Making Sense of Microbes - Jack Simpson

398 views

Published on

Metagenomic Analysis Workflow

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
398
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
7
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Making Sense of Microbes - Jack Simpson

  1. 1. Making Sense of Microbes Metagenomic Analysis Workflow Jack Simpson 10 Februrary 2014 CSIRO COMPUTATIONAL INFORMATICS
  2. 2. Cells in the human body Microbial: 100 trillion 2 | Jack Simpson Human 10 trillion
  3. 3. Human Microbiome Project • Investigate impact on human health • ~ 240 healthy individuals • 18 microbial habitats (e.g. airways) • > 5000 samples • Taxonomic marker: 16S rRNA gene 3 | Jack Simpson
  4. 4. OTU counts Samples 4 | Jack Simpson
  5. 5. Metagenomics workflow • Three areas I found interesting over the summer • Background of the data • Working with count data • Finding associations 5 | Jack Simpson
  6. 6. Where does metagenomic data start? • Where did our counts and OTUs come from? • Count data is not raw data: many processing decisions • 16S rRNA gene resolution and primers • Multiple variable regions • Lab protocols and comparing projects • Biological data starts in the real world 6 | Jack Simpson
  7. 7. Working with count data 7 | Jack Simpson
  8. 8. Zeroes: Much Ado About Nothing…? • Does absence of evidence == evidence of absence? • What do we do with zeroes? • Remove or Pseudocounts? • When to remove/replace? • Merged at the class level: visualise and replace zeroes 8 | Jack Simpson
  9. 9. Heatmap of all counts 9 | Jack Simpson
  10. 10. Beware hidden complexity 10 | Jack Simpson
  11. 11. Heatmap of grouped counts 11 | Jack Simpson
  12. 12. Zoom in on the heatmap 12 | Jack Simpson
  13. 13. Processing the Data • OTU count data analysis • Dealt with zeroes • Visualised the data • Normalization and transformation: log or Aitchison’s CLR? • What do different transformations do to the data with different sample numbers? • See artefacts related to discretization and zeroes 13 | Jack Simpson
  14. 14. Gut log compositional and log raw data 14 | Jack Simpson
  15. 15. Gut log compositional & clr compositional 15 | Jack Simpson
  16. 16. Finding associations • Warning: compositional data! • Be careful with correlation • Fractions are not independent == negative correlation • What can be done? • Proportionality 16 | Presentation title | Presenter name
  17. 17. Summary • Metagenomic data background • Processing our data • Looking for associations the right way 17 | Jack Simpson
  18. 18. Thank-you! 18 | Jack Simpson

×