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supraHex (version 1.4.0)

A supra-hexagonal map for analysing tabular omics data

Description

A supra-hexagonal map is a giant hexagon on a 2-dimensional grid seamlessly consisting of smaller hexagons. It is supposed to train, analyse and visualise a high-dimensional omics input data. The supraHex is able to carry out gene clustering/meta-clustering and sample correlation, plus intuitive visualisations to facilitate exploratory analysis. More importantly, it allows for overlaying additional data onto the trained map to explore relations between input and additional data. So with supraHex, it is also possible to carry out multilayer omics data comparisons. Newly added utilities are advanced heatmap visualisation and tree-based analysis of sample relationships. Uniquely to this package, users can ultrafastly understand any tabular omics data, both scientifically and artistically, especially in a sample-specific fashion but without loss of information on large genes (see http://www.ncbi.nlm.nih.gov/pubmed/24309102).

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Version

Version

1.4.0

License

GPL-2

Maintainer

Hai Fang

Last Published

February 15th, 2017

Functions in supraHex (1.4.0)

Function to reorder component planes

Function to calculate distance matrix in high-dimensional input space but according to neighborhood relationships in 2D output space

Function to identify local minima (in 2D output space) of distance matrix (in high-dimensional input space)

Function to define a supra-hexagonal grid

Function to calculate distances between hexagons/rectangles in a 2D grid

Function to identify the best-matching hexagons/rectangles for the input data

Human embryo gene expression dataset from Fang et al. (2010)

Leukemia gene expression dataset from Golub et al. (1999)

Function to compute the pairwise distance for a given data matrix

Function to partition a grid map into clusters

Function to define the topology of a map grid

Function to calculate any neighbors for each hexagon/rectangle in a grid

Function to implement training via batch algorithm

Function to write out the best-matching hexagons and/or cluster bases in terms of data

Function to implement training via sequential algorithm

Function to calculate direct neighbors for each hexagon/rectangle in a grid

Function to define trainology (training environment)

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

Function to initialise a sInit object given a topology and input data

Function to overlay additional data onto the trained map for viewing the distribution of that additional data

Function to define a colormap

visTreeBootstrap

Function to build and visualise the bootstrapped tree

Function to visualise multiple component planes reorded within a sheet-shape rectangle grid

Function to visualise codebook matrix or input patterns within a supra-hexagonal grid

Function to create viewports for multiple supra-hexagonal grids

Function to visualize neighborhood kernels

Function to visualise input data matrix using heatmap

Function to visualise input data matrix using advanced heatmap

Arabidopsis embryo gene expression dataset from Xiang et al. (2011)

Function to visualise clusters/bases partitioned from a supra-hexagonal grid

Function to visualise gene clusters/bases partitioned from a supra-hexagonal grid using heatmap

Function to visualise various mapping items within a supra-hexagonal grid

Function to visualise multiple component planes of a supra-hexagonal grid

Function to add transparent (alpha) into colors

Function to visualise a component plane of a supra-hexagonal grid

Function to define a colorbar

Function to visualise a supra-hexagonal grid