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Functional enrichment analysis and more via the g:Profiler toolkit

Project description

gprofiler

Project description

The official Python 3 interface to the g:Profiler toolkit for enrichment analysis of functional (GO and other) terms, conversion between identifier namespaces and mapping orhologous genes in related organisms.

It has an optional dependency on pandas.

Installing gprofiler

the recommended way of installing gprofiler is using pip

pip install gprofiler-official

Legacy version

The 0.3.x series of gprofiler-official is incompatible with the 1.0.x series. We changed the major version number to signify the breaking changes in the API. To install the previous version of gprofiler-official, use the command

pip install gprofiler-official==0.3.5

Tools:

To use any of the tools in the g:Profiler toolkit, first initialize the GProfiler object.

from gprofiler import GProfiler
gp = GProfiler(
    user_agent='ExampleTool', #optional user agent
    return_dataframe=True, #return pandas dataframe or plain python structures    
)

g:GOSt (profile)

from gprofiler import GProfiler

gp = GProfiler(return_dataframe=True)
gp.profile(organism='hsapiens',
            query=['NR1H4','TRIP12','UBC','FCRL3','PLXNA3','GDNF','VPS11'])

Output:

source      native                                            name   p_value  significant                                        description  term_size  query_size  intersection_size  effective_domain_size  precision    recall    query                               parents
GO:BP  GO:0048585     negative regulation of response to stimulus  0.004229         True  "Any process that stops, prevents, or reduces ...       1610           7                  6                  17622   0.857143  0.003727  query_1  [GO:0048583, GO:0048519, GO:0050896]
GO:BP  GO:0002224            toll-like receptor signaling pathway  0.016351         True  "Any series of molecular signals generated as ...        133           7                  3                  17622   0.428571  0.022556  query_1                          [GO:0002221]
GO:BP  GO:0048486      parasympathetic nervous system development  0.026199         True  "The process whose specific outcome is the pro...         19           7                  2                  17622   0.285714  0.105263  query_1              [GO:0048483, GO:0048731]
GO:BP  GO:0034162          toll-like receptor 9 signaling pathway  0.038733         True  "Any series of molecular signals generated as ...         23           7                  2                  17622   0.285714  0.086957  query_1                          [GO:0002224]
GO:BP  GO:0002221  pattern recognition receptor signaling pathway  0.039782         True  "Any series of molecular signals generated as ...        179           7                  3                  17622   0.428571  0.016760  query_1                          [GO:0002758]
CORUM  CORUM:5669                           PlexinA3-Nrp1 complex  0.049767         True                              PlexinA3-Nrp1 complex          2           2                  1                   3620   0.500000  0.500000  query_1                       [CORUM:0000000]
CORUM  CORUM:5759                           PLXNA3-RANBPM complex  0.049767         True                              PLXNA3-RANBPM complex          2           2                  1                   3620   0.500000  0.500000  query_1                       [CORUM:0000000]
  • source is the code for the datasource
  • native is the ID for the enriched term/functional category in its native namespace.
  • name is the readable name for the enriched term, description is the longer description if available.
  • p_value is the corrected p-value for the
  • term_size, query_size, intersection_size, effective_domain_size are parameters to the hypergeometric test.
  • query is the name of the query and is significant if multiple queries were made in one call (e.g gp.profile(query={'query1':['NR1H4'], 'query2':['NR1H4','TRIP12']}))

Setting the parameter no_evidences=False would add the column intersections (a list of genes that are annotated to the term and are present in the query ) and the column evidences (a list of lists of GO evidence codes for the intersecting genes)

NB! the parameter combined significantly changes the output structure by packing the results of distinct queries together. For example:

gp.profile(query={'query1':['NR1H4'], 'query2':['NR1H4','TRIP12']}, combined=True)

Output (truncated):

source      native                                               name                                     p_values                                        description  term_size query_sizes intersection_sizes  effective_domain_size                                           parents
GO:MF  GO:1902122                      chenodeoxycholic acid binding  [0.024822026073022193, 0.04964405214614093]  "Interacting selectively and non-covalently wi...          1      [1, 2]             [1, 1]                  17516                          [GO:0032052, GO:0005496]
GO:MF  GO:0035257                   nuclear hormone receptor binding                  [1.0, 0.033391754400990514]  "Interacting selectively and non-covalently wi...        154      [1, 2]             [1, 2]                  17516                          [GO:0051427, GO:0061629]
GO:MF  GO:0051427                           hormone receptor binding                   [1.0, 0.04929258983003374]  "Interacting selectively and non-covalently wi...        187      [1, 2]             [1, 2]                  17516                                      [GO:0005102]

g:Convert (convert)

from gprofiler import GProfiler

gp = GProfiler(return_dataframe=True)
gp.convert(organism='hsapiens',
            query=['NR1H4','TRIP12','UBC','FCRL3','PLXNA3','GDNF','VPS11'],
            target_namespace='ENTREZGENE_ACC')

