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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Bioconductor workflow for single-cell RNA sequencing: Normalization, dimensionality reduction, clustering, and

Fanny Perraudeau1, Davide Risso2, Kelly Street1

  • 1Graduate Group in Biostatistics, University of California, Berkeley, Berkeley, CA, 94720, USA.

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Summary

Novel single-cell RNA sequencing reveals gene expression at the cellular level. This study presents a computational workflow for analyzing stem cell differentiation, enabling lineage inference and rare cell type discovery.

Keywords:
RNA-seqclusteringdifferential expressiondimensionality reductionlineage inferencenormalizationsingle-cellworkflow

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Area of Science:

  • Genomics
  • Computational Biology
  • Developmental Biology

Background:

  • Single-cell transcriptome sequencing offers high-resolution gene expression analysis.
  • Investigating complex biological processes like stem cell differentiation requires advanced computational tools.

Purpose of the Study:

  • To present an integrated computational workflow for single-cell RNA sequencing data analysis.
  • To provide a tutorial for methodology and software for key tasks in analyzing stem cell differentiation.

Main Methods:

  • Dimensionality reduction with adjustments for zero inflation, overdispersion, and covariates.
  • Cell clustering using resampling-based sequential ensemble clustering.
  • Inference of cell lineages and pseudotimes.
  • Differential gene expression analysis along inferred cell lineages.

Main Results:

  • The workflow successfully applies to stem cell differentiation in mouse olfactory epithelium.
  • The methodology addresses statistical and computational challenges in single-cell RNA sequencing data.
  • The software facilitates robust analysis from dimensionality reduction to lineage inference.

Conclusions:

  • This integrated workflow provides a comprehensive approach to single-cell RNA sequencing data analysis.
  • The presented methods and software are valuable for studying stem cell differentiation and identifying rare cell types.