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In structural engineering, the analysis of beams subjected to varying loads is a critical aspect of understanding the behavior and performance of these structural elements. A common scenario involves a beam subjected to a combination of different load distributions.
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Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells
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Single Cell Explorer, collaboration-driven tools to leverage large-scale single cell RNA-seq data.

Di Feng1, Charles E Whitehurst2, Dechao Shan3

  • 1Computational Biology, Boehringer Ingelheim Pharmaceuticals, Inc., 900 Ridgebury Road, Ridgefield, CT, 06877, USA. di.feng@boehringer-ingelheim.com.

BMC Genomics
|August 29, 2019
PubMed
Summary
This summary is machine-generated.

Single Cell Explorer is a user-friendly web tool that simplifies single-cell RNA sequencing analysis. It enables collaborative annotation and exploration of cell expression data for researchers without extensive bioinformatics expertise.

Keywords:
D3DjangoPipelinePythonRNA-seqSingle cellTranscriptomicsVisualization

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Single cell transcriptome sequencing offers high-resolution biological insights but requires advanced computational skills for data analysis.
  • A gap exists between the technical demands of single-cell data and the biological expertise needed for interpretation.

Purpose of the Study:

  • To develop an accessible platform for collaborative annotation and exploration of single-cell RNA sequencing data.
  • To bridge the gap between computational and experimental biologists in single-cell data analysis.

Main Methods:

  • Developed Single Cell Explorer, a Python-based web server application.
  • Integrated Jupyter notebooks for data processing and analytic workflows.
  • Implemented features for collaborative annotation, differential gene expression analysis, and cell type identification.

Main Results:

  • Single Cell Explorer provides a user-friendly interface for iterative and collaborative annotation of cell expression phenotypes.
  • The platform facilitates easy sharing and modification of annotations among multiple users.
  • Users can identify differential gene expression and annotate cell types using marker genes without coding skills.

Conclusions:

  • Single Cell Explorer is a free, collaborative tool for exploring, annotating, and sharing single-cell transcriptomic analysis results.
  • The software promotes deeper understanding and innovation by simplifying single-cell RNA-seq data sharing and querying.
  • It offers a flexible environment with a centralized database server supporting data portal functionality.