data-raw/sysdata.R
In mskcc/facets-suite: Functions to run and parse output from FACETS
suppressPackageStartupMessages({
library(dplyr)
library(data.table)
library(stringr)
library(readr)
library(purrr)
library(usethis)
library(jsonlite)
})
# Genome builds ---------------------------------------------------------------------------------------------------
# The code use to download the genome builds is commented, the resulting data is in the form of tribbles below.
# extract_genome_info = function(url) {
#
# # url can be a path to a single file or a list of urls if split by chromosome
# build = map_dfr(url, function(x) {
# read_tsv(x, col_names = c('bin', 'chrom', 'start', 'end', 'ix', 'n', 'size', 'type', 'bridge'))
# }) %>%
# group_by(chrom) %>%
# summarize(
# size = max(end),
# centstart = min(start[type == 'centromere']),
# centend = max(end[type == 'centromere']),
# centromere = centstart + ((centend-centstart)/2)) %>%
# mutate(chrom = str_replace(chrom, 'chr', ''),
# chrom = str_replace_all(chrom, c('X' = '23', 'Y' = '24')),
# chrom = as.numeric(chrom)) %>%
# filter(chrom %in% seq(1, 24)) %>%
# arrange(chrom)
# }
# hg18 // pulled from UCSC
# hg18 = map(c(seq(1:22), 'X', 'Y'), ~paste0('http://hgdownload.cse.ucsc.edu/goldenPath/hg18/database/chr', ., '_gap.txt.gz')) %>%
# extract_genome_info()
hg18 = tibble::tribble(
~chrom, ~size, ~centstart, ~centend, ~centromere,
1, 247249719, 121236957, 123476957, 122356957,
2, 242951149, 91689898, 94689898, 93189898,
3, 199501827, 90587544, 93487544, 92037544,
4, 191273063, 49354874, 52354874, 50854874,
5, 180857866, 46441398, 49441398, 47941398,
6, 170899992, 58938125, 61938125, 60438125,
7, 154817899, 58058273, 61058273, 59558273,
8, 145403396, 43958052, 46958052, 45458052,
9, 138336818, 47107499, 50107499, 48607499,
10, 133577517, 39244941, 41624941, 40434941,
11, 95942794, 51450781, 54450781, 52950781,
12, 132349534, 34747961, 36142961, 35445461,
13, 114142980, 1.6e+07, 17868000, 16934000,
14, 106368585, 15070000, 18070000, 16570000,
15, 96367672, 15260000, 18260000, 16760000,
16, 88827254, 35143302, 36943302, 36043302,
17, 78774742, 22187133, 22287133, 22237133,
18, 73872808, 15400898, 16764896, 16082897,
19, 63811651, 26923622, 29923622, 28423622,
20, 60733814, 26267569, 28033230, 27150399.5,
21, 43507092, 10260000, 13260000, 11760000,
22, 49691432, 11330000, 14330000, 12830000,
23, 148832720, 58598737, 61598737, 60098737,
24, 57377044, 11253954, 12308578, 11781266
)
# hg19 // pulled from UCSC
# hg19 = extract_genome_info('http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/gap.txt.gz')
hg19 = tibble::tribble(
~chrom, ~size, ~centstart, ~centend, ~centromere,
1, 249250621, 121535434, 124535434, 121535434,
2, 243199373, 92326171, 95326171, 92326171,
3, 198022430, 90504854, 93504854, 90504854,
