Clustering and viewing ORA results
c_ora.Rmd
library(EnrichGT)
#> Loading required package: dplyr
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
#> Loading required package: tibble
#> Loading required package: gt
#> Loading required package: cli
#>
#> ── EnrichGT ────────────────────────────────────────────────────────────────────
#> ℹ View your enrichment result by entring `EnrichGT(result)`
#> → by Zhiming Ye, https://github.com/ZhimingYe/EnrichGTSummary
For overexpression enrichment analysis (What is overexpression
enrichment analysis? See webpage: https://yulab-smu.top/biomedical-knowledge-mining-book/enrichment-overview.html),
EnrichGT can read results from enrichResult generated by
clusterProfiler or an exported table from clusterProfiler
enrichment (including at least the columns “ID”, “Description”,
“GeneRatio”, “pvalue”, “p.adjust”, “geneID” and “Count”).
EnrichGT performs term frequency statistics and automatic
clustering based on genes enriched in hits, returns an S4 object, and
outputs a beautifully formatted result wrapped by gt.
Generating enriched Result
suppressMessages({
library(tidyverse)
library(gt)
library(clusterProfiler)
library(org.Hs.eg.db)
})
data(geneList, package="DOSE")
gene <- names(geneList)[abs(geneList) > 2]
ego <- enrichGO(gene = gene,
universe = names(geneList),
OrgDb = org.Hs.eg.db,
ont = "BP",
pAdjustMethod = "BH",
pvalueCutoff = 0.5,
qvalueCutoff = 0.5,
readable = TRUE)go EnrichGT!
From enrichResult object
Do enriching
ego
#> #
#> # over-representation test
#> #
#> #...@organism Homo sapiens
#> #...@ontology BP
#> #...@keytype ENTREZID
#> #...@gene chr [1:207] "4312" "8318" "10874" "55143" "55388" "991" "6280" "2305" ...
#> #...pvalues adjusted by 'BH' with cutoff <0.5
#> #...470 enriched terms found
#> 'data.frame': 470 obs. of 9 variables:
#> $ ID : chr "GO:0098813" "GO:0007059" "GO:0000070" "GO:0000819" ...
#> $ Description: chr "nuclear chromosome segregation" "chromosome segregation" "mitotic sister chromatid segregation" "sister chromatid segregation" ...
#> $ GeneRatio : chr "33/196" "37/196" "27/196" "29/196" ...
#> $ BgRatio : chr "236/11580" "316/11580" "151/11580" "184/11580" ...
#> $ pvalue : num 2.78e-21 4.45e-21 2.10e-20 2.92e-20 6.84e-20 ...
#> $ p.adjust : num 6.69e-18 6.69e-18 2.10e-17 2.20e-17 4.12e-17 ...
#> $ qvalue : num 6.16e-18 6.16e-18 1.94e-17 2.02e-17 3.79e-17 ...
#> $ geneID : chr "CDCA8/CDC20/KIF23/CENPE/MYBL2/CCNB2/NDC80/TOP2A/NCAPH/ASPM/DLGAP5/UBE2C/NUSAP1/TPX2/TACC3/NEK2/CDK1/MAD2L1/KIF1"| __truncated__ "CDCA8/CDC20/KIF23/CENPE/MYBL2/CCNB2/NDC80/TOP2A/NCAPH/ASPM/DLGAP5/UBE2C/HJURP/SKA1/NUSAP1/TPX2/TACC3/NEK2/CENPM"| __truncated__ "CDCA8/CDC20/KIF23/CENPE/MYBL2/NDC80/NCAPH/DLGAP5/UBE2C/NUSAP1/TPX2/NEK2/CDK1/MAD2L1/KIF18A/CDT1/BIRC5/KIF11/TTK"| __truncated__ "CDCA8/CDC20/KIF23/CENPE/MYBL2/NDC80/TOP2A/NCAPH/DLGAP5/UBE2C/NUSAP1/TPX2/TACC3/NEK2/CDK1/MAD2L1/KIF18A/CDT1/BIR"| __truncated__ ...
#> $ Count : int 33 37 27 29 31 36 37 23 38 37 ...
#> #...Citation
#> 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
obj <- ego |> EnrichGT(P.adj = 0.2,ClusterNum = 10,nTop = 10)
#> ℹ =====[SUGGESTION]=====
#> You are passing an object from GO Enrichment.
