The depmap data
Theo Killian
Laurent Gatto
Computational Biology, UCLouvain2024-08-16
Source:vignettes/depmap.Rmd
depmap.RmdIntroduction
The depmap package aims to provide a reproducible
research framework to cancer dependency data described by Tsherniak, Aviad, et
al. “Defining a cancer dependency map.” Cell 170.3 (2017): 564-576..
The data found in the depmap
package has been formatted to facilitate the use of common R packages
such as dplyr and ggplot2. We hope that this
package will allow researchers to more easily mine, explore and visually
illustrate dependency data taken from the Depmap cancer genomic
dependency study.
Installation instructions
To install depmap, the BiocManager Bioconductor Project Package Manager is required. If BiocManager is not already installed, it will need to be done so beforehand. Type (within R) install.packages(“BiocManager”) (This needs to be done just once.)
install.packages("BiocManager")
BiocManager::install("depmap")The depmap package fully depends on the
ExperimentHub Bioconductor package, which allows the data
accessed in this package to be stored and retrieved from the cloud.
Tidy depmap data
The depmap
package currently contains eight datasets available through
ExperimentHub.
The data found in this R package has been converted from a “wide”
format .csv file to “long” format .rda file. None of the
values taken from the original datasets have been changed, although the
columns have been re-arranged. Descriptions of the changes made are
described under the Details section after querying the
relevant dataset.
## create ExperimentHub query object
eh <- ExperimentHub()
query(eh, "depmap")## ExperimentHub with 82 records
## # snapshotDate(): 2024-04-29
## # $dataprovider: Broad Institute
## # $species: Homo sapiens
## # $rdataclass: tibble
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["EH2260"]]'
##
## title
## EH2260 | rnai_19Q1
## EH2261 | crispr_19Q1
## EH2262 | copyNumber_19Q1
## EH2263 | RPPA_19Q1
## EH2264 | TPM_19Q1
## ... ...
## EH7555 | copyNumber_22Q2
## EH7556 | TPM_22Q2
## EH7557 | mutationCalls_22Q2
## EH7558 | metadata_22Q2
## EH7559 | achilles_22Q2Each dataset has a ExperimentHub accession number,
(e.g. EH2260 refers to the rnai dataset from the
19Q1 release).
RNA inference knockout data
The rnai dataset contains the combined genetic
dependency data for RNAi - induced gene knockdown for select genes and
cancer cell lines. This data corresponds to the
D2_combined_genetic_dependency_scores.csv file.
Specific rnai datasets can be accessed, such as
rnai_19Q1 by EH number.
eh[["EH2260"]]The most recent rnai dataset can be automatically loaded
into R by using the depmap_rnai function.
depmap::depmap_rnai()## see ?depmap and browseVignettes('depmap') for documentation## loading from cache## # A tibble: 12,324,008 × 6
## gene cell_line dependency entrez_id gene_name depmap_id
## <chr> <chr> <dbl> <int> <chr> <chr>
## 1 A1BG (1) 127399_SOFT_TIS… NA 1 A1BG ACH-0012…
## 2 NAT2 (10) 127399_SOFT_TIS… NA 10 NAT2 ACH-0012…
## 3 ADA (100) 127399_SOFT_TIS… NA 100 ADA ACH-0012…
## 4 CDH2 (1000) 127399_SOFT_TIS… -0.195 1000 CDH2 ACH-0012…
## 5 AKT3 (10000) 127399_SOFT_TIS… -0.256 10000 AKT3 ACH-0012…
## 6 MED6 (10001) 127399_SOFT_TIS… -0.174 10001 MED6 ACH-0012…
## 7 NR2E3 (10002) 127399_SOFT_TIS… -0.140 10002 NR2E3 ACH-0012…
## 8 NAALAD2 (10003) 127399_SOFT_TIS… NA 10003 NAALAD2 ACH-0012…
## 9 DUXB (100033411) 127399_SOFT_TIS… NA 100033411 DUXB ACH-0012…
## 10 PDZK1P1 (100034743) 127399_SOFT_TIS… NA 100034743 PDZK1P1 ACH-0012…
## # ℹ 12,323,998 more rowsCRISPR-Cas9 knockout data
The crispr dataset contains the (batch corrected CERES
inferred gene effect) CRISPR-Cas9 knockout data of select genes and
cancer cell lines. This data corresponds to the
gene_effect_corrected.csv file.