4, 191154276, 49660117, 52660117, 49660117,
5, 180915260, 46405641, 49405641, 46405641,
6, 171115067, 58830166, 61830166, 58830166,
7, 159138663, 58054331, 61054331, 58054331,
8, 146364022, 43838887, 46838887, 43838887,
9, 141213431, 47367679, 50367679, 47367679,
10, 135534747, 39254935, 42254935, 39254935,
11, 135006516, 51644205, 54644205, 51644205,
12, 133851895, 34856694, 37856694, 34856694,
13, 115169878, 1.6e+07, 1.9e+07, 1.6e+07,
14, 107349540, 1.6e+07, 1.9e+07, 1.6e+07,
15, 102531392, 1.7e+07, 2e+07, 1.7e+07,
16, 90354753, 35335801, 38335801, 35335801,
17, 79759049, 22263006, 25263006, 22263006,
18, 78077248, 15460898, 18460898, 15460898,
19, 59128983, 24681782, 27681782, 24681782,
20, 63025520, 26369569, 29369569, 26369569,
21, 48129895, 11288129, 14288129, 11288129,
22, 51304566, 1.3e+07, 1.6e+07, 1.3e+07,
23, 155270560, 58632012, 61632012, 58632012,
24, 59373566, 10104553, 13104553, 10104553
)
# hg38 // pulled from UCSC
# hg38 = 'http://hgdownload.cse.ucsc.edu/goldenPath/hg38/database/gap.txt.gz' # note: centromere info not longer in this type of file, but in centromeres.txt.gz
# hg38_centromeres = 'http://hgdownload.cse.ucsc.edu/goldenPath/hg38/database/centromeres.txt.gz'
# hg38_centromeres = read_tsv(grch38_centromeres, col_names = c('bin', 'chrom', 'start', 'end', 'name')) %>%
# group_by(chrom) %>%
# summarize(centstart = min(start),
# centend = max(end),
# centromere = centstart + ((centend-centstart)/2)) %>%
# mutate(chrom = str_replace(chrom, 'chr', ''),
# chrom = str_replace_all(chrom, c('X' = '23', 'Y' = '24')),
# chrom = as.numeric(chrom))
#
# hg38 = extract_genome_info(hg38) %>%
# select(-matches('cent')) %>%
# left_join(., hg38_centromeres, by = 'chrom')
hg38 = tibble::tribble(
~chrom, ~size, ~centstart, ~centend, ~centromere,
1, 248956422, 122026459, 124932724, 123479591.5,
2, 242193529, 92188145, 94090557, 93139351,
3, 198295559, 90772458, 93655574, 92214016,
4, 190214555, 49712061, 51743951, 50728006,
5, 181538259, 46485900, 50059807, 48272853.5,
6, 170805979, 58553888, 59829934, 59191911,
7, 159345973, 58169653, 61528020, 59848836.5,
8, 145138636, 44033744, 45877265, 44955504.5,
9, 138394717, 43389635, 45518558, 44454096.5,
10, 133797422, 39686682, 41593521, 40640101.5,
11, 135086622, 51078348, 54425074, 52751711,
12, 133275309, 34769407, 37185252, 35977329.5,
13, 114364328, 1.6e+07, 18051248, 17025624,
14, 107043718, 1.6e+07, 18173523, 17086761.5,
15, 101991189, 17083673, 19725254, 18404463.5,
16, 90338345, 36311158, 38265669, 37288413.5,
17, 83257441, 22813679, 26616164, 24714921.5,
18, 80373285, 15460899, 20861206, 18161052.5,
19, 58617616, 24498980, 27190874, 25844927,
20, 64444167, 26436232, 30038348, 28237290,
21, 46709983, 10864560, 12915808, 11890184,
22, 50818468, 12954788, 15054318, 14004553,
23, 156040895, 58605579, 62412542, 60509060.5,
