Visualize and Create Reports

Dynamic document reports in HTML or Latex

Full Result: Inline or in dynamic documents

Because of the messy result table is hardly to read, EnrichGT help you convert it into pretty gt table . This only supports the re-enriched results.

You can simple print by input the object name of re-enriched object or fused object to R console, to show the table. In IDE like RStudio and Positron, you can view the table in View window. You can also integrate to dynamic document publish systems like rmarkdown and Quarto which support HTML widget. Latex output is also supported.

To add the result to your dynamic document, simply call the second slot in your rmarkdown or Quarto document using @gt_object, like this :

re_enrich@gt_object
Parse form: ora_result
Split into 10 Clusters. Generated by EnrichGT
Description Count PCT Padj geneID
Cluster_1
synaptic transmission, glutamatergic
GO:0035249
18 3.9 1.0e-06 ATP1A2, GRIA4, GRID2, GRIK2, GRIK3, GRIN1, GRIN2A, GRIN2B, GRIN2D, GRM1, GRM5, GRM8, NRXN1, NLGN1, UNC13A, MAPK8IP2, CACNG5, UNC13C
regulation of synaptic transmission, glutamatergic
GO:0051966
15 3.2 1.2e-06 ATP1A2, GRIK2, GRIK3, GRIN1, GRIN2A, GRIN2B, GRIN2D, GRM1, GRM5, GRM8, NRXN1, NLGN1, UNC13A, MAPK8IP2, CACNG5
glutamate receptor signaling pathway
GO:0007215
12 2.6 1.1e-05 GRIA4, GRID2, GRIK2, GRIK3, GRIN1, GRIN2A, GRIN2B, GRIN2D, GRM1, GRM5, GRM8, NRXN1
regulation of neuronal synaptic plasticity
GO:0048168
12 2.6 1.2e-05 APOE, CAMK2B, GRIK2, GRIN1, GRIN2A, GRIN2B, GRIN2D, GRM5, HRAS, CNTN2, VGF, SHISA9
ionotropic glutamate receptor signaling pathway
GO:0035235
8 1.7 4.1e-05 GRIA4, GRIK2, GRIK3, GRIN1, GRIN2A, GRIN2B, GRIN2D, NRXN1
ligand-gated ion channel signaling pathway
GO:1990806
9 1.9 9.8e-05 GRIA4, GRIK2, GRIK3, GRIN1, GRIN2A, GRIN2B, GRIN2D, HTR3A, NRXN1
regulation of postsynaptic membrane potential
GO:0060078
16 3.4 1.6e-04 GABRD, GRIA4, GRID2, GRIK2, GRIK3, GRIN1, GRIN2A, GRIN2B, GRIN2D, GRM1, GRM5, HTR3A, NRXN1, NLGN1, MAPK8IP2, HCN1
regulation of membrane potential
GO:0042391
30 6.5 2.5e-04 AGT, ATP1A2, HCN2, GABRD, GRIA4, GRID2, GRIK2, GRIK3, GRIN1, GRIN2A, GRIN2B, GRIN2D, GRM1, GRM5, HTR3A, KCNC3, KCNK2, NRCAM, NTRK2, PYCR1, SCN8A, TBX18, NRXN1, RIMS3, NLGN1, MAPK8IP2, KCNE4, TREM2, HCN1, RNF207
locomotory behavior
GO:0007626
19 4.1 2.5e-04 ALK, APOE, ATP1A2, DPP4, DSCAM, GAD1, GPR37, GRIN2D, GRM1, GRM5, SLC6A3, CNTN2, UCHL1, ZIC1, RASD2, ANKH, PPP1R1B, CIART, ANKFN1
transmission of nerve impulse
GO:0019226
11 2.4 4.3e-04 ATP1A2, AVPR1A, GRIK2, KCNK2, MAG, NRCAM, NTRK2, SCN8A, CNTNAP2, CACNG5, HCN1
Cluster_2
mitotic spindle assembly checkpoint signaling
