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 http://bioinfo-out.curie.fr/ittaca/
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):??-??.
Student t-test and Wilcoxon Rank Sum test
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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.
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Significance Analysis of Microarrays (SAM)
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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.
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Visualization using histograms and distribution curves
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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.
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Survival curves (Kaplan-Meier)
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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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