MRPC: Infer a causal network using the MRPC algorithm
In audreyqyfu/mrpc2: PC Algorithm with the Principle of Mendelian Randomization
MRPC R Documentation
Infer a causal network using the MRPC algorithm
Description
This function is used to infer a causal network (or a causal graph) with directed and undirected edges from observational data. It implements the MRPC (PC with the principle of Mendelian randomization) algorithm described in Badsha and Fu (2019) and Badsha et al.(2021), and the implementation is based on the pc algorithm in the pcalg package. The MRPC algorithm contains four major updates over the pc algorithm: (i) incorporating a sequential testing method to control the False Discovery Rate (FDR), (ii) improved v-structure identification; (iii) allowing for calculation of robust correlation to reduce the impact of outliers, and (iv) a new procedure for edge orientation based on the principle of Mendelian randomization (PMR) (Badsha and Fu, 2019; Badsha et al., 2021). See details below.
Usage
MRPC(data, suffStat, GV, FDR = 0.05, alpha = 0.05, indepTest, labels, p,
FDRcontrol = c("LOND", "ADDIS", "NONE"), tau = 0.5, lambda = 0.25,
verbose = FALSE)
Arguments
This function is based on the pc function in the pcalg package. Therefore, many arguments are similar to those in pc.
data
Data matrix, where the rows are observations and the columns are features (i.e., variables, or nodes). If genetic variants (GVs) are included, then the columns start from GVs (e.g., single-nucleotide polymorphisms, or SNPs; insertions and deletions, or indels; copy number variation, or CNVs; and expression quantitative trait loci, or eQTLs to genes), and followed by phenotypes (e.g., gene expression). For example, if the data contains one GV, then the first column of the input matrix is the GV and the remaining columns are the gene expression data.
suffStat
A list of sufficient statistics. When the data is continuous or can be viewed as continuous, this list contains the correlation matrix of the data and the sample size, which are the necessary elements for the conditional independence tests in gaussCItest. When the data is discrete, this list contains the entire dataset.
GV
The number of genetic variants (SNPs/indels/CNV/eQTLs) in the input data matrix. For example, if the data has one variant, which is in the first column, then GV = 1. If there are two variants, which are in the first and second Columns, then GV = 2. If there are no variants, then GV = 0.
FDR
Need to specify the desired level of the overall false discovery rate.
alpha
A scalar in [0,1]. The type I error rate for each individual test.
indepTest
A function for testing conditional independence. It is used to test the conditional independence of x and y given S, called as indepTest(x, y, S, suffStat). Where, x and y are variables, and S is a vector, possibly empty, of variables. suffStat is a list, see the argument above. The return value of indepTest is the p-value of the test for conditional independence. The different indepTest is used for different data types, for example, Gaussian data = gaussCItest, Discrete data = disCItest and Binary data = binCItest. See help(gaussCItest)
The ci.test (Marco Scutari, 2010) is also used for testing conditional independence and return value of indepTest is the p-value. If none is specified, the default test statistic is the mutual information for categorical variables, the Jonckheere-Terpstra test for ordered factors and the linear correlation for continuous variables.See help(ci.test)
Remember that need to specify the which indepTest would like for independence testing. For example, if you would like to use gaussCItest you would type indepTest = 'gaussCItest' into the function otherwise indepTest = 'citest'. Note that, we used gaussCItest to compare our MRPC with the existing pc, because of ci.test is not robust. See details in example.
labels
A character vector of variable, or node, names. All variables are denoted in column in the input matrix.
p
(optional) The number of variables, or nodes. May be specified if the labels are not provided, in which case the labels are set to 1:p.
FDRcontrol
A character string specifying whether online FDR control should be applied, and if so, what method to use. The two FDR control options are "LOND" (Javanmard and Montanari, 2015) or "ADDIS" (Tian and Ramdas, 2019). If "NONE" is specified, the type I error rate "alpha" will be used for each test.
tau
(optional) A scalar between 0 and 1. This value is used to determine if a p-value will be considered for testing, when FDRcontrol="ADDIS". For example, if a p-value is greater than tau then it is discarded and no test will be performed.
lambda
(optional) A scalar between 0 and tau. This value is used to determine if a p-value is a candidate for rejection, when FDRcontrol="ADDIS". For example, if a p-value is smaller than lambda then it can be rejected when testing the hypothesis (if the p-value is smaller than alphai).
verbose
(optional) If TRUE, detailed output is provided. The default is FALSE which does not print output details.
