author: Joshua H. Cook
last updated: June 12, 2019
This is “tidy” data from the Broad’s Project Achilles. The data files are in the “data” directory and exaplined below.
To download this repository, run the following command on the command line.
git clone https://github.com/jhrcook/tidy_Achilles.gitAll data was downloaded from the Broad’s DepMap data
repository.
web address: https://depmap.org/portal/download/all/
You can also query genes, cell lines, lineages, etc. from their
website
web address: https://depmap.org/portal/
The cell line information was obtained from the Cancer Cell Line
Encyclopedia - their query
portal is really useful, too. More data on the cell lines can be
downloaded from their website. If you have an requests for data to add
to this repo, please open a GitHub
issue.
web address: https://portals.broadinstitute.org/ccle
The following files contain information about the cell lines in the CCLE. Some, but not all, have been screen by the DepMap Project.
- sample_info.csv - information on the cell lines
- CCLE_mutations.csv.tar.gz - mutations of the cell lines
- CCLE_expression.csv.tar.gz - gene expression of the cell lines (in TPM)
- CCLE_gene_cn.csv.tar.gz - copy number of each gene in the cell lines
The Achilles project used RNAi to deplete each gene in the cell lines, but has since transitioned to a CRISPR loss-of-function screen. That data is available in this repository. The following files hold information on the effect of knocking-out each gene on the survival of the cell line.
- Achilles_gene_dependency.csv.tar.gz - probability that knocking out the gene has a real depletion effect
- Achilles_gene_effect.csv.tar.gz - CERES data with principle components strongly related to known batch effects removed, then shifted and scaled per cell line so the median nonessential KO effect is 0 and the median essential KO effect is -1
The Achilles project used RNAi to deplete each gene in the cell lines, but has since transitioned to a CRISPR loss-of-function screen. That data is available in this repository. The following files hold information on the guides used in the CRISPR screen.
- Achilles_guide_efficacy.csv - CERES inferred efficacy for the guide
- Achilles_guide_map.csv - where the guide targets on the genome (including gene name)
- Achilles_dropped_guides.csv - guides that were dropped, usually for likely off-target effects
- Achilles_replicate_map.csv - indicates which processing batch the replicate belongs to and therefore which pDNA reference it should be compared with
The data collected from the Achilles project was used to determine which genes were broadly essential.
- common_essentials.csv - genes used as positive controls, intersection of Biomen (2014) and Hart (2015) essentials; the scores of these genes are used as the dependent distribution for inferring dependency probability
- Achilles_common_essentials.csv - genes identified as dependencies in all lines
- nonessentials.csv - genes used as negative controls (Hart (2014) nonessentials)
Any others? Open an issue for any requested data to include.
library(tidyverse)All processing was done in “data_preparation.R”. The tidy data were
stored as “tibbles” (tbl_df instead of R’s standard data.frame
object) in RDS files. They can all be read directly into R.
library(tibble)
readRDS("data/example_data_table.tib")More information in the “tidy data” format can be found in R for Data Science - Tidy data.
The following data frame holds meta data on the cell lines used in the
CRISPR screen (an other cell lines in the CCLE). Use the dep_map_id
column for unique identification and matching with the data frames
included in this repository. The columns disease and disease_sutype
provide information on where the cell line originated.
