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NGS for natural scientist
  • 1. Preface
    • How to use this book
    • Motivation
    • Genomic data science as a tool to biologist
    • Next Generation Science (also NGS)
  • 2. Getting started
    • A step by step pipeline tutorial
    • Sequencing chemistry explained by Illumina
    • Joining a course
    • RNA quality and Library prep
    • (optional) My click moment about "Why Linux"
  • 3. Good-to-know beforehand
    • Experiment design
    • Single-end and Paired-end
    • Read per sample and data size
    • Normalization - RPKM/FPKM/TPM
    • Gene annotation
  • 4. Setting up terminal
    • My Linux terminal
    • Linux environment
    • R and RStudio
    • PATH
  • 5. FASTQ and quality control
    • Getting FASTQ files from online database
    • FASTQ quality assessment
  • 6. Mapping/alignment and quantification
    • Salmon
    • DESeq2
  • 7. Visualization
  • 8. Single cell RNA-Seq
  • 9. AWS cloud and Machine Learning
    • Machine Learning in a nutshell
    • R vs Python
    • Setting up ML terminal
    • Data exploration
  • (pending material)
    • graphPad
    • readings for ML
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  1. 3. Good-to-know beforehand

Normalization - RPKM/FPKM/TPM

What is you favorite dividend?

PreviousRead per sample and data sizeNextGene annotation

Last updated 2 years ago

Normalization is essential when comes to statistics. Absolute quantification is not wrong but more of the time we are more capable to understand the graph when control is always 1. In the case of RNA-Seq, to make it super simple, the main stream at the time of writing is TPM and if you are using DESeq2 or edgeR for the differential expression analysis you are automatically using TPM.　 I strongly recommend to watch this blog-post for a deeper understanding of the normalization model.

https://www.rna-seqblog.com/rpkm-fpkm-and-tpm-clearly-explained