For each of the edges calculate the gini correlation coefficient.
If it is greater than `statCutoff`

, also calculate a p-value by doing
`bootstrapIterations`

random permutations of the expression data.
Returns an **R** `data.frame`

containing source ,target, gini, and pval columns

1 | ```
gini(edges, expression, bootstrapIterations, statCutoff)
``` |

1 2 3 | ```
edges <- read.delim("data/network.txt")
expr <- as.matrix(read.delim("data/expression.txt", row.names = 1))
gini(edges, expr, 10000, 0.5)
``` |

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