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(): 2020-10-02
query(eh, "depmap")
## ExperimentHub with 43 records
## # snapshotDate(): 2020-10-02
## # $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          
##   ...      ...               
##   EH3797 | crispr_20Q3       
##   EH3798 | copyNumber_20Q3   
##   EH3799 | TPM_20Q3          
##   EH3800 | mutationCalls_20Q3
##   EH3801 | metadata_20Q3

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 20Q3 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 x 6
##    depmap_id  cell_line         gene              gene_name entrez_id dependency
##    <chr>      <chr>             <chr>             <chr>     <chr>          <dbl>
##  1 ACH-001270 127399_SOFT_TISS… A1BG (1)          A1BG      1             NA    
##  2 ACH-001270 127399_SOFT_TISS… NAT2 (10)         NAT2      10            NA    
##  3 ACH-001270 127399_SOFT_TISS… ADA (100)         ADA       100           NA    
##  4 ACH-001270 127399_SOFT_TISS… CDH2 (1000)       CDH2      1000          -0.195
##  5 ACH-001270 127399_SOFT_TISS… AKT3 (10000)      AKT3      10000         -0.256
##  6 ACH-001270 127399_SOFT_TISS… MED6 (10001)      MED6      10001         -0.174
##  7 ACH-001270 127399_SOFT_TISS… NR2E3 (10002)     NR2E3     10002         -0.140
##  8 ACH-001270 127399_SOFT_TISS… NAALAD2 (10003)   NAALAD2   10003         NA    
##  9 ACH-001270 127399_SOFT_TISS… DUXB (100033411)  DUXB      100033411     NA    
## 10 ACH-001270 127399_SOFT_TISS… PDZK1P1 (1000347… 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(): 2020-10-02
## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache
## # A tibble: 12,324,008 x 6
##    gene               cell_line         dependency entrez_id gene_name depmap_id
##    <chr>              <chr>                  <dbl>     <int> <chr>     <chr>    
##  1 A1BG (1)           127399_SOFT_TISS…     NA             1 A1BG      ACH-0012…
##  2 NAT2 (10)          127399_SOFT_TISS…     NA            10 NAT2      ACH-0012…
##  3 ADA (100)          127399_SOFT_TISS…     NA           100 ADA       ACH-0012…
##  4 CDH2 (1000)        127399_SOFT_TISS…     -0.195      1000 CDH2      ACH-0012…
##  5 AKT3 (10000)       127399_SOFT_TISS…     -0.256     10000 AKT3      ACH-0012…
##  6 MED6 (10001)       127399_SOFT_TISS…     -0.174     10001 MED6      ACH-0012…
##  7 NR2E3 (10002)      127399_SOFT_TISS…     -0.140     10002 NR2E3     ACH-0012…
##  8 NAALAD2 (10003)    127399_SOFT_TISS…     NA         10003 NAALAD2   ACH-0012…
##  9 DUXB (100033411)   127399_SOFT_TISS…     NA     100033411 DUXB      ACH-0012…
## 10 PDZK1P1 (10003474… 127399_SOFT_TISS…     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 20Q3 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 x 6
##    depmap_id  cell_line                    gene   gene_name entrez_id dependency
##    <chr>      <chr>                        <chr>  <chr>     <chr>          <dbl>
##  1 ACH-000004 HEL_HAEMATOPOIETIC_AND_LYMP… A1BG … A1BG      1             0.135 
##  2 ACH-000005 HEL9217_HAEMATOPOIETIC_AND_… 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(): 2020-10-02
## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache
## # A tibble: 14,295,891 x 6
##    depmap_id  gene    dependency entrez_id gene_name cell_line                  
##    <chr>      <chr>        <dbl>     <int> <chr>     <chr>                      
##  1 ACH-000004 A1BG (…     0.181          1 A1BG      HEL_HAEMATOPOIETIC_AND_LYM…
##  2 ACH-000005 A1BG (…    -0.0902         1 A1BG      HEL9217_HAEMATOPOIETIC_AND…
##  3 ACH-000007 A1BG (…     0.0716         1 A1BG      LS513_LARGE_INTESTINE      
##  4 ACH-000009 A1BG (…     0.110          1 A1BG      C2BBE1_LARGE_INTESTINE     
##  5 ACH-000011 A1BG (…     0.275          1 A1BG      253J_URINARY_TRACT         