24, 57227415, 10316944, 10544039, 10430491.5
)
# Gene positions --------------------------------------------------------------------------------------------------
tumor_suppressors = fromJSON(readLines('https://legacy.oncokb.org/api/v1/genes', warn = FALSE)) %>%
filter(tsg == TRUE) %>%
select(hugoSymbol) %>%
mutate(hugoSymbol = mapvalues(hugoSymbol,
c('FAM175A', 'FAM58A', 'MRE11A', 'PARK2', 'FAM46C'), # these are not the official symbols
c('ABRAXAS1', 'CCNQ', 'MRE11', 'PRKN', 'TENT5C'))) # these are
genes_hg19 = fread('ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_29/GRCh37_mapping/gencode.v29lift37.basic.annotation.gtf.gz',
header = F, skip = 'chr1',
col.names = c('chrom', 'source', 'type', 'start', 'end', 'na_1', 'strand', 'na_2', 'info')) %>%
filter(type == 'gene', info %like% 'protein_coding') %>%
mutate(chrom = str_replace(chrom, 'chr', ''),
gene = str_extract(info, '(?<=gene_name ")[A-Za-z0-9\\.\\-]+(?=";)')) %>%
group_by(gene, chrom) %>%
summarize(start = min(start),
end = max(end)) %>%
ungroup() %>%
mutate(tsg = gene %in% tumor_suppressors$hugoSymbol)
genes_hg38 = fread('ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_29/gencode.v29.basic.annotation.gtf.gz',
header = F, skip = 'chr1',
col.names = c('chrom', 'source', 'type', 'start', 'end', 'na_1', 'strand', 'na_2', 'info')) %>%
filter(type == 'gene', info %like% 'protein_coding') %>%
mutate(chrom = str_replace(chrom, 'chr', ''),
gene = str_extract(info, '(?<=gene_name ")[A-Za-z0-9\\.\\-]+(?=";)')) %>%
group_by(gene, chrom) %>%
summarize(start = min(start),
end = max(end)) %>%
ungroup() %>%
mutate(tsg = gene %in% tumor_suppressors$hugoSymbol)
# use_data(hg18, hg19, hg38, genes_hg19, genes_hg38, internal = T, overwrite = T)
# Copy-number states ----------------------------------------------------------------------------------------------
copy_number_states = tibble::tribble(
~wgd, ~tcn, ~mcn, ~lcn, ~numeric_call, ~call,
# No genome doubling
FALSE, 0, 0, 0, -2, 'HOMDEL',
FALSE, 0, NA, NA, -2, 'HOMDEL',
FALSE, 1, 1, 0, -1, 'HETLOSS',
FALSE, 1, NA, NA, -1, 'HETLOSS',
FALSE, 2, 2, 0, -1, 'CNLOH',
FALSE, 2, NA, NA, NA, 'DIPLOID or CNLOH', #numeric_call will be NA if it cannot be unambiguously ascertained. here it could be either 0 or -1
FALSE, 3, 3, 0, 1, 'CNLOH & GAIN',
FALSE, 3, NA, NA, 1, 'GAIN (many states)',
FALSE, 4, 4, 0, 1, 'CNLOH & GAIN',
FALSE, 4, NA, NA, 1, 'GAIN (many states)',
FALSE, 5, 5, 0, 2, 'AMP (LOH)',
FALSE, 5, NA, NA, 2, 'AMP (many states)',
FALSE, 6, 6, 0, 2, 'AMP (LOH)',
FALSE, 6, NA, NA, 2, 'AMP (many states)',
FALSE, 2, 1, 1, 0, 'DIPLOID',
FALSE, 3, 2, 1, 1, 'GAIN',
FALSE, 4, 3, 1, 1, 'GAIN',
FALSE, 5, 4, 1, 2, 'AMP',
FALSE, 6, 5, 1, 2, 'AMP',
FALSE, 7, 6, 1, 2, 'AMP',
FALSE, 4, 2, 2, 1, 'TETRAPLOID',
FALSE, 5, 3, 2, 2, 'AMP',
FALSE, 6, 4, 2, 2, 'AMP',
