Description Usage Arguments Author(s)

The function uses sPLS or PLS and network functions in mixOmics package to perform pairwise integrative and correlation analysis. The pairwise graphs are merged using igraph and community detection is performed using the Multilevel clustering algorithm. Association networks can be visualized in R or using Cytoscape.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
run_xmwas(xome_fname = NA, yome_fname = NA, zome_fname = NA, wome_fname = NA,
outloc = NA,
class_fname = NA, Xome_data = NA, Yome_data = NA, Zome_data = NA, Wome_data = NA,
classlabels = NA, xmwasmethod = "spls", plsmode = "canonical", max_xvar = 5000,
max_yvar = 5000, max_zvar = 5000, max_wvar = 5000, rsd.filt.thresh = 1,
all.missing.thresh = 0.1, missing.val = 0, corthresh = 0.4, keepX = 1000,
keepY = 1000, keepZ = 1000, keepW = 1000, pairedanalysis = FALSE,
optselect = TRUE, rawPthresh = 0.05, numcomps = 10,
net_edge_colors = c("blue", "red"), net_node_colors = c("orange", "green", "blue", "gold"),
Xname = "X", Yname = "Y", Zname = "Z", Wname = "W",
net_node_shape = c("circle", "rectangle", "triangle", "star"), seednum = 100,
label.cex = 0.3, vertex.size = 6, max_connections = NA,
centrality_method = "eigenvector", use.X.reference = FALSE,
removeRda = TRUE, compare.classes = TRUE, class.comparison.allvar = TRUE, ...)
``` |

`xome_fname` |
Full path with filename for dataset A. Default: NA; The software uses the value provided for Xome_data when this is set to NA. |

`yome_fname` |
Full path with filename for dataset B Default: NA; The software uses the value provided for Yome_data when this is set to NA. |

`zome_fname` |
Full path with filename for dataset C Default: NA; The software uses the value provided for Zome_data when this is set to NA. |

`wome_fname` |
Full path with filename for dataset D Default: NA; The software uses the value provided for Wome_data when this is set to NA. |

`Xome_data` |
Data matrix for dataset A. Run: data(exnci60); head(exnci60$mrna) to see how to format data matrices. |

`Yome_data` |
Data matrix for dataset B |

`Zome_data` |
Data matrix for dataset C |

`Wome_data` |
Data matrix for dataset D |

`outloc` |
Output directory |

`classlabels` |
Data matrix with phenotype information. Set to NA if this information is not available. see: data(classlabels_casecontrol) for case vs control design or data(classlabels_repeatmeasures) for repeat measures |

`class_fname` |
File with phenotype information. Set to NA if this information is not available. see: data(classlabels_casecontrol) for case vs control design or data(classlabels_repeatmeasures) for repeat measures |

`xmwasmethod` |
Method for data integration. eg: "pls": partial least squares regression "spls": sparse partial least squares regression "o1pls": orthogonal partial least squares regression |

`plsmode` |
"canonical" for bi-directional relationships; "regression" for regression/predictive relationships |

`max_xvar` |
Maximum number of X variables to select based on relative standard deviation (RSD). e.g. 10000 |

`max_yvar` |
Maximum number of Y variables to select based on relative standard deviation (RSD). e.g. 10000 |

`max_zvar` |
Maximum number of Z variables to select based on relative standard deviation (RSD). e.g. 10000 |

`max_wvar` |
Maximum number of W variables to select based on relative standard deviation (RSD). e.g. 10000 |

`rsd.filt.thresh` |
Relative standard deviation (sd/mean) threshold |

`all.missing.thresh` |
Remove variables (rows) that do not meet the minimum threshold for presence of non-missing values. e.g. 0.8 |

`missing.val` |
How are the missing values represented in the input data files? Default: 0 |

`corthresh` |
Correlation threshold. eg: 0.7 |

`keepX` |
Maximum number of X variables to select in sPLS. Note: keepX, keepY, keepZ, and keepW are only used when xmwasmethod is set to "spls" |

`keepY` |
Maximum number of Y variables to select in sPLS. Note: keepX, keepY, keepZ, and keepW are only used when xmwasmethod is set to "spls" |

`keepZ` |
Maximum number of Z variables to select in sPLS. Note: keepX, keepY, keepZ, and keepW are only used when xmwasmethod is set to "spls" |

`keepW` |
Maximum number of W variables to select in sPLS. Note: keepX, keepY, keepZ, and keepW are only used when xmwasmethod is set to "spls" |

`pairedanalysis` |
Are their repeated measurements? TRUE or FALSE |

`optselect` |
Find optimal number of PLS components. TRUE or FALSE |

`rawPthresh` |
p-value threshold calculated using Student's t-test. eg: 0.05 |

`numcomps` |
Number of components to use in PLS model. eg: 3 |

`net_edge_colors` |
Colors for edges. |

`net_node_colors` |
Colors for nodes. |

`Xname` |
Name for X dataset. eg: "Genes" |

`Yname` |
Name for Y dataset. eg: "Proteins" |

`Zname` |
Name for Z dataset. eg: "Metabolites" |

`Wname` |
Name for W dataset. eg: "EnvironmentalExposures" |

`net_node_shape` |
Shapes for nodes. |

`seednum` |
Seed for random number generator used for plotting the network. |

`label.cex` |
Size of the labels. eg: 0.8 |

`vertex.size` |
Size of the nodes. |

`max_connections` |
Maximum number of associations to include in the network. The connections between nodes are ranked based on the strength of association (+ve and -ve). Only the top "max_connections" connections are shown and used for centrality and community detection analyses. Set max_connections=NA if you want to use all connections. e.g. 1e5, 1e6, or NA |

`centrality_method` |
Method for centrality analysis. Options: 1) "eigenvector" for eigenvector centrality, which is based on the number and quality of connections - centrality scores range from 0 to 1, where 1 means high centrality and 0 means low or no centrality. Nodes/vertices with high centrality scores are connected to many other nodes, which are in turn connected to many other nodes. Please see igraph::eigen_centrality function for more details. 2) "betweenness" for betweenness centrality, which is based on the number of shortest paths going through a node/vertex - centrality scores are normalized and scaled to 0 to 1 range, where 1 means high betweenness centrality and 0 means low or no betweenness centrality. Please see igraph::betweenness function for more details. 3) "degree.count" based on the number of connections of a node. -centrality scores are normalized and scaled to 0 to 1 range. High centrality means more connections. Please see igraph::degree function for more details. 4) "degree.weight" is based on the sum of absolute weights of connections of a node. -centrality scores are normalized and scaled to 0 to 1 range. High centrality means stronger connections. 5) "closeness" based on the reciprocal of the sum of the distances of a node/vertex to all other nodes. -centrality scores are normalized. High centrality means the node is closer to all other nodes. |

`use.X.reference` |
TRUE or FALSE if you want to use Xome_data as reference. If TRUE, only X<->Y, X<->Z, and X<->W pairwise analysis will be performed. |

`removeRda` |
TRUE or FALSE; set to TRUE if you want to remove the intermediate files. |

`compare.classes` |
TRUE or FALSE; set to TRUE if you want to compare individual classes as provided in class labels file. |

`class.comparison.allvar` |
TRUE or FALSE; set to TRUE if all nodes shown |

Karan Uppal [email protected]

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