GO:0007094
11 2.4 1.2e-05 BIRC5, BUB1, CCNB1, CDC20, CENPF, PLK1, NDC80, ZWINT, CDCA8, NUF2, SPC24
mitotic spindle checkpoint signaling
GO:0071174
11 2.4 1.2e-05 BIRC5, BUB1, CCNB1, CDC20, CENPF, PLK1, NDC80, ZWINT, CDCA8, NUF2, SPC24
spindle assembly checkpoint signaling
GO:0071173
11 2.4 1.2e-05 BIRC5, BUB1, CCNB1, CDC20, CENPF, PLK1, NDC80, ZWINT, CDCA8, NUF2, SPC24
spindle checkpoint signaling
GO:0031577
11 2.4 1.2e-05 BIRC5, BUB1, CCNB1, CDC20, CENPF, PLK1, NDC80, ZWINT, CDCA8, NUF2, SPC24
regulation of mitotic sister chromatid separation
GO:0010965
12 2.6 1.2e-05 BIRC5, BUB1, CCNB1, CDC20, CENPF, PLK1, NDC80, UBE2C, ZWINT, CDCA8, NUF2, SPC24
negative regulation of mitotic metaphase/anaphase transition
GO:0045841
11 2.4 1.2e-05 BIRC5, BUB1, CCNB1, CDC20, CENPF, PLK1, NDC80, ZWINT, CDCA8, NUF2, SPC24
negative regulation of mitotic sister chromatid segregation
GO:0033048
11 2.4 1.2e-05 BIRC5, BUB1, CCNB1, CDC20, CENPF, PLK1, NDC80, ZWINT, CDCA8, NUF2, SPC24
negative regulation of mitotic sister chromatid separation
GO:2000816
11 2.4 1.2e-05 BIRC5, BUB1, CCNB1, CDC20, CENPF, PLK1, NDC80, ZWINT, CDCA8, NUF2, SPC24
negative regulation of sister chromatid segregation
GO:0033046
11 2.4 1.2e-05 BIRC5, BUB1, CCNB1, CDC20, CENPF, PLK1, NDC80, ZWINT, CDCA8, NUF2, SPC24
mitotic sister chromatid separation
GO:0051306
12 2.6 1.5e-05 BIRC5, BUB1, CCNB1, CDC20, CENPF, PLK1, NDC80, UBE2C, ZWINT, CDCA8, NUF2, SPC24
Cluster_3
regulation of neuron differentiation
GO:0045664
17 3.7 8.4e-04 JAG1, ALK, MAG, MAP1B, NKX6-1, NRCAM, RAC3, SFRP2, SIX1, CNTN2, TP73, ZNF536, NLGN1, HEY2, SOX8, ASPM, WDR62
positive regulation of nervous system development
GO:0051962
17 3.7 2.5e-02 CAMK2B, DSCAM, GFAP, GRID2, GRM5, MAG, MAP1B, NKX6-1, NTRK2, TP73, NRXN1, NLGN1, SOX8, SYNDIG1, IL34, ASPM, WDR62
regulation of nervous system development
GO:0051960
23 4.9 2.9e-02 CAMK2B, DSCAM, GFAP, GRID2, GRM5, MAG, MAP1B, NKX6-1, NTRK2, TP73, WNT5A, NRXN1, NLGN1, FSTL4, HEY2, DAAM2, SOX8, TREM2, DPYSL5, SYNDIG1, IL34, ASPM, WDR62
regulation of developmental growth
GO:0048638
18 3.9 3.6e-02 APOE, DSCAM, FOXC2, FOXS1, KCNK2, MAG, MAP1B, NKX6-1, NRCAM, SIX1, SLC6A3, TP73, WNT5A, AGR2, UNC13A, FSTL4, HEY2, GPAM
negative regulation of neuron differentiation
GO:0045665
7 1.5 4.5e-02 JAG1, MAG, CNTN2, TP73, ZNF536, SOX8, ASPM
regulation of neurogenesis
GO:0050767
19 4.1 4.8e-02 CAMK2B, DSCAM, GFAP, GRM5, MAG, MAP1B, NKX6-1, NTRK2, TP73, WNT5A, FSTL4, HEY2, DAAM2, SOX8, TREM2, DPYSL5, IL34, ASPM, WDR62
Cluster_4
behavioral fear response
GO:0001662
8 1.7 9.7e-04 APOE, ATP1A2, DPP4, GRIK2, NPAS2, MAPK8IP2, LYPD1, ANKFN1
