Calculate a metric for combination data.
Source:R/combinations-calculate_matrix_metric.R
calculate_matrix_metric.RdCalculate a metric based off of single-agent values in combination screens.
Usage
calculate_HSA(sa1, series_id1, sa2, series_id2, metric)
calculate_Bliss(
sa1,
series_id1,
sa2,
series_id2,
metric,
measured_col = "smooth"
)
.calculate_matrix_metric(
sa1,
series_id1,
sa2,
series_id2,
metric,
FXN,
measured_col = "x"
)Arguments
- sa1
data.table containing single agent data where entries in
series_id2are all0. Columns of the data.table include identifiers and themetricof interest. Metric is stored in the 'x' column.- series_id1
String representing the column within
sa1that represents id1.- sa2
data.table containing single agent data where entries in
series_id1are all0. Columns of the data.table include identifiers and themetricof interest.n Metric is stored in the 'x' column.- series_id2
String representing the column within
sa2that represents id2.- metric
String specifying the metric of interest. Usually either 'GRvalue' or 'RelativeViability'.
- measured_col
String specyfying the measured colname.
- FXN
Function to apply to the single-agent fits to calculate a metric.
Value
data.table containing a single row for every unique combination of the two series identifiers and the corresponding calculated metric for each row.
Details
calculate_HSA takes the minimum of the two single agents readouts.
calculate_Bliss performs Bliss additivity calculation based on the
single agent effects, defined as 1-x for the corresponding
normalization.
See https://www.sciencedirect.com/science/article/pii/S1359644619303460?via%3Dihub#tb0005
for more details.
Examples
n <- 10
sa1 <- data.table::data.table(conc = seq(n), conc2 = rep(0, n), smooth = seq(n))
sa2 <- data.table::data.table(conc = rep(0, n), conc2 = seq(n), smooth = seq(n))
calculate_HSA(sa1, "conc", sa2, "conc2", "smooth")
#> conc conc2 metric1 metric2 metric
#> <int> <int> <int> <int> <int>
#> 1: 1 1 1 1 1
#> 2: 2 1 2 1 1
#> 3: 3 1 3 1 1
#> 4: 4 1 4 1 1
#> 5: 5 1 5 1 1
#> 6: 6 1 6 1 1
#> 7: 7 1 7 1 1
#> 8: 8 1 8 1 1
#> 9: 9 1 9 1 1
#> 10: 10 1 10 1 1
#> 11: 1 2 1 2 1
#> 12: 2 2 2 2 2
#> 13: 3 2 3 2 2
#> 14: 4 2 4 2 2
#> 15: 5 2 5 2 2
#> 16: 6 2 6 2 2
#> 17: 7 2 7 2 2
#> 18: 8 2 8 2 2
#> 19: 9 2 9 2 2
#> 20: 10 2 10 2 2
#> 21: 1 3 1 3 1
#> 22: 2 3 2 3 2
#> 23: 3 3 3 3 3
#> 24: 4 3 4 3 3
#> 25: 5 3 5 3 3
#> 26: 6 3 6 3 3
#> 27: 7 3 7 3 3
#> 28: 8 3 8 3 3
#> 29: 9 3 9 3 3
#> 30: 10 3 10 3 3
#> 31: 1 4 1 4 1
#> 32: 2 4 2 4 2
#> 33: 3 4 3 4 3
#> 34: 4 4 4 4 4
#> 35: 5 4 5 4 4
#> 36: 6 4 6 4 4
#> 37: 7 4 7 4 4
#> 38: 8 4 8 4 4
#> 39: 9 4 9 4 4
#> 40: 10 4 10 4 4
#> 41: 1 5 1 5 1
#> 42: 2 5 2 5 2
#> 43: 3 5 3 5 3
#> 44: 4 5 4 5 4
#> 45: 5 5 5 5 5
#> 46: 6 5 6 5 5
#> 47: 7 5 7 5 5
#> 48: 8 5 8 5 5
#> 49: 9 5 9 5 5
#> 50: 10 5 10 5 5
#> 51: 1 6 1 6 1
#> 52: 2 6 2 6 2
#> 53: 3 6 3 6 3
#> 54: 4 6 4 6 4
#> 55: 5 6 5 6 5
#> 56: 6 6 6 6 6
#> 57: 7 6 7 6 6
