ITTACA : Integrated Tumor Transcriptome Array and Clinical data Analysis

Participants: Adil El Filali, Séverine Lair, Catia Verbeke, Philippe La Rosa, François Radvanyi, Emmanuel Barillot

Many researchers in oncology would like to confront their results to transcriptomic data taken from the scientific literature. For this reason, ITTACA (Integrated Tumor Transcriptome Array and Clinical data Analysis) was designed and developped by the Bioinformatics group and Molecular oncology group at the Institute Curie. This platform offers users the possibility to access transcriptomic and clinical data from published studies on tumors. ITTACA combines a relational database (MySQL) with a web interface (written in PHP, HTML and javascript) to allow the visualization and analysis of genetic expression. ITTACA makes it possible to highlight genes that are differentially expressed (using Student test, Wilcoxon test, and SAM) between two groups presenting different user-defined anatomo-clinical or biological characteristics. ITTACA currently includes data sets from breast, bladder, eyes tumours as well as normal tissue gene expression levels, and is available at
ITTACA is also described in Elfilali A. et al. ITTACA: a new database for integrated tumor transcriptome array and clinical data analysis. Nucleic Acids Res. 2006 Jan 1;34(1):??-??.

The tools available on ITTACA as follows:

Student t-test and Wilcoxon Rank Sum test

The t-test and Wilcoxon Rank Sum test are used to determine if two population means are equal. By performing these tests in ITTACA, it is possible to compare the transcriptomic expression means of one or more genes (chosen by user), for two sets of samples. The results appear as boxplots showing the p-value for each gene.

Significance Analysis of Microarrays (SAM)

SAM is a statistical tool used to detect genes that are differentially expressed between two groups (for example, two groups with different experimental conditions) (Tusher et al, 2001). By defining two patient groups and a False Discovery Rate (FDR), it is possible to obtain a Q-Q plot and a list of differentially expressed genes.

Visualization using histograms and distribution curves

Using ITTACA, it is possible to have a global vision of expression data. For one or more genes selected by the user, ITTACA can generate a histogram (representing the expression level for each sample) and a distribution curve. This graphical representation permits the user to constitute 2 groups according to the median or to visualize the overall expression.

Survival curves (Kaplan-Meier)

Survival curves show the fraction of all individuals that survive at each time point plotted on the X axis. ITTACA creates survival curves using the Kaplan-Meier method and allows the comparison of two survival curves using the log-rank test. To create these curves, data about overall survival (and/or relapse free-survival) and patient status must be available.


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