Package: MRPC 3.1.0

Audrey Fu

MRPC: PC Algorithm with the Principle of Mendelian Randomization

A PC Algorithm with the Principle of Mendelian Randomization. This package implements the MRPC (PC with the principle of Mendelian randomization) algorithm to infer causal graphs. It also contains functions to simulate data under a certain topology, to visualize a graph in different ways, and to compare graphs and quantify the differences. See Badsha and Fu (2019) <doi:10.3389/fgene.2019.00460>,Badsha, Martin and Fu (2021) <doi:10.3389/fgene.2021.651812>.

Authors:Md Bahadur Badsha [aut,cre],Evan A Martin [ctb] and Audrey Qiuyan Fu [aut]

MRPC_3.1.0.tar.gz
MRPC_3.1.0.zip(r-4.5)MRPC_3.1.0.zip(r-4.4)
MRPC_3.1.0.tgz(r-4.4-any)
MRPC_3.1.0.tar.gz(r-4.5-noble)MRPC_3.1.0.tar.gz(r-4.4-noble)
MRPC_3.1.0.tgz(r-4.4-emscripten)
MRPC.pdf |MRPC.html✨
MRPC/json (API)

# Install 'MRPC' in R:

install.packages('MRPC', repos = c('https://mrcieu.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/audreyqyfu/mrpc/issues

Datasets:

MRPCtruth - Graphs used as truth in simulation
data_GEUVADIS - GEUVADIS data with 62 eQTL-gene sets
data_GEUVADIS_combined - Combined genotype and gene expression data from 62 eQTL-gene sets in 373 Europeans from GEUVADIS
data_examples - Example data under simple and complex models
data_with_outliers - Example data with outliers
data_without_outliers - Example data without outliers
simu_data_M0 - Data for Model 0
simu_data_M1 - Data for Model 1
simu_data_M2 - Data for Model 2
simu_data_M3 - Data for Model 3
simu_data_M4 - Data for Model 4
simu_data_layered - Data for the layered model
simu_data_multiparent - Data for the multiple-parent model
simu_data_starshaped - Data for the star model

On CRAN:

4.68 score 8 stars 20 scripts 318 downloads 2 mentions 25 exports 169 dependencies

Last updated 3 years agofrom:6b9385db8a. Checks:OK: 3 WARNING: 1 NOTE: 1. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Nov 08 2024
R-4.5-win	NOTE	Nov 08 2024
R-4.5-linux	WARNING	Nov 08 2024
R-4.4-win	OK	Nov 08 2024
R-4.4-mac	OK	Nov 08 2024

Exports:AdjustMatrix aSHD CompareMethodsNodeOrdering CompareMethodsVStructure CutModules EdgeOrientation empty IdentifyAssociatedPCs ModiSkeleton mpinv MRPC plot PlotDendrogram PlotGraphWithModules RecallPrecision RobustCor seqDiff SeqFDR show SimulateData SimulateData1P SimulateData2P SimulateData3P SimulateDataNP summary

Dependencies:abind AnnotationDbi askpass backports base64enc bayesm bdsmatrix BH Biobase BiocGenerics BiocManager Biostrings bit bit64 blob bnlearn boot broom bslib cachem checkmate cli clipr clue cluster coda codetools colorspace compositions corpcor cpp11 crayon curl data.table DBI DEoptimR digest doParallel dplyr dynamicTreeCut evaluate fansi farver fastcluster fastICA fastmap fontawesome forcats foreach foreign Formula fs generics GenomeInfoDb GenomeInfoDbData GGally ggm ggplot2 ggstats glmnet glue GO.db GPArotation graph gridExtra gtable gtools haven highr Hmisc hms htmlTable htmltools htmlwidgets httr igraph impute IRanges isoband iterators jomo jquerylib jsonlite KEGGREST knitr labeling lattice lifecycle lme4 lmtest magrittr MASS Matrix matrixStats memoise mgcv mice mime minqa mitml mnormt munsell network nlme nloptr nnet numDeriv openssl ordinal pan patchwork pcalg pillar pkgconfig plogr plyr png preprocessCore prettyunits progress psych purrr R6 rappdirs RBGL RColorBrewer Rcpp RcppArmadillo RcppEigen readr Rgraphviz rlang rmarkdown robustbase rpart RSQLite rstudioapi S4Vectors sass scales sfsmisc shape sna statnet.common stringi stringr survival sys tensorA tibble tidyr tidyselect tinytex tzdb ucminf UCSC.utils utf8 vcd vctrs viridis viridisLite vroom WGCNA withr xfun XVector yaml zlibbioc zoo

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Adjust the columns of the input matrix same as in the reference matrix	AdjustMatrix
Adjusted structural hamming distance (aSHD)	aSHD
Comparison of inference accuracy using the same data but with different node orderings.	CompareMethodsNodeOrdering
Comparison of inference accuracy of different methods on data with and without a v-structure	CompareMethodsVStructure
Cut a numeric variable into intervals	CutModules
Example data under simple and complex models	data_examples
GEUVADIS data with 62 eQTL-gene sets	data_GEUVADIS
Combined genotype and gene expression data from 62 eQTL-gene sets in 373 Europeans from GEUVADIS	data_GEUVADIS_combined
Example data with outliers	data_with_outliers
Example data without outliers	data_without_outliers
Perform edge orientation under the MRPC algorithm	EdgeOrientation
Check empty matrix	empty
Identifyprincipal components (PCs) that are significantly associated with eQTLs and genes	IdentifyAssociatedPCs
Infer a graph skeleton (undirected graph)	ModiSkeleton
Calculate the inverse matrix	mpinv
Infer a causal network using the MRPC algorithm	MRPC
Class of MRPC algorithm results	MRPCclass-class plot,MRPCclass,ANY-method print.MRPCclass show,MRPCclass-method summary,MRPCclass-method
Graphs used as truth in simulation	MRPCtruth
Plot a dendrogram and display node groups in colored modules	PlotDendrogram
Plot a graph with nodes in modules indicated by colors	PlotGraphWithModules
Calculate recall and precision for two graphs	RecallPrecision
Calculate robust correlation matrix	RobustCor
Deviation between two graphs represented by two sequences	seqDiff
Sequential FDR	SeqFDR
Data for the layered model	simu_data_layered
Data for Model 0	simu_data_M0
Data for Model 1	simu_data_M1
Data for Model 2	simu_data_M2
Data for Model 3	simu_data_M3
Data for Model 4	simu_data_M4
Data for the multiple-parent model	simu_data_multiparent
Data for the star model	simu_data_starshaped
Simulate data under certain graphs	SimulateData
Simulate data for a node with one parent	SimulateData1P
Simulate data for a node with two parents	SimulateData2P
Simulate data for a node with three parents	SimulateData3P
Simulate data for a node with no parent	SimulateDataNP