sMapOverlay: Function to overlay additional data onto the trained map for...
In supraHex: supraHex: a supra-hexagonal map for analysing tabular omics data

Description Usage Arguments Value Note See Also Examples

sMapOverlay is supposed to overlay additional data onto the trained map for viewing the distribution of that additional data. It returns an object of class "sMap". It is realised by first estimating the hit histogram weighted by the neighborhood kernel, and then calculating the distribution of the additional data over the map (similarly weighted by the neighborhood kernel). The final overlaid distribution of additional data is normalised by the hit histogram.

1	sMapOverlay(sMap, data = NULL, additional)

`sMap`	an object of class "sMap"
`data`	a data frame or matrix of input data or NULL
`additional`	a numeric vector or numeric matrix used to overlay onto the trained map. It must have the length (if being vector) or row number (if matrix) being equal to the number of rows in input data

an object of class "sMap", a list with following components:

nHex: the total number of hexagons/rectanges in the grid
xdim: x-dimension of the grid
ydim: y-dimension of the grid
r: the hypothetical radius of the grid
lattice: the grid lattice
shape: the grid shape
coord: a matrix of nHex x 2, with rows corresponding to the coordinates of all hexagons/rectangles in the 2D map grid
ig: the igraph object
polygon: a tibble of 7 columns ('x','y','index','node','edge','stepCentroid','angleCentroid') storing polygon location per hexagon
init: an initialisation method
neighKernel: the training neighborhood kernel
codebook: a codebook matrix of nHex x ncol(additional), with rows corresponding to overlaid vectors
hits: a vector of nHex, each element meaning that a hexagon/rectangle contains the number of input data vectors being hit wherein
mqe: the mean quantization error for the "best" BMH
data: an input data matrix
response: a tibble of 3 columns ('did' for rownames of input data matrix, 'index', and 'qerr' (quantization error; the distance to the "best" BMH))
call: the call that produced this result

Weighting by neighbor kernel is to avoid rigid overlaying by only focusing on the best-matching map nodes as there may exist several closest best-matching nodes for an input data vector.

sPipeline, sBMH, sHexDist, visHexMulComp

# 1) generate an iid normal random matrix of 100x10 
data <- matrix( rnorm(100*10,mean=0,sd=1), nrow=100, ncol=10)
colnames(data) <- paste(rep('S',10), seq(1:10), sep="")

# 2) get trained using by default setup
sMap <- sPipeline(data=data)

# 3) overlay additional data onto the trained map
# here using the first two columns of the input "data" as "additional"
# codebook in "sOverlay" is the same as the first two columns of codebook in "sMap"
sOverlay <- sMapOverlay(sMap=sMap, data=data, additional=data[,1:2])

# 4) viewing the distribution of that additional data
visHexMulComp(sOverlay)