Details
The PC algorithm is computationally efficient for learning a directed acyclic graph (Spirtes et al., 2000). Several variants of the original PC algorithms are available (Kalisch and Buhlmann, 2007; Kalisch et al., 2012). Similar to these PC-like algorithms, our MRPC algorithm also contains two main steps:
Step-1: Inference of the graph skeleton. A graph skeleton is an undirected graph with edges that are supported by the data. Similar to existing PC-like algorithms, we perform statistical tests for marginal and conditional independence tests. If the null hypothesis of independence is not rejected, then the corresponding edge is removed and never tested again.
However, unlike existing algorithms, which control only the type I error rate for each individual test, MRPC implements the LOND (Level On the Number of Discoveries) method (Javanmard and Montanari, 2015), which is a sequential hypothesis testing procedure and sets the significance level for each test based on the number of discoveries (i.e., rejections), to control the overall false discovery rate (FDR). See ModiSkeleton.
Genome data may have outliers that drastically alter the topology of the inferred graph. MRPC allows for the estimate of robust correlation, which may be the substitute of the Pearson correlation as the input to graph inference (Badsha et al., 2013).
Step-2: Edge orientation. With the graph skeleton inferred from Step 1, we orient each edge that is present in the graph. MRPC is fundamentally different from algorithms in the pcalg (Kalisch and Buhlmann, 2007; Kalisch et al., 2012) and bnlearn (Scutari, 2010) packages in the following ways (see EdgeOrientation):
(i) When analyzing genomic data, genetic variants provide additional information that helps distinguish the casual direction between two genes. Our MRPC algorithm incorporates the principle of Mendelian randomization in graph inference, which greatly reduces the space of possible graphs and increases the inference efficiency.
(ii) Next or if the input is not genomic data, we search for possible triplets
that may form a v-structure (e.g.,X–>Y<–Z). We check conditional test results
from step I to see whether X and Z are independent given Y. If they are, then
this is not a v-structure; alternative models for the triplet may be any of the
following three Markov equivalent graphs: X–>Y–>Z, X<–Y<–Z, and
X<–Y–>Z. If this test is not performed in the first step, we conduct it in
this step. This step improves the accuracy of the v-structure identification over
existing methods.
(iii) If there are undirected edges after steps (i) and (ii), we examine iteratively triplets of nodes with at least one directed edge and no more than one undirected edge. We check the marginal and conditional test results from Step I to determine which of the basic models is consistent with the test results. It is plausible that some undirected edges cannot be oriented, and we leave them as undirected.
Value
An object of class that contains an estimate of the equivalence class of the underlying DAG.
call:a call object: the original function call.
n:The sample
size used to estimate the graph.
max.ord:The
maximum size of the conditioning set used
in the conditional independence tests in
the first part of the algorithm.
n.edgetests:The number of
conditional independence tests performed by
the first part of the algorithm.
sepset:Separation sets.
pMax:A numeric square matrix
, where the (i, j)th entry contains the
maximal p-value of all conditional
independence tests for edge i–j.
graph:Object of class "graph":
the undirected or partially directed graph that was estimated.
zMin:Deprecated.
test:The number of tests that have been performed.
alpha:The level of significance
for the current test.
R:All of the decisions made from tests that have been performed. A 1 indicates a rejected null hypothesis and 0 represents a null hypothesis that was not rejected.
K:The total number of rejections.
pval:A vector of p-values calculated for each test.
normalizer:The value that ensures the vector gammai sums to one.
exponent:The exponent of the p-series used to calculate each value of the gammai vector.
alphai:A vector containing the alpha value calculated for each test.
kappai:A vector containing the iteration at which each rejected test occurs.
kappai_star:Each element of this vector is the sum of the Si vector up to the iteration at which each rejection occurs.
