characterisation by Artificial Neural Network]
Ghislain Bidaut 1,2,*
Chris Stoeckert 1
1: Center for Bioinformatics, Department of Genetics, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
2: Integrative Bioinformatics, Centre de Recherche en Cancérologie de Marseille, INSERM
U891 - Institut Paoli-Calmettes, 27 Boulevard Leï Roure, 13009
It is believed that stem cells differentiation and fate are triggered
by a common molecular program that gives those cells the ability to
differentiate into specialized progenitors and fully differentiated
cells. To extract the stemness signature of several cells
types at the transcriptional level, we integrated heterogeneous
microarray experiments performed in different adult and embryonic
tissues (liver, blood, bone, prostate and stomach in Homo
sapiens and Mus musculus). Data were integrated by
generalization of the hematopoietic stem cell hierarchy and by homology
between mouse and human. The variation-filtered and integrated
gene expression dataset was fed to a single-layered neural network to
create a classifier to (i) extract the stemness signature
and (ii) characterize unknown stem cell tissue samples by
attribution of a stem cell differentiation stage. We were able to
characterize mouse stomach progenitor and human prostate progenitor
samples and isolate gene signatures playing a fundamental role for
every level of the stem cell generalized hierarchy. Cross-validation
training led to a 63 gene signature minimizing classsification error on
the input dataset.
Bidaut G, Stoeckert CJ Jr. (in prep.) Large Scale Transcriptome Data Integration Across Multiple Tissues to Decipher Stem Cell Signatures. Methods in Enzymology.
Bidaut G, Stoeckert CJ Jr. Characterization of unknown adult stem cell samples by large scale data integration and artificial neural networks. Pac Symp Biocomput. 2009:356-67.
- The full data are (input dataset and list of markers) is avalable as a single package here. [scann-data.tar.gz].
- The complete SCGAP database is available here with downloads to individual datasets.
Source code (version 1.0) of the classifier can be downloaded here [scann-1.0.tar.gz] - a machine capable of interpreting perl code is needed in order to run it.
Questions? Comments ? Please do not
hesitate to contact directly the corresponding author (Ghislain Bidaut)
from the sourceforge web site.