Specific crispr datasets can be accessed, such as
crispr_19Q1 by EH number.
eh[["EH2261"]]The most recent crispr dataset can be automatically
loaded into R by using the depmap_crispr function.
depmap::depmap_crispr()## see ?depmap and browseVignettes('depmap') for documentation## loading from cache## # A tibble: 18,881,196 × 6
## depmap_id gene dependency entrez_id gene_name cell_line
## <chr> <chr> <dbl> <int> <chr> <chr>
## 1 ACH-000001 A1BG (1) -0.135 1 A1BG NIHOVCAR3_OVARY
## 2 ACH-000004 A1BG (1) 0.0819 1 A1BG HEL_HAEMATOPOIETIC_AND_LY…
## 3 ACH-000005 A1BG (1) -0.0942 1 A1BG HEL9217_HAEMATOPOIETIC_AN…
## 4 ACH-000007 A1BG (1) -0.0115 1 A1BG LS513_LARGE_INTESTINE
## 5 ACH-000009 A1BG (1) -0.0508 1 A1BG C2BBE1_LARGE_INTESTINE
## 6 ACH-000011 A1BG (1) 0.0918 1 A1BG 253J_URINARY_TRACT
## 7 ACH-000012 A1BG (1) -0.147 1 A1BG HCC827_LUNG
## 8 ACH-000013 A1BG (1) -0.0592 1 A1BG ONCODG1_OVARY
## 9 ACH-000014 A1BG (1) -0.0348 1 A1BG HS294T_SKIN
## 10 ACH-000015 A1BG (1) -0.204 1 A1BG NCIH1581_LUNG
## # ℹ 18,881,186 more rowsWES copy number data
The copyNumber dataset contains the WES copy number
data, relating to the numerical log-fold copy number change measured
against the baseline copy number of select genes and cell lines. This
dataset corresponds to the public_19Q1_gene_cn.csv
Specific copyNumber datasets can be accessed, such as
copyNumber_19Q1 by EH number.
eh[["EH2262"]]The most recent copyNumber dataset can be automatically
loaded into R by using the depmap_copyNumber function.
depmap::depmap_copyNumber()## see ?depmap and browseVignettes('depmap') for documentation## loading from cache## # A tibble: 44,799,888 × 6
## depmap_id gene log_copy_number entrez_id gene_name cell_line
## <chr> <chr> <dbl> <int> <chr> <chr>
## 1 ACH-000267 DDX11L1 (84771) 1.15 84771 DDX11L1 HDLM2_HAEMATO…
## 2 ACH-001408 DDX11L1 (84771) 1.04 84771 DDX11L1 UMUC14_URINAR…
## 3 ACH-000617 DDX11L1 (84771) 0.762 84771 DDX11L1 OVCAR4_OVARY
## 4 ACH-002123 DDX11L1 (84771) 1.14 84771 DDX11L1 H2369_PLEURA
## 5 ACH-000519 DDX11L1 (84771) 1.01 84771 DDX11L1 PEER_HAEMATOP…
## 6 ACH-000750 DDX11L1 (84771) 0.711 84771 DDX11L1 LOXIMVI_SKIN
## 7 ACH-000544 DDX11L1 (84771) 0.981 84771 DDX11L1 OE21_OESOPHAG…
## 8 ACH-001214 DDX11L1 (84771) 1.05 84771 DDX11L1 U138MG_CENTRA…
## 9 ACH-002223 DDX11L1 (84771) 0.630 84771 DDX11L1 D245MG_CENTRA…
## 10 ACH-000713 DDX11L1 (84771) 0.823 84771 DDX11L1 CAOV3_OVARY
## # ℹ 44,799,878 more rowsCCLE Reverse Phase Protein Array data
The RPPA dataset contains the CCLE Reverse Phase Protein
Array (RPPA) data which corresponds to the
CCLE_RPPA_20180123.csv file.
