Description Usage Arguments Value Examples

A method that integrates differential expression (DE) analysis and differential network (DN) analysis to select biomarker candidates for cancer studies. non_partial_cor is a one step function for user to perform the analysis, no pre-processing step required.

1 2 3 | ```
non_partial_cor(data = NULL, class_label = NULL, id = NULL,
method = "pearson", p_val = NULL, permutation = 1000,
permutation_thres = 0.025)
``` |

`data` |
This is a matrix of expression from all metabolites from all samples. |

`class_label` |
this is a binary array with 0 for group 1 and 1 for group 2. |

`id` |
This is an array of biomolecule ID to label. |

`method` |
This is a character string indicating which correlation coefficient is to be computed. The options are either "pearson" (the default) or "spearman". |

`p_val` |
This is optional, it is a data frame containing p-values for each metabolite/molecule. |

`permutation` |
This is a positive integer representing the desired number of permutations, default is 1000. |

`permutation_thres` |
This is a threshold for permutation. The defalut is 0.025 for each side to result in 95 percent confidience. |

A list containing a score data frame with "MetID", "P_value", "Node_Degree", "Activity_Score" and a differential network data frame with "Node1", "Node2", the binary link value and the weight link value.

1 2 | ```
non_partial_cor(data = Met_GU, class_label = Met_Group_GU, id = Met_name_GU,
method = "spearman", permutation_thres = 0.05, permutation = 1000)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.