Ci:A vector indicating whether or not a p-value is a candidate for being rejected.
Si:A vector indicating whether or not a p-value was discarded.
Ci_plus:Each element of this vector represents the number of times each kappai value was counted when calculating each alphai value.
gammai:The elements of this vector are the values of the p-series 0.4374901658/(m^(1.6)), where m is the iteration at which each test is performed.
Author(s)
Md Bahadur Badsha (mbbadshar@gmail.com)
References
1. Badsha MB and Fu AQ (2019). Learning causal biological networks with the principle of Mendelian randomization. Frontiers in Genetics, 10:460.
2. Badsha MB, Martin EA and Fu AQ (2021). MRPC: An R package for inference of causal graphs. Frontiers in Genetics, 10:651812.
3. Badsha MB, Mollah MN, Jahan N and Kurata H (2013). Robust complementary hierarchical clustering for gene expression data analysis by beta-divergence. J Biosci Bioeng, 116(3): 397-407.
4. Javanmard A and Montanari A (2015). On Online Control of False Discovery Rate. arXiv:150206197 [statME].
5. Kalisch M and Buhlmann P (2007). Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm, Journal of Machine Learning Research, 8, 613-636.
6. Kalisch M, Machler M, Colombo D, Maathuis MH and Buhlmann P (2012). Causal Inference Using Graphical Models with the R Package pcalg. Journal of Statistical Software, 47, 26.
7. Scutari M (2010). Learning Bayesian Networks with the bnlearn R Package. Journal of Statistical Software, 35(3), 1-22.
8. Spirtes P, Glymour C and Scheines R (2000). Causation, Prediction, and Search, 2nd edition. The MIT Press.
9. Tian J and Ramdas A (2019). ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls. In Advances in Neural Information Processing Systems (pp. 9388-9396).
See Also
ModiSkeleton for inferring a graph skeleton (i.e., an undirected graph); EdgeOrientation for edge orientation in the inferred graph skeleton; SimulateData for generating data under a topology.
Examples
## Not run:
# Load packages
# Compare six methods on simulated data:
# MRPC,
# pc in pcalg (Kalisch et al., 2012),
# and pc.stable, mmpc mmhc and hc in bnlearn (Scutari, 2010)
library(MRPC) # MRPC
library(pcalg) # pc
library(bnlearn) # pc.stable, mmpc, mmhc and hc
####################################-->
# Load simulated data
# Model 1 (mediation)
Truth <- MRPCtruth$M1 # Truth for model 1
# The 1st column of the data matrix is a genetic variant
# and the remaining columns are gene expression nodes.
data <- simu_data_M1 # load data for model 1
n <- nrow (data) # Number of rows
V <- colnames(data) # Column names
# Calculate Pearson correlation
suffStat_C <- list(C = cor(data),
n = n)
# Infer the graph by MRPC
# using the LOND method for FDR control
MRPC.fit <- MRPC(data,
suffStat = suffStat_C,
GV = 1,
FDR = 0.05,
indepTest = 'gaussCItest',
labels = V,
FDRcontrol = 'LOND',
verbose = FALSE)
# Infer the graph by pc
pc.fit <- pc(suffStat = suffStat_C,
indepTest = gaussCItest,
alpha = 0.05,
labels = V,
verbose = FALSE)
# arcs (from gene expression to genotype) to be excluded
# in pc.stable and mmpc
bl <- data.frame (from = colnames (data)[-1],
to = 'V1')
# Infer the graph by pc.stable
pc.stable.fit <- pc.stable(data.frame(data),
blacklist = bl,
undirected = FALSE)
# Infer the graph by mmpc
mmpc.fit <- mmpc(data.frame(data),
blacklist = bl,
undirected = FALSE,
debug = FALSE)
# Infer the graph by mmhc
mmhc.fit <- mmhc(data.frame(data),
blacklist = bl,
debug = FALSE)