Specific RPPA datasets can be accessed, such as
RPPA_19Q1 by EH number.
eh[["EH2263"]]The most recent RPPA dataset can be automatically loaded
into R by using the depmap_RPPA function.
depmap::depmap_RPPA()## see ?depmap and browseVignettes('depmap') for documentation## loading from cache## # A tibble: 192,386 × 4
## cell_line antibody expression depmap_id
## <chr> <chr> <dbl> <chr>
## 1 DMS53_LUNG 14-3-3_beta -0.105 ACH-000698
## 2 SW1116_LARGE_INTESTINE 14-3-3_beta 0.359 ACH-000489
## 3 NCIH1694_LUNG 14-3-3_beta 0.0287 ACH-000431
## 4 P3HR1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE 14-3-3_beta 0.120 ACH-000707
## 5 HUT78_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE 14-3-3_beta -0.269 ACH-000509
## 6 UMUC3_URINARY_TRACT 14-3-3_beta -0.171 ACH-000522
## 7 HOS_BONE 14-3-3_beta -0.0253 ACH-000613
## 8 HUNS1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE 14-3-3_beta -0.170 ACH-000829
## 9 AML193_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE 14-3-3_beta 0.0819 ACH-000557
## 10 RVH421_SKIN 14-3-3_beta 0.222 ACH-000614
## # ℹ 192,376 more rowsCCLE RNAseq gene expression data
The TPM dataset contains the CCLE RNAseq gene expression
data. This shows expression data only for protein coding genes (using
scale log2(TPM+1)). This data corresponds to the
CCLE_depMap_19Q1_TPM.csv file.
Specific TPM datasets can be accessed, such as
TPM_19Q1 by EH number.
eh[["EH2264"]]The TPM dataset can also be accessed by using the
depmap_TPM function.
depmap::depmap_TPM()## see ?depmap and browseVignettes('depmap') for documentation## loading from cache## # A tibble: 27,024,726 × 6
## depmap_id gene rna_expression entrez_id gene_name cell_line
## <chr> <chr> <dbl> <int> <chr> <chr>
## 1 ACH-001113 TSPAN6 (7105) 4.33 7105 TSPAN6 LC1SQSF_LUNG
## 2 ACH-001289 TSPAN6 (7105) 4.57 7105 TSPAN6 COGAR359_SOFT_TI…
## 3 ACH-001339 TSPAN6 (7105) 3.15 7105 TSPAN6 COLO794_SKIN
## 4 ACH-001538 TSPAN6 (7105) 5.09 7105 TSPAN6 KKU213_BILIARY_T…
## 5 ACH-000242 TSPAN6 (7105) 6.73 7105 TSPAN6 RT4_URINARY_TRACT
## 6 ACH-000708 TSPAN6 (7105) 4.27 7105 TSPAN6 SNU283_LARGE_INT…
## 7 ACH-000327 TSPAN6 (7105) 3.34 7105 TSPAN6 NCIH1395_LUNG
## 8 ACH-000233 TSPAN6 (7105) 0.0566 7105 TSPAN6 DEL_HAEMATOPOIET…
## 9 ACH-000461 TSPAN6 (7105) 4.02 7105 TSPAN6 SNU1196_BILIARY_…
## 10 ACH-000705 TSPAN6 (7105) 4.41 7105 TSPAN6 LC1F_LUNG
## # ℹ 27,024,716 more rowsCancer cell lines
The metadata dataset contains the metadata about all of
the cancer cell lines. It corresponds to the
depmap_19Q1_cell_lines.csv file.
