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.
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.
library("depmap")
library("ExperimentHub")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(): 2023-10-24
## # $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).
## see ?depmap and browseVignettes('depmap') for documentation## loading from cacheThe 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 found in the
22Q2 depmap release and includes 17309
genes, 712 cell lines, 30 primary diseases and 31
lineages.
Specific rnai datasets can be accessed, such as rnai_19Q1 by EH number.
rnai <- eh[["EH2260"]]rnai## # A tibble: 12,324,008 × 6
## depmap_id cell_line gene gene_name entrez_id dependency
## <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 ACH-001270 127399_SOFT_TISSUE A1BG (1) A1BG 1 NA
## 2 ACH-001270 127399_SOFT_TISSUE NAT2 (10) NAT2 10 NA
## 3 ACH-001270 127399_SOFT_TISSUE ADA (100) ADA 100 NA
## 4 ACH-001270 127399_SOFT_TISSUE CDH2 (1000) CDH2 1000 -0.195
## 5 ACH-001270 127399_SOFT_TISSUE AKT3 (10000) AKT3 10000 -0.256
## 6 ACH-001270 127399_SOFT_TISSUE MED6 (10001) MED6 10001 -0.174
## 7 ACH-001270 127399_SOFT_TISSUE NR2E3 (10002) NR2E3 10002 -0.140
## 8 ACH-001270 127399_SOFT_TISSUE NAALAD2 (10003) NAALAD2 10003 NA
## 9 ACH-001270 127399_SOFT_TISSUE DUXB (100033411) DUXB 100033411 NA
## 10 ACH-001270 127399_SOFT_TISSUE PDZK1P1 (100034… PDZK1P1 100034743 NA
## # ℹ 12,323,998 more rowsThe 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 rows## see ?depmap and browseVignettes('depmap') for documentation## loading from cacheThe 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 from the 22Q2
depmap release. Data from this dataset includes 17634
genes, 558 cell lines, 26 primary diseases, 28
lineages.
Specific crispr datasets can be accessed, such as crispr_19Q1 by EH number.
crispr <- eh[["EH2261"]]crispr## # A tibble: 9,839,772 × 6
## depmap_id cell_line gene gene_name entrez_id dependency
## <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 ACH-000004 HEL_HAEMATOPOIETIC_AND_LYMPH… A1BG… A1BG 1 0.135
## 2 ACH-000005 HEL9217_HAEMATOPOIETIC_AND_L… A1BG… A1BG 1 -0.212
## 3 ACH-000007 LS513_LARGE_INTESTINE A1BG… A1BG 1 0.0433
## 4 ACH-000009 C2BBE1_LARGE_INTESTINE A1BG… A1BG 1 0.0705
## 5 ACH-000011 253J_URINARY_TRACT A1BG… A1BG 1 0.191
## 6 ACH-000012 HCC827_LUNG A1BG… A1BG 1 -0.0104
## 7 ACH-000013 ONCODG1_OVARY A1BG… A1BG 1 0.0210
## 8 ACH-000014 HS294T_SKIN A1BG… A1BG 1 0.113
## 9 ACH-000015 NCIH1581_LUNG A1BG… A1BG 1 -0.0742
## 10 ACH-000017 SKBR3_BREAST A1BG… A1BG 1 0.133
## # ℹ 9,839,762 more rowsThe 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 rows## see ?depmap and browseVignettes('depmap') for documentation## loading from cacheThe 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 from the 22Q2 depmap release.
This dataset includes 23299 genes,
1604 cell lines, 38 primary diseases and 33
lineages.