# Infer the graph by hc
hc.fit <- hc (data.frame (data),
blacklist = bl,
debug = FALSE)
# Plot the inferred graphs
par(mfrow = c(1, 7))
plot(Truth,
main = "(A) Truth")
plot(MRPC.fit,
main = "(B) MRPC")
plot(pc.fit,
main ="(C) pc")
graphviz.plot(pc.stable.fit,
main = "(D) pc.stable")
graphviz.plot(mmpc.fit,
main = "(E) mmpc")
graphviz.plot(mmhc.fit,
main = "(F) mmhc")
graphviz.plot(hc.fit,
main = "(G) hc")
####################################<--
####################################-->
# Use alpha, instead of FDR control
# in MRPC
# Model 1
Truth <- MRPCtruth$M1 # Truth for model 1
data <- simu_data_M1 # load data for model 1
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
# using the LOND method for FDR control
MRPC.fit <- MRPC(data,
suffStat = suffStat_C,
GV = 1,
alpha = 0.01,
indepTest = 'gaussCItest',
labels = V,
FDRcontrol = 'NONE',
verbose = FALSE)
# The inferred adjacency matrix
as(MRPC.fit@graph, "matrix")
####################################<--
####################################-->
# 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')
# Other graph visualizations
# Adjacency matrix from directed graph
Adj_directed <- as(MRPC.addis@graph,
"matrix")
# Plot of dendrogram with modules of different colors
PlotDendrogramObj <- PlotDendrogram(Adj_directed,
minModuleSize = 5)
# Visualization of inferred graph with module colors
PlotGraphWithModulesObj <- PlotGraphWithModules(Adj_directed,
PlotDendrogramObj,
GV = 14,
node.size = 8,
arrow.size = 5,
label.size = 3,
alpha = 1)
plot(PlotGraphWithModulesObj)
####################################<--
####################################-->
# Other models are available and may be called as follows:
# Model 0
# Truth <- MRPCtruth$M0
# data <- simu_data_M0
# Model 2
# Truth <- MRPCtruth$M2
# data <- simu_data_M2
# Model 3
# Truth <- MRPCtruth$M3
# data <- simu_data_M3
# Model 4
# Truth <- MRPCtruth$M4
# data <- simu_data_M4
# Model Multiparent
# Truth <- MRPCtruth$Multiparent
# data <- simu_data_multiparent
# Model Star
# Truth <- MRPCtruth$Star
# data <- simu_data_starshaped
# Model Layered
# Truth <- MRPCtruth$Layered
# data <- simu_data_layered
####################################<--
####################################-->
# Discrete data with genetic variants
data <- data_examples$simple$disc$withGV$data
n <- nrow (data) # Sample size
V <- colnames (data) # Node labels
# need different suffStat for discrete data
suffStat <- list (dm = data, adaptDF = FALSE, n.min = 1000)
# Infer the graph by MRPC
data.mrpc.disc.withGV <- MRPC (data,
suffStat = suffStat,
GV = 1,
FDR = 0.05,
indepTest = 'disCItest',
labels = V,
FDRcontrol = 'LOND',
verbose = FALSE)
# Discrete data without genetic variants
data <- data_examples$simple$disc$withoutGV$data
n <- nrow (data) # Sample size
V <- colnames (data) # Node labels
suffStat <- list (dm = data, adaptDF = FALSE)
# Infer the graph by MRPC
data.mrpc.disc.withoutGV <- MRPC (data,
suffStat = suffStat,
GV = 0,
FDR = 0.05,
indepTest = 'disCItest',
labels = V,
FDRcontrol = 'LOND',
verbose = FALSE)
# Plots of true and inferred graphs on discrete data
par (mfrow = c (2,2))
plot (data_examples$simple$disc$withGV$graph, main = "truth")
plot (data.mrpc.disc.withGV, main = "inferred")
plot (data_examples$simple$disc$withoutGV$graph, main = "truth")
plot (data.mrpc.disc.withoutGV, main = "inferred")
####################################<--
## End(Not run)
audreyqyfu/mrpc2 documentation built on April 17, 2022, 7:36 a.m.
