part 6 of rna-seq for de analysis: detecting biology from differential expression analysis results
DESCRIPTION
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.htmlTRANSCRIPT
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RNA-seq for DE analysis training
The biology behind expression differencesJoachim Jacob22 and 24 April 2014
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Overview
http://www.nature.com/nprot/journal/v8/n9/full/nprot.2013.099.html
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Analyzing the DE analysis results
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● ...
http://wiki.bits.vib.be/index.php/Gene_set_enrichment_analysis
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Getting lists of genes
● Gene Ontology consortium
● Reactome:
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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
5/30
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Contingency approach
637/5830
DE results Gene set 3
34/78
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).
Pick me!
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Contingency table approach tools
http://wiki.bits.vib.be/index.php/Gene_set_enrichment_analysis
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DAVID uses the contingency approach
Need to define the complete gene set tested!
Your list of DE genes
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Cut-off free approach: GSEA
No cut-off needs to be chosen using GSEA and derived methods!
We take into account all genes for which we get a reliable p-value. (see the p-value histogram chart).
The genes are sorted/ranked according to 'level of surprise', i.e. by their p-value. (other options are test-statistics (T,...))
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Intuition of GSEA
0 1p-value
Gene set 1
Mootha et al. http://www.nature.com/ng/journal/v34/n3/full/ng1180.html
Running sum:Every occurrence
increases the sum, every absence
decreases the sum.The maximum is
the MES, the final score
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Intuition of GSEA
0 1p-value
Gene set 2 Higher running sum MES
Gene set 3
Gene set 4
Median running sum MES
Low running sum MES
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
Significant DE genes (p-value <0,05)
Genes involved in oxidative phosphorylation
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Cut-off free approach
Genes involved in oxidative phosphorylation are nearly all slightly overexpressed. This can be detected by gene set analysis.
Mootha et al. http://www.nature.com/ng/journal/v34/n3/full/ng1180.html
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GSEA has inspired others.
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.
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GSEA has inspired others
Piano
SPIA
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Piano provides a consensus output
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.
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KeywordsGene set
Contingency approach
T-statistic
P-value histogram
GSEA
heatmap
Directionality of expression changes
The meaning of the p-value cut-off
Write in your own words what the terms mean
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Exercise
● → Exploring the biology behind observed changes
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Finish!
● Congratulate yourself for your new skills! Enjoy!
Figure: http://kristiholl.net/writers-blog/2013/10/press-on-to-finish-strong/