behavioral defense response
GO:0002209
8 1.7 1.1e-03 APOE, ATP1A2, DPP4, GRIK2, NPAS2, MAPK8IP2, LYPD1, ANKFN1
fear response
GO:0042596
8 1.7 2.2e-03 APOE, ATP1A2, DPP4, GRIK2, NPAS2, MAPK8IP2, LYPD1, ANKFN1
startle response
GO:0001964
6 1.3 4.0e-03 CTNNA2, GRID2, GRIN2A, GRIN2D, SLC6A3, CNTNAP2
protein localization to synapse
GO:0035418
10 2.2 4.6e-03 GRIN2A, HRAS, HSPB1, KIF5C, WNT5A, NRXN1, NLGN1, GRIP1, GRIP2, LHFPL4
neuromuscular process
GO:0050905
14 3.0 7.3e-03 CTNNA2, FOXS1, GRID2, GRIN2A, GRIN2D, RAC3, SLC6A3, TNNT1, UCHL1, NRXN1, CNTNAP2, STRA6, ANKFN1, STAC2
multicellular organismal response to stress
GO:0033555
10 2.2 7.9e-03 APOE, ATP1A2, DPP4, GRIK2, NPAS2, THBS4, MAPK8IP2, LYPD1, ANKFN1, HCN1
gas transport
GO:0015669
5 1.1 8.2e-03 CA2, HBA1, HBA2, HBB, MB
cellular oxidant detoxification
GO:0098869
10 2.2 8.2e-03 APOE, CP, NQO1, GPX1, HBA1, HBA2, HBB, MB, MGST1, TXNDC17
response to hydrogen peroxide
GO:0042542
10 2.2 8.2e-03 COL1A1, CRYAB, NQO1, GPX1, HBA1, HBA2, HBB, HMOX1, SDC1, TRPM2
Cluster_5
ATP synthesis coupled electron transport
GO:0042773
11 2.4 3.3e-03 CCNB1, MT-CO2, MT-CO3, MT-CYB, MT-ND1, MT-ND2, MT-ND3, MT-ND4, NDUFB2, NDUFS6, UQCR10
mitochondrial ATP synthesis coupled electron transport
GO:0042775
11 2.4 3.3e-03 CCNB1, MT-CO2, MT-CO3, MT-CYB, MT-ND1, MT-ND2, MT-ND3, MT-ND4, NDUFB2, NDUFS6, UQCR10
aerobic electron transport chain
GO:0019646
10 2.2 6.3e-03 MT-CO2, MT-CO3, MT-CYB, MT-ND1, MT-ND2, MT-ND3, MT-ND4, NDUFB2, NDUFS6, UQCR10
aerobic respiration
GO:0009060
15 3.2 8.1e-03 CCNB1, IDH1, MT-ATP6, MT-CO2, MT-CO3, MT-CYB, MT-ND1, MT-ND2, MT-ND3, MT-ND4, NDUFB2, NDUFS6, UQCR10, OGDHL, TRPV4
respiratory electron transport chain
GO:0022904
11 2.4 8.1e-03 CCNB1, MT-CO2, MT-CO3, MT-CYB, MT-ND1, MT-ND2, MT-ND3, MT-ND4, NDUFB2, NDUFS6, UQCR10
response to oxygen levels
GO:0070482
21 4.5 1.0e-02 CA9, COL1A1, CRYAB, DPP4, ENO1, KCNK2, LOXL2, MB, MT-ATP6, MT-CO2, MT-CYB, MT-ND1, MT-ND2, MT-ND4, PGF, RYR1, TWIST1, ANGPTL4, TREM2, TRPV4, CD24
proton transmembrane transport
GO:1902600
14 3.0 1.2e-02 ATP1A2, MT-ATP6, MT-CO2, MT-CO3, MT-CYB, MT-ND1, MT-ND2, MT-ND3, MT-ND4, NDUFB2, NDUFS6, SLC15A1, UQCR10, ATP6V1C2
electron transport chain
GO:0022900
11 2.4 1.3e-02 CCNB1, MT-CO2, MT-CO3, MT-CYB, MT-ND1, MT-ND2, MT-ND3, MT-ND4, NDUFB2, NDUFS6, UQCR10
response to hypoxia
GO:0001666
19 4.1 1.4e-02 CA9, CRYAB, DPP4, ENO1, KCNK2, LOXL2, MB, MT-CO2, MT-CYB, MT-ND1, MT-ND2, MT-ND4, PGF, RYR1, TWIST1, ANGPTL4, TREM2, TRPV4, CD24
oxidative phosphorylation
GO:0006119
12 2.6 1.5e-02 CCNB1, MT-ATP6, MT-CO2, MT-CO3, MT-CYB, MT-ND1, MT-ND2, MT-ND3, MT-ND4, NDUFB2, NDUFS6, UQCR10