readRDS(file.path("data", "cell_line_metadata.tib"))
#> # A tibble: 1,714 x 16
#> dep_map_id stripped_cell_l… ccle_name alias cosmic_id sanger_id disease
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr>
#> 1 ACH-000001 NIHOVCAR3 NIHOVCAR… OVCA… 905933 2201 ovary
#> 2 ACH-000002 HL60 HL60_HAE… <NA> 905938 55 leukem…
#> 3 ACH-000003 CACO2 CACO2_LA… CACO… NA NA colore…
#> 4 ACH-000004 HEL HEL_HAEM… <NA> 907053 783 leukem…
#> 5 ACH-000005 HEL9217 HEL9217_… <NA> NA NA leukem…
#> 6 ACH-000006 MONOMAC6 MONOMAC6… <NA> 908148 2167 leukem…
#> 7 ACH-000007 LS513 LS513_LA… <NA> 907795 569 colore…
#> 8 ACH-000008 A101D A101D_SK… <NA> 910921 1806 <NA>
#> 9 ACH-000009 C2BBE1 C2BBE1_L… <NA> 910700 2104 colore…
#> 10 ACH-000010 NCIH2077 NCIH2077… NCI-… NA NA lung
#> # … with 1,704 more rows, and 9 more variables: disease_sutype <chr>,
#> # disease_sub_subtype <chr>, gender <chr>, source <chr>,
#> # achilles_n_replicates <dbl>, cell_line_nnmd <dbl>, culture_type <chr>,
#> # culture_medium <chr>, cas9_activity <chr>All of the cell lines in the CCLE have been whole exome sequenced. Their
mutations are contained in the following file. Both the Hugo and Entrez
gene identifiers are available (hugo_symbol and entrez_gene_id,
respectively). Use the dep_map_id column for unique identification and
matching with the data frames included in this repository. The specific
mutations to the proteins are in the protein_change column.
readRDS(file.path("data", "cell_line_mutations.tib"))
#> # A tibble: 1,227,713 x 34
#> hugo_symbol entrez_gene_id ncbi_build chromosome start_position
#> <chr> <dbl> <dbl> <chr> <dbl>
#> 1 VPS13D 55187 37 1 12359347
#> 2 AADACL4 343066 37 1 12726308
#> 3 IFNLR1 163702 37 1 24484172
#> 4 TMEM57 55219 37 1 25785018
#> 5 ZSCAN20 7579 37 1 33954141
#> 6 POU3F1 5453 37 1 38512139
#> 7 MAST2 23139 37 1 46498028
#> 8 GBP4 115361 37 1 89657103
#> 9 VAV3 10451 37 1 108247170
#> 10 NBPF20 100288142 37 1 148346689
#> # … with 1,227,703 more rows, and 29 more variables: end_position <dbl>,
#> # strand <chr>, variant_classification <chr>, variant_type <chr>,
#> # reference_allele <chr>, tumor_seq_allele1 <chr>, db_snp_rs <chr>,
#> # db_snp_val_status <chr>, genome_change <chr>,
#> # annotation_transcript <chr>, tumor_sample_barcode <chr>,
#> # c_dna_change <chr>, codon_change <chr>, protein_change <chr>,
#> # is_deleterious <lgl>, is_tcg_ahotspot <lgl>, tcg_ahs_cnt <dbl>,
#> # is_cosmi_chotspot <lgl>, cosmi_chs_cnt <dbl>, ex_ac_af <dbl>,
#> # cga_wes_ac <chr>, sanger_wes_ac <chr>, sanger_recalib_wes_ac <chr>,
#> # rn_aseq_ac <chr>, hc_ac <chr>, rd_ac <chr>, wgs_ac <chr>,
#> # variant_annotation <chr>, dep_map_id <chr>Due to size restrictions, the gene copy number data was separated into
multiple files by tissue of origin of the cell line. The Hugo gene
identifier is available in the gene column. Use the dep_map_id
column for unique identification and matching with the data frames
included in this repository. The copy number information is in the
copy_number column.
