# Given a set of genes of interest, full bipartite networks with scores (one network for each sample), a significance cutoff for statistical testing, and a hop constraint, BLOBFISH finds a subnetwork of significant edges connecting the genes.

Source:`R/BLOBFISH.R`

`RunBLOBFISH.Rd`

Given a set of genes of interest, full bipartite networks with scores (one network for each sample), a significance cutoff for statistical testing, and a hop constraint, BLOBFISH finds a subnetwork of significant edges connecting the genes.

## Usage

```
RunBLOBFISH(
geneSet,
networks,
alpha,
hopConstraint,
nullDistribution,
verbose = FALSE,
topX = NULL,
doFDRAdjustment = TRUE,
pValueChunks = 100,
loadPValues = FALSE,
pValueFile = "pvalues.RDS"
)
```

## Arguments

- geneSet
A character vector of genes comprising the targets of interest.

- networks
A list of bipartite (PANDA-like) networks, where each network is a data frame with the following format: tf,gene,score

- alpha
The significance cutoff for the statistical test.

- hopConstraint
The maximum number of hops to be considered between gene pairs. Must be an even number.

- nullDistribution
The null distribution, specified as a vector of values.

- verbose
Whether or not to print detailed information about the run.

- topX
Select the X lowest significant p-values for each gene. NULL by default.

- doFDRAdjustment
Whether or not to perform FDR adjustment.

- pValueChunks
The number of chunks to split when calculating the p-value. This parameter allows the edges to be split into chunks to prevent memory errors.

- loadPValues
Whether p-values should be loaded from pValueFile or re-generated. Default is FALSE.

- pValueFile
The file where the p-values should be saved. If NULL, they are not saved and need to be recalculated.