Cluster_6
ear development
GO:0043583
17 3.7 4.9e-03 JAG1, COL11A1, GSDME, DLX5, KCNK2, MSX1, SIX1, TFAP2A, TWIST1, WNT5A, ZIC1, TBX18, SIX2, HEY2, STRA6, CTHRC1, MYO3B
sensory organ morphogenesis
GO:0090596
19 4.1 6.3e-03 JAG1, COL11A1, DLX5, DSCAM, MSX1, NTRK2, SIX1, TFAP2A, TWIST1, WNT5A, ZIC1, TBX18, SIX2, FJX1, SOX8, STRA6, CTHRC1, MYO3B, HCN1
ear morphogenesis
GO:0042471
12 2.6 6.3e-03 COL11A1, DLX5, MSX1, SIX1, TFAP2A, TWIST1, WNT5A, ZIC1, TBX18, SIX2, CTHRC1, MYO3B
regulation of animal organ morphogenesis
GO:2000027
9 1.9 9.5e-03 AGT, MSX1, SFRP2, SIX1, WNT5A, SIX2, SOX8, WNT4, SAPCD2
cochlea development
GO:0090102
7 1.5 1.3e-02 KCNK2, SIX1, WNT5A, TBX18, HEY2, CTHRC1, MYO3B
cochlea morphogenesis
GO:0090103
5 1.1 1.5e-02 SIX1, WNT5A, TBX18, CTHRC1, MYO3B
muscle cell development
GO:0055001
14 3.0 1.8e-02 GPX1, RYR1, SDC1, SIX1, TNNT1, UCHL1, TBX18, HEY2, MEGF10, MYO18B, DNER, ALPK2, WFIKKN1, SGCZ
inner ear development
GO:0048839
14 3.0 2.1e-02 JAG1, COL11A1, GSDME, DLX5, KCNK2, MSX1, SIX1, TFAP2A, WNT5A, ZIC1, TBX18, HEY2, CTHRC1, MYO3B
muscle tissue development
GO:0060537
23 4.9 2.5e-02 CENPF, COL11A1, EYA2, FOXC2, GPX1, KCNK2, MSX1, RYR1, SIX1, TP73, TWIST1, BARX2, TBX18, HEY2, SOX8, NOX4, STRA6, MEGF10, MYO18B, MYLK2, DNER, ALPK2, SGCZ
embryonic cranial skeleton morphogenesis
GO:0048701
6 1.3 2.5e-02 FOXC2, SIX1, TBX15, TFAP2A, TWIST1, SIX2
Cluster_7
positive regulation of chemotaxis
GO:0050921
13 2.8 4.9e-03 C3AR1, DSCAM, HSPB1, PGF, THBS4, WNT5A, SCG2, TREM2, CAMK1D, S100A14, TRPV4, SMOC2, IL34
regulation of granulocyte chemotaxis
GO:0071622
7 1.5 1.4e-02 C3AR1, DPP4, THBS4, CAMK1D, S100A14, TRPV4, IL34
regulation of chemotaxis
GO:0050920
14 3.0 3.0e-02 C3AR1, DPP4, DSCAM, HSPB1, PGF, THBS4, WNT5A, SCG2, TREM2, CAMK1D, S100A14, TRPV4, SMOC2, IL34
Cluster_8
renal system development
GO:0072001
21 4.5 6.3e-03 JAG1, AGT, AKR1B1, CENPF, EMX2, ENPEP, FOXD1, FOXC2, MMP17, SDC1, SIX1, TFAP2A, TP73, WNT5A, TBX18, GCNT3, SIX2, SOX8, WNT4, STRA6, CD24
regulation of kidney development
GO:0090183
6 1.3 1.0e-02 AGT, FOXD1, SIX1, SIX2, SOX8, WNT4
kidney development
GO:0001822
19 4.1 1.8e-02 JAG1, AGT, AKR1B1, CENPF, ENPEP, FOXD1, FOXC2, MMP17, SDC1, SIX1, TFAP2A, TP73, WNT5A, GCNT3, SIX2, SOX8, WNT4, STRA6, CD24
regulation of morphogenesis of an epithelium
GO:1905330
7 1.5 3.2e-02 AGT, SIX1, WNT5A, SIX2, SOX8, WNT4, RNF207
Cluster_9
olefinic compound metabolic process
GO:0120254
13 2.8 1.0e-02 ABCA4, AKR1B1, ALOX15B, AKR1C1, AKR1C2, GPX1, GRIN1, SRD5A1, PLA2G4C, RDH16, DHRS3, WNT4, CTHRC1
C21-steroid hormone metabolic process
GO:0008207
5 1.1 4.7e-02 AKR1B1, AKR1C1, AKR1C2, SRD5A1, WNT4