FALSE, 7, 5, 2, 2, 'AMP',
FALSE, 8, 6, 2, 2, 'AMP',
FALSE, 6, 3, 3, 2, 'AMP (BALANCED)',
FALSE, 7, 4, 3, 2, 'AMP',
FALSE, 8, 5, 3, 2, 'AMP',
FALSE, 9, 6, 3, 2, 'AMP',
# With genome doubling
TRUE, 0, 0, 0, -2, 'HOMDEL',
TRUE, 0, NA, NA, -2, 'HOMDEL',
TRUE, 1, 1, 0, -1, 'LOSS BEFORE & AFTER',
TRUE, 1, NA, NA, -1, 'LOSS BEFORE & AFTER',
TRUE, 2, 2, 0, -1, 'LOSS BEFORE',
TRUE, 2, NA, NA, -1, 'LOSS BEFORE or DOUBLE LOSS AFTER',
TRUE, 3, 3, 0, -1, 'CNLOH BEFORE & LOSS',
TRUE, 3, NA, NA, -1, 'LOSS (many states)',
TRUE, 4, 4, 0, -1, 'CNLOH BEFORE',
TRUE, 4, NA, NA, -1, 'TETRAPLOID or CNLOH BEFORE',
TRUE, 5, 5, 0, 1, 'CNLOH BEFORE & GAIN',
TRUE, 5, NA, NA, -1, 'GAIN (many states)',
TRUE, 6, 6, 0, 2, 'AMP (LOH)',
TRUE, 6, NA, NA, 2, 'AMP (many states)',
TRUE, 2, 1, 1, -1, 'DOUBLE LOSS AFTER',
TRUE, 3, 2, 1, -1, 'LOSS AFTER',
TRUE, 4, 3, 1, -1, 'CNLOH AFTER',
TRUE, 5, 4, 1, 1, 'LOSS & GAIN',
TRUE, 6, 5, 1, 2, 'AMP',
TRUE, 7, 6, 1, 2, 'AMP',
TRUE, 4, 2, 2, 0, 'TETRAPLOID',
TRUE, 5, 3, 2, 1, 'GAIN',
TRUE, 6, 4, 2, 2, 'AMP',
TRUE, 7, 5, 2, 2, 'AMP',
TRUE, 8, 6, 2, 2, 'AMP',
TRUE, 6, 3, 3, 2, 'AMP (BALANCED)',
TRUE, 7, 4, 3, 2, 'AMP',
TRUE, 8, 5, 3, 2, 'AMP',
TRUE, 9, 6, 3, 2, 'AMP'
) %>% mutate(map_string := paste(wgd, tcn, mcn, lcn, sep = ':'))
use_data(hg18, hg19, hg38, genes_hg19, genes_hg38, copy_number_states, internal = T, overwrite = T)
mskcc/facets-suite documentation built on Sept. 13, 2022, 4:14 a.m.
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suppressPackageStartupMessages({
library(dplyr)
library(data.table)
library(stringr)
library(readr)
library(purrr)
library(usethis)
library(jsonlite)
})
# Genome builds ---------------------------------------------------------------------------------------------------
# The code use to download the genome builds is commented, the resulting data is in the form of tribbles below.
# extract_genome_info = function(url) {
#
# # url can be a path to a single file or a list of urls if split by chromosome
# build = map_dfr(url, function(x) {
# read_tsv(x, col_names = c('bin', 'chrom', 'start', 'end', 'ix', 'n', 'size', 'type', 'bridge'))
# }) %>%
# group_by(chrom) %>%
# summarize(
# size = max(end),
# centstart = min(start[type == 'centromere']),
# centend = max(end[type == 'centromere']),
# centromere = centstart + ((centend-centstart)/2)) %>%
# mutate(chrom = str_replace(chrom, 'chr', ''),
# chrom = str_replace_all(chrom, c('X' = '23', 'Y' = '24')),
# chrom = as.numeric(chrom)) %>%
# filter(chrom %in% seq(1, 24)) %>%
# arrange(chrom)
# }
# hg18 // pulled from UCSC
# hg18 = map(c(seq(1:22), 'X', 'Y'), ~paste0('http://hgdownload.cse.ucsc.edu/goldenPath/hg18/database/chr', ., '_gap.txt.gz')) %>%
# extract_genome_info()
hg18 = tibble::tribble(
~chrom, ~size, ~centstart, ~centend, ~centromere,