Specific copyNumber datasets can be accessed, such as copyNumber_19Q1 by EH
number.
copyNumber <- eh[["EH2262"]]copyNumber## # A tibble: 37,371,596 × 6
## depmap_id cell_line gene gene_name entrez_id log_copy_number
## <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 ACH-000011 253J_URINARY_TRACT A1BG… A1BG 1 0.131
## 2 ACH-000026 253JBV_URINARY_TRACT A1BG… A1BG 1 -0.237
## 3 ACH-000086 ACCMESO1_PLEURA A1BG… A1BG 1 0.134
## 4 ACH-000557 AML193_HAEMATOPOIETIC_A… A1BG… A1BG 1 -0.0208
## 5 ACH-000838 AMO1_HAEMATOPOIETIC_AND… A1BG… A1BG 1 0.170
## 6 ACH-000080 BDCM_HAEMATOPOIETIC_AND… A1BG… A1BG 1 0.00703
## 7 ACH-000992 BICR18_UPPER_AERODIGEST… A1BG… A1BG 1 -0.376
## 8 ACH-000228 BICR31_UPPER_AERODIGEST… A1BG… A1BG 1 1.16
## 9 ACH-000771 BICR56_UPPER_AERODIGEST… A1BG… A1BG 1 0.0197
## 10 ACH-000415 BICR6_UPPER_AERODIGESTI… A1BG… A1BG 1 0.280
## # ℹ 37,371,586 more rowsThe 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 rows## see ?depmap and browseVignettes('depmap') for documentation## loading from cacheThe RPPA dataset contains the CCLE Reverse Phase Protein Array (RPPA) data
which corresponds to the CCLE_RPPA_20180123.csv file from the 22Q2
depmap release. This dataset includes 214 genes, 899
cell lines, 28 primary diseases, 28 lineages.
Specific RPPA datasets can be accessed, such as RPPA_19Q1 by EH number.
RPPA <- eh[["EH2263"]]RPPA## # A tibble: 192,386 × 4
## depmap_id cell_line antibody expression
## <chr> <chr> <chr> <dbl>
## 1 ACH-000698 DMS53_LUNG 14-3-3_beta -0.105
## 2 ACH-000489 SW1116_LARGE_INTESTINE 14-3-3_beta 0.359
## 3 ACH-000431 NCIH1694_LUNG 14-3-3_beta 0.0287
## 4 ACH-000707 P3HR1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE 14-3-3_beta 0.120
## 5 ACH-000509 HUT78_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE 14-3-3_beta -0.269
## 6 ACH-000522 UMUC3_URINARY_TRACT 14-3-3_beta -0.171
## 7 ACH-000613 HOS_BONE 14-3-3_beta -0.0253
## 8 ACH-000829 HUNS1_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE 14-3-3_beta -0.170
## 9 ACH-000557 AML193_HAEMATOPOIETIC_AND_LYMPHOID_TISSUE 14-3-3_beta 0.0819
## 10 ACH-000614 RVH421_SKIN 14-3-3_beta 0.222
## # ℹ 192,376 more rowsThe 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 rows## see ?depmap and browseVignettes('depmap') for documentation## loading from cacheThe 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 from the 22Q2
depmap release. This dataset includes 55825 genes,
1165 cell lines, 33 primary Diseases, 32 lineages.
Specific TPM datasets can be accessed, such as TPM_19Q1 by EH number.
TPM <- eh[["EH2264"]]TPM## # A tibble: 67,360,300 × 6
## depmap_id cell_line gene gene_name ensembl_id expression
## <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 ACH-000956 22RV1_PROSTATE TSPA… TSPAN6 ENSG00000… 2.65
## 2 ACH-000948 2313287_STOMACH TSPA… TSPAN6 ENSG00000… 3.00
## 3 ACH-000026 253JBV_URINARY_TRACT TSPA… TSPAN6 ENSG00000… 4.57
## 4 ACH-000011 253J_URINARY_TRACT TSPA… TSPAN6 ENSG00000… 4.58
## 5 ACH-000323 42MGBA_CENTRAL_NERVOUS_SYST… TSPA… TSPAN6 ENSG00000… 4.59
## 6 ACH-000905 5637_URINARY_TRACT TSPA… TSPAN6 ENSG00000… 5.88
## 7 ACH-000520 59M_OVARY TSPA… TSPAN6 ENSG00000… 4.11
## 8 ACH-000973 639V_URINARY_TRACT TSPA… TSPAN6 ENSG00000… 5.05
## 9 ACH-000896 647V_URINARY_TRACT TSPA… TSPAN6 ENSG00000… 5.94
## 10 ACH-000070 697_HAEMATOPOIETIC_AND_LYMP… TSPA… TSPAN6 ENSG00000… 0.151
## # ℹ 67,360,290 more rowsThe 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 rows## see ?depmap and browseVignettes('depmap') for documentation## loading from cacheThe metadata dataset contains the metadata about all of the cancer cell lines.
