sPipeline: Function to setup the pipeline for completing ab initio...

Description Usage Arguments Value Note References See Also Examples

View source: R/sPipeline.r

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", "triangle",
"diamond", "hourglass", "trefoil", "ladder", "butterfly", "ring",
"bridge"),
init = c("linear", "uniform", "sample"), algorithm = c("batch",
"sequential"), alphaType = c("invert", "linear", "power"),
neighKernel = c("gaussian", "bubble", "cutgaussian", "ep", "gamma"),
finetuneSustain = FALSE, verbose = TRUE)

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. Also supported are suprahex's variants (including "triangle" for the triangle-shaped variant, "diamond" for the diamond-shaped variant, "hourglass" for the hourglass-shaped variant, "trefoil" for the trefoil-shaped variant, "ladder" for the ladder-shaped variant, "butterfly" for the butterfly-shaped variant, "ring" for the ring-shaped variant, and "bridge" for the bridge-shaped variant)

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:

Note

The pipeline sequentially consists of:

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.

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))

# 4) get trained using by default setup but using the shape "butterfly"
sMap <- sPipeline(data=data, shape="trefoil",
algorithm=c("batch","sequential")[2])
visHexMulComp(sMap, colormap="jet", ncolors=20, zlim=c(-1,1),
gp=grid::gpar(cex=0.8))

supraHex documentation built on May 20, 2017, 9:12 p.m.

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