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| MRPC | R Documentation |
Infer a causal network using the MRPC algorithm
Description
This function is used to infer a causal network (or a causal graph) with directed and undirected edges from observational data. It implements the MRPC (PC with the principle of Mendelian randomization) algorithm described in Badsha and Fu (2019) and Badsha et al.(2021), and the implementation is based on the pc algorithm in the pcalg package. The MRPC algorithm contains four major updates over the pc algorithm: (i) incorporating a sequential testing method to control the False Discovery Rate (FDR), (ii) improved v-structure identification; (iii) allowing for calculation of robust correlation to reduce the impact of outliers, and (iv) a new procedure for edge orientation based on the principle of Mendelian randomization (PMR) (Badsha and Fu, 2019; Badsha et al., 2021). See details below.
Usage
MRPC(data, suffStat, GV, FDR = 0.05, alpha = 0.05, indepTest, labels, p,
FDRcontrol = c("LOND", "ADDIS", "NONE"), tau = 0.5, lambda = 0.25,
verbose = FALSE)
Arguments
This function is based on the pc function in the pcalg package. Therefore, many arguments are similar to those in pc.
data |
Data matrix, where the rows are observations and the columns are features (i.e., variables, or nodes). If genetic variants (GVs) are included, then the columns start from GVs (e.g., single-nucleotide polymorphisms, or SNPs; insertions and deletions, or indels; copy number variation, or CNVs; and expression quantitative trait loci, or eQTLs to genes), and followed by phenotypes (e.g., gene expression). For example, if the data contains one GV, then the first column of the input matrix is the GV and the remaining columns are the gene expression data. |
suffStat |
A list of sufficient statistics. When the data is continuous or can be viewed as continuous, this list contains the correlation matrix of the data and the sample size, which are the necessary elements for the conditional independence tests in gaussCItest. When the data is discrete, this list contains the entire dataset. |
GV |
The number of genetic variants (SNPs/indels/CNV/eQTLs) in the input data matrix. For example, if the data has one variant, which is in the first column, then GV = 1. If there are two variants, which are in the first and second Columns, then GV = 2. If there are no variants, then GV = 0. |
FDR |
Need to specify the desired level of the overall false discovery rate. |
alpha |
A scalar in [0,1]. The type I error rate for each individual test. |
indepTest |
A function for testing conditional independence. It is used to test the conditional independence of x and y given S, called as indepTest(x, y, S, suffStat). Where, x and y are variables, and S is a vector, possibly empty, of variables. suffStat is a list, see the argument above. The return value of indepTest is the p-value of the test for conditional independence. The different indepTest is used for different data types, for example, Gaussian data = gaussCItest, Discrete data = disCItest and Binary data = binCItest. See help(gaussCItest) The ci.test (Marco Scutari, 2010) is also used for testing conditional independence and return value of indepTest is the p-value. If none is specified, the default test statistic is the mutual information for categorical variables, the Jonckheere-Terpstra test for ordered factors and the linear correlation for continuous variables.See help(ci.test) Remember that need to specify the which indepTest would like for independence testing. For example, if you would like to use gaussCItest you would type indepTest = 'gaussCItest' into the function otherwise indepTest = 'citest'. Note that, we used gaussCItest to compare our MRPC with the existing pc, because of ci.test is not robust. See details in example. |
labels |
A character vector of variable, or node, names. All variables are denoted in column in the input matrix. |
p |
(optional) The number of variables, or nodes. May be specified if the labels are not provided, in which case the labels are set to 1:p. |
FDRcontrol |
A character string specifying whether online FDR control should be applied, and if so, what method to use. The two FDR control options are "LOND" (Javanmard and Montanari, 2015) or "ADDIS" (Tian and Ramdas, 2019). If "NONE" is specified, the type I error rate "alpha" will be used for each test. |
tau |
(optional) A scalar between 0 and 1. This value is used to determine if a p-value will be considered for testing, when FDRcontrol="ADDIS". For example, if a p-value is greater than tau then it is discarded and no test will be performed. |
lambda |
(optional) A scalar between 0 and tau. This value is used to determine if a p-value is a candidate for rejection, when FDRcontrol="ADDIS". For example, if a p-value is smaller than lambda then it can be rejected when testing the hypothesis (if the p-value is smaller than alphai). |
verbose |
(optional) If TRUE, detailed output is provided. The default is FALSE which does not print output details. |
Details
The PC algorithm is computationally efficient for learning a directed acyclic graph (Spirtes et al., 2000). Several variants of the original PC algorithms are available (Kalisch and Buhlmann, 2007; Kalisch et al., 2012). Similar to these PC-like algorithms, our MRPC algorithm also contains two main steps:
Step-1: Inference of the graph skeleton. A graph skeleton is an undirected graph with edges that are supported by the data. Similar to existing PC-like algorithms, we perform statistical tests for marginal and conditional independence tests. If the null hypothesis of independence is not rejected, then the corresponding edge is removed and never tested again.