Specific metadata datasets can be accessed, such as
metadata_19Q1 by EH number.
eh[["EH2266"]]The most recent metadata dataset can be automatically
loaded into R by using the depmap_metadata function.
depmap::depmap_metadata()## see ?depmap and browseVignettes('depmap') for documentation## loading from cache## # A tibble: 1,840 × 29
## depmap_id cell_line_name stripped_cell_line_name cell_line aliases cosmic_id
## <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 ACH-000016 SLR 21 SLR21 SLR21_KI… NA NA
## 2 ACH-000032 MHH-CALL-3 MHHCALL3 MHHCALL3… NA NA
## 3 ACH-000033 NCI-H1819 NCIH1819 NCIH1819… NA NA
## 4 ACH-000043 Hs 895.T HS895T HS895T_F… NA NA
## 5 ACH-000049 HEK TE HEKTE HEKTE_KI… NA NA
## 6 ACH-000051 TE 617.T TE617T TE617T_S… NA NA
## 7 ACH-000064 SALE SALE SALE_LUNG NA NA
## 8 ACH-000068 REC-1 REC1 REC1_HAE… NA NA
## 9 ACH-000071 NA HS706T HS706T_B… NA NA
## 10 ACH-000076 NCO2 NCO2 NCO2_HAE… NA NA
## # ℹ 1,830 more rows
## # ℹ 23 more variables: sex <chr>, source <chr>, RRID <chr>,
## # WTSI_master_cell_ID <dbl>, sample_collection_site <chr>,
## # primary_or_metastasis <chr>, primary_disease <chr>, subtype_disease <chr>,
## # age <chr>, sanger_id <chr>, additional_info <chr>, lineage <chr>,
## # lineage_subtype <chr>, lineage_sub_subtype <chr>,
## # lineage_molecular_subtype <chr>, default_growth_pattern <chr>, …Mutation calls
The mutationCalls dataset contains all merged mutation
calls (coding region, germline filtered) found in the depmap dependency
study. This dataset corresponds with the
depmap_19Q1_mutation_calls.csv file.
Specific mutationCalls datasets can be accessed, such as
mutationCalls_19Q1 by EH number.
eh[["EH2265"]]The most recent mutationCalls dataset can be
automatically loaded into R by using the
depmap_mutationCalls function.
depmap::depmap_mutationCalls()## see ?depmap and browseVignettes('depmap') for documentation## loading from cache## # A tibble: 1,235,466 × 32
## depmap_id gene_name entrez_id ncbi_build chromosome start_pos end_pos strand
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <chr>
## 1 ACH-000001 VPS13D 55187 37 1 12359347 1.24e7 +
## 2 ACH-000001 AADACL4 343066 37 1 12726308 1.27e7 +
## 3 ACH-000001 IFNLR1 163702 37 1 24484172 2.45e7 +
## 4 ACH-000001 TMEM57 55219 37 1 25785018 2.58e7 +
## 5 ACH-000001 ZSCAN20 7579 37 1 33954141 3.40e7 +
## 6 ACH-000001 POU3F1 5453 37 1 38512139 3.85e7 +
## 7 ACH-000001 MAST2 23139 37 1 46498028 4.65e7 +
## 8 ACH-000001 GBP4 115361 37 1 89657103 8.97e7 +
## 9 ACH-000001 VAV3 10451 37 1 108247170 1.08e8 +
## 10 ACH-000001 NBPF20 100288142 37 1 148346689 1.48e8 +
## # ℹ 1,235,456 more rows
## # ℹ 24 more variables: var_class <chr>, var_type <chr>, ref_allele <chr>,
## # alt_allele <chr>, dbSNP_RS <chr>, dbSNP_val_status <chr>,
## # genome_change <chr>, annotation_trans <chr>, cDNA_change <chr>,
## # codon_change <chr>, protein_change <chr>, is_deleterious <lgl>,
## # is_tcga_hotspot <lgl>, tcga_hsCnt <dbl>, is_cosmic_hotspot <lgl>,
## # cosmic_hsCnt <dbl>, ExAC_AF <dbl>, var_annotation <chr>, …Drug Sensitivity
The drug_sensitivity dataset contains dependency data
for cancer cell lines treated with various compounds. This dataset
corresponds with the
primary_replicate_collapsed_logfold_change.csv file.
