1 Introduction

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.

2 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.

library("depmap")
library("ExperimentHub")

3 Available 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()
## snapshotDate(): 2022-04-19
query(eh, "depmap")
## ExperimentHub with 68 records
## # snapshotDate(): 2022-04-19
## # $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          
##   ...      ...               
##   EH7290 | crispr_21Q4       
##   EH7291 | copyNumber_21Q4   
##   EH7292 | TPM_21Q4          
##   EH7293 | mutationCalls_21Q4
##   EH7294 | metadata_21Q4

Each dataset has a ExperimentHub accession number, (e.g. EH2260 refers to the rnai dataset from the 19Q1 release).

3.1 RNA inference knockout data

## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache

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 found in the 22Q1 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    
## # … with 12,323,998 more rows

The most recent rnai dataset can be automatically loaded into R by using the depmap_rnai function.

depmap::depmap_rnai()
## snapshotDate(): 2022-04-19
## 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…
## # … with 12,323,998 more rows

3.2 CRISPR-Cas9 knockout data

## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache

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 from the 22Q1 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 
## # … with 9,839,762 more rows

The most recent crispr dataset can be automatically loaded into R by using the depmap_crispr function.

depmap::depmap_crispr()
## snapshotDate(): 2022-04-19
## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache
## # A tibble: 18,324,844 × 6
##    depmap_id  gene     dependency entrez_id gene_name cell_line                 
##    <chr>      <chr>         <dbl>     <int> <chr>     <chr>                     
##  1 ACH-000001 A1BG (1)    -0.131          1 A1BG      NIHOVCAR3_OVARY           
##  2 ACH-000004 A1BG (1)     0.0774         1 A1BG      HEL_HAEMATOPOIETIC_AND_LY…
##  3 ACH-000005 A1BG (1)    -0.0861         1 A1BG      HEL9217_HAEMATOPOIETIC_AN…
##  4 ACH-000007 A1BG (1)    -0.0197         1 A1BG      LS513_LARGE_INTESTINE     
##  5 ACH-000009 A1BG (1)    -0.0305         1 A1BG      C2BBE1_LARGE_INTESTINE    
##  6 ACH-000011 A1BG (1)     0.0639         1 A1BG      253J_URINARY_TRACT        
##  7 ACH-000012 A1BG (1)    -0.130          1 A1BG      HCC827_LUNG               
##  8 ACH-000013 A1BG (1)    -0.0449         1 A1BG      ONCODG1_OVARY             
##  9 ACH-000014 A1BG (1)    -0.0236         1 A1BG      HS294T_SKIN               
## 10 ACH-000015 A1BG (1)    -0.184          1 A1BG      NCIH1581_LUNG             
## # … with 18,324,834 more rows

3.3 WES copy number data

## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache

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 from the 22Q1 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  
## # … with 37,371,586 more rows

The most recent copyNumber dataset can be automatically loaded into R by using the depmap_copyNumber function.

depmap::depmap_copyNumber()
## snapshotDate(): 2022-04-19
## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache
## # A tibble: 44,394,000 × 6
##    depmap_id  gene            log_copy_number entrez_id gene_name cell_line     
##    <chr>      <chr>                     <dbl>     <int> <chr>     <chr>         
##  1 ACH-001280 DDX11L1 (84771)          0.914      84771 DDX11L1   SCS214_SOFT_T…
##  2 ACH-001521 DDX11L1 (84771)          0.994      84771 DDX11L1   HKA1_SKIN     
##  3 ACH-000550 DDX11L1 (84771)          0.986      84771 DDX11L1   IGR39_SKIN    
##  4 ACH-002066 DDX11L1 (84771)          0.0119     84771 DDX11L1   HSSCH2_CENTRA…
##  5 ACH-001959 DDX11L1 (84771)          1.05       84771 DDX11L1   CCLP1_BILIARY…
##  6 ACH-001794 DDX11L1 (84771)          1.38       84771 DDX11L1   93T449_SOFT_T…
##  7 ACH-002026 DDX11L1 (84771)          1.03       84771 DDX11L1   HHUA_ENDOMETR…
##  8 ACH-000171 DDX11L1 (84771)          0.745      84771 DDX11L1   VMRCRCZ_KIDNEY
##  9 ACH-002201 DDX11L1 (84771)          0.969      84771 DDX11L1   TCYIK_CERVIX  
## 10 ACH-000111 DDX11L1 (84771)          1.16       84771 DDX11L1   HCC1187_BREAST
## # … with 44,393,990 more rows