#> 58: 8 6 8 6 6
#> 59: 9 6 9 6 6
#> 60: 10 6 10 6 6
#> 61: 1 7 1 7 1
#> 62: 2 7 2 7 2
#> 63: 3 7 3 7 3
#> 64: 4 7 4 7 4
#> 65: 5 7 5 7 5
#> 66: 6 7 6 7 6
#> 67: 7 7 7 7 7
#> 68: 8 7 8 7 7
#> 69: 9 7 9 7 7
#> 70: 10 7 10 7 7
#> 71: 1 8 1 8 1
#> 72: 2 8 2 8 2
#> 73: 3 8 3 8 3
#> 74: 4 8 4 8 4
#> 75: 5 8 5 8 5
#> 76: 6 8 6 8 6
#> 77: 7 8 7 8 7
#> 78: 8 8 8 8 8
#> 79: 9 8 9 8 8
#> 80: 10 8 10 8 8
#> 81: 1 9 1 9 1
#> 82: 2 9 2 9 2
#> 83: 3 9 3 9 3
#> 84: 4 9 4 9 4
#> 85: 5 9 5 9 5
#> 86: 6 9 6 9 6
#> 87: 7 9 7 9 7
#> 88: 8 9 8 9 8
#> 89: 9 9 9 9 9
#> 90: 10 9 10 9 9
#> 91: 1 10 1 10 1
#> 92: 2 10 2 10 2
#> 93: 3 10 3 10 3
#> 94: 4 10 4 10 4
#> 95: 5 10 5 10 5
#> 96: 6 10 6 10 6
#> 97: 7 10 7 10 7
#> 98: 8 10 8 10 8
#> 99: 9 10 9 10 9
#> 100: 10 10 10 10 10
#> conc conc2 metric1 metric2 metric
n <- 10
sa1 <- data.table::data.table(conc = seq(n), conc2 = rep(0, n), smooth = seq(n))
sa2 <- data.table::data.table(conc = rep(0, n), conc2 = seq(n), smooth = seq(n))
calculate_Bliss(sa1, "conc", sa2, "conc2", "smooth")
#> conc conc2 metric1 metric2 metric
#> <int> <int> <int> <int> <int>
#> 1: 1 1 1 1 1
#> 2: 2 1 2 1 2
#> 3: 3 1 3 1 3
#> 4: 4 1 4 1 4
#> 5: 5 1 5 1 5
#> 6: 6 1 6 1 6
#> 7: 7 1 7 1 7
#> 8: 8 1 8 1 8
#> 9: 9 1 9 1 9
#> 10: 10 1 10 1 10
#> 11: 1 2 1 2 2
#> 12: 2 2 2 2 4
#> 13: 3 2 3 2 6
#> 14: 4 2 4 2 8
#> 15: 5 2 5 2 10
#> 16: 6 2 6 2 12
#> 17: 7 2 7 2 14
#> 18: 8 2 8 2 16
#> 19: 9 2 9 2 18
#> 20: 10 2 10 2 20
#> 21: 1 3 1 3 3
#> 22: 2 3 2 3 6
#> 23: 3 3 3 3 9
#> 24: 4 3 4 3 12
#> 25: 5 3 5 3 15
#> 26: 6 3 6 3 18
#> 27: 7 3 7 3 21
#> 28: 8 3 8 3 24
#> 29: 9 3 9 3 27
#> 30: 10 3 10 3 30
#> 31: 1 4 1 4 4
#> 32: 2 4 2 4 8
#> 33: 3 4 3 4 12
#> 34: 4 4 4 4 16
#> 35: 5 4 5 4 20
#> 36: 6 4 6 4 24
#> 37: 7 4 7 4 28
#> 38: 8 4 8 4 32
#> 39: 9 4 9 4 36
#> 40: 10 4 10 4 40
#> 41: 1 5 1 5 5
#> 42: 2 5 2 5 10
#> 43: 3 5 3 5 15
#> 44: 4 5 4 5 20
#> 45: 5 5 5 5 25
#> 46: 6 5 6 5 30
#> 47: 7 5 7 5 35
#> 48: 8 5 8 5 40
#> 49: 9 5 9 5 45
#> 50: 10 5 10 5 50
#> 51: 1 6 1 6 6
#> 52: 2 6 2 6 12
#> 53: 3 6 3 6 18
#> 54: 4 6 4 6 24
#> 55: 5 6 5 6 30
#> 56: 6 6 6 6 36
#> 57: 7 6 7 6 42
#> 58: 8 6 8 6 48
#> 59: 9 6 9 6 54
#> 60: 10 6 10 6 60
#> 61: 1 7 1 7 7
#> 62: 2 7 2 7 14
#> 63: 3 7 3 7 21
#> 64: 4 7 4 7 28
#> 65: 5 7 5 7 35
#> 66: 6 7 6 7 42
#> 67: 7 7 7 7 49
#> 68: 8 7 8 7 56
#> 69: 9 7 9 7 63
#> 70: 10 7 10 7 70
#> 71: 1 8 1 8 8
#> 72: 2 8 2 8 16
#> 73: 3 8 3 8 24
#> 74: 4 8 4 8 32
#> 75: 5 8 5 8 40
#> 76: 6 8 6 8 48
#> 77: 7 8 7 8 56
#> 78: 8 8 8 8 64
#> 79: 9 8 9 8 72
#> 80: 10 8 10 8 80
#> 81: 1 9 1 9 9
#> 82: 2 9 2 9 18
#> 83: 3 9 3 9 27
#> 84: 4 9 4 9 36
#> 85: 5 9 5 9 45
#> 86: 6 9 6 9 54
#> 87: 7 9 7 9 63
#> 88: 8 9 8 9 72
#> 89: 9 9 9 9 81
#> 90: 10 9 10 9 90
#> 91: 1 10 1 10 10
#> 92: 2 10 2 10 20
#> 93: 3 10 3 10 30
#> 94: 4 10 4 10 40
#> 95: 5 10 5 10 50
#> 96: 6 10 6 10 60
#> 97: 7 10 7 10 70
#> 98: 8 10 8 10 80
#> 99: 9 10 9 10 90
#> 100: 10 10 10 10 100
#> conc conc2 metric1 metric2 metric