Function to plot the loading of the supervised conformational signature analysis results

Function to plot the loading of the supervised conformational signature analysis results

plotLoadingSCSA(
  states.plsda,
  labels,
  whichlabel = 1,
  whichLoading = "predictive",
  threshold = 0.125
)

Arguments

states.plsda

The OPLS-DA object from the supervisedCSA function

labels

The labels to use for the plot. 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 colouring of the labels.

whichlabel

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

whichLoading

The type of loading to plot. Default is "predictive" but could be "orthogonal"

threshold

The threshold to use for the critical residues. Default is 0.125.

Value

A ggplot 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 0.15 0.1


plotLoadingSCSA(states.plsda = scsa,
               labels = labels,
               whichlabel = "ABCA1",
               whichLoading = "predictive",
               threshold = 0.125)