3.4 CCLE Reverse Phase Protein Array data

## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache

The RPPA dataset contains the CCLE Reverse Phase Protein Array (RPPA) data which corresponds to the CCLE_RPPA_20180123.csv file from the 22Q1 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 
## # … with 192,376 more rows

The most recent RPPA dataset can be automatically loaded into R by using the depmap_RPPA function.

depmap::depmap_RPPA()
## snapshotDate(): 2022-04-19
## 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
## # … with 192,376 more rows

3.5 CCLE RNAseq gene expression data

## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache

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 from the 22Q1 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
## # … with 67,360,290 more rows

The TPM dataset can also be accessed by using the depmap_TPM function.

depmap::depmap_TPM()
## snapshotDate(): 2022-04-19
## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache
## # A tibble: 26,636,853 × 6
##    depmap_id  gene          rna_expression entrez_id gene_name cell_line        
##    <chr>      <chr>                  <dbl>     <int> <chr>     <chr>            
##  1 ACH-001113 TSPAN6 (7105)         4.99        7105 TSPAN6    LC1SQSF_LUNG     
##  2 ACH-001289 TSPAN6 (7105)         5.21        7105 TSPAN6    COGAR359_SOFT_TI…
##  3 ACH-001339 TSPAN6 (7105)         3.78        7105 TSPAN6    COLO794_SKIN     
##  4 ACH-001538 TSPAN6 (7105)         5.73        7105 TSPAN6    KKU213_BILIARY_T…
##  5 ACH-000242 TSPAN6 (7105)         7.47        7105 TSPAN6    RT4_URINARY_TRACT
##  6 ACH-000708 TSPAN6 (7105)         4.91        7105 TSPAN6    SNU283_LARGE_INT…
##  7 ACH-000327 TSPAN6 (7105)         4.03        7105 TSPAN6    NCIH1395_LUNG    
##  8 ACH-000233 TSPAN6 (7105)         0.0976      7105 TSPAN6    DEL_HAEMATOPOIET…
##  9 ACH-000461 TSPAN6 (7105)         4.71        7105 TSPAN6    SNU1196_BILIARY_…
## 10 ACH-000705 TSPAN6 (7105)         5.10        7105 TSPAN6    LC1F_LUNG        
## # … with 26,636,843 more rows

3.6 Cancer cell lines

## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache

The 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 22Q1 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 
## # … with 1,666 more rows, and 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()
## snapshotDate(): 2022-04-19
## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache
## # A tibble: 1,825 × 26
##    depmap_id  cell_line_name stripped_cell_li… cell_line aliases cosmic_id sex  
##    <chr>      <chr>          <chr>             <chr>     <chr>       <dbl> <chr>
##  1 ACH-000001 NIH:OVCAR-3    NIHOVCAR3         NIHOVCAR… OVCAR3     905933 Fema…
##  2 ACH-000002 HL-60          HL60              HL60_HAE… <NA>       905938 Fema…
##  3 ACH-000003 CACO2          CACO2             CACO2_LA… CACO2,…        NA Male 
##  4 ACH-000004 HEL            HEL               HEL_HAEM… <NA>       907053 Male 
##  5 ACH-000005 HEL 92.1.7     HEL9217           HEL9217_… <NA>           NA Male 
##  6 ACH-000006 MONO-MAC-6     MONOMAC6          MONOMAC6… <NA>       908148 Male 
##  7 ACH-000007 LS513          LS513             LS513_LA… <NA>       907795 Male 
##  8 ACH-000008 A101D          A101D             A101D_SK… <NA>       910921 Male 
##  9 ACH-000009 C2BBe1         C2BBE1            C2BBE1_L… <NA>       910700 Male 
## 10 ACH-000011 253J           253J              253J_URI… <NA>           NA Male 
## # … with 1,815 more rows, and 19 more variables: source <chr>,
## #   Achilles_n_replicates <dbl>, cell_line_NNMD <dbl>, culture_type <chr>,
## #   culture_medium <chr>, cas9_activity <dbl>, RRID <chr>,
## #   WTSI_master_cell_ID <dbl>, sample_collection_site <chr>,
## #   primary_or_metastasis <chr>, primary_disease <chr>, subtype_disease <chr>,
## #   age <dbl>, sanger_id <chr>, additional_info <lgl>, lineage <chr>,
## #   lineage_subtype <chr>, lineage_sub_subtype <chr>, …