This just shows the second slot inside the EnrichGT_obj object. The second slot - gt_object is a pure object of gt table package, you can use any function on it.

For further usage of gt table package, please refer to gt package website.

See re-enrichment example for further demo.

Each Cluster: HTML Widgets

If you want to show cluster 1 in your documents(the publish system or IDE should support HTML widgets, like RStudio), you can use one of below:

egt_summary(deepseekAnno, "1")
egt_summary(deepseekAnno, 1)
egt_summary(deepseekAnno, "Cluster_1")

In Quarto or rmarkdown, you can use #| output: asis header in code chunk and print it to show HTML widgets.

HTMLres <- egt_summary(re_enrich, "1")
HTMLres

Enrichment Result of Cluster_1 (Local Summary)

This cluster contains: synaptic transmission, glutamatergic, regulation of synaptic transmission, glutamatergic, glutamate receptor signaling pathway, regulation of neuronal synaptic plasticity, ionotropic glutamate receptor signaling pathway ...

Candidate genes include: ACHE, ADGRF1, AGT, ALK, ANKFN1, ANKH, APLP1, APOE, ASPM, ATP1A2, ATP8B3, AVPR1A, BCAN, C4BPB, CA2, CACNG5, CAMK1D, CAMK2B, CDC20, CELSR3, CIART, CNTN2, CNTNAP2, CPNE4, CPNE7, CTNNA2, DAAM2, DLX5, DNAJB1, DNER, DPP4, DPYSL5, DSCAM, E2F1, EMX2, EPHA10, FOLR2, FOXD1, FSTL4, GABRD, GAD1, GAP43, GFAP, GIPR, GPR37, GRIA4, GRID2, GRIK2, GRIK3, GRIN1, GRIN2A, GRIN2B, GRIN2D, GRM1, GRM5, GRM8, HCN1, HCN2, HMOX1, HRAS, HTR3A, IGSF9, IL1RAPL2, IMPA2, INA, KCNC3, KCNE4, KCNK2, KIAA1755, KIF1A, KIF5C, LHFPL4, LOXL2, LRFN2, MAG, MAP1B, MAPK8IP2, MEGF10, MSX1, MT-CYB, MYO3B, NKX6-1, NLGN1, NMU, NQO1, NRCAM, NRXN1, NTRK2, NUDT1, OPCML, PPP1R1B, PTPRH, PYCR1, RAC3, RASD2, RIMS3, RNF207, RYR1, SCN8A, SDC1, SDC2, SEMA5B, SEZ6L2, SFRP2, SHISA9, SIX1, SLC30A10, SLC30A3, SLC6A1, SLC6A3, SPOCK1, SRD5A1, STRA6, SYNDIG1, TBX18, TF, TFAP2A, TREM2, TRPM2, TRPV4, TWIST1, UCHL1, UNC13A, UNC13C, VGF, WDR62, WNT4, WNT5A, ZAN, ZIC1