However, unlike existing algorithms, which control only the type I error rate for each individual test, MRPC implements the LOND (Level On the Number of Discoveries) method (Javanmard and Montanari, 2015), which is a sequential hypothesis testing procedure and sets the significance level for each test based on the number of discoveries (i.e., rejections), to control the overall false discovery rate (FDR). See ModiSkeleton.
Genome data may have outliers that drastically alter the topology of the inferred graph. MRPC allows for the estimate of robust correlation, which may be the substitute of the Pearson correlation as the input to graph inference (Badsha et al., 2013).
Step-2: Edge orientation. With the graph skeleton inferred from Step 1, we orient each edge that is present in the graph. MRPC is fundamentally different from algorithms in the pcalg (Kalisch and Buhlmann, 2007; Kalisch et al., 2012) and bnlearn (Scutari, 2010) packages in the following ways (see EdgeOrientation):
(i) When analyzing genomic data, genetic variants provide additional information that helps distinguish the casual direction between two genes. Our MRPC algorithm incorporates the principle of Mendelian randomization in graph inference, which greatly reduces the space of possible graphs and increases the inference efficiency.
(ii) Next or if the input is not genomic data, we search for possible triplets that may form a v-structure (e.g.,X–>Y<–Z). We check conditional test results from step I to see whether X and Z are independent given Y. If they are, then this is not a v-structure; alternative models for the triplet may be any of the following three Markov equivalent graphs: X–>Y–>Z, X<–Y<–Z, and X<–Y–>Z. If this test is not performed in the first step, we conduct it in this step. This step improves the accuracy of the v-structure identification over existing methods.
(iii) If there are undirected edges after steps (i) and (ii), we examine iteratively triplets of nodes with at least one directed edge and no more than one undirected edge. We check the marginal and conditional test results from Step I to determine which of the basic models is consistent with the test results. It is plausible that some undirected edges cannot be oriented, and we leave them as undirected.
Value
An object of class that contains an estimate of the equivalence class of the underlying DAG.
call:a call object: the original function call.
n:The sample size used to estimate the graph.
max.ord:The maximum size of the conditioning set used in the conditional independence tests in the first part of the algorithm.
n.edgetests:The number of conditional independence tests performed by the first part of the algorithm.
sepset:Separation sets.
pMax:A numeric square matrix , where the (i, j)th entry contains the maximal p-value of all conditional independence tests for edge i–j.
graph:Object of class
"graph": the undirected or partially directed graph that was estimated.zMin:Deprecated.
test:The number of tests that have been performed.
alpha:The level of significance for the current test.
R:All of the decisions made from tests that have been performed. A 1 indicates a rejected null hypothesis and 0 represents a null hypothesis that was not rejected.
K:The total number of rejections.
pval:A vector of p-values calculated for each test.
normalizer:The value that ensures the vector gammai sums to one.
exponent:The exponent of the p-series used to calculate each value of the gammai vector.
alphai:A vector containing the alpha value calculated for each test.
kappai:A vector containing the iteration at which each rejected test occurs.
kappai_star:Each element of this vector is the sum of the Si vector up to the iteration at which each rejection occurs.