3.7 Mutation calls

## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache

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 found in the 22Q1 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 +     
## # … with 1,243,135 more rows, and 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>,
## #   ExAC_AF <dbl>, VA_WES_AC <chr>, CGA_WES_AC <chr>, sanger_WES_AC <chr>, …

The most recent mutationCalls dataset can be automatically loaded into R by using the depmap_mutationCalls function.

depmap::depmap_mutationCalls()
## snapshotDate(): 2022-04-19
## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache
## # A tibble: 1,230,245 × 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 +     
## # … with 1,230,235 more rows, and 24 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_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>, CGA_WES_AC <chr>, HC_AC <chr>, RD_AC <chr>, …

3.8 Drug Sensitivity

## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache

The 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 22Q1 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 
## # … with 2,708,498 more rows

The most recent drug_sensitivity dataset can be automatically loaded into R by using the depmap_drug_sensitivity function.

depmap::depmap_drug_sensitivity()
## snapshotDate(): 2022-04-19
## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache
## # 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 
## # … with 2,708,498 more rows

3.9 Proteomic

## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache

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 found in the 22Q1 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_express… protein_id desc 
##    <chr>      <chr>         <dbl> <chr>                   <dbl> <chr>      <chr>
##  1 ACH-000849 SLC12A2        6558 MDAMB468_BR…          2.11    sp|P55011… S12A…
##  2 ACH-000441 SLC12A2        6558 SH4_SKIN_Te…          0.0705  sp|P55011… S12A…
##  3 ACH-000248 SLC12A2        6558 AU565_BREAS…         -0.464   sp|P55011… S12A…
##  4 ACH-000684 SLC12A2        6558 KMRC1_KIDNE…         -0.884   sp|P55011… S12A…
##  5 ACH-000856 SLC12A2        6558 CAL51_BREAS…          0.789   sp|P55011… S12A…
##  6 ACH-000348 SLC12A2        6558 RPMI7951_SK…         -0.912   sp|P55011… S12A…
##  7 ACH-000062 SLC12A2        6558 RERFLCMS_LU…          0.729   sp|P55011… S12A…
##  8 ACH-000650 SLC12A2        6558 IGR37_SKIN_…         -0.658   sp|P55011… S12A…
##  9 ACH-000484 SLC12A2        6558 VMRCRCW_KID…         -1.15    sp|P55011… S12A…
## 10 ACH-000625 SLC12A2        6558 HEP3B217_LI…          0.00882 sp|P55011… S12A…
## # … with 4,821,380 more rows, and 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()
## snapshotDate(): 2022-04-19
## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache
## # A tibble: 4,821,390 × 12
##    depmap_id  gene_name entrez_id protein      protein_express… protein_id desc 
##    <chr>      <chr>         <dbl> <chr>                   <dbl> <chr>      <chr>
##  1 ACH-000849 SLC12A2        6558 MDAMB468_BR…          2.11    sp|P55011… S12A…
##  2 ACH-000441 SLC12A2        6558 SH4_SKIN_Te…          0.0705  sp|P55011… S12A…
##  3 ACH-000248 SLC12A2        6558 AU565_BREAS…         -0.464   sp|P55011… S12A…
##  4 ACH-000684 SLC12A2        6558 KMRC1_KIDNE…         -0.884   sp|P55011… S12A…
##  5 ACH-000856 SLC12A2        6558 CAL51_BREAS…          0.789   sp|P55011… S12A…
##  6 ACH-000348 SLC12A2        6558 RPMI7951_SK…         -0.912   sp|P55011… S12A…
##  7 ACH-000062 SLC12A2        6558 RERFLCMS_LU…          0.729   sp|P55011… S12A…
##  8 ACH-000650 SLC12A2        6558 IGR37_SKIN_…         -0.658   sp|P55011… S12A…
##  9 ACH-000484 SLC12A2        6558 VMRCRCW_KID…         -1.15    sp|P55011… S12A…
## 10 ACH-000625 SLC12A2        6558 HEP3B217_LI…          0.00882 sp|P55011… S12A…
## # … with 4,821,380 more rows, and 5 more variables: group_id <dbl>,
## #   uniprot <chr>, uniprot_acc <chr>, TenPx <chr>, cell_line <chr>

