This presentation is available under the Creative Commons Attribution-ShareAlike 3.0 Unported License. Please refer to http://www.bits.vib.be/ if you use this presentation or parts hereof. RNA-seq for DE analysis training The biology behind expression differences Joachim Jacob 22 and 24 April 2014
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Part 6 of RNA-seq for DE analysis: Detecting biology from differential expression analysis results
Sixth part of the training session 'RNA-seq for Differential expression analysis'. We explain how we extract biological meaningful results from differential expression analysis results, based on RNA-seq. Interested in following this session? Please contact http://www.jakonix.be/contact.html
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This presentation is available under the Creative Commons Attribution-ShareAlike 3.0 Unported License. Please refer to http://www.bits.vib.be/ if you use this presentation or parts hereof.
RNA-seq for DE analysis training
The biology behind expression differencesJoachim Jacob22 and 24 April 2014
The 'detect differential expression' tool gives you four results: the first is the report including graphs.
Only lower than cut-off and with indep filtering.
All genes, with indep filtering applied.
Complete DESeq results, without indep filtering applied.
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Analyzing the DE analysis results
Only lower than cut-off and with indep filtering.
All genes, with indep filtering applied.
Complete DESeq results, without indep filtering applied.
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Setting a cut-off
You choose a cut-off! You can go over the genes one by one, and look for 'interesting' genes, and try to link it to the experimental conditions.
Alternative: we can take all genes, ranked by their p-value (which stands a 'level of surprise'). Pro: we don't need our arbitrary cut-off.
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Analysis of the list of DE genes
All genes (6666 yeast genes)Genes sensible to test (filtered out 10% of the lowest genes) (5830 yeast genes)
DE genes with p-value cut-off of 0,01 (637 genes)
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Gene set enrichment
● We use the knowledge already available on biology. We construct list of genes for:● Pathways● Biological processes● Cellular components● Molecular functions● Transcription binding sites● ...
A many-to-many relationLinking gene IDs to molecular function.
… to binding partners
... to transcription factorbinding sites.
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Biomart can help you fetch sets
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Biomart can help you
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Contingency approach
637/5830
DE results Gene set 1
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Is the portion ofDE Genes equal?
(hypergeometric test)
Significantly DE genes
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Contingency approach
637/5830
DE results Gene set 2
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Contingency approach
637/5830
DE results Gene set 3
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Not equal! Gene set enriched
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Artificial?DE results
But our cut-off remains artificial, arbitrarily chosen. Rerun with different cut-off: you will detect other significant sets!
The background needs to be carefully chosen. This approach favors gene sets with genes whose expression differs a lot ('high level of surprise', p-value).
The scores are compared to permutated/shuffled gene set (sample label versus gene label permutation).
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Cut-off free approach: GSEA
The advantages:● Robustness about mapping errors influencing counts● The set can be detected even if some genes are not present.● Tolerance if gene set contains incorrect genes.● Strong signal if all genes are only seemingly lightly overexpressed.
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With cut-off applied
Mootha et al. http://www.nature.com/ng/journal/v34/n3/full/ng1180.html
Varemo et al. http://nar.oxfordjournals.org/content/early/2013/02/26/nar.gkt111
Different methods exist to rank the genes, to calculate the running sum, and to check significance of the running sum. In addition, directionality of the changes can be incorporated.
Piano has combined different GSEA methods and calculates a consensus score. It does this for 5 different types of 'directionality classes'.
The main output is a heatmap with gene set significantly enriched, depleted or just changed.
Ranks! Lower is 'more important'Ranks! Lower is 'more important'
The sets
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Piano provides a consensus output
1) distinct-directional down: gene set as a whole is downregulated.2) mixed-directional down: A subset of the set is significantly downregulated3) non-directional: the set is enriched in significant DE genes without takinginto account directionality.4) mixed-directional up: A subset of the set is significantly upregulated5) distinct-directional up: gene set as a whole is upregulated.