Documentations

Training and Analysis functions

These functions are used for training and analysis

• sPipeline
  Function to setup the pipeline for completing ab initio training given the input data
• sHexGrid
  Function to define a supra-hexagonal grid
• sHexGridVariant
  Function to define a variant of a supra-hexagonal grid
• sHexPolygon
  Function to extract polygon location per hexagon within a supra-hexagonal grid
• sTopology
  Function to define the topology of a map grid
• sInitial
  Function to initialise a sInit object given a topology and input data
• sTrainology
  Function to define trainology (training environment)
• sTrainSeq
  Function to implement training via sequential algorithm
• sTrainBatch
  Function to implement training via batch algorithm
• sBMH
  Function to identify the best-matching hexagons/rectangles for the input data
• sNeighDirect
  Function to calculate direct neighbors for each hexagon/rectangle in a grid
• sNeighAny
  Function to calculate any neighbors for each hexagon/rectangle in a grid
• sHexDist
  Function to calculate distances between hexagons/rectangles in a 2D grid
• sDistance
  Function to compute the pairwise distance for a given data matrix
• sDmat
  Function to calculate distance matrix in high-dimensional input space but according to neighborhood relationships in 2D output space
• sDmatMinima
  Function to identify local minima (in 2D output space) of distance matrix (in high-dimensional input space)
• sDmatCluster
  Function to partition a grid map into clusters
• sCompReorder
  Function to reorder component planes
• sWriteData
  Function to write out the best-matching hexagons and/or cluster bases in terms of data
• sMapOverlay
  Function to overlay additional data onto the trained map for viewing the distribution of that additional data

Visualisation functions

These functions are used for visualisation

• visHexPattern
  Function to visualise codebook matrix or input patterns within a supra-hexagonal grid
• visHexGrid
  Function to visualise a supra-hexagonal grid
• visHexMapping
  Function to visualise various mapping items within a supra-hexagonal grid
• visHexBarplot
  Function to visualise codebook matrix using barplot for all hexagons or a specific one
• visHexComp
  Function to visualise a component plane of a supra-hexagonal grid
• visColormap
  Function to define a colormap
• visColoralpha
  Function to add transparent (alpha) into colors
• visColorbar
  Function to define a colorbar
• visVp
  Function to create viewports for multiple supra-hexagonal grids
• visHexMulComp
  Function to visualise multiple component planes of a supra-hexagonal grid
• visHexAnimate
  Function to animate multiple component planes of a supra-hexagonal grid
• visCompReorder
  Function to visualise multiple component planes reorded within a sheet-shape rectangle grid
• visDmatCluster
  Function to visualise clusters/bases partitioned from a supra-hexagonal grid
• visDmatHeatmap
  Function to visualise gene clusters/bases partitioned from a supra-hexagonal grid using heatmap
• visKernels
  Function to visualize neighborhood kernels
• visHeatmap
  Function to visualise input data matrix using heatmap
• visHeatmapAdv
  Function to visualise input data matrix using advanced heatmap
• visTreeBootstrap
  Function to build and visualise the bootstrapped tree
• visTreeBSclust
  Function to obtain clusters from a bootstrapped tree

Built-in datasets

• Fang(Fang.sampleinfo, Fang.geneinfo)
  Human embryo gene expression dataset from Fang et al. (2010)
• Golub
  Leukemia gene expression dataset from Golub et al. (1999)
• Xiang
  Arabidopsis embryo gene expression dataset from Xiang et al.