The biology behind expression differences

RNA-seq for DE analysis training

Joachim Jacob20 and 27 January 2014

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Overview

http://www.nature.com/nprot/journal/v8/n9/full/nprot.2013.099.html

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.

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.

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.

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)

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

Getting lists of genes

● Gene Ontology consortium

● Reactome:

A many-to-many relationLinking gene IDs to molecular function.

… to binding partners

... to transcription factorbinding sites.

Biomart can help you fetch sets

Biomart can help you

Contingency approach

637/5830

DE results Gene set 1

15/56

Equal?(hypergeometric test)

Contingency approach

637/5830

DE results Gene set 2

5/30

Contingency approach

637/5830

DE results Gene set 3

34/78

! Gene set enriched

Artificial?DE results

But 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).

Contingency table approach tools

http://wiki.bits.vib.be/index.php/Gene_set_enrichment_analysis

Cut-off free approach

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,...))

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

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).

Cut-off free approach

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.

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

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

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.

GSEA has inspired many

Piano

SPIA

Piano provides a consensus output

Piano has combined different 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

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.

KeywordsGene set

Contingency approach

T-statistic

P-value histogram

GSEA

heatmap

Directionality of expression changes

Write in your own words what the terms mean

Break