readRDS(file.path("data", "copy_number", "PANCREAS_copynum.tib"))
#> # A tibble: 1,304,744 x 19
#> dep_map_id gene copy_number stripped_cell_l… ccle_name alias cosmic_id
#> <chr> <chr> <dbl> <chr> <chr> <chr> <dbl>
#> 1 ACH-000178 A1BG 0.877 HS766T HS766T_P… <NA> 1298141
#> 2 ACH-000281 A1BG 0.944 KP2 KP2_PANC… <NA> 1298218
#> 3 ACH-000685 A1BG 1.20 L33 L33_PANC… <NA> NA
#> 4 ACH-000042 A1BG 1.33 PANC0203 PANC0203… <NA> 1298475
#> 5 ACH-000031 A1BG 0.874 PANC0213 PANC0213… <NA> NA
#> 6 ACH-000235 A1BG 0.832 PANC0403 PANC0403… <NA> 1298476
#> 7 ACH-000093 A1BG 1.03 PANC0504 PANC0504… <NA> NA
#> 8 ACH-000599 A1BG 0.791 PATU8902 PATU8902… <NA> 1298526
#> 9 ACH-000307 A1BG 1.07 PK1 PK1_PANC… <NA> NA
#> 10 ACH-000205 A1BG 1.42 PK59 PK59_PAN… <NA> NA
#> # … with 1,304,734 more rows, and 12 more variables: sanger_id <dbl>,
#> # disease <chr>, disease_sutype <chr>, disease_sub_subtype <chr>,
#> # gender <chr>, source <chr>, achilles_n_replicates <dbl>,
#> # cell_line_nnmd <dbl>, culture_type <chr>, culture_medium <chr>,
#> # cas9_activity <chr>, tissue <chr>Due to size restrictions, the gene expression (in TPM) data was
separated into multiple files by tissue of origin of the cell line. The
Hugo gene identifier is available in the gene column. Use the
dep_map_id column for unique identification and matching with the data
frames included in this repository. The expression values are in the
gene_expression column.
readRDS(file.path("data", "gene_expression", "PANCREAS_geneexpr.tib"))
#> # A tibble: 876,757 x 19
#> dep_map_id gene gene_expression stripped_cell_l… ccle_name alias
#> <chr> <chr> <dbl> <chr> <chr> <chr>
#> 1 ACH-000222 TSPA… 5.97 ASPC1 ASPC1_PA… <NA>
#> 2 ACH-000535 TSPA… 5.89 BXPC3 BXPC3_PA… <NA>
#> 3 ACH-000354 TSPA… 4.53 CAPAN1 CAPAN1_P… <NA>
#> 4 ACH-000107 TSPA… 5.21 CAPAN2 CAPAN2_P… <NA>
#> 5 ACH-000138 TSPA… 5.23 CFPAC1 CFPAC1_P… <NA>
#> 6 ACH-000243 TSPA… 4.79 DANG DANG_PAN… <NA>
#> 7 ACH-000270 TSPA… 4.85 HPAC HPAC_PAN… <NA>
#> 8 ACH-000094 TSPA… 6.23 HPAFII HPAFII_P… <NA>
#> 9 ACH-000178 TSPA… 5.79 HS766T HS766T_P… <NA>
#> 10 ACH-000118 TSPA… 4.47 HUPT3 HUPT3_PA… <NA>
#> # … with 876,747 more rows, and 13 more variables: cosmic_id <dbl>,
#> # sanger_id <dbl>, disease <chr>, disease_sutype <chr>,
#> # disease_sub_subtype <chr>, gender <chr>, source <chr>,
#> # achilles_n_replicates <dbl>, cell_line_nnmd <dbl>, culture_type <chr>,
#> # culture_medium <chr>, cas9_activity <chr>, tissue <chr>A cell line is dependent on a gene if the deletion of the gene causes a decrease in vitality of the cell line. The Achilles project is using a genome-wide CRISPR-Cas9 loss-of-function screen to test hundreds of cell line’s dependencies.
