minPath | R Documentation |
Obtain the path of links through the feature space that links two traits together such that the probability of obtaining any link from the null distribution is minimized. For the nodeNames vector, note that trait names come before feature names.
minPath(s, t, gm, clusters, r = 1, plotpath = TRUE, nodeNames = NULL)
s |
a string (if nodeNames is supplied) or an index (otherwise) corresponding to the starting node of the path |
t |
a string (if nodeNames is supplied) or an index (otherwise) corresponding to the ending node of the path |
gm |
the output of |
clusters |
a numeric vector of cluster labels, usually obtained from the
|
r |
a regularization term. More info in the details section. |
plotpath |
whether to plot the minimal path. |
nodeNames |
a character vector of names for the nodes in the network. Trait names come before feature names |
For regularization, consider the following three options, where N is the total number of nodes:
1. r = 1 (no regularization)
2. r = 1 - 2 / (N*(N-1)) (the smallest meaningful p-value is the probability of drawing the single greatest association out of the entire network)
3. r = 1 - 1/(N-1) (the smallest meaningful p-value is the probability of drawing a given feature's closest neighbor out of the entire network)
pvals |
the individual p-values of each link from the first node to the last node. The sum of these p-values is the total distance |
clusters |
cluster labels for each feature in the path |
names |
the names of the nodes in the path |
Anooj Arkatkar
distBoot
, pBoot
# Load data data(foldChange) data(traitData) # Obtain clusters c.gm <- gelMatrix(foldChange) clust <- gammaCluster(c.gm, 7) # Set up the path data path.gm <- gelMatrix(foldChange, phen=traitData, squared=TRUE, output="orig") nodeNames <- c(rownames(traitData), rownames(foldChange)) # Find the minimal path mp <- minPath("TCDD", "Fat", path.gm, clust$labels, nodeNames=nodeNames)
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