Too few or too many genes!

[Niranjan Shirgaonkar]

I have a list of only 176 genes i want to run GSEA on. I selected KEGG.

If I put in 176, I get the error

"All gene sets are skipped! Please try to descrease the minimum set size."

So these are apparently too many

Same error if i put in a 150 or a 100 genes

Now if i reduce the number and run it on 50 genes

ERROR: The number of annotated IDs for all functional categories are not from 5 to 2000 for the GSEA enrichment method.

So this is apparently too less now

Could you please help figure out the problem?

This is my gene list

SSR4    0.995995248
IGHG4    0.873736271
SELENOK    0.833328525
SPCS3    0.779148637
IGHV4-80    0.766559205
MANF    0.760748513
PLPP5    0.758969439
DNAJB9    0.730452413
TXNDC15    0.722928574
TMEM59    0.680752617
IGLL5    0.677626172
IGHV4-28    0.675726645
IGLC2    0.659050684
IGLV2-8    0.647498798
IGLV2-11    0.643780926
IGLV2-23    0.630895388
PRSS21    0.630035506
DHRS9    0.613239788
PIM1    0.606779955
JSRP1    0.606221422
PTP4A3    0.572108489
IGLV2-18    0.571524445
IGHJ4    0.571022905
U62317.5    0.56065642
PRDM1    0.558072948
IGHV1-69    0.535255521
TMEM39A    0.525989917
IGLV3-1    0.524345353
ITGA8    0.502602975
YIPF2    0.502267723
GPR160    0.500531532
B9D1    0.498110635
SIL1    0.492119169
SERPINI1    0.483608628
AGA    0.474832585
AC022509.3    0.4731302
IGHV4-31    0.471221236
TMED9    0.455217867
TOR3A    0.453777421
TBCEL    0.442426272
PRPSAP2    0.440056324
IGLV3-9    0.424126502
CHODL    0.414850383
SLC25A23    0.412907325
NUGGC    0.410263117
SLC39A11    0.40151748
UBA5    0.401397502
CEACAM21    0.401185496
NEB    0.398261089
QPRT    0.389665131
MANEA-DT    0.389619818
MAGEH1    0.387614085
IGHV2-5    0.38556746
CYBC1    0.38203569
ICAM4    0.38143859
MESP1    0.380095479
IGLV7-46    0.37433173
SCNN1B    0.37283311
PRR34-AS1    0.372240389
CNKSR1    0.362981877
IGHV4-61    0.360793408
IGHV4-4    0.357268678
AC007728.2    0.356094683
DGCR6    0.352209401
KNTC1    0.351706993
ABHD8    0.347650831
TMEM263    0.342679473
IGHV3-64D    0.340223178
FAM30A    0.33950963
ST6GAL1    0.326856032
AC026202.2    0.324394066
CASP3    0.323585774
GGH    0.323039624
IGHV4-55    0.322565604
APOM    0.320466105
SMOX    0.312342336
ASTN2    0.304918381
AC020911.2    0.304143552
DENND5B    0.304037171
GEMIN7    0.303730933
CIP2A    0.301325499
MSL3    0.297470281
TMEM205    0.287454248
IMP4    0.284462838
IGLV9-49    0.284182704
AC062017.1    0.274579248
REM2    0.27418051
SPATC1L    0.272382389
DPM3    0.272012716
PGM3    0.270706493
GPAA1    0.267538557
IGLV4-60    0.266484359
MFSD3    0.25849318
AC078795.1    0.257847497
LINC01759    0.257847497
U62317.3    0.257847497
BEX5    0.257239489
H1FX    0.255555983
LMNTD2    0.253768408
SCN2A    0.253768408

[Niranjan Shirgaonkar]

The analysis worked when I put the

minimum number of genes for a category = 3