ITTACA: a new database for integrated tumor transcriptome array and clinical data analysis - PubMed (original) (raw)
ITTACA: a new database for integrated tumor transcriptome array and clinical data analysis
Adil Elfilali et al. Nucleic Acids Res. 2006.
Abstract
Transcriptome microarrays have become one of the tools of choice for investigating the genes involved in tumorigenesis and tumor progression, as well as finding new biomarkers and gene expression signatures for the diagnosis and prognosis of cancer. Here, we describe a new database for Integrated Tumor Transcriptome Array and Clinical data Analysis (ITTACA). ITTACA centralizes public datasets containing both gene expression and clinical data. ITTACA currently focuses on the types of cancer that are of particular interest to research teams at Institut Curie: breast carcinoma, bladder carcinoma and uveal melanoma. A web interface allows users to carry out different class comparison analyses, including the comparison of expression distribution profiles, tests for differential expression and patient survival analyses. ITTACA is complementary to other databases, such as GEO and SMD, because it offers a better integration of clinical data and different functionalities. It also offers more options for class comparison analyses when compared with similar projects such as Oncomine. For example, users can define their own patient groups according to clinical data or gene expression levels. This added flexibility and the user-friendly web interface makes ITTACA especially useful for comparing personal results with the results in the existing literature. ITTACA is accessible online at http://bioinfo.curie.fr/ittaca.
Figures
Figure 1
The workflow/outline of ITTACA. The first step (I, upper part of the figure) in ITTACA is the choice of the publication the user wishes to study. Then (step II, middle part of the figure) the user can define groups of tumors or select genes (or both). There are four methods for building tumor groups (represented by a right-angled box): manual choice from the list of tumors, selection based on clinical parameters or on survival time (Kaplan-Meier curve), or on a gene expression threshold. The ovals represent the different analyses that can be carried out with ITTACA (step III, bottom part of the figure): (A) KM is the Kaplan-Meier survival curve; it also includes a log-rank test to assess significance of different survival. (B) SAM (8) is a statistical method for finding significantly differentially expressed genes in a set of microarray experiments for a given FDR. (C) The differential expression tests used by ITTACA are the Wilcoxon (non-parametric) test and the Student's (parametric) _t_-test. ITTACA allows a descriptive analysis of the data, with a gene expression barplot (D) and/or the sample's frequency according to the expression level for a gene (E) (frequency distribution).
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