It corresponds to the depmap_19Q1_cell_lines.csv file found in the 22Q2
depmap release. This dataset includes 0 genes, 1676
cell lines, 38 primary diseases and 33 lineages.
Specific metadata datasets can be accessed, such as metadata_19Q1 by EH
number.
metadata <- eh[["EH2266"]]metadata## # A tibble: 1,676 × 9
## depmap_id cell_line aliases cosmic_id sanger_id primary_disease
## <chr> <chr> <chr> <dbl> <dbl> <chr>
## 1 ACH-000001 NIHOVCAR3_OVARY NIH:OV… 905933 2201 Ovarian Cancer
## 2 ACH-000002 HL60_HAEMATOPOIETIC_A… HL-60 905938 55 Leukemia
## 3 ACH-000003 CACO2_LARGE_INTESTINE CACO2;… NA NA Colon/Colorect…
## 4 ACH-000004 HEL_HAEMATOPOIETIC_AN… HEL 907053 783 Leukemia
## 5 ACH-000005 HEL9217_HAEMATOPOIETI… HEL 92… NA NA Leukemia
## 6 ACH-000006 MONOMAC6_HAEMATOPOIET… MONO-M… 908148 2167 Leukemia
## 7 ACH-000007 LS513_LARGE_INTESTINE LS513 907795 569 Colon/Colorect…
## 8 ACH-000009 C2BBE1_LARGE_INTESTINE C2BBe1 910700 2104 Colon/Colorect…
## 9 ACH-000010 NCIH2077_LUNG NCI-H2… NA NA Lung Cancer
## 10 ACH-000011 253J_URINARY_TRACT 253J NA NA Bladder Cancer
## # ℹ 1,666 more rows
## # ℹ 3 more variables: subtype_disease <chr>, gender <chr>, source <chr>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>, …## see ?depmap and browseVignettes('depmap') for documentation## loading from cacheThe 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 found in the
22Q2 depmap release and includes
19350 genes,
1601 cell lines, 37 primary diseases and
33 lineages.
Specific mutationCalls datasets can be accessed, such as mutationCalls_19Q1
by EH number.
mutationCalls <- eh[["EH2265"]]mutationCalls## # A tibble: 1,243,145 × 35
## 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 1,243,135 more rows
## # ℹ 27 more variables: var_class <chr>, var_type <chr>, ref_allele <chr>,
## # tumor_seq_allele1 <chr>, dbSNP_RS <chr>, dbSNP_val_status <chr>,
## # genome_change <chr>, annotation_transcript <chr>,
## # tumor_sample_barcode <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>, …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>, …## see ?depmap and browseVignettes('depmap') for documentation## loading from cacheThe drug_sensitivity dataset contains dependency data for cancer cell lines
treated with 4686 compounds. This
dataset corresponds with the primary_replicate_collapsed_logfold_change.csv
file found in the 22Q2 depmap release and includes
578 cell lines, 23 primary diseases
and 25 lineages.