Specific drug_sensitivity datasets can be accessed, such
as drug_sensitivity_19Q3 by EH number.
eh[["EH3087"]]The most recent drug_sensitivity dataset can be
automatically loaded into R by using the
depmap_drug_sensitivity function.
depmap::depmap_drug_sensitivity()## see ?depmap and browseVignettes('depmap') for documentation## loading from cache## # A tibble: 2,708,508 × 14
## depmap_id cell_line compound dependency broad_id name dose screen_id moa
## <chr> <chr> <chr> <dbl> <chr> <chr> <dbl> <chr> <chr>
## 1 ACH-000001 NIHOVCAR… BRD-A00… -0.0156 BRD-A00… 8-br… 2.5 HTS PKA …
## 2 ACH-000007 LS513_LA… BRD-A00… -0.0957 BRD-A00… 8-br… 2.5 HTS PKA …
## 3 ACH-000008 A101D_SK… BRD-A00… 0.379 BRD-A00… 8-br… 2.5 HTS PKA …
## 4 ACH-000010 NCIH2077… BRD-A00… 0.119 BRD-A00… 8-br… 2.5 HTS PKA …
## 5 ACH-000011 253J_URI… BRD-A00… 0.145 BRD-A00… 8-br… 2.5 HTS PKA …
## 6 ACH-000012 HCC827_L… BRD-A00… 0.103 BRD-A00… 8-br… 2.5 HTS PKA …
## 7 ACH-000013 ONCODG1_… BRD-A00… 0.353 BRD-A00… 8-br… 2.5 HTS PKA …
## 8 ACH-000014 HS294T_S… BRD-A00… 0.128 BRD-A00… 8-br… 2.5 HTS PKA …
## 9 ACH-000015 NCIH1581… BRD-A00… 0.167 BRD-A00… 8-br… 2.5 HTS PKA …
## 10 ACH-000018 T24_URIN… BRD-A00… 0.832 BRD-A00… 8-br… 2.5 HTS PKA …
## # ℹ 2,708,498 more rows
## # ℹ 5 more variables: target <chr>, disease_area <chr>, indication <chr>,
## # smiles <chr>, phase <chr>Proteomic
The proteomic dataset contains normalized quantitative
profiling of proteins of cancer cell lines by mass spectrometry. This
dataset corresponds with the
https://gygi.med.harvard.edu/sites/gygi.med.harvard.edu/files/documents/protein_quant_current_normalized.csv.gz
file.
Specific proteomic datasets can be accessed, such as
proteomic_20Q2 by EH number.
eh[["EH3459"]]The most recent proteomic dataset can be automatically
loaded into R by using the depmap_proteomic function.
depmap::depmap_proteomic()## see ?depmap and browseVignettes('depmap') for documentation## loading from cache## # A tibble: 4,821,390 × 12
## depmap_id gene_name entrez_id protein protein_expression protein_id desc
## <chr> <chr> <dbl> <chr> <dbl> <chr> <chr>
## 1 ACH-000849 SLC12A2 6558 MDAMB468_… 2.11 sp|P55011… S12A…
## 2 ACH-000441 SLC12A2 6558 SH4_SKIN_… 0.0705 sp|P55011… S12A…
## 3 ACH-000248 SLC12A2 6558 AU565_BRE… -0.464 sp|P55011… S12A…
## 4 ACH-000684 SLC12A2 6558 KMRC1_KID… -0.884 sp|P55011… S12A…
## 5 ACH-000856 SLC12A2 6558 CAL51_BRE… 0.789 sp|P55011… S12A…
## 6 ACH-000348 SLC12A2 6558 RPMI7951_… -0.912 sp|P55011… S12A…
## 7 ACH-000062 SLC12A2 6558 RERFLCMS_… 0.729 sp|P55011… S12A…
## 8 ACH-000650 SLC12A2 6558 IGR37_SKI… -0.658 sp|P55011… S12A…
## 9 ACH-000484 SLC12A2 6558 VMRCRCW_K… -1.15 sp|P55011… S12A…
## 10 ACH-000625 SLC12A2 6558 HEP3B217_… 0.00882 sp|P55011… S12A…
## # ℹ 4,821,380 more rows
## # ℹ 5 more variables: group_id <dbl>, uniprot <chr>, uniprot_acc <chr>,
## # TenPx <chr>, cell_line <chr>Repackaged data source
If desired, the original data from which the depmap package were
derived from can be downloaded from the Broad Institute website.
