part 6 of rna-seq for de analysis: detecting biology from differential expression analysis results

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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

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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).

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/