genefinder {genefilter} | R Documentation |
Given an exprSet
or a matrix of gene expressions, and the
indices of the genes of
interest, genefinder
returns a list of the
numbResults
closest genes.
The user can specify one of the standard distance measures listed
below.
The number of values to return can be specified. The return value is a
list with two components:
genes (measured through the desired distance method) to the genes
of interest (where X is the number of desired results returned) and
their distances.
genefinder(X, ilist, numResults=25, scale="none", weights, method="euclidean")
X |
A numeric matrix where columns represent patients, and rows represent genes. |
ilist |
Vector of genes of interest. Contains indices of genes in matrix X. |
numResults |
Number of results to display, starting from the least distance to the greatest. |
scale |
one of 'none', 'range', or 'zscore'. Scaling is carried out separately on each row. |
weights |
A vector of weights applied across the columns of
X . If no weights are supplied, no weights are applied |
method |
one of "euclidean", "maximum", "manhattan", "canberra", "correlation", "binary". |
If the scale option is "range", then the input matrix is scaled using genescale(). If it is "zscore", then the input matrix is scaled using the 'scale' builtin with no arguments.
The method option specifies the metric used for gene comparisons. The
metric is applied, row by row, for each gene specified in ilist
.
The "correlation" option for the distance method will return a value equal to 1-correlation(x).
See dist
for a more detailed description of the distances.
The returned value is a list containing an entry for each gene specified in ilist. Each list entry contains an array of distances for that gene of interest.
J. Gentry and M. Kajen
set.seed(12345) #create some fake expression profiles m1 <- matrix (1:12, 4, 3) v1 <- 1 nr <- 2 #find the 2 rows of m1 that are closest to row 1 genefinder (m1, v1, nr, method="euc") v2 <- c(1,3) genefinder (m1, v2, nr) genefinder (m1, v2, nr, scale="range") genefinder (m1, v2, nr, method="manhattan") m2 <- matrix (rnorm(100), 10, 10) v3 <- c(2, 5, 6, 8) nr2 <- 6 genefinder (m2, v3, nr2, scale="zscore")