The Achilles_gene_dependency.tib contains the probability that
knocking out the gene has a real depletion effect. The Hugo gene
identifier is available in the gene column. Use the dep_map_id
column for unique identification and matching with the data frames
included in this repository. The dependency score is in the
gene_dependency column. This file includes the CCLE sample
information, too.
readRDS(file.path("data", "Achilles_gene_dependency.tib"))
#> # A tibble: 9,927,942 x 19
#> dep_map_id gene gene_dependency stripped_cell_l… ccle_name alias
#> <chr> <chr> <dbl> <chr> <chr> <chr>
#> 1 ACH-000004 A1BG 0.00160 HEL HEL_HAEM… <NA>
#> 2 ACH-000007 A1BG 0.00362 LS513 LS513_LA… <NA>
#> 3 ACH-000009 A1BG 0.00348 C2BBE1 C2BBE1_L… <NA>
#> 4 ACH-000011 A1BG 0.000310 253J 253J_URI… <NA>
#> 5 ACH-000012 A1BG 0.00789 HCC827 HCC827_L… <NA>
#> 6 ACH-000013 A1BG 0.00685 ONCODG1 ONCODG1_… <NA>
#> 7 ACH-000014 A1BG 0.00101 HS294T HS294T_S… A101…
#> 8 ACH-000015 A1BG 0.0216 NCIH1581 NCIH1581… NCI-…
#> 9 ACH-000017 A1BG 0.00112 SKBR3 SKBR3_BR… <NA>
#> 10 ACH-000018 A1BG 0.00977 T24 T24_URIN… <NA>
#> # … with 9,927,932 more rows, and 13 more variables: cosmic_id <dbl>,
#> # sanger_id <dbl>, disease <chr>, disease_sutype <chr>,
#> # disease_sub_subtype <chr>, gender <chr>, source <chr>,
#> # achilles_n_replicates <dbl>, cell_line_nnmd <dbl>, culture_type <chr>,
#> # culture_medium <chr>, cas9_activity <chr>, tissue <chr>
The depletion effect of targeting each gene is in
Achilles_gene_effect.tib. This contains CERES data with principle
components strongly related to known batch effects removed, then shifted
and scaled per cell line so the median nonessential knock-out effect is
0 and the median essential knock-out effect is -1. The gene effect
values are in the gene_effect column.
readRDS(file.path("data", "Achilles_gene_effect.tib"))
#> # A tibble: 9,927,942 x 19
#> dep_map_id gene gene_effect stripped_cell_l… ccle_name alias cosmic_id
#> <chr> <chr> <dbl> <chr> <chr> <chr> <dbl>
#> 1 ACH-000004 A1BG 0.158 HEL HEL_HAEM… <NA> 907053
#> 2 ACH-000007 A1BG 0.0674 LS513 LS513_LA… <NA> 907795
#> 3 ACH-000009 A1BG 0.0512 C2BBE1 C2BBE1_L… <NA> 910700
#> 4 ACH-000011 A1BG 0.270 253J 253J_URI… <NA> NA
#> 5 ACH-000012 A1BG -0.00426 HCC827 HCC827_L… <NA> 1240146
#> 6 ACH-000013 A1BG 0.0409 ONCODG1 ONCODG1_… <NA> NA
#> 7 ACH-000014 A1BG 0.150 HS294T HS294T_S… A101… NA
#> 8 ACH-000015 A1BG -0.0753 NCIH1581 NCIH1581… NCI-… 908471
#> 9 ACH-000017 A1BG 0.167 SKBR3 SKBR3_BR… <NA> NA
#> 10 ACH-000018 A1BG -0.0244 T24 T24_URIN… <NA> 724812
#> # … with 9,927,932 more rows, and 12 more variables: sanger_id <dbl>,
#> # disease <chr>, disease_sutype <chr>, disease_sub_subtype <chr>,
#> # gender <chr>, source <chr>, achilles_n_replicates <dbl>,
#> # cell_line_nnmd <dbl>, culture_type <chr>, culture_medium <chr>,
#> # cas9_activity <chr>, tissue <chr>
The DepMap released information on the guides they used including, mapping information, efficacy, and those that they removed (for various reasons, though likely for off-target effects). These data were collated into one data frame.
