The document discusses differential expression analysis using RNA-Seq, focusing on the tools and methods available for interpreting results, including setting cut-offs and gene set enrichment analysis. It explains the advantages of a cut-off-free approach like Gene Set Enrichment Analysis (GSEA) that allows for robust detection of significant gene sets based on their p-values. The presentation also mentions various methods and tools for visualizing and analyzing gene expression data.

1. The biology behind
expression differences
RNA-seq for DE analysis training
Joachim Jacob
20 and 27 January 2014
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.

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

3. 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.

4. 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.

5. 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.

6. 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)

7. 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

8. Getting lists of genes
●
Gene Ontology consortium
●
Reactome:

9. A many-to-many relation
Linking gene IDs to molecular function.
… to binding partners
... to transcription factor
binding sites.

10. Biomart can help you fetch sets

11. Biomart can help you

12. Contingency approach
DE results
Gene set 1
637/5830
15/56
Equal?
(hypergeometric test)

13. Contingency approach
DE results
Gene set 2
637/5830
5/30

14. Contingency approach
DE results
Gene set 3
637/5830
34/78
! Gene set
enriched

15. 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).

16. Contingency table approach tools
http://wiki.bits.vib.be/index.php/Gene_set_enrichment_analysis

17. 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,...))

18. Intuition of GSEA
Gene set 1
Running sum:
Every occurrence
increases the sum,
every absence
decreases the sum.
The maximum is
the MES, the
final score
0
p-value
1
Mootha et al. http://www.nature.com/ng/journal/v34/n3/full/ng1180.html

19. Intuition of GSEA
Gene set 2
Higher running sum MES
Gene set 3
Median running sum MES
Low running sum MES
Gene set 4
The scores are compared to permutated/shuffled gene
set (sample label versus gene label permutation).
0
p-value
1

20. 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.

21. With cut-off applied
Genes involved in
oxidative phosphorylation
Significant DE genes
(p-value <0,05)
Mootha et al. http://www.nature.com/ng/journal/v34/n3/full/ng1180.html

22. 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

23. GSEA has inspired others.
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.
Varemo et al. http://nar.oxfordjournals.org/content/early/2013/02/26/nar.gkt111

24. GSEA has inspired many
Piano
SPIA

25. 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.
The sets
Ranks! Lower is
'more important'

26. 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 downregulated
3) non-directional: the set is enriched in significant DE genes without taking
into account directionality.
4) mixed-directional up: A subset of the set is significantly upregulated
5) distinct-directional up: gene set as a whole is upregulated.

27. Keywords
Gene set
Contingency approach
T-statistic
P-value histogram
GSEA
heatmap
Directionality of expression changes
Write in your own words what the terms mean

28. Break