##  6 ACH-000012 A1BG (…     0.0367         1 A1BG      HCC827_LUNG                
##  7 ACH-000013 A1BG (…     0.0644         1 A1BG      ONCODG1_OVARY              
##  8 ACH-000014 A1BG (…     0.154          1 A1BG      HS294T_SKIN                
##  9 ACH-000015 A1BG (…    -0.0742         1 A1BG      NCIH1581_LUNG              
## 10 ACH-000017 A1BG (…     0.178          1 A1BG      SKBR3_BREAST               
## # … with 14,295,881 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 20Q3 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 x 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_… A1BG … A1BG      1                -0.0208 
##  5 ACH-000838 AMO1_HAEMATOPOIETIC_AN… A1BG … A1BG      1                 0.170  
##  6 ACH-000080 BDCM_HAEMATOPOIETIC_AN… A1BG … A1BG      1                 0.00703
##  7 ACH-000992 BICR18_UPPER_AERODIGES… A1BG … A1BG      1                -0.376  
##  8 ACH-000228 BICR31_UPPER_AERODIGES… A1BG … A1BG      1                 1.16   
##  9 ACH-000771 BICR56_UPPER_AERODIGES… A1BG … A1BG      1                 0.0197 
## 10 ACH-000415 BICR6_UPPER_AERODIGEST… 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(): 2020-10-02
## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache
## # A tibble: 1,804 x 25
##    depmap_id stripped_cell_l… cell_line aliases cosmic_id sex   source
##    <chr>     <chr>            <chr>     <chr>       <dbl> <chr> <chr> 
##  1 ACH-0000… NIHOVCAR3        NIHOVCAR… OVCAR3     905933 Fema… ATCC  
##  2 ACH-0000… HL60             HL60_HAE… <NA>       905938 Fema… ATCC  
##  3 ACH-0000… CACO2            CACO2_LA… CACO2,…        NA Male  ATCC  
##  4 ACH-0000… HEL              HEL_HAEM… <NA>       907053 Male  DSMZ  
##  5 ACH-0000… HEL9217          HEL9217_… <NA>           NA Male  ATCC  
##  6 ACH-0000… MONOMAC6         MONOMAC6… <NA>       908148 Male  DSMZ  
##  7 ACH-0000… LS513            LS513_LA… <NA>       907795 Male  ATCC  
##  8 ACH-0000… A101D            A101D_SK… <NA>       910921 Male  ATCC  
##  9 ACH-0000… C2BBE1           C2BBE1_L… <NA>       910700 Male  ATCC  
## 10 ACH-0000… NCIH2077         NCIH2077… NCI-H1…        NA Male  Acade…
## # … with 1,794 more rows, and 18 more variables: Achilles_n_replicates <dbl>,
## #   cell_line_NNMD <dbl>, culture_type <chr>, culture_medium <chr>,
## #   cas9_activity <chr>, RRID <chr>, sample_collection_site <chr>,
## #   primary_or_metastasis <chr>, primary_disease <chr>, subtype_disease <chr>,
## #   age <dbl>, sanger_id <chr>, WTSI_master_cell_ID <dbl>,
## #   additional_info <lgl>, lineage <chr>, lineage_subtype <chr>,
## #   lineage_sub_subtype <chr>, lineage_molecular_subtype <chr>

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 20Q3 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 x 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(): 2020-10-02
## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache
## # A tibble: 192,386 x 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 20Q3 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 x 6
##    depmap_id  cell_line            gene         gene_name ensembl_id  expression
##    <chr>      <chr>                <chr>        <chr>     <chr>            <dbl>
##  1 ACH-000956 22RV1_PROSTATE       TSPAN6 (ENS… TSPAN6    ENSG000000…      2.65 
##  2 ACH-000948 2313287_STOMACH      TSPAN6 (ENS… TSPAN6    ENSG000000…      3.00 
##  3 ACH-000026 253JBV_URINARY_TRACT TSPAN6 (ENS… TSPAN6    ENSG000000…      4.57 
##  4 ACH-000011 253J_URINARY_TRACT   TSPAN6 (ENS… TSPAN6    ENSG000000…      4.58 
##  5 ACH-000323 42MGBA_CENTRAL_NERV… TSPAN6 (ENS… TSPAN6    ENSG000000…      4.59 
##  6 ACH-000905 5637_URINARY_TRACT   TSPAN6 (ENS… TSPAN6    ENSG000000…      5.88 
##  7 ACH-000520 59M_OVARY            TSPAN6 (ENS… TSPAN6    ENSG000000…      4.11 
##  8 ACH-000973 639V_URINARY_TRACT   TSPAN6 (ENS… TSPAN6    ENSG000000…      5.05 
##  9 ACH-000896 647V_URINARY_TRACT   TSPAN6 (ENS… TSPAN6    ENSG000000…      5.94 
## 10 ACH-000070 697_HAEMATOPOIETIC_… TSPAN6 (ENS… TSPAN6    ENSG000000…      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(): 2020-10-02
## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache
## # A tibble: 24,982,920 x 6
##    depmap_id  gene     rna_expression ensembl_id gene_name cell_line            
##    <chr>      <chr>             <dbl>      <int> <chr>     <chr>                
##  1 ACH-001097 TSPAN6 …          0           7105 TSPAN6    KARPAS384_HAEMATOPOI…
##  2 ACH-001636 TSPAN6 …          0           7105 TSPAN6    RAMOS_HAEMATOPOIETIC…
##  3 ACH-001804 TSPAN6 …          4.93        7105 TSPAN6    LPS510_SOFT_TISSUE   
##  4 ACH-000534 TSPAN6 …          0.840       7105 TSPAN6    WSUDLCL2_HAEMATOPOIE…
##  5 ACH-001498 TSPAN6 …          1.74        7105 TSPAN6    FARAGE_HAEMATOPOIETI…
##  6 ACH-000742 TSPAN6 …          3.72        7105 TSPAN6    DM3_FIBROBLAST       
##  7 ACH-002018 TSPAN6 …          5.12        7105 TSPAN6    OMM25_UVEA           
##  8 ACH-000836 TSPAN6 …          4.63        7105 TSPAN6    YD15_SALIVARY_GLAND  
##  9 ACH-000545 TSPAN6 …          4.25        7105 TSPAN6    VMCUB1_URINARY_TRACT 
## 10 ACH-001617 TSPAN6 …          0           7105 TSPAN6    OCILY7_HAEMATOPOIETI…
## # … with 24,982,910 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 20Q3 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 x 9
##    depmap_id cell_line aliases cosmic_id sanger_id primary_disease
##    <chr>     <chr>     <chr>       <dbl>     <dbl> <chr>          
##  1 ACH-0000… NIHOVCAR… NIH:OV…    905933      2201 Ovarian Cancer 
##  2 ACH-0000… HL60_HAE… HL-60      905938        55 Leukemia       
##  3 ACH-0000… CACO2_LA… CACO2;…        NA        NA Colon/Colorect…
##  4 ACH-0000… HEL_HAEM… HEL        907053       783 Leukemia       
##  5 ACH-0000… HEL9217_… HEL 92…        NA        NA Leukemia       
##  6 ACH-0000… MONOMAC6… MONO-M…    908148      2167 Leukemia       
##  7 ACH-0000… LS513_LA… LS513      907795       569 Colon/Colorect…
##  8 ACH-0000… C2BBE1_L… C2BBe1     910700      2104 Colon/Colorect…
##  9 ACH-0000… NCIH2077… NCI-H2…        NA        NA Lung Cancer    
## 10 ACH-0000… 253J_URI… 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(): 2020-10-02
## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache
## # A tibble: 1,804 x 25
##    depmap_id stripped_cell_l… cell_line aliases cosmic_id sex   source
##    <chr>     <chr>            <chr>     <chr>       <dbl> <chr> <chr> 
##  1 ACH-0000… NIHOVCAR3        NIHOVCAR… OVCAR3     905933 Fema… ATCC  
##  2 ACH-0000… HL60             HL60_HAE… <NA>       905938 Fema… ATCC  
##  3 ACH-0000… CACO2            CACO2_LA… CACO2,…        NA Male  ATCC  
##  4 ACH-0000… HEL              HEL_HAEM… <NA>       907053 Male  DSMZ  
##  5 ACH-0000… HEL9217          HEL9217_… <NA>           NA Male  ATCC  
##  6 ACH-0000… MONOMAC6         MONOMAC6… <NA>       908148 Male  DSMZ  
##  7 ACH-0000… LS513            LS513_LA… <NA>       907795 Male  ATCC  
##  8 ACH-0000… A101D            A101D_SK… <NA>       910921 Male  ATCC  
##  9 ACH-0000… C2BBE1           C2BBE1_L… <NA>       910700 Male  ATCC  
## 10 ACH-0000… NCIH2077         NCIH2077… NCI-H1…        NA Male  Acade…
## # … with 1,794 more rows, and 18 more variables: Achilles_n_replicates <dbl>,
## #   cell_line_NNMD <dbl>, culture_type <chr>, culture_medium <chr>,
## #   cas9_activity <chr>, RRID <chr>, sample_collection_site <chr>,
## #   primary_or_metastasis <chr>, primary_disease <chr>, subtype_disease <chr>,
## #   age <dbl>, sanger_id <chr>, WTSI_master_cell_ID <dbl>,
## #   additional_info <lgl>, lineage <chr>, lineage_subtype <chr>,
## #   lineage_sub_subtype <chr>, lineage_molecular_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 20Q3 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 x 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-0000… VPS13D        55187         37 1           12359347  1.24e7 +     
##  2 ACH-0000… AADACL4      343066         37 1           12726308  1.27e7 +     
##  3 ACH-0000… IFNLR1       163702         37 1           24484172  2.45e7 +     
##  4 ACH-0000… TMEM57        55219         37 1           25785018  2.58e7 +     
##  5 ACH-0000… ZSCAN20        7579         37 1           33954141  3.40e7 +     
##  6 ACH-0000… POU3F1         5453         37 1           38512139  3.85e7 +     
##  7 ACH-0000… MAST2         23139         37 1           46498028  4.65e7 +     
##  8 ACH-0000… GBP4         115361         37 1           89657103  8.97e7 +     
##  9 ACH-0000… VAV3          10451         37 1          108247170  1.08e8 +     
## 10 ACH-0000… 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>,
## #   sanger_recalib_WES_AC <chr>, RNAseq_AC <chr>, HC_AC <chr>, RD_AC <chr>,
## #   WGS_AC <chr>, var_annotation <chr>

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

depmap::depmap_mutationCalls()
## snapshotDate(): 2020-10-02
## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache
## # A tibble: 1,804 x 25
##    depmap_id stripped_cell_l… cell_line aliases cosmic_id sex   source
##    <chr>     <chr>            <chr>     <chr>       <dbl> <chr> <chr> 
##  1 ACH-0000… NIHOVCAR3        NIHOVCAR… OVCAR3     905933 Fema… ATCC  
##  2 ACH-0000… HL60             HL60_HAE… <NA>       905938 Fema… ATCC  
##  3 ACH-0000… CACO2            CACO2_LA… CACO2,…        NA Male  ATCC  
##  4 ACH-0000… HEL              HEL_HAEM… <NA>       907053 Male  DSMZ  
##  5 ACH-0000… HEL9217          HEL9217_… <NA>           NA Male  ATCC  
##  6 ACH-0000… MONOMAC6         MONOMAC6… <NA>       908148 Male  DSMZ  
##  7 ACH-0000… LS513            LS513_LA… <NA>       907795 Male  ATCC  
##  8 ACH-0000… A101D            A101D_SK… <NA>       910921 Male  ATCC  
##  9 ACH-0000… C2BBE1           C2BBE1_L… <NA>       910700 Male  ATCC  
## 10 ACH-0000… NCIH2077         NCIH2077… NCI-H1…        NA Male  Acade…
## # … with 1,794 more rows, and 18 more variables: Achilles_n_replicates <dbl>,
## #   cell_line_NNMD <dbl>, culture_type <chr>, culture_medium <chr>,
## #   cas9_activity <chr>, RRID <chr>, sample_collection_site <chr>,
## #   primary_or_metastasis <chr>, primary_disease <chr>, subtype_disease <chr>,
## #   age <dbl>, sanger_id <chr>, WTSI_master_cell_ID <dbl>,
## #   additional_info <lgl>, lineage <chr>, lineage_subtype <chr>,
## #   lineage_sub_subtype <chr>, lineage_molecular_subtype <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 20Q3 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 x 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(): 2020-10-02
## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache
## # A tibble: 2,708,508 x 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 20Q3 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 x 12
##    depmap_id gene_name entrez_id protein protein_express… protein_id desc 
##    <chr>     <chr>         <dbl> <chr>              <dbl> <chr>      <chr>
##  1 ACH-0008… SLC12A2        6558 MDAMB4…          2.11    sp|P55011… S12A…
##  2 ACH-0004… SLC12A2        6558 SH4_SK…          0.0705  sp|P55011… S12A…
##  3 ACH-0002… SLC12A2        6558 AU565_…         -0.464   sp|P55011… S12A…
##  4 ACH-0006… SLC12A2        6558 KMRC1_…         -0.884   sp|P55011… S12A…
##  5 ACH-0008… SLC12A2        6558 CAL51_…          0.789   sp|P55011… S12A…
##  6 ACH-0003… SLC12A2        6558 RPMI79…         -0.912   sp|P55011… S12A…
##  7 