The instructions on how to download these files and how the data was
transformed and loaded into the depmap package can
be found in the make_data.R file found in
./inst/scripts. (It should be noted that the original
uncompressed .csv files are > 1.5GB in total and take a
moderate amount of time to download remotely.)
Original depmap data
In addition to the re-packaged files, the package also allows to download any of the original files provided by the DepMap project on Figshare.
A list of all the datasets is available with the
dmsets() function:
dmsets()## # A tibble: 27 × 5
## title dataset_id n_files url data
## <chr> <int> <int> <chr> <list>
## 1 DepMap 24Q2 Public 25880521 66 https://figshare.com/articl… <DpMpDtst>
## 2 DepMap 23Q4 Public 24667905 56 https://figshare.com/articl… <DpMpDtst>
## 3 DepMap 23Q2 Public 22765112 52 https://figshare.com/articl… <DpMpDtst>
## 4 DepMap 22Q4 Public 21637199 47 https://figshare.com/articl… <DpMpDtst>
## 5 DepMap 22Q2 Public 19700056 42 https://figshare.com/articl… <DpMpDtst>
## 6 DepMap 22Q1 Public 19139906 42 https://figshare.com/articl… <DpMpDtst>
## 7 DepMap 21Q4 Public 16924132 40 https://figshare.com/articl… <DpMpDtst>
## 8 DepMap 21Q2 Public 14541774 44 https://figshare.com/articl… <DpMpDtst>
## 9 DepMap 21Q3 Public 15160110 46 https://figshare.com/articl… <DpMpDtst>
## 10 DepMap 21Q1 Public 13681534 37 https://figshare.com/articl… <DpMpDtst>
## # ℹ 17 more rowsWe could check what datasets from any quarter of 2020 are available
by searching for "20Q" in the datasets titles:
## # A tibble: 4 × 5
## title dataset_id n_files url data
## <chr> <int> <int> <chr> <list>
## 1 DepMap 20Q4 Public 13237076 31 https://figshare… <DpMpDtst>
## 2 DepMap 20Q2 Public 12280541 24 https://figshare… <DpMpDtst>
## 3 DepMap 20Q1 Public 11791698 24 https://figshare… <DpMpDtst>
## 4 PRISM Repurposing 20Q2 Dataset 20564034 16 https://figshare… <DpMpDtst>Let’s focus on the PRISM Repurposing 20Q2 Dataset dataset,
with identifier 20564034.
A list of all the files is available with the dmfiles()
function:
dmfiles()## # A tibble: 772 × 7
## dataset_id id name size download_url md5 mimetype
## <int> <int> <chr> <dbl> <chr> <chr> <chr>
## 1 25880521 46486180 AchillesCommonEssenti… 1.70e4 https://ndo… 1cbf… text/pl…
## 2 25880521 46486192 AchillesHighVarianceG… 7.07e3 https://ndo… 3ac0… text/pl…
## 3 25880521 46486201 AchillesNonessentialC… 1.15e4 https://ndo… 9b21… text/pl…
## 4 25880521 46486213 AchillesScreenQCRepor… 3.19e5 https://ndo… 678c… text/pl…
## 5 25880521 46486222 AchillesSequenceQCRep… 4.50e5 https://ndo… 13a8… text/pl…
## 6 25880521 46486231 AvanaGuideMap.csv 1.60e7 https://ndo… b694… text/pl…
## 7 25880521 46486252 AvanaLogfoldChange.csv 3.34e9 https://ndo… ab2f… text/pl…
## 8 25880521 46488025 AvanaRawReadcounts.csv 1.02e9 https://ndo… c902… text/pl…
## 9 25880521 46489021 CRISPRGeneDependency.… 4.12e8 https://ndo… 6aaa… applica…
## 10 25880521 46489063 CRISPRGeneEffect.csv 4.19e8 https://ndo… d10d… applica…
## # ℹ 762 more rowsIf we want to find all files from the PRISM Repurposing 20Q2
Dataset identified above, we could filter all files with its
