Function to perform supervised conformational signature analysis. In this case the construction of the signatures using labels as part of the dimensionality reduction which are defined in labels. The type of the labels can be catagorical or continuous. The function returns the OPLS-DA object. The labels are allowed to contain "Unknown" values which are ignored in the analysis if the type is catagorical. If the type is continuous, the "Unknown" values should be recorded as NAs.

Function to perform supervised conformational signature analysis. In this case the construction of the signatures using labels as part of the dimensionality reduction which are defined in labels. The type of the labels can be catagorical or continuous. The function returns the OPLS-DA object. The labels are allowed to contain "Unknown" values which are ignored in the analysis if the type is catagorical. If the type is continuous, the "Unknown" values should be recorded as NAs.

supervisedCSA(
  RexDifferentialList,
  quantity = "TRE",
  states,
  labels,
  whichlabel = 1,
  whichTimepoint = 600,
  type = "catagorical",
  orthoI = 1
)

Arguments

RexDifferentialList

A list of RexDifferential objects

quantity

The quantity to use for the analysis. Default is "TRE"

states

The state name to use for the analysis. e.g. ligand used in differential analysis

labels

The labels to use for the analysis. Construct labels carefully using example below. The labels should be a data frame with the rownames as the states and the columns as the labels to use for the analysis.

whichlabel

The column in the labels to use for the analysis. Default is 1

whichTimepoint

The timepoint to use for the analysis. Default is 600

type

The type of the labels. Default is "catagorical" but could be "continuous". If "continuous" the "Unknown" values should be recorded as NAs.

orthoI

The number of orthogonal components to use in the OPLS-DA analysis. Default is 1

Value

An OPLS-DA object

Examples


# Construct labels carefully using known properties of the states (Ligands)
# first a catagorical example
library("RexMS")
data("out_lxr_compound_proccessed")
data("LXRalpha_compounds")

states <- names(LXRalpha_compounds) 

labels <- data.frame(ABCA1 = rep("Unknown", length(states)),
lipogenic = rep("Unknown", length(states)))
rownames(labels) <- states

labels$ABCA1[rownames(labels) %in% c("LXR.623", "AZ9", "AZ8", "AZ5")] <- "low"
labels$ABCA1[rownames(labels) %in% c("Az1", "AZ2", "AZ3", "AZ4", "AZ6",
                                    "AZ7", "AZ876", "T0.901317", "WAY.254011",
                                    "F1", "GW3965", "BMS.852927")] <- "high"


labels$lipogenic[rownames(labels) %in% c("AZ6", "AZ7", "AZ9",
                                        "AZ8", "GW3965", "BMS.852927",
                                        "LXR.623")] <- "Non-Lipogenic"

labels$lipogenic[rownames(labels) %in% c("AZ876", "AZ1",
                                        "T0.901317", "F1", "WAY.254011")] <- "Lipogenic"

labels$ABCA1 <- factor(labels$ABCA1,
                       levels = c("low", "high", "Unknown"))
labels$lipogenic <- factor(labels$lipogenic,
                          levels = c("Non-Lipogenic", "Lipogenic", "Unknown"))

# First using ABCA1 as an example

scsa <- supervisedCSA(RexDifferentialList = out_lxr_compound_proccessed,
                        quantity = "TRE",
                        states = states,
                        labels = labels,
                        whichlabel = "ABCA1",
                        whichTimepoint = 600,
                        orthoI = 1)
#> Warning: OPLS: number of predictive components ('predI' argument) set to 1
#> Warning: The variance of the 263 following variables is less than 2.2e-16 in the full or partial (cross-validation) dataset: these variables will be removed:
#> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 204, 208, 214, 215, 234, 235, 237, 239, 242, 244, 258, 259, 260, 261, 262, 268, 272, 276, 279, 280, 291, 300, 308, 317, 318, 319, 320, 332, 333, 334, 335, 336, 338, 342, 350, 357, 358, 359, 360, 361, 366, 370, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 428, 436, 437
#> OPLS-DA
#> 14 samples x 184 variables and 1 response
#> standard scaling of predictors and response(s)
#> 263 excluded variables (near zero variance)
#>       R2X(cum) R2Y(cum) Q2(cum) RMSEE pre ort pR2Y  pQ2
#> Total        1    0.398   0.358 0.395   1   1  0.1 0.05

                        
# Now using lipogenic as an example

scsa2 <- supervisedCSA(RexDifferentialList = out_lxr_compound_proccessed,
                       quantity = "TRE",
                       states = states,
                       labels = labels,
                       whichlabel = "lipogenic",
                       whichTimepoint = 600,
                       orthoI = 1)
#> Warning: OPLS: number of predictive components ('predI' argument) set to 1
#> Warning: The variance of the 263 following variables is less than 2.2e-16 in the full or partial (cross-validation) dataset: these variables will be removed:
#> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 204, 208, 214, 215, 234, 235, 237, 239, 242, 244, 258, 259, 260, 261, 262, 268, 272, 276, 279, 280, 291, 300, 308, 317, 318, 319, 320, 332, 333, 334, 335, 336, 338, 342, 350, 357, 358, 359, 360, 361, 366, 370, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 428, 436, 437
#> OPLS-DA
#> 11 samples x 184 variables and 1 response
#> standard scaling of predictors and response(s)
#> 263 excluded variables (near zero variance)
#>       R2X(cum) R2Y(cum) Q2(cum) RMSEE pre ort pR2Y  pQ2
#> Total        1     0.12 -0.0506 0.529   1   1 0.75 0.35


# Now using a continuous example, add additional annotation to the labels
# Here we use the ED50 values of the ligands, using the log values to 
# because of the large range

labels$ED50 <- NA

labels[, "ED50"] <- log(c(4.11, NA, NA, 0.956, NA, 9.64, 1.49,
                         5.65, 0.969, 2.10, 11.3, NA, 31.5, 341, 32.2, 17.2,
                          NA))
                          
scsa3 <- supervisedCSA(RexDifferentialList = out_lxr_compound_proccessed,
                       quantity = "TRE",
                       states = states,
                       labels = labels,
                       whichlabel = "ED50",
                       whichTimepoint = 600,
                       type = "continuous",
                       orthoI = 1)             
#> Warning: OPLS: number of predictive components ('predI' argument) set to 1
#> Warning: The variance of the 263 following variables is less than 2.2e-16 in the full or partial (cross-validation) dataset: these variables will be removed:
#> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 204, 208, 214, 215, 234, 235, 237, 239, 242, 244, 258, 259, 260, 261, 262, 268, 272, 276, 279, 280, 291, 300, 308, 317, 318, 319, 320, 332, 333, 334, 335, 336, 338, 342, 350, 357, 358, 359, 360, 361, 366, 370, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 428, 436, 437
#> OPLS
#> 12 samples x 184 variables and 1 response
#> standard scaling of predictors and response(s)
#> 263 excluded variables (near zero variance)
#>       R2X(cum) R2Y(cum) Q2(cum) RMSEE pre ort pR2Y  pQ2
#> Total    0.997    0.363  0.0176  1.52   1   1 0.35 0.35