Datasets from 10X

Single-cell datasets visualizations : PCA Versus tSNE

Datasets from 10X

Single-cell datasets visualizations : PCA Versus tSNE

Overview

The ISPED Summer School has been a perfect moment to talk about single-cell RNA-seq visualization and its importance in the current researches. Indeed, PCA tends to be set aside by methods such as tSNE because it gives deeper pieces of information on the given datasets. Serendipitously the conclusions over that tool must be manipulated with parsimony.

That course discusses most of the pros and cons of the two main visualization methods in immunoogy which are PCA and tSNE.

Current Course

  • What is single-cell RNA-Seq ?

  • What is the structure of a single-cell dataset and how to prepare it to further analyses ?

  • Objectives of PCA : interprete and visualize ?

  • Main objective of tSNE : compress the interesting information in 1 or 2 components. How to communicate ?

Practical work on R

tSNE and PCA on single-cell RNA-Seq. How to ?: Simply how to perform PCA and tSNE on a given RNA-Seq Single-Cell dataset. The correct eventual conclusions is left to the reader

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Hadrien Lorenzo
PhD student in Biostatistics