dataset_id:
## # A tibble: 16 × 7
## dataset_id id name size download_url md5 mimetype
## <int> <int> <chr> <dbl> <chr> <chr> <chr>
## 1 20564034 36794562 prism-repurposing-20q… 1.92e8 https://ndo… d5c2… text/pl…
## 2 20564034 36794565 prism-repurposing-20q… 4.68e4 https://ndo… 580b… text/pl…
## 3 20564034 36794571 prism-repurposing-20q… 1.27e8 https://ndo… 854c… text/pl…
## 4 20564034 36794574 prism-repurposing-20q… 7.73e7 https://ndo… 793d… text/pl…
## 5 20564034 36794577 prism-repurposing-20q… 6.20e3 https://ndo… 88f8… text/pl…
## 6 20564034 36794580 prism-repurposing-20q… 4.67e7 https://ndo… 8f3e… text/pl…
## 7 20564034 36794583 prism-repurposing-20q… 1.31e6 https://ndo… 1e69… text/pl…
## 8 20564034 36794586 prism-repurposing-20q… 5.72e6 https://ndo… f1a2… text/pl…
## 9 20564034 36794589 prism-repurposing-20q… 8.47e4 https://ndo… 4c3a… text/pl…
## 10 20564034 36794595 prism-repurposing-20q… 2.90e8 https://ndo… eb0c… text/pl…
## 11 20564034 36794607 prism-repurposing-20q… 3.08e8 https://ndo… 5140… text/pl…
## 12 20564034 36794610 prism-repurposing-20q… 3.32e5 https://ndo… 8a48… text/pl…
## 13 20564034 36794613 prism-repurposing-20q… 8.05e3 https://ndo… 4741… text/pl…
## 14 20564034 36794616 prism-repurposing-20q… 1.15e8 https://ndo… d89c… text/pl…
## 15 20564034 36794619 prism-repurposing-20q… 4.28e6 https://ndo… 162d… text/pl…
## 16 20564034 36794622 prism-repurposing-20q… 1.61e7 https://ndo… 610f… text/pl…Let’s now focus on the
prism-repurposing-20q2-primary-screen-cell-line-info.csv
file. We can filter it by its name and downloaded it with
dmget():
## File: prism-repurposing-20q2-primary-screen-cell-line-info.csv (36794565)## Loading 36794565 from cache.## BFC1
## "/github/home/.cache/R/depmap/22f1f53272f_36794565"The dmget() function will first check if it hasn’t
already been downloaded and cached in the depmap cache directory (see
?dmCache()). If so, it will retrieve if from there.
Otherwise, it will download the file and store it in the package cache
directory. It will return the location of the cached file.
Given that the file is in csv format, we can directly open it with
read_csv():
dmfiles() |>
filter(name == "prism-repurposing-20q2-primary-screen-cell-line-info.csv") |>
dmget() |>
read_csv()## File: prism-repurposing-20q2-primary-screen-cell-line-info.csv (36794565)## Loading 36794565 from cache.## Rows: 588 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): row_name, depmap_id, ccle_name, primary_tissue, secondary_tissue, t...
