Running Enrichr in python using a background gene list
I am trying to run Enrichr in python using a background gene list as per https://gseapy.readthedocs.io/en/latest/gseapy_example.html (2.3.2.2. Enrichr Web Service (with background input)). I got the following to work without specifying a background:
enr_bg = gp.enrichr(gene_list="3383CCGs.txt",
gene_sets=['KEGG_2019_Mouse'],
outdir=None
)
But then I try add the background argument to the script and it doesn't work:
enr_bg_back_KEGG = gp.enrichr(gene_list="3383CCGs.txt",
gene_sets=['KEGG_2019_Mouse'],
background="Background13569.txt",
outdir=None,
)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Input In [23], in <cell line: 1>()
----> 1 enr_bg_back_KEGG = gp.enrichr(gene_list="3383CCGs.txt",
2 gene_sets=['KEGG_2019_Mouse'],
3 background="Background13569.txt",
4 outdir=None,
5 )
TypeError: enrichr() got an unexpected keyword argument 'background'
Any ideas on how to input the background list into the command??
1 answer
This is for human though. I just tested and it works fine. Please make sure to provide correct path to your gene_list and background
enr_bg = gp.enrichr(gene_list="gene_list.txt",
gene_sets=['MSigDB_Hallmark_2020','KEGG_2021_Human'],
# organism='human', # organism argment is ignored because user input a background
background= "all_human_genes.txt", #"hsapiens_gene_ensembl",
outdir=None, # don't write to disk
)
enr_bg.results.head()
Gene_set Term P-value Adjusted P-value Old P-value Old adjusted P-value Odds Ratio Combined Score Genes
0 MSigDB_Hallmark_2020 TNF-alpha Signaling via NF-kB 8.934977e-16 2.233744e-14 0 0 8.597135 297.902671 BTG2;BCL2A1;PLEK;IRS2;LITAF;IFIH1;PANX1;DRAM1;...
1 MSigDB_Hallmark_2020 Complement 8.934977e-16 2.233744e-14 0 0 8.597135 297.902671 FCN1;LRP1;PLEK;LIPA;CA2;CASP3;LAMP2;S100A12;FY...
2 MSigDB_Hallmark_2020 IL-6/JAK/STAT3 Signaling 1.891480e-15 3.152466e-14 0 0 15.259576 517.321263 IL4R;TGFB1;IL1R1;IFNGR1;IL10RB;ITGB3;IFNGR2;IL...
3 MSigDB_Hallmark_2020 Inflammatory Response 4.938414e-13 6.173017e-12 0 0 7.479140 211.933110 LYN;IFITM1;BTG2;IL4R;CD82;IL1R1;IFNGR2;ITGB3;F...
4 MSigDB_Hallmark_2020 heme Metabolism 3.693816e-12 3.693816e-11 0 0 7.116750 187.343901 SLC22A4;MPP1;BNIP3L;BTG2;ARHGEF12;NEK7;GDE1;FO...
It should work no matter what species it is. I saw your corresponding R code in other POST. Please so spend some time in basic R and Python; you will get there.
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