Ci:A vector indicating whether or not a p-value is a candidate for being rejected.
Si:A vector indicating whether or not a p-value was discarded.
Ci_plus:Each element of this vector represents the number of times each kappai value was counted when calculating each alphai value.
gammai:The elements of this vector are the values of the p-series 0.4374901658/(m^(1.6)), where m is the iteration at which each test is performed.
Author(s)
Md Bahadur Badsha (mbbadshar@gmail.com)
References
1. Badsha MB and Fu AQ (2019). Learning causal biological networks with the principle of Mendelian randomization. Frontiers in Genetics, 10:460.
2. Badsha MB, Martin EA and Fu AQ (2021). MRPC: An R package for inference of causal graphs. Frontiers in Genetics, 10:651812.
3. Badsha MB, Mollah MN, Jahan N and Kurata H (2013). Robust complementary hierarchical clustering for gene expression data analysis by beta-divergence. J Biosci Bioeng, 116(3): 397-407.
4. Javanmard A and Montanari A (2015). On Online Control of False Discovery Rate. arXiv:150206197 [statME].
5. Kalisch M and Buhlmann P (2007). Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm, Journal of Machine Learning Research, 8, 613-636.
6. Kalisch M, Machler M, Colombo D, Maathuis MH and Buhlmann P (2012). Causal Inference Using Graphical Models with the R Package pcalg. Journal of Statistical Software, 47, 26.
7. Scutari M (2010). Learning Bayesian Networks with the bnlearn R Package. Journal of Statistical Software, 35(3), 1-22.
8. Spirtes P, Glymour C and Scheines R (2000). Causation, Prediction, and Search, 2nd edition. The MIT Press.
9. Tian J and Ramdas A (2019). ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls. In Advances in Neural Information Processing Systems (pp. 9388-9396).
See Also
ModiSkeleton for inferring a graph skeleton (i.e., an undirected graph); EdgeOrientation for edge orientation in the inferred graph skeleton; SimulateData for generating data under a topology.
Examples
## Not run:
# Load packages
# Compare six methods on simulated data:
# MRPC,
# pc in pcalg (Kalisch et al., 2012),
# and pc.stable, mmpc mmhc and hc in bnlearn (Scutari, 2010)
library(MRPC) # MRPC
library(pcalg) # pc
library(bnlearn) # pc.stable, mmpc, mmhc and hc
####################################-->
# Load simulated data
# Model 1 (mediation)
Truth <- MRPCtruth$M1 # Truth for model 1
# The 1st column of the data matrix is a genetic variant
# and the remaining columns are gene expression nodes.
data <- simu_data_M1 # load data for model 1
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
# using the LOND method for FDR control
MRPC.fit <- MRPC(data,
suffStat = suffStat_C,
GV = 1,
FDR = 0.05,
indepTest = 'gaussCItest',
labels = V,
FDRcontrol = 'LOND',
verbose = FALSE)
# Infer the graph by pc
pc.fit <- pc(suffStat = suffStat_C,
indepTest = gaussCItest,
alpha = 0.05,
labels = V,
verbose = FALSE)
# arcs (from gene expression to genotype) to be excluded
# in pc.stable and mmpc
bl <- data.frame (from = colnames (data)[-1],
to = 'V1')
# Infer the graph by pc.stable
pc.stable.fit <- pc.stable(data.frame(data),
blacklist = bl,
undirected = FALSE)
# Infer the graph by mmpc
mmpc.fit <- mmpc(data.frame(data),
blacklist = bl,
undirected = FALSE,
debug = FALSE)
# Infer the graph by mmhc
mmhc.fit <- mmhc(data.frame(data),
blacklist = bl,
debug = FALSE)
# Infer the graph by hc
hc.fit <- hc (data.frame (data),
blacklist = bl,
debug = FALSE)
# Plot the inferred graphs
par(mfrow = c(1, 7))
plot(Truth,
main = "(A) Truth")
plot(MRPC.fit,
main = "(B) MRPC")
plot(pc.fit,
main ="(C) pc")
graphviz.plot(pc.stable.fit,
main = "(D) pc.stable")
graphviz.plot(mmpc.fit,
main = "(E) mmpc")
graphviz.plot(mmhc.fit,
main = "(F) mmhc")
graphviz.plot(hc.fit,
main = "(G) hc")
####################################<--
####################################-->
# Use alpha, instead of FDR control
# in MRPC
# Model 1
Truth <- MRPCtruth$M1 # Truth for model 1
data <- simu_data_M1 # load data for model 1
n <- nrow (data) # Number of rows
V <- colnames(data) # Column names
# Calculate Pearson correlation
suffStat_C <- list(C = cor(data),
n = n)
# Infer the graph by MRPC
# using the LOND method for FDR control
MRPC.fit <- MRPC(data,
suffStat = suffStat_C,
GV = 1,
alpha = 0.01,
indepTest = 'gaussCItest',
labels = V,
FDRcontrol = 'NONE',
verbose = FALSE)
# The inferred adjacency matrix
as(MRPC.fit@graph, "matrix")
