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  • Associate Professor
  • Clinical Research Assistant
  • Full Professor
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  • Pharmacist
  • PhD Student
  • Physician
  • Post-doc
  • Project Manager
  • Research Associate
  • Research Engineer
  • Retired scientist
  • Technician
  • Undergraduate Student
  • Veterinary
  • Visiting Scientist
  • Deputy Director of Center
  • Deputy Director of Department
  • Deputy Director of National Reference Center
  • Deputy Head of Facility
  • Director of Center
  • Director of Department
  • Director of Institute
  • Director of National Reference Center
  • Group Leader
  • Head of Facility
  • Head of Operations
  • Head of Structure
  • Honorary President of the Departement
  • Labex Coordinator
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Starting Date
08
Nov 2019
Status
Ongoing
Members
3
Structures
1

About

The CB UTechS and the Bioinformatics and Biostatistics Hub work closely together to support the community involved in single cell data analysis. The single cell data analysis is a highly challenging field that is continuously evolving to tackle complex data originating from omics-technologies.

Two types of bioinformatic support are proposed by the CB UTechS:

  1. We developed a tool that permits data analysis via a graphical interface, meant for scientists who are willing to have a certain autonomy in exploration of their data. Training and assistance are ensured. Currently employed bioinformatic tools an pipelines include:
    • R/Shiny based tools available and maintained on a common computer (Mac4)
    • SCHNAPPs: Shiny app for exploratory single-cell RNAseq data analysis
    • iSEE: Shiny app for visualizing previously analyzed scRNAseq results.
    • pagoda2: Shiny app for scRNAseq data analysis including pathways analyses.

A computer with all necessary software and hardware to handle small to mid sized projects is accessible via CB UTechS’s ppms reservation site in the Aquarium of CFJ building.

   2.  Collaboration for full data analysis is proposed by dedicated bioinformaticians.

A group of experts from the Bioinformatics and Biostatistics Hub proposes exploratory and statistical analysis of scRNA-Seq data tailored to the needs of each project. Starting from the raw data or the count matrix, and depending on the specific biological question, we work hand-in-hand with researchers to perform exploratory analysis of their data and develop new pipelines adapted to their needs.  These analyses allow to uncover the heterogeneity of cell population and to characterise the different cell sub-populations by identifying marker genes and enriched gene sets using functional analysis. When relevant, we also trace virtual lineages by applying pseudo-time analysis, and integrate and compare the identified cell sub-populations with published single-cell datasets.

Single cell data analysis – sharing within the community. In order to enucleate the single-cell bioinformatic community, we organize monthly meetings open to the entire scientific community. The meetings are open to the entire research community, welcoming colleagues from different academic institutions. Topics such as comparing data analysis pipelines, troublshooting, sharing feedback from relevant scientific conferences are discussed.

To participate to the meetings please contact bernd.jagla@pasteur.fr.

How to access.

single_cell@pasteur.fr