Golub


Leukemia dataset (learning set) contains gene expression levels (3051
genes and 38 patient samples) from Golub et al. (1999). This dataset
has been pre-processed: capping into floor of 100 and ceiling of 16000;
filtering by exclusion of genes with $max/min

datasets

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).

Hai Fang

supraHex

A Supra-Hexagonal Map for Analysing Tabular Omics Data

Golub function

Leukemia dataset (learning set) contains gene expression levels (3051
genes and 38 patient samples) from Golub et al. (1999). This dataset
has been pre-processed: capping into floor of 100 and ceiling of 16000;
filtering by exclusion of genes with $max/min<=5$ or
$max-min<=500$, where max and min refer respectively to the maximum
and minimum intensities for a particular gene across mRNA samples;
2-base logarithmic transformation.

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

Description

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References