data_without_outliers: Example data without outliers

In audreyqyfu/mrpc: PC Algorithm with the Principle of Mendelian Randomization

Example data without outliers

Description

The data contain two genotype nodes, V1 and V2, and three phenotype nodes, T1, T2 and T3. The code below compares the performance of MRPC, pc pc.stable, mmpc, mmhc, and hc on this data set.

Value

Matrix

Author(s)

Md Bahadur Badsha ([e-mail])

Examples

  ## Not run: 
# Load packages

library(MRPC)     # MRPC
library(pcalg)    # pc
library(bnlearn)  # pc.stable, mmpc, mmhc, and hc

# Truth without outlier
tarmat <- matrix(0,
                 nrow = ncol(data_with_outliers),
                 ncol = ncol(data_with_outliers))
                
colnames(tarmat) <- colnames(data_with_outliers)
rownames(tarmat) <- colnames(data_with_outliers)

tarmat[1,2] <- 1
tarmat[2,1] <- 1
tarmat[1,3] <- 1
tarmat[4,3] <- 1
tarmat[4,5] <- 1

Truth <- as(tarmat,
            "graphNEL")

# Data without outliers
n <- nrow(data_without_outliers)     # Number of rows
V <- colnames(data_without_outliers) # Column names

# Calculate Pearson correlation
suffStat_C1 <- list(C = cor(data_without_outliers),
                    n = n)

# Infer the graph by MRPC
MRPC.fit_withoutoutliers <- MRPC (data_without_outliers, 
                                  suffStat = suffStat_C1, 
                                  GV = 2, 
                                  FDR = 0.05, 
                                  indepTest ='gaussCItest', 
                                  labels = V, 
                                  FDRcontrol = 'LOND', 
                                  verbose = FALSE)

# Infer the graph by pc with Pearson correlation
pc.fit_withoutoutliers <- pc(suffStat = suffStat_C1,
                            indepTest = gaussCItest,
                            alpha = 0.05, 
                            labels = V,
                            verbose = FALSE)

# arcs not to be included from gene expression to genotype for blacklist argument 
# in pc.stable and mmpc

GV <- 2
to <- rep (colnames (data_without_outliers)[1:GV], each = (ncol (data_without_outliers) - GV))
from <- rep (colnames (data_without_outliers)[(GV + 1):ncol (data_without_outliers)], GV)
bl <- cbind (from, to)

# Infer the graph by pc.stable
pc.stable_withoutoutliers <- pc.stable (data.frame (data_without_outliers), 
                                        blacklist = bl, 
                                        alpha = 0.05, 
                                        debug = FALSE, 
                                        undirected = FALSE)
# Infer the graph by mmpc
mmpc_withoutoutliers <- mmpc (data.frame (data_without_outliers), 
                              blacklist = bl, 
                              alpha = 0.05, 
                              debug = FALSE, 
                              undirected = FALSE)
# Infer the graph by mmhc
mmhc_withoutoutliers <- mmhc (data.frame (data_without_outliers), 
                              blacklist = bl, 
                              debug = FALSE)
# Infer the graph by hc
hc_withoutoutliers <- hc (data.frame (data_without_outliers), 
                          blacklist = bl, 
                          debug = FALSE)
# True graph
plot (Truth, main = "Truth")

#-------------
# Plot inferred graphs
par (mfrow = c (2,3))

# Data without outliers
# Inference with Pearson correlation
plot (MRPC.fit_withoutoutliers, main = "MRPC")
plot (pc.fit_withoutoutliers, main = "pc")
graphviz.plot (pc.stable_withoutoutliers, main = "pc.stable")
graphviz.plot (mmpc_withoutoutliers, main = "mmpc")
graphviz.plot (mmhc_withoutoutliers, main = "mmhc")
graphviz.plot (hc_withoutoutliers, main = "hc")

## End(Not run)
  

audreyqyfu/mrpc documentation built on April 17, 2022, 7:35 a.m.