geNet: Execution geNet algorithm
In haneylab/geNet: Generating large network of co-occurring genes

Description Usage Arguments Value Examples

function to execute the geNet algorithm on a binary dataframe of genes occurrences (presence/absence data)

geNet(
  input_binary_df,
  clust_method = "infomap",
  type_weight = "coeff",
  cores = 1,
  out_dir,
  export_results = F,
  test_pvalue = "cor_test",
  pval_thr_pos = 0.01,
  pval_thr_neg = 0.1
)

`input_binary_df`	input binary dataframe containing genes occurrences. Mandatory argument. input_binary_df is an object of class "dataframe". The rownames are the strains names, the columns names are unique genes/nodes IDs.
`clust_method`	clustering method. Default to "infomap".
`type_weight`	possible values: logpvalue: the weights of the edges will be the negative log adjusted p-values coeff: the weights of the edges will be the correlation coefficients values Note: This is true only for the positive edges. The negative edges will have 0 weights in both cases. The negative edges doesn't influence the topology of the network or the clustering.
`cores`	number of cores to use for parallel processing
`out_dir`	if export_results=T, select output directory
`export_results`	exporting geNet output in csv files?
`test_pvalue`	significance test to use. cor_test: use Pearson correlation test approach (faster) chisquare: use chi-square test approach (slower)
`pval_thr_pos`	threhold p-value positive edges. Default to 0.01.
`pval_thr_neg`	threshold p-value negative edges. Default to 0.1.

list of two objects of class "ffdf".

nodes: reports info about the nodes and clustering
edges: reports info about the connections between the nodes This format is designed to be used with the visNetwork package.