####################################<--
####################################-->
# Run MRPC on the complex data set with ADDIS as the FDR control method.
data <- data_examples$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$graph,
main = 'True graph')
plot(MRPC.addis,
main = 'Inferred graph')
# Other graph visualizations
# Adjacency matrix from directed graph
Adj_directed <- as(MRPC.addis@graph,
"matrix")
# Plot of dendrogram with modules of different colors
PlotDendrogramObj <- PlotDendrogram(Adj_directed,
minModuleSize = 5)
# Visualization of inferred graph with module colors
PlotGraphWithModulesObj <- PlotGraphWithModules(Adj_directed,
PlotDendrogramObj,
GV = 14,
node.size = 8,
arrow.size = 5,
label.size = 3,
alpha = 1)
plot(PlotGraphWithModulesObj)
####################################<--
####################################-->
# Other models are available and may be called as follows:
# Model 0
# Truth <- MRPCtruth$M0
# data <- simu_data_M0
# Model 2
# Truth <- MRPCtruth$M2
# data <- simu_data_M2
# Model 3
# Truth <- MRPCtruth$M3
# data <- simu_data_M3
# Model 4
# Truth <- MRPCtruth$M4
# data <- simu_data_M4
# Model Multiparent
# Truth <- MRPCtruth$Multiparent
# data <- simu_data_multiparent
# Model Star
# Truth <- MRPCtruth$Star
# data <- simu_data_starshaped
# Model Layered
# Truth <- MRPCtruth$Layered
# data <- simu_data_layered
####################################<--
####################################-->
# Discrete data with genetic variants
data <- data_examples$simple$disc$withGV$data
n <- nrow (data) # Sample size
V <- colnames (data) # Node labels
# need different suffStat for discrete data
suffStat <- list (dm = data, adaptDF = FALSE, n.min = 1000)
# Infer the graph by MRPC
data.mrpc.disc.withGV <- MRPC (data,
suffStat = suffStat,
GV = 1,
FDR = 0.05,
indepTest = 'disCItest',
labels = V,
FDRcontrol = 'LOND',
verbose = FALSE)
# Discrete data without genetic variants
data <- data_examples$simple$disc$withoutGV$data
n <- nrow (data) # Sample size
V <- colnames (data) # Node labels
suffStat <- list (dm = data, adaptDF = FALSE)
# Infer the graph by MRPC
data.mrpc.disc.withoutGV <- MRPC (data,
suffStat = suffStat,
GV = 0,
FDR = 0.05,
indepTest = 'disCItest',
labels = V,
FDRcontrol = 'LOND',
verbose = FALSE)
# Plots of true and inferred graphs on discrete data
par (mfrow = c (2,2))
plot (data_examples$simple$disc$withGV$graph, main = "truth")
plot (data.mrpc.disc.withGV, main = "inferred")
plot (data_examples$simple$disc$withoutGV$graph, main = "truth")
plot (data.mrpc.disc.withoutGV, main = "inferred")
####################################<--
## End(Not run)
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