Enichment-analysis-goseq
unawaz1996
2023-03-16
Last updated: 2023-05-11
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Knit directory: NMD-analysis/
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| Rmd | 84be588 | unawaz1996 | 2023-03-17 | Enrichment analysis |
In this workbook, we are testing for enrichment within discrete sets of DE genes as defined in the DEG analysis.
In order to do this, we will be using goseq, an R
package that performs enrichment analysis whilst taking length bias into
account. It does so by calculating a Probability Weighting Function
(PWF), which gives the probability that a gene will be differentially
expressed based on length alone.
The PWD is calculated by fitting a monotonic spline to the binary data series of differential expression (1 = DE, 0= Not DE) as a function of gene length. The PWF is used to weight the chance of selecting each gene when forming a null distribution for GO category membership. The fact that the PWF is calculated directly from the dataset under consideration makes this approach robust, only correcting for the length bias present in the data.
Results can be interpreted as following:
overrpresented p-value: Over representation in this analysis means that there are more DE genes in categort than expected. P-value relates to the probability of observing this number of DE genes in category by chancenumDEinCat: number of DE genes in categoryExpected: Expected number of genes in categoryadjP: Bonferroni adjustment of over represented p-valueFDR: FDR adjustment of over represented p-value
Databases used for testing
Data was sourced using the misigdbr package.
Hallmark gene sets
Mappings were required from gene to pathway, and Ensembl identifies were used to map from gene to pathway. A total of 4,391 Ensembl IDs were mapped to pathways from the Hallmark set.
C2 gene set
The same mapping process was applied to datasets from c2 signatures. For this analysis, only Wikipathways, KEGG and Reactome gene sets were retrieved, and a total of 11,774 Ensembl IDs were mapped to c2 genesets.
Gene Ontology Gene set
For the analysis of gene-sets from GO database, gene-sets were restricted to those with 3 or more steps back to the ontology root terms.
Enrichment in the DE Gene Set
The first step of analysis using goseq, regardless of the gene-set, is estimation of the probability weight function (PWF) which quantifies the probability of a gene being considered as DE based on a single covariate.
GO terms
For gene ontology analysis, we will be using the GO summaries method.
Essentially, this analysis involves creating a graph for each ontology
term, and removing the node all, as this redundant. For
each GO term, we get the ontology it belongs to, the shortest path back
to the root note, the longest path to the root node and whether it GO
term is a terminal node.
UPF3B KD vs Controls
UPF3A KD vs Controls
Double KD
UPF3A OE
2. C2 database
- UPF3B KD vs Controls
- UPF3A KD vs Controls
- Double KDs vs Controls
- UPF3A OE vs Controls
- UPF3A OE UPF3B KD vs Controls
Results summary
Overlap of terms between enrichment analyses of different groups
Hallmark datasets
- UPF3B KD vs Controls
- UPF3A KD vs Controls
- Double KD vs Controls
- UPF3A OE vs Controls
- UPF3A OE vs Controls
Results summary
- Overlap of Hallmark gene sets
- Summary plots