1, 247249719, 121236957, 123476957, 122356957,
2, 242951149, 91689898, 94689898, 93189898,
3, 199501827, 90587544, 93487544, 92037544,
4, 191273063, 49354874, 52354874, 50854874,
5, 180857866, 46441398, 49441398, 47941398,
6, 170899992, 58938125, 61938125, 60438125,
7, 154817899, 58058273, 61058273, 59558273,
8, 145403396, 43958052, 46958052, 45458052,
9, 138336818, 47107499, 50107499, 48607499,
10, 133577517, 39244941, 41624941, 40434941,
11, 95942794, 51450781, 54450781, 52950781,
12, 132349534, 34747961, 36142961, 35445461,
13, 114142980, 1.6e+07, 17868000, 16934000,
14, 106368585, 15070000, 18070000, 16570000,
15, 96367672, 15260000, 18260000, 16760000,
16, 88827254, 35143302, 36943302, 36043302,
17, 78774742, 22187133, 22287133, 22237133,
18, 73872808, 15400898, 16764896, 16082897,
19, 63811651, 26923622, 29923622, 28423622,
20, 60733814, 26267569, 28033230, 27150399.5,
21, 43507092, 10260000, 13260000, 11760000,
22, 49691432, 11330000, 14330000, 12830000,
23, 148832720, 58598737, 61598737, 60098737,
24, 57377044, 11253954, 12308578, 11781266
)
# hg19 // pulled from UCSC
# hg19 = extract_genome_info('http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/gap.txt.gz')
hg19 = tibble::tribble(
~chrom, ~size, ~centstart, ~centend, ~centromere,
1, 249250621, 121535434, 124535434, 121535434,
2, 243199373, 92326171, 95326171, 92326171,
3, 198022430, 90504854, 93504854, 90504854,
4, 191154276, 49660117, 52660117, 49660117,
5, 180915260, 46405641, 49405641, 46405641,
6, 171115067, 58830166, 61830166, 58830166,
7, 159138663, 58054331, 61054331, 58054331,
8, 146364022, 43838887, 46838887, 43838887,
9, 141213431, 47367679, 50367679, 47367679,
10, 135534747, 39254935, 42254935, 39254935,
11, 135006516, 51644205, 54644205, 51644205,
12, 133851895, 34856694, 37856694, 34856694,
13, 115169878, 1.6e+07, 1.9e+07, 1.6e+07,
14, 107349540, 1.6e+07, 1.9e+07, 1.6e+07,
15, 102531392, 1.7e+07, 2e+07, 1.7e+07,
16, 90354753, 35335801, 38335801, 35335801,
17, 79759049, 22263006, 25263006, 22263006,
18, 78077248, 15460898, 18460898, 15460898,
19, 59128983, 24681782, 27681782, 24681782,
20, 63025520, 26369569, 29369569, 26369569,
21, 48129895, 11288129, 14288129, 11288129,
22, 51304566, 1.3e+07, 1.6e+07, 1.3e+07,
23, 155270560, 58632012, 61632012, 58632012,
24, 59373566, 10104553, 13104553, 10104553
)
# hg38 // pulled from UCSC
# hg38 = 'http://hgdownload.cse.ucsc.edu/goldenPath/hg38/database/gap.txt.gz' # note: centromere info not longer in this type of file, but in centromeres.txt.gz
# hg38_centromeres = 'http://hgdownload.cse.ucsc.edu/goldenPath/hg38/database/centromeres.txt.gz'
# hg38_centromeres = read_tsv(grch38_centromeres, col_names = c('bin', 'chrom', 'start', 'end', 'name')) %>%
# group_by(chrom) %>%
# summarize(centstart = min(start),
# centend = max(end),
# centromere = centstart + ((centend-centstart)/2)) %>%
# mutate(chrom = str_replace(chrom, 'chr', ''),
# chrom = str_replace_all(chrom, c('X' = '23', 'Y' = '24')),
# chrom = as.numeric(chrom))
#
# hg38 = extract_genome_info(hg38) %>%
# select(-matches('cent')) %>%
# left_join(., hg38_centromeres, by = 'chrom')
hg38 = tibble::tribble(