We can't find LLM annotations. Please using egt_llm_* functions for further analysis.

Quarto Reports

Function egt_generate_quarto_report() can help you convert re-enrichment object to an all-in-one Quarto document containing all enrichment results. You can use RStudio or Positron to knit them (preview) into HTML document.

Ploting functions

HTML gt table table satisfied most of things, but for others. Though we don’t want this package become complex (i.e., you can simple draw your figure using ggplot2 for enriched tables by yourself.) But we still provide limited figure ploting functions.

Basic result plot

The Dot Plot provide basic viewing of results. So it supports both simple enrichment result data.frame and re-enriched egt_object.

You can adjust this figure by these params:

  • ntop: Show top N in each cluster. In default, for origin enriched result, showing top 15; for re-clustered object, showing top 5 in each cluster.
  • showIDs: bool, show pathway IDs or not. Default is FALSE
  • max_len_descript: the label format length, default as 40.
  • P.adj: If pass an origin data.frame from original enriched result, you can specify the P-adjust value cut off. If is null, default is 0.05. When passing EnrichGT_obj, this filter is previously done by egt_recluster_analysis.
  • low.col: the color for the lowest
  • hi.col: the color for the highest

Ploting ORA result

egt_plot_results(ora_result)
ℹ Use Default P-adjust cut-off 0.05. You can pass `P.adj=xxx` arugument to filter. 
! You are drawing origin results, for better result you can re-cluster it by egt_recluster_analysis()

Ploting GSEA result

egt_plot_results(resGSEA)
ℹ Use Default P-adjust cut-off 0.05. You can pass `P.adj=xxx` arugument to filter. 
! You are drawing origin results, for better result you can re-cluster it by egt_recluster_analysis()

Ploting re-enrichment result

egt_plot_results(re_enrich)

Additional GSEA plots

Since version 0.8.6, EnrichGT provides a function called egt_plot_gsea() to help users to get typical ranking plots. If you pass a pathway name to the first param of this function, egt_plot_gsea() will return a ranking plot of single pathway. For multiple pathway, you need to subset the data.frame result of egt_gsea_analysis().

The param contains:

  • x: A GSEA result object. Can be either:
    • A data frame containing GSEA results (requires columns: pvalue, p.adjust, Description)
    • A character string specifying a single pathway name
  • genes and database should be the same as you doing egt_gsea_analysis()
Why multiple results plotting function needs a data.frame

You can see the result figure, we need the NES and p-values for display.

Single ranking plot

You need to prepare your favourite pathway’s name:

1class(resGSEA$Description[1])
2resGSEA$Description[1]
1
This is a vector.
2
If you need the single-plot, all you pass to egt_plot_gsea() is the name of this pathway, subset from resGSEA. 
[1] "character"
[1] "meiotic chromosome segregation"

And then using egt_plot_gsea() for drawing. The other params should be the same as you provided in origin GSEA analysis.

egt_plot_gsea(resGSEA$Description[1],
              genes = genes_with_weights(genes = DEGexample$...1, 
                                              weights = DEGexample$log2FoldChange),
              database = database_GO_BP(org.Hs.eg.db))
✔ success loaded database, time used : 15.935772895813 sec.

Table-like visualization GSEA result

Warning

If you want to plot this plot, remember to filter the GSEA results to ~10 to ~20, you can base on NES or p-values. This is to avoid too many loading to this plotting.