Output:

incoming converted  n_incoming  n_converted    name                                        description                           namespaces    query
  NR1H4      9971           1            1   NR1H4  nuclear receptor subfamily 1 group H member 4 ...  ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE  query_1
 TRIP12      9320           2            1  TRIP12  thyroid hormone receptor interactor 12 [Source...  ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE  query_1
    UBC      7316           3            1     UBC    ubiquitin C [Source:HGNC Symbol;Acc:HGNC:12468]  ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE  query_1
  FCRL3    115352           4            1   FCRL3  Fc receptor like 3 [Source:HGNC Symbol;Acc:HGN...  ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE  query_1
 PLXNA3     55558           5            1  PLXNA3       plexin A3 [Source:HGNC Symbol;Acc:HGNC:9101]             ENTREZGENE,HGNC,WIKIGENE  query_1
   GDNF      2668           6            1    GDNF  glial cell derived neurotrophic factor [Source...  ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE  query_1
  VPS11     55823           7            1   VPS11  VPS11, CORVET/HOPS core subunit [Source:HGNC S...  ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE  query_1
 PLXNA3     55558           5            1  PLXNA3       plexin A3 [Source:HGNC Symbol;Acc:HGNC:9101]             ENTREZGENE,HGNC,WIKIGENE  query_1

incoming column lists the input gene, converted lists the gene in the target namespace (Entrez Gene accession number in this case).

g:Orth (orth)

from gprofiler import GProfiler

gp = GProfiler(return_dataframe=True)
gp.orth(organism='hsapiens',
            query=['NR1H4','TRIP12','UBC','FCRL3','PLXNA3','GDNF','VPS11'],
            target='mmusculus')

Output:

incoming        converted       ortholog_ensg  n_incoming  n_converted  n_result    name                                        description                           namespaces
  NR1H4  ENSG00000012504  ENSMUSG00000047638           1            1         1   Nr1h4  nuclear receptor subfamily 1, group H, member ...  ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE
 TRIP12  ENSG00000153827  ENSMUSG00000026219           2            1         1  Trip12  thyroid hormone receptor interactor 12 [Source...  ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE
    UBC  ENSG00000150991  ENSMUSG00000008348           3            1         1     Ubc      ubiquitin C [Source:MGI Symbol;Acc:MGI:98889]  ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE
  FCRL3  ENSG00000160856                 N/A           4            1         1     N/A                                                N/A  ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE
 PLXNA3  ENSG00000130827  ENSMUSG00000031398           5            1         1  Plxna3       plexin A3 [Source:MGI Symbol;Acc:MGI:107683]             ENTREZGENE,HGNC,WIKIGENE
   GDNF  ENSG00000168621  ENSMUSG00000022144           6            1         1    Gdnf  glial cell line derived neurotrophic factor [S...  ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE
  VPS11  ENSG00000160695  ENSMUSG00000032127           7            1         1   Vps11  VPS11, CORVET/HOPS core subunit [Source:MGI Sy...  ENTREZGENE,HGNC,UNIPROT_GN,WIKIGENE

incoming is the input gene, converted is the canonical Ensembl ID for the input gene, ortholog_ensg is the canonical Ensembl ID for the orthologous gene in the target organism.

g:SNPense (snpense)

from gprofiler import GProfiler

gp = GProfiler(return_dataframe=True)
gp.snpense(query=['rs11734132', 'rs7961894', 'rs4305276', 'rs17396340'])

Output:

rs_id chromosome strand      start        end              ensgs gene_names                                           variants
rs11734132                           -1         -1                 []         []  {'intron_variant': 0, 'non_coding_transcript_v...
 rs7961894         12      +  121927677  121927677  [ENSG00000158023]    [WDR66]  {'intron_variant': 3, 'non_coding_transcript_v...
 rs4305276          2      +  240555596  240555596  [ENSG00000144504]   [ANKMY1]  {'intron_variant': 57, 'non_coding_transcript_...
rs17396340          1      +   10226118   10226118  [ENSG00000054523]    [KIF1B]  {'intron_variant': 8, 'non_coding_transcript_v...

  • rs_id is the input rs-number
  • chromosome, strand, start and end encode the position of the variation
  • ensgs and gene_names are lists of protein-encoding genes associated with the rs-number.
  • variants are predicted variant effects.

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