~chrom, ~size, ~centstart, ~centend, ~centromere,
1, 248956422, 122026459, 124932724, 123479591.5,
2, 242193529, 92188145, 94090557, 93139351,
3, 198295559, 90772458, 93655574, 92214016,
4, 190214555, 49712061, 51743951, 50728006,
5, 181538259, 46485900, 50059807, 48272853.5,
6, 170805979, 58553888, 59829934, 59191911,
7, 159345973, 58169653, 61528020, 59848836.5,
8, 145138636, 44033744, 45877265, 44955504.5,
9, 138394717, 43389635, 45518558, 44454096.5,
10, 133797422, 39686682, 41593521, 40640101.5,
11, 135086622, 51078348, 54425074, 52751711,
12, 133275309, 34769407, 37185252, 35977329.5,
13, 114364328, 1.6e+07, 18051248, 17025624,
14, 107043718, 1.6e+07, 18173523, 17086761.5,
15, 101991189, 17083673, 19725254, 18404463.5,
16, 90338345, 36311158, 38265669, 37288413.5,
17, 83257441, 22813679, 26616164, 24714921.5,
18, 80373285, 15460899, 20861206, 18161052.5,
19, 58617616, 24498980, 27190874, 25844927,
20, 64444167, 26436232, 30038348, 28237290,
21, 46709983, 10864560, 12915808, 11890184,
22, 50818468, 12954788, 15054318, 14004553,
23, 156040895, 58605579, 62412542, 60509060.5,
24, 57227415, 10316944, 10544039, 10430491.5
)
# Gene positions --------------------------------------------------------------------------------------------------
tumor_suppressors = fromJSON(readLines('https://legacy.oncokb.org/api/v1/genes', warn = FALSE)) %>%
filter(tsg == TRUE) %>%
select(hugoSymbol) %>%
mutate(hugoSymbol = mapvalues(hugoSymbol,
c('FAM175A', 'FAM58A', 'MRE11A', 'PARK2', 'FAM46C'), # these are not the official symbols
c('ABRAXAS1', 'CCNQ', 'MRE11', 'PRKN', 'TENT5C'))) # these are
genes_hg19 = fread('ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_29/GRCh37_mapping/gencode.v29lift37.basic.annotation.gtf.gz',
header = F, skip = 'chr1',
col.names = c('chrom', 'source', 'type', 'start', 'end', 'na_1', 'strand', 'na_2', 'info')) %>%
filter(type == 'gene', info %like% 'protein_coding') %>%
mutate(chrom = str_replace(chrom, 'chr', ''),
gene = str_extract(info, '(?<=gene_name ")[A-Za-z0-9\\.\\-]+(?=";)')) %>%
group_by(gene, chrom) %>%
summarize(start = min(start),
end = max(end)) %>%
ungroup() %>%
mutate(tsg = gene %in% tumor_suppressors$hugoSymbol)
genes_hg38 = fread('ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_29/gencode.v29.basic.annotation.gtf.gz',
header = F, skip = 'chr1',
col.names = c('chrom', 'source', 'type', 'start', 'end', 'na_1', 'strand', 'na_2', 'info')) %>%
filter(type == 'gene', info %like% 'protein_coding') %>%