## lgl (1): passed_str_profiling
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## # A tibble: 588 × 10
## row_name depmap_id ccle_name primary_tissue secondary_tissue tertiary_tissue
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 ACH-0008… ACH-0008… KYSE510_… esophagus esophagus_squam… NA
## 2 ACH-0009… ACH-0009… HEC1A_EN… uterus uterus_endometr… NA
## 3 ACH-0006… ACH-0006… MIAPACA2… pancreas NA NA
## 4 ACH-0006… ACH-0006… SW620_LA… colorectal NA NA
## 5 ACH-0003… ACH-0003… SKHEP1_L… liver NA NA
## 6 ACH-0008… ACH-0008… SW480_LA… colorectal NA NA
## 7 ACH-0005… ACH-0005… YKG1_CEN… central_nervo… glioma glioblastoma
## 8 ACH-0003… ACH-0003… J82_URIN… urinary_tract NA NA
## 9 ACH-0007… ACH-0007… KYSE30_O… esophagus esophagus_squam… NA
## 10 ACH-0009… ACH-0009… NCIH2172… lung lung_NSC NA
## # ℹ 578 more rows
## # ℹ 4 more variables: passed_str_profiling <lgl>, str_profiling_notes <chr>,
## # originally_assigned_depmap_id <chr>, originally_assigned_ccle_name <chr>It is also possible to pass multiple rows of the
dmfiles() table to dmget() to retrieve
multiple file paths. Below, let’s get all the README.txt files from
2020:
ids_2020 <- filter(dmsets(), grepl("20Q", title)) |>
pull(dataset_id)
dmfiles() |>
filter(dataset_id %in% ids_2020) |>
filter(grepl("README", name)) |>
dmget()## File: README (25797005)## Loading 25797005 from cache.## File: README (25817903)## Loading 25817903 from cache.## File: README (22897970)## Loading 22897970 from cache.## File: README (22543694)## Loading 22543694 from cache.## BFC2
## "/github/home/.cache/R/depmap/22f612a4f32_25797005"
## BFC3
## "/github/home/.cache/R/depmap/22f747dd462_25817903"
## BFC4
## "/github/home/.cache/R/depmap/22f13f6750b_22897970"
## BFC5
## "/github/home/.cache/R/depmap/22f412805c2_22543694"Session information
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ExperimentHub_2.12.0 AnnotationHub_3.12.0 BiocFileCache_2.12.0
## [4] dbplyr_2.5.0 BiocGenerics_0.50.0 depmap_1.19.1
## [7] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1
## [10] dplyr_1.1.4 purrr_1.0.2 readr_2.1.5
## [13] tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1
## [16] tidyverse_2.0.0 BiocStyle_2.32.1
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 blob_1.2.4 filelock_1.0.3
## [4] Biostrings_2.72.1 fastmap_1.2.0 digest_0.6.36
## [7] mime_0.12 timechange_0.3.0 lifecycle_1.0.4
## [10] KEGGREST_1.44.1 RSQLite_2.3.7 magrittr_2.0.3
## [13] compiler_4.4.1 rlang_1.1.4 sass_0.4.9
## [16] tools_4.4.1 utf8_1.2.4 yaml_2.3.10
## [19] knitr_1.48 htmlwidgets_1.6.4 bit_4.0.5
## [22] curl_5.2.1 withr_3.0.1 desc_1.4.3
## [25] grid_4.4.1 stats4_4.4.1 fansi_1.0.6
## [28] colorspace_2.1-1 scales_1.3.0 cli_3.6.3
## [31] rmarkdown_2.27 crayon_1.5.3 ragg_1.3.2
## [34] generics_0.1.3 httr_1.4.7 tzdb_0.4.0
## [37] DBI_1.2.3 cachem_1.1.0 zlibbioc_1.50.0
## [40] parallel_4.4.1 AnnotationDbi_1.66.0 BiocManager_1.30.23
## [43] XVector_0.44.0 vctrs_0.6.5 jsonlite_1.8.8
## [46] bookdown_0.40 IRanges_2.38.1 hms_1.1.3
## [49] S4Vectors_0.42.1 bit64_4.0.5 systemfonts_1.1.0
## [52] jquerylib_0.1.4 glue_1.7.0 pkgdown_2.1.0.9000
## [55] stringi_1.8.4 gtable_0.3.5 BiocVersion_3.19.1
## [58] GenomeInfoDb_1.40.1 UCSC.utils_1.0.0 munsell_0.5.1
## [61] pillar_1.9.0 rappdirs_0.3.3 htmltools_0.5.8.1
## [64] GenomeInfoDbData_1.2.12 httr2_1.0.2 R6_2.5.1
## [67] textshaping_0.4.0 vroom_1.6.5 evaluate_0.24.0
## [70] Biobase_2.64.0 png_0.1-8 memoise_2.0.1
## [73] bslib_0.8.0 xfun_0.46 fs_1.6.4
## [76] pkgconfig_2.0.3