supraHex 1.13.3

Does visDmatCluster serve as a gene cluster legend to the samples visualised by visCompReorder?

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    # visDmatCluster allows for visualisation of gene meta-clusters.

# The identification of gene meta-clusters is done by its sister fucntion sDmatCluster.

# Yes. These meta-clusters can also be useful to correlate with sample relationships displayed by visCompReorder. See an example below:

data <- matrix( rnorm(100*10,mean=0,sd=1), nrow=100, ncol=10)
colnames(data) <- paste('S', seq(1:10), sep="")
sMap <- sPipeline(data=data)

    
Start at 2017-03-27 18:59:40

First, define topology of a map grid (2017-03-27 18:59:40)...
Second, initialise the codebook matrix (61 X 10) using 'linear' initialisation, given a topology and input data (2017-03-27 18:59:40)...
Third, get training at the rough stage (2017-03-27 18:59:40)...
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Fourth, get training at the finetune stage (2017-03-27 18:59:40)...
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Next, identify the best-matching hexagon/rectangle for the input data (2017-03-27 18:59:40)...
Finally, append the response data (hits and mqe) into the sMap object (2017-03-27 18:59:40)...

Below are the summaries of the training results:
   dimension of input data: 100x10
   xy-dimension of map grid: xdim=9, ydim=9, r=5
   grid lattice: hexa
   grid shape: suprahex
   dimension of grid coord: 61x2
   initialisation method: linear
   dimension of codebook matrix: 61x10
   mean quantization error: 4.60312441206232

Below are the details of trainology:
   training algorithm: batch
   alpha type: invert
   training neighborhood kernel: gaussian
   trainlength (x input data length): 7 at rough stage; 25 at finetune stage
   radius (at rough stage): from 3 to 1
   radius (at finetune stage): from 1 to 1

End at 2017-03-27 18:59:40
Runtime in total is: 0 secs


    sReorder <- sCompReorder(sMap=sMap)

    
Start at 2017-03-27 18:59:40

First, define topology of a map grid (2017-03-27 18:59:40)...
Second, initialise the codebook matrix (30 X 61) using 'linear' initialisation, given a topology and input data (2017-03-27 18:59:40)...
Third, get training at the rough stage (2017-03-27 18:59:40)...
	1 out of 300 (2017-03-27 18:59:40)
	30 out of 300 (2017-03-27 18:59:40)
	60 out of 300 (2017-03-27 18:59:40)
	90 out of 300 (2017-03-27 18:59:40)
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	240 out of 300 (2017-03-27 18:59:40)
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Fourth, get training at the finetune stage (2017-03-27 18:59:40)...
	1 out of 1200 (2017-03-27 18:59:40)
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Next, identify the best-matching hexagon/rectangle for the input data (2017-03-27 18:59:40)...
Finally, append the response data (hits and mqe) into the sMap object (2017-03-27 18:59:40)...

Below are the summaries of the training results:
   dimension of input data: 10x61
   xy-dimension of map grid: xdim=6, ydim=5, r=3
   grid lattice: rect
   grid shape: sheet
   dimension of grid coord: 30x2
   initialisation method: linear
   dimension of codebook matrix: 30x61
   mean quantization error: 4.0169186787623

Below are the details of trainology:
   training algorithm: sequential
   alpha type: invert
   training neighborhood kernel: gaussian
   trainlength (x input data length): 30 at rough stage; 120 at finetune stage
   radius (at rough stage): from 1 to 1
   radius (at finetune stage): from 1 to 1

End at 2017-03-27 18:59:40
Runtime in total is: 0 secs


    visCompReorder(sMap=sMap, sReorder=sReorder)
sBase <- sDmatCluster(sMap=sMap)

    


    visDmatCluster(sMap,sBase)

# As you have seen the previous two images, not only can you tell sample relationships but also the meta-clusters wherein.

    Source faq

    FAQ4.r

    Functions used in this FAQ