Description Usage Arguments Value Author(s) References Examples

Construct differential correlation network with expressionSet,st5 is recommand for testing equal correlation.

1 2 3 4 5 6 7 | ```
construct_network(es,
cor_method = "st5",
var.grp,
pseudo_adjust_cutoff = FALSE,
pAdjMethod = 'fdr',
cutoff = 0.05,
nPseudo = 25)
``` |

`es` |
an ExpressionSet object of microRNA dataset |

`cor_method` |
a string represents the method for equal correlation, 'st5' is recommand. |

`var.grp` |
character. phenotype variable name indicating case-control status,0 as control, 1 as case. |

`pseudo_adjust_cutoff` |
if the value is TRUE, pseudo probes will be used for setting the cutoff of p-value for differential corrlation test. Otherwise, adjusted p-value will be compared with |

`pAdjMethod` |
if |

`cutoff` |
if p value is smaller than the cutoff, there will be an edge between the two nodes. |

`nPseudo` |
if |

A list with 6 elements:

`my_graph` |
obtained network as igraph object |

`my_dat` |
obtained netork as data frame with 3 columns: edge id, node_id1,node_id2 |

`pvalMat` |
raw p-values for testing differential correlation for each pair of genes |

`pAdjMat` |
adjusted p-values for testing differential correlation for each pair of genes |

`pvalPseudo` |
p-values for testing differential correlation between pseudo genes and other genes |

`alpha1` |
cutoff for p-values for testing differential correlation |

Danyang Yu <dyu33@jhu.edu>, Weiliang Qiu <weiliang.qiu@gmail.com>

Danyang Yu, Zeyu Zhang, Kimberly Glass, Jessica Su, Dawn L. DeMeo, Kelan Tantisira, Scott T. Weiss, Weiliang Qiu(corresponding author). New Statistical Methods for Constructing Robust Differential Correlation Networks to characterize the interactions among microRNAs. Scientific Reports 9, Article number: 3499 (2019)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ```
set.seed(1234567)
res = generate_data(n1 = 50, n2 = 60, p1 = 5, p2 = 50)
es = res$es
print(es)
covCtrl = res$covCtrl
covCase = res$covCase
# we expect cov for 1st 5 genes are different between cases and controls
print(round(covCtrl[1:5, 1:5], 2))
print(round(covCase[1:5, 1:5], 2))
# we expect cov for other genes are same between cases and controls
print(round(covCtrl[6:10, 6:10], 2))
print(round(covCase[6:10, 6:10], 2))
res2 = construct_network(es = es,
cor_method = "st5",
pseudo_adjust_cutoff = FALSE,
var.grp = "grp",
pAdjMethod = 'fdr',
cutoff = 0.05,
nPseudo = 25)
print(res2$graph)
print(res2$network_dat)
``` |

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