data_examples | R Documentation |
Example data under the simple and complex graphs. Data may be continuous or discrete.
data(data_examples)
For each model, the graph and a simulated data matrix are available for both continuous and discrete data.
For continuous data with genetic information: 1000 samples in row and 6 variables in column. First two columns are the genetic variants and remaning columns are gene expression.
Continuous data without genetic information: 1000 samples in row and 8 variables in column.
Discrete data with genetic information: 1000 samples in row and 6 variables in column. First column is the genetic variant and remaning columns are the gene expression.
Discrete data without genetic information: 1000 samples in row and 5 variables in column.
Continuous data with genetic information for complex model: 1000 samples in row and 22 variables in column. First 14 column is the genetic variants and remaning columns are the genes expression.
A list that containing the numeric data matrix and components of a graph.
simple
: Simple model.
complex
: Complex model.
cont
: Continuous.
disc
: Discrete.
withGV
: With genetic information.
withoutGV
: Without genetic information.
data
: Data matrix.
graph
: Components of a graph.
Md Bahadur Badsha (mbbadshar@gmail.com)
## Not run: # Continuous data with genetic varitant (GV) # load the data data("data_examples") data <- data_examples$simple$cont$withGV$data # Extract the sample size n <- nrow(data) # Extract the node/column names V <- colnames(data) # Calculate Pearson correlation suffStat_C <- list(C = cor(data), n = n) # Infer the graph by MRPC data.mrpc.cont.withGV <- MRPC(data = data, suffStat = suffStat_C, GV = 2, FDR = 0.05, indepTest = 'gaussCItest', labels = V, FDRcontrol = 'LOND', verbose = FALSE) # Plot the results par(mfrow = c(1, 2)) # plot the true graph plot(data_examples$simple$cont$withGV$graph, main = "truth") # plot the inferred graph plot(data.mrpc.cont.withGV, main = "inferred") # Continuous data without genetic information # load the data data("data_examples") data <- data_examples$simple$cont$withoutGV$data # Extract the sample size n <- nrow(data) # Extract the node/column names V <- colnames(data) # Calculate Pearson correlation suffStat_C <- list(C = cor(data), n = n) # Infer the graph by MRPC data.mrpc.cont.withoutGV <- MRPC(data = data, suffStat = suffStat_C, GV = 0, FDR = 0.05, indepTest = 'gaussCItest', labels = V, FDRcontrol = 'LOND', verbose = FALSE) # Plot the results par(mfrow = c(1, 2)) # plot the true graph plot(data_examples$simple$cont$withoutGV$graph, main = "truth") # plot the inferred graph plot(data.mrpc.cont.withoutGV, main = "inferred") # Discrete data with genetic information # load the data data("data_examples") data <- data_examples$simple$disc$withGV$data # Extract the sample size n <- nrow(data) # Extract the node/column names V <- colnames(data) suffStat_C <- list (dm = data, adaptDF = FALSE, n.min = 1000) # Infer the graph by MRPC data.mrpc.disc.withGV <- MRPC(data = data, suffStat = suffStat_C, GV = 1, FDR = 0.05, indepTest = 'disCItest', labels = V, FDRcontrol = 'LOND', verbose = FALSE) # Plot the results par (mfrow = c(1, 2)) # plot the true graph plot(data_examples$simple$disc$withGV$graph, main = "truth") # Plot the inferred causal graph plot(data.mrpc.disc.withGV, main = "inferred") # Discrete data without genetic information # load the data data("data_examples") data <- data_examples$simple$disc$withoutGV$data # Extract the sample size n <- nrow (data) # Extract the node/column names V <- colnames(data) suffStat_C <- list (dm = data, adaptDF = FALSE, n.min = 1000) # Infer the graph by MRPC data.mrpc.disc.withoutGV <- MRPC(data = data, suffStat = suffStat_C, GV = 1, FDR = 0.05, indepTest = 'disCItest', labels = V, FDRcontrol = 'LOND', verbose = FALSE) # Plot the results par(mfrow = c(1, 2)) # plot the true graph plot(data_examples$simple$disc$withoutGV$graph, main = "truth") # plot the inferred graph plot(data.mrpc.disc.withoutGV, main = "inferred") # Continuous data with genetic information for complex model # load the data data("data_examples") # Graph without clustering plot(data_examples$complex$cont$withGV$graph) # Adjacency matrix from directed example graph Adj_directed <- as(data_examples$complex$cont$withGV$graph, "matrix") # Plot of dendrogram with modules colors of nodes PlotDendrogramObj <- PlotDendrogram(Adj_directed, minModuleSize = 5) # Visualization of inferred graph with modules colors PlotGraphWithModulesObj <- PlotGraphWithModules(Adj_directed, PlotDendrogramObj, GV = 14, node.size = 8, arrow.size = 5, label.size = 3, alpha = 1) # plot plot(PlotGraphWithModulesObj) # Run MRPC on the complex data set with ADDIS as the FDR control method. data <- data_examples$complex$cont$withGV$data n <- nrow (data) # Number of rows V <- colnames(data) # Column names # Calculate Pearson correlation suffStat_C <- list(C = cor(data), n = n) # Infer the graph by MRPC MRPC.addis <- MRPC(data, suffStat = suffStat_C, GV = 14, FDR = 0.05, indepTest = 'gaussCItest', labels = V, FDRcontrol = 'ADDIS', tau = 0.5, lambda = 0.25, verbose = FALSE) # Plot the true and inferred graphs. par(mfrow = c(1, 2)) plot(data_examples$complex$cont$withGV$graph, main = 'True graph') plot(MRPC.addis, main = 'Inferred graph') ## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.