readRDS(file.path("data", "Achilles_guides.tib"))
#> # A tibble: 71,143 x 7
#> sgrna genome_alignment gene n_alignments offset efficacy dropped
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <lgl>
#> 1 AAAAAAA… chr10_112724378_+ SHOC2… 1 -3.84e-2 1.000 FALSE
#> 2 AAAAAAC… chr12_95397391_+ NDUFA… 1 -8.34e-1 0.190 FALSE
#> 3 AAAAAAG… chr4_76891509_- SDAD1… 1 4.44e-1 1.000 FALSE
#> 4 AAAAAAG… chr2_33813513_- FAM98… 1 -1.86e-1 1.000 FALSE
#> 5 AAAAAAG… chr19_20002409_+ ZNF25… 1 -2.03e-2 0.707 FALSE
#> 6 AAAAAAG… chr6_26199835_+ HIST1… 1 9.74e-2 1.000 FALSE
#> 7 AAAAACA… chr14_64688272_- SYNE2… 1 -1.13e-2 0.691 FALSE
#> 8 AAAAACT… chr1_229440884_+ SPHAR… 1 4.79e-6 1.000 FALSE
#> 9 AAAAAGA… chr11_64757251_- BATF2… 1 3.92e-1 0.999 FALSE
#> 10 AAAAAGA… chr1_59154718_- MYSM1… 1 2.05e-1 0.998 FALSE
#> # … with 71,133 more rowsThe DepMap project released a file containing which batch each sample
(cell line) was processed in. Note, the scores in
Achilles_gene_effect.tib have already been corrected for batch
effects.
readRDS(file.path("data", "Achilles_replicate_map.tib"))
#> # A tibble: 1,253 x 3
#> replicate_id dep_map_id p_dna_batch
#> <chr> <chr> <dbl>
#> 1 MIA Paca-2-311Cas9 Rep A p6_batch0 ACH-000601 0
#> 2 MIA Paca-2-311Cas9 Rep B p6_batch0 ACH-000601 0
#> 3 MIA Paca-2-311Cas9 Rep C p6_batch0 ACH-000601 0
#> 4 MIA Paca-2-311Cas9 Rep D p6_batch0 ACH-000601 0
#> 5 NCI-H1792-311Cas9 Rep A p6_batch0 ACH-000496 0
#> 6 NCI-H1792-311Cas9 Rep B p6_batch0 ACH-000496 0
#> 7 NCI-H1792-311Cas9 Rep C p6_batch0 ACH-000496 0
#> 8 NCI-H1792-311Cas9 Rep D p6_batch0 ACH-000496 0
#> 9 ASPC-1-311Cas9 Rep A p6_batch0 ACH-000222 0
#> 10 ASPC-1-311Cas9 Rep B p6_batch0 ACH-000222 0
#> # … with 1,243 more rowsFinally, the DepMap created three lists of genes according tho their
essentialiaty. Those were merged into one data frame, here. The
common_essential column indicates which genes were previously known to
be essential in all cells, while the achilles_essential column
indicates which genes were found to be pan-essential during the Achilles
screen. The nonessential column indicates which genes are universally
non-essential
readRDS(file.path("data", "gene_essentiality.tib"))
#> # A tibble: 2,917 x 4
#> gene common_essential achilles_essential nonessential
#> <chr> <lgl> <lgl> <lgl>
#> 1 RPS11 (6205) TRUE TRUE FALSE
#> 2 RPS17 (6218) TRUE TRUE FALSE
#> 3 RPL4 (6124) TRUE TRUE FALSE
#> 4 EIF3D (8664) TRUE TRUE FALSE
#> 5 RPL27 (6155) TRUE TRUE FALSE
#> 6 RPL10A (4736) TRUE TRUE FALSE
#> 7 RPS13 (6207) TRUE TRUE FALSE
#> 8 U2AF1 (7307) TRUE TRUE FALSE
#> 9 POLR2D (5433) TRUE TRUE FALSE
#> 10 RPS15A (6210) TRUE TRUE FALSE
#> # … with 2,907 more rowsThe following Venn diagram shows the overlap of these three groups.
If there are any mistake or places for explanation, please open an issue or create a pull request if you want to address it yourself.