rda",
compress = "xz", compression_level = 9)
## access the data on ExperimentHub
# hub <- ExperimentHub()
# x <- query(hub, "depmap")
# EH3081 <- x[["EH3081"]]
##########################################
## EH3084 depmap `TPM_19Q3` dataset
##########################################
## data cleaning of `TPM` dataset
### loading data (downloading .csv file from online source)
url_19 <- "https://ndownloader.figshare.com/files/16757690"
CCLE_expression_full_19Q3 <- read_csv(url_19)
### CCLE_depMap_19Q3_TPM.csv data renamed to `TPM`
TPM_19Q3 <- CCLE_expression_full_19Q3
# names(TPM_19Q3)
### rename column first column to "depmap_id"
names(TPM_19Q3)[1] <-"depmap_id"
### gather `TPM` into long form on columns: `depmap_id`, `gene`, `expression`
TPM_19Q3_long <- gather(TPM_19Q3, gene, expression, -depmap_id)
### mutate gene into gene_name and ensembl_id
TPM_19Q3_long <- TPM_19Q3_long %>%
mutate(ensembl_id = gsub("&", ";", sub("\\)", "", sub("^.+ \\(", "",gene))),
gene_name = gsub("&", ";", sub(" \\(.+\\)$", "", gene)))
### left_join join `TPM` and `depmap_id_to_name_19Q3` to add `cell_line` column
TPM_19Q3 <- TPM_19Q3_long %>% left_join(depmap_id_to_name_19Q3,
by = c("depmap_id" = "depmap_id"))
### rearrange columns into same column format as other datasets
TPM_19Q3 %>%
dplyr::select(depmap_id, gene, expression, entrez_id, gene_name,
cell_line) %>%
type_convert(cols(entrez_id = "i")) -> TPM_19Q3
### saving cleaned and converted `TPM` data as .rda file
save(TPM_19Q3, file = "../eh_data/TPM_19Q3.rda", compress="xz",
compression_level=9)
## access the data on ExperimentHub
# hub <- ExperimentHub()
# x <- query(hub, "depmap")
# EH3084 <- x[["EH3084"]]
##########################################
## EH3083 depmap `RPPA_19Q3` dataset
##########################################
## data cleaning of `RPPA` dataset
### loading data (downloading .csv file from online source)
url_20 <- "https://depmap.org/portal/download/api/download/external?file_name=ccle%2Fccle_2019%2FCCLE_RPPA_20181003.csv"
CCLE_RPPA_20180123_19Q3 <- read_csv(url_20)
### CCLE_RPPA_20180123.csv data renamed to `RPPA`
RPPA_19Q3 <- CCLE_RPPA_20180123_19Q3
# head(RPPA_19Q3)
### rename column first column to "cell_line"
names(RPPA_19Q3)[1] <-"cell_line"
### gather cell line columns and split gene name into gene_name and entrez_id
RPPA_19Q3_long <- RPPA_19Q3 %>% gather(antibody, expression, -cell_line)
### join cell_line name
RPPA_19Q3 <- RPPA_19Q3_long %>% left_join(depmap_id_to_name_19Q3,
by = c("cell_line" = "cell_line"))
### rearrange columns into same column format as other datasets
RPPA_19Q3 <- RPPA_19Q3 %>% dplyr::select(cell_line, antibody, expression,
depmap_id)
### visual check
# head(RPPA_19Q3)
### saving cleaned and converted `RPPA` data as .rda file
save(RPPA_19Q3, file="../eh_data/RPPA_19Q3.rda", compress="xz",
compression_level=9)
## access the data on ExperimentHub
# hub <- ExperimentHub()
# x <- query(hub, "depmap")
# EH3083 <- x[["EH3083"]]
##########################################
## EH3080 depmap `rnai_19Q3` dataset
##########################################
## data cleaning of `rnai` dataset
### loading data (downloading .csv file from online source)
url_21 <- "https://ndownloader.figshare.com/files/13515395"
D2_combined_gene_dep_scores_19Q3 <- read_csv(url_21)
### D2_combined_genetic_dependency_scores.csv data renamed to `rnai`
rnai_19Q3 <- D2_combined_gene_dep_scores_19Q3
# head(rnai_19Q3)
### rename column first column to "gene"
names(rnai_19Q3)[1] <- "gene"
### gather rnai into long form with columns: depmap_id, gene and dependency
rnai_19Q3 <- gather(rnai_19Q3, key = cell_line, value = dependency, -gene) %>%
mutate(entrez_id = gsub("&", ";", sub("\\)", "", sub("^.+ \\(", "", gene))),
gene_name = gsub("&", ";", sub(" \\(.+\\)$", "", gene))) %>%
left_join(depmap_id_to_name_19Q3, by = c("cell_line" = "cell_line"))
### rearrange columns into same column format as other datasets
rnai_19Q3 <- rnai_19Q3 %>% dplyr::select(gene, cell_line, dependency, entrez_id,
gene_name, depmap_id) %>%
type_convert(cols(entrez_id = "i"))
### visual check
# head(rnai_19Q3)
### saving cleaned and converted `rnai` data as .rda file
save(rnai_19Q3, file="../eh_data/rnai_19Q3.rda", compress="xz",
compression_level=9)
## access the data on ExperimentHub
# hub <- ExperimentHub()
# x <- query(hub, "depmap")
# EH3080 <- x[["EH3080"]]
##########################################
## EH3087 depmap `drug_sensativity_19Q3` dataset
##########################################
## data cleaning of `drug_sensativity` dataset
### loading data (downloading .csv file from online source)
url_22 <- "https://depmap.org/portal/download/api/download/external?file_name=drug%2Fprimary-screen-e5c7.7%2Fprimary_replicate_collapsed_logfold_change.csv"
primary_replicate_collapsed_logfold_change_19Q3 <- read_csv(url_22)
### primary_replicate_collapsed_logfold_change_19Q3.csv data renamed to
### `drug_sensativity_19Q3`
drug_sensativity_19Q3 <- primary_replicate_collapsed_logfold_change_19Q3
# head(drug_sensativity_19Q3)
### rename column first column to "depmap_id"
names(drug_sensativity_19Q3)[1] <- "depmap_id"
### gather drug_sensativity into long form with columns: depmap_id, compound and
### dependency
drug_sensativity_19Q3 <- gather(drug_sensativity_19Q3, key = compound,
value = dependency, -depmap_id) %>%
left_join(depmap_id_to_name_19Q3,
by = c("depmap_id" = "depmap_id"))
### rearrange columns into same column format as other datasets
drug_sensativity_19Q3 <- drug_sensativity_19Q3 %>%
dplyr::select(depmap_id, cell_line, compound, dependency)
### visual check
head(drug_sensativity_19Q3)
### saving cleaned and converted `drug_sensativity` data as .rda file
save(drug_sensativity_19Q3, file="../eh_data/drug_sensativity_19Q3.rda",
compress="xz", compression_level=9)
## access the data on ExperimentHub
# hub <- ExperimentHub()
# x <- query(hub, "depmap")
# EH3087 <- x[["EH3087"]]
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