4 The Broad Institute data

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.)

5 Session information

## R version 4.2.0 RC (2022-04-19 r82224)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.15-bioc/R/lib/libRlapack.so
## 
## 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       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] ExperimentHub_2.4.0 AnnotationHub_3.4.0 BiocFileCache_2.4.0
## [4] dbplyr_2.1.1        BiocGenerics_0.42.0 depmap_1.10.0      
## [7] dplyr_1.0.8         BiocStyle_2.24.0   
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.8.3                  png_0.1-7                    
##  [3] Biostrings_2.64.0             assertthat_0.2.1             
##  [5] digest_0.6.29                 utf8_1.2.2                   
##  [7] mime_0.12                     R6_2.5.1                     
##  [9] GenomeInfoDb_1.32.0           stats4_4.2.0                 
## [11] RSQLite_2.2.12                evaluate_0.15                
## [13] httr_1.4.2                    pillar_1.7.0                 
## [15] zlibbioc_1.42.0               rlang_1.0.2                  
## [17] curl_4.3.2                    jquerylib_0.1.4              
## [19] blob_1.2.3                    S4Vectors_0.34.0             
## [21] rmarkdown_2.14                stringr_1.4.0                
## [23] RCurl_1.98-1.6                bit_4.0.4                    
## [25] shiny_1.7.1                   compiler_4.2.0               
## [27] httpuv_1.6.5                  xfun_0.30                    
## [29] pkgconfig_2.0.3               htmltools_0.5.2              
## [31] tidyselect_1.1.2              KEGGREST_1.36.0              
## [33] GenomeInfoDbData_1.2.8        tibble_3.1.6                 
## [35] interactiveDisplayBase_1.34.0 bookdown_0.26                
## [37] IRanges_2.30.0                fansi_1.0.3                  
## [39] withr_2.5.0                   crayon_1.5.1                 
## [41] later_1.3.0                   bitops_1.0-7                 
## [43] rappdirs_0.3.3                jsonlite_1.8.0               
## [45] xtable_1.8-4                  lifecycle_1.0.1              
## [47] DBI_1.1.2                     magrittr_2.0.3               
## [49] cli_3.3.0                     stringi_1.7.6                
## [51] cachem_1.0.6                  XVector_0.36.0               
## [53] promises_1.2.0.1              bslib_0.3.1                  
## [55] ellipsis_0.3.2                filelock_1.0.2               
## [57] generics_0.1.2                vctrs_0.4.1                  
## [59] tools_4.2.0                   bit64_4.0.5                  
## [61] Biobase_2.56.0                glue_1.6.2                   
## [63] purrr_0.3.4                   BiocVersion_3.15.2           
## [65] fastmap_1.1.0                 yaml_2.3.5                   
## [67] AnnotationDbi_1.58.0          BiocManager_1.30.17          
## [69] memoise_2.0.1                 knitr_1.39                   
## [71] sass_0.4.1