You need to subset the origin GSEA result data.frame according to NES or p-val to gain your targets. In this demo, we choose row 1 to row 8.

1class(resGSEA[1:8,])
2resGSEA[1:8,]
1
This is a data.frame. Please subset it to avoid too many results and the waste of time.
2
You need to subset data frame according to NES (suggests: abs(NES)>1) and p-value, too avoiding too many outputs and wasting. 
[1] "tbl_df"     "tbl"        "data.frame"
# A tibble: 8 × 12
  ID        Description    ES   NES  pvalue p.adjust core_enrichment  rank tags 
  <chr>     <chr>       <dbl> <dbl>   <dbl>    <dbl> <chr>           <dbl> <chr>
1 GO:00451… meiotic ch… 0.612  1.96 3.73e-5  0.00117 SPATA22/KASH5/…  2679 43%  
2 GO:00092… purine rib… 0.575  1.92 4.15e-5  0.00126 ADCY10/NME9/ND…  4558 55%  
3 GO:00509… detection … 0.767  1.92 3.41e-4  0.00611 OR51E1/CST1/OR…  2023 63%  
4 GO:00091… purine nuc… 0.570  1.91 3.21e-5  0.00104 ADCY10/NME9/ND…  4558 54%  
5 GO:00067… ATP biosyn… 0.582  1.90 6.38e-5  0.00179 ADCY10/NDUFS6/…  4403 56%  
6 GO:00352… synaptic t… 0.557  1.88 3.23e-5  0.00104 GRIK3/UNC13C/G…  1330 31%  
7 GO:00076… adult walk… 0.714  1.88 6.84e-4  0.0103  ZIC1/CNTN2/CAC…   866 25%  
8 GO:00315… spindle ch… 0.637  1.88 3.12e-4  0.00574 SPC24/BIRC5/PL…  1435 39%  
# ℹ 3 more variables: list <chr>, signal <chr>, leading_edge <chr>

And then using egt_plot_gsea() for drawing. The other params should be the same as you provided in origin GSEA analysis.

egt_plot_gsea(resGSEA[1:8,], 
              genes = genes_with_weights(genes = DEGexample$...1, 
                                              weights = DEGexample$log2FoldChange),
              database = database_GO_BP(org.Hs.eg.db))
✔ Use cached database: GO_BP_org.Hs.eg.db

UMAP plot for re-enrichment (no longer supported)

Before version 0.8.6, UMAP plot is provided in re-enriched egt_object, to show the dimensionality reduction view of enriched results.

A word frequency matrix represents the frequency of words or tokens across different documents or text samples. Each row corresponds to a document, and each column represents a word or token, with the cell values indicating the frequency of the respective word in that document.However, high-dimensional data like word frequency matrices can be challenging to interpret directly. To make such data easier to analyze, we can reduce its dimensionality and visualize the patterns or clusters in a 2D or 3D space. UMAP (Uniform Manifold Approximation and Projection) is a powerful, non-linear dimensionality reduction technique widely used for this purpose.

But since 0.8.6, because umap and ggrepel have too many dependencies, and this function is not nesessary in most of cases. We now don’t support it. If you still needs this figure, you can execute following code.

library(umap)
library(ggrepel)
mat<-x@document_term_matrix
umap_result <- umap::umap(mat)
umap_df <- data.frame(ID=rownames(umap_result[["layout"]]),
                      UMAP1 = umap_result$layout[, 1],
                      UMAP2 = umap_result$layout[, 2])
udf<-x@enriched_result |> left_join(umap_df,by="ID")
fig<-ggplot(udf, aes(x = UMAP1, y = UMAP2, color = Cluster)) +
  geom_point(size = 2) +
  geom_text_repel(aes(label = Description),
                  size = 3,
                  max.overlaps = 20,
                  box.padding = 0.3,
                  point.padding = 0.2) +
  labs(title = "Enrichment Results",
        x = "UMAP1", y = "UMAP2") +
  theme_classic()
fig
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