2018 - Bioinformatics Tutorial - Advanced (2018)
  • Bioinformatics Tutorial - Advanced (2018)
  • Getting Startted
  • PART I Basic Skills
    • Introduction of PART I
    • 1.Setup
    • 2.Linux
    • 3.Bash and Github
    • 4.R
    • 5.Python
    • 6.Perl
    • Conclusion of PART I
  • PART II. Basic Bioinfo Analyses
    • Introduction of PART II
    • 1.Mapping, Annotation and QC
    • 2.Expression Matrix
    • 3.Differential Expression
    • Midterm Conclusion
    • 4.Normalization
    • 5.Control Data
    • 6.Motif and Structure
  • PART III. Advanced Bioinfo Analyses
    • Introduction of PART III
    • 1.Machine Learning
    • 2.Feature Selection
    • 3.Deep Learning
  • Appendix
    • Appendix I. Keep Learning
    • Appendix II. Docker Manual
    • Appendix III. Mapping Protocol
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On this page
  • Background
  • Problems and issues
  • Spike-in for Normalization
  • Computational Normalization Tools
  • References
  • More Reading & Practice
  • Video
  • a) Normalization 1
  • b) Normalization 2

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  1. PART II. Basic Bioinfo Analyses

4.Normalization

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Last updated 5 years ago

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Background

Problems and issues

  • Sparsity of data and technical noise ("batch effects") --> will mask the signal of interest

Causes:

Spike-in for Normalization

RNA content (total amount and species) varies

for mRNAs:

  • 92 ERCC molecules

  • 8 mRNAs

  • whole transcriptome HeLa RNAs

for sRNAs:

  • 52(?) sRNA sequences

Caveat:

Typically only half of the spike-in were detected.

Computational Normalization Tools

for Single cell RNA-seq (and exRNA-seq)

  1. scran:

    1. pools multiple cells (samples) in order to estimate cell-specific size factors in the presence of zero inflation and unbalanced differential expression of genes across groups of cells;

    2. precluster (using e.g. rank-based clustering) the cells into smaller, more homogeneous sets

  2. SCnorm

  3. Census

If considering spike-ins:

  1. SAMstrt

  2. GRM

References

More Reading & Practice

See more about normalization, imputation and confounder (e.g. batch effect) in

Video

a) Normalization 1

b) Normalization 2

: 4.QC and Normalization; 5. Imputation and Confounders

Normalizing single-cell RNA sequencing data: challenges and opportunities, Nature Methods, 2017
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Additional Tutorial