Function to setup the pipeline for completing ab initio training given the input data

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Description

sPipeline is supposed to finish ab inito training for the input data. It returns an object of class "sMap".

Usage

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sPipeline(data = NULL, xdim = NULL, ydim = NULL, nHex = NULL,
lattice = c("hexa", "rect"), shape = c("suprahex", "sheet"),
init = c("linear", "uniform", "sample"), algorithm = c("batch",
"sequential"), alphaType = c("invert", "linear", "power"),
neighKernel = c("gaussian", "bubble", "cutgaussian", "ep", "gamma"),
finetuneSustain = F, verbose = T)

Arguments

data

a data frame or matrix of input data

xdim

an integer specifying x-dimension of the grid

ydim

an integer specifying y-dimension of the grid

nHex

the number of hexagons/rectangles in the grid

lattice

the grid lattice, either "hexa" for a hexagon or "rect" for a rectangle

shape

the grid shape, either "suprahex" for a supra-hexagonal grid or "sheet" for a hexagonal/rectangle sheet

init

an initialisation method. It can be one of "uniform", "sample" and "linear" initialisation methods

algorithm

the training algorithm. It can be one of "sequential" and "batch" algorithm. By default, it uses 'batch' algorithm purely because of its fast computations (probably also without the compromise of accuracy). However, it is highly recommended not to use 'batch' algorithm if the input data contain lots of zeros; it is because matrix multiplication used in the 'batch' algorithm can be problematic in this context. If much computation resource is at hand, it is alwasy safe to use the 'sequential' algorithm

alphaType

the alpha type. It can be one of "invert", "linear" and "power" alpha types

neighKernel

the training neighborhood kernel. It can be one of "gaussian", "bubble", "cutgaussian", "ep" and "gamma" kernels

finetuneSustain

logical to indicate whether sustain the "finetune" training. If true, it will repeat the "finetune" stage until the mean quantization error does get worse. By default, it sets to true

verbose

logical to indicate whether the messages will be displayed in the screen. By default, it sets to false for no display

Value

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

  • 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

  • init: an initialisation method

  • neighKernel: the training neighborhood kernel

  • codebook: a codebook matrix of nHex x ncol(data), with rows corresponding to prototype vectors in input high-dimensional space

  • 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

  • call: the call that produced this result

Note

The pipeline sequentially consists of:

  • i) sTopology used to define the topology of a grid (with "suprahex" shape by default ) according to the input data;

  • ii) sInitial used to initialise the codebook matrix given the pre-defined topology and the input data (by default using "uniform" initialisation method);

  • iii) sTrainology and sTrainSeq used to get the grid map trained at both "rough" and "finetune" stages. If instructed, sustain the "finetune" training until the mean quantization error does get worse;

  • iv) sBMH used to identify the best-matching hexagons/rectangles (BMH) for the input data, and these response data are appended to the resulting object of "sMap" class.

References

Hai Fang and Julian Gough. (2014) supraHex: an R/Bioconductor package for tabular omics data analysis using a supra-hexagonal map. Biochemical and Biophysical Research Communications, 443(1), 285-289. DOI: http://dx.doi.org/10.1016/j.bbrc.2013.11.103, PMID: http://www.ncbi.nlm.nih.gov/pubmed/?term=24309102

See Also

sTopology, sInitial, sTrainology, sTrainSeq, sTrainBatch, sBMH, visHexMulComp

Examples

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# 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 but with different neighborhood kernels
# 2a) with "gaussian" kernel
sMap <- sPipeline(data=data, neighKernel="gaussian")
# 2b) with "bubble" kernel
# sMap <- sPipeline(data=data, neighKernel="bubble")
# 2c) with "cutgaussian" kernel
# sMap <- sPipeline(data=data, neighKernel="cutgaussian")
# 2d) with "ep" kernel
# sMap <- sPipeline(data=data, neighKernel="ep")
# 2e) with "gamma" kernel
# sMap <- sPipeline(data=data, neighKernel="gamma")

# 3) visualise multiple component planes of a supra-hexagonal grid
visHexMulComp(sMap, colormap="jet", ncolors=20, zlim=c(-1,1),
gp=grid::gpar(cex=0.8))

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