Error importing in API mode: ImportError('On Windows, cffi mode "ANY" is only "ABI".')
Trying to import in ABI mode.
R callback write-console: 
  
R callback write-console: clusterProfiler v4.18.4 Learn more at https://yulab-smu.top/contribution-knowledge-mining/

Please cite:

T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan,
X Fu, S Liu, X Bo, and G Yu. clusterProfiler 4.0: A universal
enrichment tool for interpreting omics data. The Innovation. 2021,
2(3):100141
  
R callback write-console: <class 'UnicodeDecodeError'> 'utf-8' codec can't decode byte 0xd4 in position 1: invalid continuation byte <traceback object at 0x0000014EE961C540>
R callback write-console: The following object is masked from 'package:stats':

    filter

  
R callback write-console: In addition:   
R callback write-console: Warning message:
  
R callback write-console: <class 'UnicodeDecodeError'> 'utf-8' codec can't decode byte 0xb3 in position 0: invalid start byte <traceback object at 0x0000014EEC7E0200>
R callback write-console: In addition:   
R callback write-console: Warning messages:
  
R callback write-console: 1:   
R callback write-console: In preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam,  :  
R callback write-console: 
   
R callback write-console:  There are ties in the preranked stats (3.27% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
  
R callback write-console: 2:   
R callback write-console: In fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize,  :  
R callback write-console: 
   
R callback write-console:  For some of the pathways the P-values were likely overestimated. For such pathways log2err is set to NA.
  
R callback write-console: 3:   
R callback write-console: In fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize,  :  
R callback write-console: 
   
R callback write-console:  For some pathways, in reality P-values are less than 1e-10. You can set the `eps` argument to zero for better estimation.
  
R callback write-console: In addition:   
R callback write-console: Warning messages:
  
R callback write-console: 1:   
R callback write-console: In preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam,  :  
R callback write-console: 
   
R callback write-console:  There are ties in the preranked stats (0.96% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
  
R callback write-console: 2:   
R callback write-console: In fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize,  :  
R callback write-console: 
   
R callback write-console:  There were 7 pathways for which P-values were not calculated properly due to unbalanced (positive and negative) gene-level statistic values. For such pathways pval, padj, NES, log2err are set to NA. You can try to increase the value of the argument nPermSimple (for example set it nPermSimple = 10000)
  
R callback write-console: In addition:   
R callback write-console: Warning messages:
  
R callback write-console: 1:   
R callback write-console: In preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam,  :  
R callback write-console: 
   
R callback write-console:  There are ties in the preranked stats (1.13% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
  
R callback write-console: 2:   
R callback write-console: In fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize,  :  
R callback write-console: 
   
R callback write-console:  There were 3 pathways for which P-values were not calculated properly due to unbalanced (positive and negative) gene-level statistic values. For such pathways pval, padj, NES, log2err are set to NA. You can try to increase the value of the argument nPermSimple (for example set it nPermSimple = 10000)
  
R callback write-console: 3:   
R callback write-console: In fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize,  :  
R callback write-console: 
   
R callback write-console:  For some of the pathways the P-values were likely overestimated. For such pathways log2err is set to NA.
  
R callback write-console: 4:   
R callback write-console: In fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize,  :  
R callback write-console: 
   
R callback write-console:  For some pathways, in reality P-values are less than 1e-10. You can set the `eps` argument to zero for better estimation.
  
R callback write-console: In addition:   
R callback write-console: Warning messages:
  
R callback write-console: 1:   
R callback write-console: In preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam,  :  
R callback write-console: 
   
R callback write-console:  There are ties in the preranked stats (1.09% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
  
R callback write-console: 2:   
R callback write-console: In fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize,  :  
R callback write-console: 
   
R callback write-console:  There were 2 pathways for which P-values were not calculated properly due to unbalanced (positive and negative) gene-level statistic values. For such pathways pval, padj, NES, log2err are set to NA. You can try to increase the value of the argument nPermSimple (for example set it nPermSimple = 10000)
  
R callback write-console: 3:   
R callback write-console: In fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize,  :  
R callback write-console: 
   
R callback write-console:  For some of the pathways the P-values were likely overestimated. For such pathways log2err is set to NA.
  
R callback write-console: 4:   
R callback write-console: In fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize,  :  
R callback write-console: 
   
R callback write-console:  For some pathways, in reality P-values are less than 1e-10. You can set the `eps` argument to zero for better estimation.
  
Processing ST...
  ST: 23 active TFs
Processing SSC...
  SSC: 9 active TFs
Processing SPGing...
  SPGing: 0 active TFs
Processing SPGed...
  SPGed: 1 active TFs

R clusterProfiler bridge: Pearson=0.9045 n=33 FP=1 FN=10
  SPGed: R=1 Py=1 agree=1 FP=[] FN=[]
  SPGing: R=1 Py=0 agree=0 FP=[] FN=['PARP1']
  SSC: R=17 Py=9 agree=9 FP=[] FN=['ESR1', 'FOXO1', 'HIF1A', 'ID4', 'NFATC3', 'NFYB', 'PPARG', 'RUNX2']
  ST: R=23 Py=23 agree=22 FP=['NFKB2'] FN=['NFATC3']