mutate(chrom = str_replace(chrom, 'chr', ''),
gene = str_extract(info, '(?<=gene_name ")[A-Za-z0-9\\.\\-]+(?=";)')) %>%
group_by(gene, chrom) %>%
summarize(start = min(start),
end = max(end)) %>%
ungroup() %>%
mutate(tsg = gene %in% tumor_suppressors$hugoSymbol)
# use_data(hg18, hg19, hg38, genes_hg19, genes_hg38, internal = T, overwrite = T)
# Copy-number states ----------------------------------------------------------------------------------------------
copy_number_states = tibble::tribble(
~wgd, ~tcn, ~mcn, ~lcn, ~numeric_call, ~call,
# No genome doubling
FALSE, 0, 0, 0, -2, 'HOMDEL',
FALSE, 0, NA, NA, -2, 'HOMDEL',
FALSE, 1, 1, 0, -1, 'HETLOSS',
FALSE, 1, NA, NA, -1, 'HETLOSS',
FALSE, 2, 2, 0, -1, 'CNLOH',
FALSE, 2, NA, NA, NA, 'DIPLOID or CNLOH', #numeric_call will be NA if it cannot be unambiguously ascertained. here it could be either 0 or -1
FALSE, 3, 3, 0, 1, 'CNLOH & GAIN',
FALSE, 3, NA, NA, 1, 'GAIN (many states)',
FALSE, 4, 4, 0, 1, 'CNLOH & GAIN',
FALSE, 4, NA, NA, 1, 'GAIN (many states)',
FALSE, 5, 5, 0, 2, 'AMP (LOH)',
FALSE, 5, NA, NA, 2, 'AMP (many states)',
FALSE, 6, 6, 0, 2, 'AMP (LOH)',
FALSE, 6, NA, NA, 2, 'AMP (many states)',
FALSE, 2, 1, 1, 0, 'DIPLOID',
FALSE, 3, 2, 1, 1, 'GAIN',
FALSE, 4, 3, 1, 1, 'GAIN',
FALSE, 5, 4, 1, 2, 'AMP',
FALSE, 6, 5, 1, 2, 'AMP',
FALSE, 7, 6, 1, 2, 'AMP',
FALSE, 4, 2, 2, 1, 'TETRAPLOID',
FALSE, 5, 3, 2, 2, 'AMP',
FALSE, 6, 4, 2, 2, 'AMP',
FALSE, 7, 5, 2, 2, 'AMP',
FALSE, 8, 6, 2, 2, 'AMP',
FALSE, 6, 3, 3, 2, 'AMP (BALANCED)',
FALSE, 7, 4, 3, 2, 'AMP',
FALSE, 8, 5, 3, 2, 'AMP',
FALSE, 9, 6, 3, 2, 'AMP',
# With genome doubling
TRUE, 0, 0, 0, -2, 'HOMDEL',
TRUE, 0, NA, NA, -2, 'HOMDEL',
TRUE, 1, 1, 0, -1, 'LOSS BEFORE & AFTER',
TRUE, 1, NA, NA, -1, 'LOSS BEFORE & AFTER',
TRUE, 2, 2, 0, -1, 'LOSS BEFORE',
TRUE, 2, NA, NA, -1, 'LOSS BEFORE or DOUBLE LOSS AFTER',
TRUE, 3, 3, 0, -1, 'CNLOH BEFORE & LOSS',
TRUE, 3, NA, NA, -1, 'LOSS (many states)',
TRUE, 4, 4, 0, -1, 'CNLOH BEFORE',
TRUE, 4, NA, NA, -1, 'TETRAPLOID or CNLOH BEFORE',
TRUE, 5, 5, 0, 1, 'CNLOH BEFORE & GAIN',
TRUE, 5, NA, NA, -1, 'GAIN (many states)',
TRUE, 6, 6, 0, 2, 'AMP (LOH)',
TRUE, 6, NA, NA, 2, 'AMP (many states)',
TRUE, 2, 1, 1, -1, 'DOUBLE LOSS AFTER',
TRUE, 3, 2, 1, -1, 'LOSS AFTER',
TRUE, 4, 3, 1, -1, 'CNLOH AFTER',
TRUE, 5, 4, 1, 1, 'LOSS & GAIN',
TRUE, 6, 5, 1, 2, 'AMP',
TRUE, 7, 6, 1, 2, 'AMP',
TRUE, 4, 2, 2, 0, 'TETRAPLOID',
TRUE, 5, 3, 2, 1, 'GAIN',
TRUE, 6, 4, 2, 2, 'AMP',
TRUE, 7, 5, 2, 2, 'AMP',
TRUE, 8, 6, 2, 2, 'AMP',
TRUE, 6, 3, 3, 2, 'AMP (BALANCED)',
TRUE, 7, 4, 3, 2, 'AMP',
TRUE, 8, 5, 3, 2, 'AMP',
TRUE, 9, 6, 3, 2, 'AMP'
) %>% mutate(map_string := paste(wgd, tcn, mcn, lcn, sep = ':'))
use_data(hg18, hg19, hg38, genes_hg19, genes_hg38, copy_number_states, internal = T, overwrite = T)
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