Specific drug_sensitivity datasets can be accessed, such as
drug_sensitivity_19Q3 by EH number.
drug_sensitivity <- eh[["EH3087"]]drug_sensitivity## # A tibble: 2,708,508 × 4
## depmap_id cell_line compound dependency
## <chr> <chr> <chr> <dbl>
## 1 ACH-000001 NIHOVCAR3_OVARY BRD-A00077618-236-07-6::2.5::HTS -0.0156
## 2 ACH-000007 LS513_LARGE_INTESTINE BRD-A00077618-236-07-6::2.5::HTS -0.0957
## 3 ACH-000008 A101D_SKIN BRD-A00077618-236-07-6::2.5::HTS 0.379
## 4 ACH-000010 NCIH2077_LUNG BRD-A00077618-236-07-6::2.5::HTS 0.119
## 5 ACH-000011 253J_URINARY_TRACT BRD-A00077618-236-07-6::2.5::HTS 0.145
## 6 ACH-000012 HCC827_LUNG BRD-A00077618-236-07-6::2.5::HTS 0.103
## 7 ACH-000013 ONCODG1_OVARY BRD-A00077618-236-07-6::2.5::HTS 0.353
## 8 ACH-000014 HS294T_SKIN BRD-A00077618-236-07-6::2.5::HTS 0.128
## 9 ACH-000015 NCIH1581_LUNG BRD-A00077618-236-07-6::2.5::HTS 0.167
## 10 ACH-000018 T24_URINARY_TRACT BRD-A00077618-236-07-6::2.5::HTS 0.832
## # ℹ 2,708,498 more rowsThe 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>## see ?depmap and browseVignettes('depmap') for documentation## loading from cacheThe 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 found in the 22Q2 depmap release and includes
375 cell lines, 24 primary diseases
and 27 lineages.
Specific proteomic datasets can be accessed, such as proteomic_20Q2
by EH number.
proteomic <- eh[["EH3459"]]proteomic## # 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>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>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.)
## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [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: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ExperimentHub_2.10.0 AnnotationHub_3.10.0 BiocFileCache_2.10.0
## [4] dbplyr_2.3.4 BiocGenerics_0.48.0 depmap_1.16.0
## [7] dplyr_1.1.3 BiocStyle_2.30.0
##
## loaded via a namespace (and not attached):
## [1] KEGGREST_1.42.0 xfun_0.40
## [3] bslib_0.5.1 Biobase_2.62.0
## [5] bitops_1.0-7 vctrs_0.6.4
## [7] tools_4.3.1 generics_0.1.3
## [9] stats4_4.3.1 curl_5.1.0
## [11] tibble_3.2.1 fansi_1.0.5
## [13] AnnotationDbi_1.64.0 RSQLite_2.3.1
## [15] blob_1.2.4 pkgconfig_2.0.3
## [17] S4Vectors_0.40.0 GenomeInfoDbData_1.2.11
## [19] lifecycle_1.0.3 compiler_4.3.1
## [21] Biostrings_2.70.1 GenomeInfoDb_1.38.0
## [23] httpuv_1.6.12 htmltools_0.5.6.1
## [25] sass_0.4.7 RCurl_1.98-1.12
## [27] yaml_2.3.7 interactiveDisplayBase_1.40.0
## [29] pillar_1.9.0 later_1.3.1
## [31] crayon_1.5.2 jquerylib_0.1.4
## [33] ellipsis_0.3.2 cachem_1.0.8
## [35] mime_0.12 tidyselect_1.2.0
## [37] digest_0.6.33 purrr_1.0.2
## [39] bookdown_0.36 BiocVersion_3.18.0
## [41] fastmap_1.1.1 cli_3.6.1
## [43] magrittr_2.0.3 utf8_1.2.4
## [45] withr_2.5.1 filelock_1.0.2
## [47] promises_1.2.1 rappdirs_0.3.3
## [49] bit64_4.0.5 XVector_0.42.0
## [51] rmarkdown_2.25 httr_1.4.7
## [53] bit_4.0.5 png_0.1-8
## [55] memoise_2.0.1 shiny_1.7.5.1
## [57] evaluate_0.22 knitr_1.44
## [59] IRanges_2.36.0 rlang_1.1.1
## [61] Rcpp_1.0.11 xtable_1.8-4
## [63] glue_1.6.2 DBI_1.1.3
## [65] BiocManager_1.30.22 jsonlite_1.8.7
## [67] R6_2.5.1 zlibbioc_1.48.0