Autarchy of the Private Cave

Amazonia! – explore the jungle of microarray results

Paradoxically, the tremendous downpour of microarray results prevents a simple use of expression data. Therefore, we propose a thematic entry to public transcriptomes: you may for instance query a gene on a “Stem Cells page”, where you will see the expression of your favorite gene across selected microarray experiments related to stem cell biology. This selection of samples can be customized at will among the 6462 samples currently present in the database.

Every transcriptome study results in the identification of lists of genes relevant to a given biological condition. In order to include this valuable information in any new query in the Amazonia! database, we indicate for each gene in which lists it is included. This is a straightforward and efficient way to synthesize hundreds of microarray publications.

A special feature of Amazonia! is the field of human stem cells, notably embryonic stem cells.

Superimposing gene expression data onto pathways from databases is a common task in the final steps of microarray data analysis – that is, biological interpretation and results discussion.

I have found many tools which claim to facilitate this procedure. Some of them are reviewed below (in no specific order).
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If you ever need to glimpse at the properties of the Poisson distribution with lambda=0.16, or find the factorial of 6163338 (as a gamma function), then Wolfram|Alpha is a perfect tool for you (unless you have some math package at hand).

The motto of Wolfram|Alpha is Making the world’s knowledge computable. Basically, it is like Mathematica plus a growing corpus of factual numeric data, plus a system to interpret user’s input. This is a nice online reference and computation platform.

The title of this post is my current – “forthcome”, as in “done” – field of interest.

First article on topic: Fast network component analysis (FastNCA) for gene regulatory network reconstruction from microarray data.

Another one, on combining different high-throughput data sources to get higher-quality results: Uncovering signal transduction networks from high-throughput data by integer linear programming.

I’m especially interested in time-series network reconstruction algorithms. If you have a good advice to share with a newcomer to the networks field – don’t hesitate