Split raw data into list based on the data types
Arguments
- dt
data.table of raw drug response data containing both treated and untreated values with column specified in
type_colargument.- type_col
string with column names in
dtwith info about data type. Defaults to"type".
Author
Bartosz Czech bartosz.czech@contractors.roche.com
Examples
cell_lines <- gDRtestData::create_synthetic_cell_lines()
drugs <- gDRtestData::create_synthetic_drugs()
dt_layout <- drugs[4:6, as.list(cell_lines[7:8, ]), names(drugs)]
dt_layout <- gDRtestData::add_data_replicates(dt_layout)
dt_layout <- gDRtestData::add_concentration(
dt_layout,
concentrations = 10 ^ (seq(-3, .5, .5))
)
dt_2 <-
drugs[c(21, 26), as.list(cell_lines[which(cell_lines$clid %in% dt_layout$clid)]), names(drugs)]
dt_2 <- gDRtestData::add_data_replicates(dt_2)
dt_2 <- gDRtestData::add_concentration(
dt_2,
concentrations = 10 ^ (seq(-3, .5, .5))
)
colnames(dt_2)[colnames(dt_2) %in% c(colnames(drugs), "Concentration")] <-
paste0(
colnames(dt_2)[colnames(dt_2) %in% c(colnames(drugs), "Concentration")],
"_2"
)
dt_layout_2 <- dt_layout[dt_2, on = intersect(names(dt_layout), names(dt_2)),
allow.cartesian = TRUE]
dt_merged_data <- gDRtestData::generate_response_data(dt_layout_2, 0)
dt <- identify_data_type(dt_merged_data)
split_raw_data(dt)
#> $combination
#> Barcode Gnumber DrugName drug_moa clid CellLineName Tissue
#> <char> <char> <char> <char> <char> <char> <char>
#> 1: plate_1 G00004 drug_004 moa_A CL00016 cellline_GB tissue_y
#> 2: plate_1 G00005 drug_005 moa_A CL00016 cellline_GB tissue_y
#> 3: plate_1 G00006 drug_006 moa_A CL00016 cellline_GB tissue_y
#> 4: plate_1 G00004 drug_004 moa_A CL00016 cellline_GB tissue_y
#> 5: plate_1 G00005 drug_005 moa_A CL00016 cellline_GB tissue_y
#> ---
#> 3596: plate_3 G00026 drug_026 moa_E CL00017 cellline_HB tissue_y
#> 3597: plate_3 G00026 drug_026 moa_E CL00017 cellline_HB tissue_y
#> 3598: plate_3 G00026 drug_026 moa_E CL00017 cellline_HB tissue_y
#> 3599: plate_3 G00026 drug_026 moa_E CL00017 cellline_HB tissue_y
#> 3600: plate_3 G00026 drug_026 moa_E CL00017 cellline_HB tissue_y
#> ReferenceDivisionTime Concentration Gnumber_2 DrugName_2 drug_moa_2
#> <num> <num> <char> <char> <char>
#> 1: 46 0.001000000 G00021 drug_021 moa_D
#> 2: 46 0.001000000 G00021 drug_021 moa_D
#> 3: 46 0.001000000 G00021 drug_021 moa_D
#> 4: 46 0.003162278 G00021 drug_021 moa_D
#> 5: 46 0.003162278 G00021 drug_021 moa_D
#> ---
#> 3596: 50 3.162277660 vehicle vehicle vehicle
#> 3597: 50 3.162277660 vehicle vehicle vehicle
#> 3598: 50 3.162277660 vehicle vehicle vehicle
#> 3599: 50 3.162277660 vehicle vehicle vehicle
#> 3600: 50 3.162277660 vehicle vehicle vehicle
#> Concentration_2 ReadoutValue BackgroundValue Duration record_id
#> <num> <num> <num> <num> <int>
#> 1: 0.001 98.29017 0 72 727
#> 2: 0.001 95.09049 0 72 728
#> 3: 0.001 99.79002 0 72 729
#> 4: 0.001 78.78972 0 72 730
#> 5: 0.001 55.22270 0 72 731
#> ---
#> 3596: 0.000 100.00000 0 72 3572
#> 3597: 0.000 100.00000 0 72 3573