ACH-0000… SLC12A2        6558 RERFLC…          0.729   sp|P55011… S12A…
##  8 ACH-0006… SLC12A2        6558 IGR37_…         -0.658   sp|P55011… S12A…
##  9 ACH-0004… SLC12A2        6558 VMRCRC…         -1.15    sp|P55011… S12A…
## 10 ACH-0006… SLC12A2        6558 HEP3B2…          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(): 2020-10-02
## see ?depmap and browseVignettes('depmap') for documentation
## loading from cache
## # A tibble: 4,821,390 x 12
##    depmap_id gene_name entrez_id protein protein_express… protein_id desc 
##    <chr>     <chr>         <dbl> <chr>              <dbl> <chr>      <chr>
##  1 ACH-0008… SLC12A2        6558 MDAMB4…          2.11    sp|P55011… S12A…
##  2 ACH-0004… SLC12A2        6558 SH4_SK…          0.0705  sp|P55011… S12A…
##  3 ACH-0002… SLC12A2        6558 AU565_…         -0.464   sp|P55011… S12A…
##  4 ACH-0006… SLC12A2        6558 KMRC1_…         -0.884   sp|P55011… S12A…
##  5 ACH-0008… SLC12A2        6558 CAL51_…          0.789   sp|P55011… S12A…
##  6 ACH-0003… SLC12A2        6558 RPMI79…         -0.912   sp|P55011… S12A…
##  7 ACH-0000… SLC12A2        6558 RERFLC…          0.729   sp|P55011… S12A…
##  8 ACH-0006… SLC12A2        6558 IGR37_…         -0.658   sp|P55011… S12A…
##  9 ACH-0004… SLC12A2        6558 VMRCRC…         -1.15    sp|P55011… S12A…
## 10 ACH-0006… SLC12A2        6558 HEP3B2…          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 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.12-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.12-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        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] parallel  stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
## [1] ExperimentHub_1.16.0 AnnotationHub_2.22.0 BiocFileCache_1.14.0
## [4] dbplyr_1.4.4         BiocGenerics_0.36.0  depmap_1.4.0        
## [7] dplyr_1.0.2          BiocStyle_2.18.0    
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.1.0              xfun_0.18                    
##  [3] BiocVersion_3.12.0            purrr_0.3.4                  
##  [5] vctrs_0.3.4                   generics_0.0.2               
##  [7] htmltools_0.5.0               stats4_4.0.3                 
##  [9] yaml_2.2.1                    utf8_1.1.4                   
## [11] interactiveDisplayBase_1.28.0 blob_1.2.1                   
## [13] rlang_0.4.8                   pillar_1.4.6                 
## [15] later_1.1.0.1                 glue_1.4.2                   
## [17] DBI_1.1.0                     rappdirs_0.3.1               
## [19] bit64_4.0.5                   lifecycle_0.2.0              
## [21] stringr_1.4.0                 memoise_1.1.0                
## [23] evaluate_0.14                 Biobase_2.50.0               
## [25] knitr_1.30                    IRanges_2.24.0               
## [27] fastmap_1.0.1                 httpuv_1.5.4                 
## [29] curl_4.3                      AnnotationDbi_1.52.0         
## [31] fansi_0.4.1                   Rcpp_1.0.5                   
## [33] xtable_1.8-4                  promises_1.1.1               
## [35] BiocManager_1.30.10           S4Vectors_0.28.0             
## [37] mime_0.9                      bit_4.0.4                    
## [39] digest_0.6.27                 stringi_1.5.3                
## [41] bookdown_0.21                 shiny_1.5.0                  
## [43] cli_2.1.0                     tools_4.0.3                  
## [45] magrittr_1.5                  tibble_3.0.4                 
## [47] RSQLite_2.2.1                 crayon_1.3.4                 
## [49] pkgconfig_2.0.3               ellipsis_0.3.1               
## [51] assertthat_0.2.1              rmarkdown_2.5                
## [53] httr_1.4.2                    R6_2.5.0                     
## [55] compiler_4.0.3