centrality_pipeline: Pipeline function to run complete noderank workflow.
In taylorpourtaheri/nr: Node prioritization optimizer

Given a dataframe of differential gene expression results and a set of parameters, 'centrality_pipeline()' will generate network of the most important genes, ranked by the specified centrality measure.

centrality_pipeline(
  deg,
  ppi = NULL,
  string_db = NULL,
  sim = NULL,
  edge_conf_score_min = NULL,
  logFC_min,
  pvalue_max,
  connected_filter = TRUE,
  method = "betweenness",
  causal_gene_symbol,
  export_network = FALSE,
  export_dir = NULL,
  sim_method = "jaccard",
  n_sim = 9999,
  weighted = FALSE
)

`deg`	Object of class '`dataframe`'. Differential gene expression analysis results including log fold-changes and p-values, i.e. the output of `topTable()` from limma or `results()` from DESeq2.
`edge_conf_score_min`	Numeric. A The minimum confidence score between edges in the STRING protein-protein interaction network.
`logFC_min`	Numeric. The minimum log2 fold-change value for a gene to be included in the final network.
`pvalue_max`	Numeric. The maximum p-value value for a gene to be included in the final network.
`method`	A string. One of the following centrality methods: strength degree avg_strength degree_frac strength_scaled avg_strength_scaled evcent_w evcent_uw betweenness
`causal_gene_symbol`	A string. The gene symbol associated with the causal gene.
`export_network`	Logical. If TRUE, the network object will be returned.
`export_dir`	String. If export_network = TRUE, the network object will be saved using the file path provided in export_dir.
`sim_method`	A string. The method for calculating the similarity between each gene in the final network and the causal gene. One of the following: #' jaccard dice invlogweighted
`n_sim`	Numeric. The number of simulations passed to `evaluate_performance()`.

A list of length 4:

network: Object of class 'igraph'. The network of important genes.
top_genes: An annotated dataframe of genes ranked by centrality method.
mean_score: The mean score of the network, which is equal to the average similarity score between each node in the network and the causal gene.
pvalue: The p-value associated with the mean_score.