#> 3598: 0.000 100.00000 0 72 3574
#> 3599: 0.000 100.00000 0 72 3575
#> 3600: 0.000 100.00000 0 72 3576
#>
#> $`single-agent`
#> Barcode Gnumber DrugName drug_moa clid CellLineName Tissue
#> <char> <char> <char> <char> <char> <char> <char>
#> 1: plate_1 G00004 drug_004 moa_A CL00016 cellline_GB tissue_y
#> 2: plate_1 G00005 drug_005 moa_A CL00016 cellline_GB tissue_y
#> 3: plate_1 G00006 drug_006 moa_A CL00016 cellline_GB tissue_y
#> 4: plate_1 G00004 drug_004 moa_A CL00016 cellline_GB tissue_y
#> 5: plate_1 G00005 drug_005 moa_A CL00016 cellline_GB tissue_y
#> ---
#> 1292: plate_3 vehicle vehicle vehicle CL00017 cellline_HB tissue_y
#> 1293: plate_3 vehicle vehicle vehicle CL00017 cellline_HB tissue_y
#> 1294: plate_3 vehicle vehicle vehicle CL00017 cellline_HB tissue_y
#> 1295: plate_3 vehicle vehicle vehicle CL00017 cellline_HB tissue_y
#> 1296: plate_3 vehicle vehicle vehicle CL00017 cellline_HB tissue_y
#> ReferenceDivisionTime Concentration ReadoutValue BackgroundValue Duration
#> <num> <num> <num> <num> <num>
#> 1: 46 0.001000000 98.29017 0 72
#> 2: 46 0.001000000 95.09049 0 72
#> 3: 46 0.001000000 99.79002 0 72
#> 4: 46 0.003162278 78.78972 0 72
#> 5: 46 0.003162278 55.22270 0 72
#> ---
#> 1292: 50 0.000000000 100.00000 0 72
#> 1293: 50 0.000000000 100.00000 0 72
#> 1294: 50 0.000000000 100.00000 0 72
#> 1295: 50 0.000000000 100.00000 0 72
#> 1296: 50 0.000000000 100.00000 0 72
#> record_id
#> <int>
#> 1: 7
#> 2: 8
#> 3: 9
#> 4: 10
#> 5: 11
#> ---
#> 1292: 692
#> 1293: 693
#> 1294: 694
#> 1295: 695
#> 1296: 696
#>
conc <- rep(seq(0, 0.3, 0.1), 2)
ctrl_dt <- S4Vectors::DataFrame(
ReadoutValue = c(2, 2, 1, 1, 2, 1),
Concentration = rep(0, 6),
masked = FALSE,
DrugName = rep(c("DRUG_10", "vehicle", "DRUG_8"), 2),
CellLineName = "CELL1"
)
trt_dt <- S4Vectors::DataFrame(
ReadoutValue = rep(seq(1, 4, 1), 2),
Concentration = conc,
masked = rep(FALSE, 8),
DrugName = c("DRUG_10", "DRUG_8"),
CellLineName = "CELL1"
)
input_dt <- data.table::as.data.table(rbind(ctrl_dt, trt_dt))
input_dt$Duration <- 72
input_dt$CorrectedReadout2 <- input_dt$ReadoutValue
split_dt <- identify_data_type(input_dt)
split_raw_data(split_dt)
#> $`single-agent`
#> ReadoutValue Concentration masked DrugName CellLineName Duration
#> <num> <num> <lgcl> <char> <char> <num>
#> 1: 2 0.1 FALSE DRUG_8 CELL1 72
#> 2: 3 0.2 FALSE DRUG_10 CELL1 72
#> 3: 4 0.3 FALSE DRUG_8 CELL1 72
#> 4: 2 0.1 FALSE DRUG_8 CELL1 72
#> 5: 3 0.2 FALSE DRUG_10 CELL1 72
#> 6: 4 0.3 FALSE DRUG_8 CELL1 72
#> 7: 2 0.0 FALSE DRUG_10 CELL1 72
#> 8: 2 0.0 FALSE vehicle CELL1 72
#> 9: 1 0.0 FALSE DRUG_8 CELL1 72
#> 10: 1 0.0 FALSE DRUG_10 CELL1 72
#> 11: 2 0.0 FALSE vehicle CELL1 72
#> 12: 1 0.0 FALSE DRUG_8 CELL1 72
#> 13: 1 0.0 FALSE DRUG_10 CELL1 72
#> 14: 1 0.0 FALSE DRUG_10 CELL1 72
#> CorrectedReadout2 record_id
#> <num> <int>
#> 1: 2 8
#> 2: 3 9
#> 3: 4 10
#> 4: 2 12
#> 5: 3 13
#> 6: 4 14
#> 7: 2 1
#> 8: 2 2
#> 9: 1 3
#> 10: 1 4
#> 11: 2 5
#> 12: 1 6
#> 13: 1 7
#> 14: 1 11
#>