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A patient in Birmingham has undergone landmark gene therapy to treat prostate cancer.
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SignatureClust clusters microarray data after projecting it into a signature space defined by a set of landmark genes chosen by the user, allowing biologists to get different perspectives of the same underlying data simply by changing the landmark genes.
In adult mouse hypothalami, the expression of landmark genes such as Npy, Pomc1 and Agrp is changed upon food deprivation [16].
The correlation signature projects heterogeneous microarray expression data onto a coherent information space where a gene is represented by the vector of its correlations against a series of landmark genes.
Both landmark genes and the whole genome were analyzed separately.
The best overall correlation coefficient is 0.026 when ECFP4 and Pearson of landmark genes are used.
The complete list of landmark genes is available on http://www.lincscloud.org/l1000/.org/l1000/
A set of 1,000 landmark genes is selected that captures approximately 80%% of the correlations in gene expression profiles.
We found that using landmark genes leads to higher transcriptomic similarity than using the whole genome, so we focused our analysis on the landmark genes rather than the whole genome.
For transcriptomic similarity, we used the landmark genes and the whole genome independently at the beginning of the analysis.
However, the landmark genes may not be sufficient to capture all the transcriptomic changes of some compounds.
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