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ANALYTICS

FGSEA

Fast gene set enrichment analysis

Fast gene set enrichment analysis

Input data

Table files (with .txt or .tsv file extension) either from:

  • DESeq2 output, or
  • LIMMA output, or
  • A tab-delimited file with two columns: gene identifiers + and a numeric column, for example
gene_symbol	t_statistics
TP53	3.43
MUC16	4.59
TTN	1.02
CSMD3	-0.23
...

Note:

  • Gene identifiers can be either ENSG, Affy probeset ID, Entrez ID, or HNSC gene symbols.
  • If the table is derived from differential expession analysis, the gene list should be a full list (i.e. including both significant and non-significant genes)

Output files

In output folder, for each input gene list, a corresponding subfolder will be created with following files

  • toplist_fgsea.tsv: A table with pathway enrichment scores and additional statistics
  • enrichment_scores.json: A json file containing data for enrichment plots

For example,

`-- output
    |-- gordon_gse71203
        |-- enrichment_scores.json
        `-- toplist_fgsea.tsv

Parameter settings

Options

  • collection — Name of pathways or gene sets collection, e.g. “H: hallmark gene sets”, “C2: curated gene sets”, “C5: gene ontology (GO)”
  • number_of_pathways — Max number of top pathways to show (with p.value <0.05), default: 20
  • collapse_pathways — Whether to collpase related pathways into independent ones, default: true

References

Please cite:

Korotkevich et al., bioRxiv 2020

FGSEA

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