PT  - JOURNAL ARTICLE
AU  - Mónica Tapia Pacheco
AU  - Pierre Baudot
AU  - Martial A. Dufour
AU  - Christine Formisano-Tréziny
AU  - Simone Temporal
AU  - Manon Lasserre
AU  - Jean Gabert
AU  - Kazuto Kobayashi
AU  - Jean-Marc Goaillard
TI  - Information topology of gene expression profile in dopaminergic neurons
AID  - 10.1101/168740
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 168740
4099  - http://biorxiv.org/content/early/2017/07/26/168740.short
4100  - http://biorxiv.org/content/early/2017/07/26/168740.full
AB  - Extracting high-degree interactions and dependences between variables (pairs, triplets, … k-tuples) is a challenge posed by all omics approaches1, 2. Here we used multivariate mutual information (Ik) analysis3 on single-cell retro-transcription quantitative PCR (sc-RTqPCR) data obtained from midbrain neurons to estimate the k-dimensional topology of their gene expression profiles. 41 mRNAs were quantified and statistical dependences in gene expression levels could be fully described for 21 genes: Ik analysis revealed a complex combinatorial structure including modules of pairs, triplets (up to 6-tuples) sharing strong positive, negative or zero Ik, corresponding to co-varying, clustering and independent sets of genes, respectively. Therefore, Ik analysis simultaneously identified heterogeneity (negative Ik) of the cell population under study and regulatory principles conserved across the population (homogeneity, positive Ik). Moreover, maximum information paths enabled to determine the size and stability of such transcriptional modules. Ik analysis represents a new topological and statistical method of data analysis.