makeEnrichmentGraph: Makes an enrichment graph for a sorted binary vector
In Carldeboer/BrockmanR: K-mer-based analysis of chromatin state.

Description Usage Arguments Value Examples

Applies the minimum hypergeometric test to given data and plots the enrichment of observed over expected across the data, including the ln(P) value (title), log2(observed/expected) (y-axis; e.g. observed/expected cognate k-mers), and element rank (x-axis, e.g. k-mer). The blue line indicates the point of minimum hypergeometric p-value.

1	makeEnrichmentGraph(x, n_max = length(x) - 1, sortedBy = NULL)

`x`	A sorted vector of binary values, where 1 is a "hit" (e.g. cognate k-mer) and 0 is a "miss" (e.g. non-cognate k-mer).
`n_max`	The maximum number of k-mers to consider for each minimum hypergeometric test. Defaults to testing all possible entries in the vector for maximal enrichment.
`sortedBy`	An alternate x-axis (instead of vector index) to use for the x-axis of the graph. Generally, what the vector x was sorted by.

Returns a list containing the plot (plot), the data.frame used to make the plot (rawData), and the minHG test (minHGTest).

kmerMat = inputKMerFreqs(sprintf("kMerFiles/%s.freq.gz",sampleDesc$id), IDs = sampleDesc$id)
myPCA = doKMerPCA(kmerMat, nPCs = "jackstraw")
treatmentPCs = findDistinguishingPCs(myPCA$rotation[,1:myPCA$nPCs], sampleDesc[c("id","treated")])
plot = makeEnrichmentGraph(as.logical(cisbp$binaryPBMZScores[order(pcs$rotation[row.names(cisbp$binaryPBMZScores),treatmentPCs$PC[1]],decreasing = T),"M0312_1.02.PBM"])); # highly weighted k-mers