#> Please ensure that `obj |> clusterProfiler::simplify()` is executed, to pre-simplify result,
#> For better enriched result.
#> Loading required package: text2vec
#>
#> Attaching package: 'text2vec'
#> The following object is masked from 'package:BiocGenerics':
#>
#> normalize
You can define how many clusters and how many top terms would be shown. And you can adjust the p-adj cut-off.
Understanding EnrichGT_obj
The created obj contains several slots:
str(obj,max.level=2)
#> Formal class 'EnrichGT_obj' [package "EnrichGT"] with 6 slots
#> ..@ enriched_result : tibble [34 × 7] (S3: tbl_df/tbl/data.frame)
#> ..@ gt_object :List of 17
#> .. ..- attr(*, "class")= chr [1:2] "gt_tbl" "list"
#> ..@ gene_modules :List of 7
#> ..@ pathway_clusters :List of 7
#> ..@ clustering_tree :List of 7
#> .. ..- attr(*, "class")= chr "hclust"
#> ..@ raw_enriched_result:'data.frame': 147 obs. of 7 variables:Simply enter obj in console also can returns a rendered
gt view in viewer plane in RStudio.
objgt object is storaged in gt_object. You can
use all gt functions on it like gt::gtsave() by calling
obj@gt_object.
obj@gt_object| Parse form: ego | ||||
| Split into 10 Clusters. Generated by github@zhimingye/EnrichGT | ||||
| Description | Count | PCT | Padj | geneID |
|---|---|---|---|---|
| Cluster_1 | ||||
|
nuclear chromosome segregation
GO:0098813
|
33 | 17.0 | 6.7e-18 | CDCA8, CDC20, KIF23, CENPE, MYBL2, CCNB2, NDC80, TOP2A, NCAPH, ASPM, DLGAP5, UBE2C, NUSAP1, TPX2, TACC3, NEK2, CDK1, MAD2L1, KIF18A, CDT1, BIRC5, KIF11, TTK, NCAPG, AURKB, TRIP13, PRC1, KIFC1, KIF18B, AURKA, CCNB1, KIF4A, PTTG1 |
|
chromosome segregation
GO:0007059
|
37 | 19.0 | 6.7e-18 | CDCA8, CDC20, KIF23, CENPE, MYBL2, CCNB2, NDC80, TOP2A, NCAPH, ASPM, DLGAP5, UBE2C, HJURP, SKA1, NUSAP1, TPX2, TACC3, NEK2, CENPM, CENPN, CDK1, MAD2L1, KIF18A, CDT1, BIRC5, KIF11, TTK, NCAPG, AURKB, TRIP13, PRC1, KIFC1, KIF18B, AURKA, CCNB1, KIF4A, PTTG1 |
|
mitotic sister chromatid segregation
GO:0000070
|
27 | 14.0 | 2.1e-17 | CDCA8, CDC20, KIF23, CENPE, MYBL2, NDC80, NCAPH, DLGAP5, UBE2C, NUSAP1, TPX2, NEK2, CDK1, MAD2L1, KIF18A, CDT1, BIRC5, KIF11, TTK, NCAPG, AURKB, TRIP13, PRC1, KIFC1, KIF18B, CCNB1, KIF4A |
|
sister chromatid segregation
GO:0000819
|
29 | 15.0 | 2.2e-17 | CDCA8, CDC20, KIF23, CENPE, MYBL2, NDC80, TOP2A, NCAPH, DLGAP5, UBE2C, NUSAP1, TPX2, TACC3, NEK2, CDK1, MAD2L1, KIF18A, CDT1, BIRC5, KIF11, TTK, NCAPG, AURKB, TRIP13, PRC1, KIFC1, KIF18B, CCNB1, KIF4A |
|
mitotic nuclear division
GO:0140014
|
31 | 16.0 | 4.1e-17 | CDCA8, CDC20, KIF23, CENPE, MYBL2, NDC80, NCAPH, DLGAP5, UBE2C, NUSAP1, TPX2, NEK2, UBE2S, CDK1, MAD2L1, KIF18A, CDT1, BIRC5, KIF11, TTK, NCAPG, AURKB, CHEK1, TRIP13, PRC1, KIFC1, KIF18B, AURKA, CCNB1, KIF4A, BMP4 |
|
nuclear division
GO:0000280
|
36 | 18.0 | 5.3e-17 | CDCA8, CDC20, KIF23, CENPE, MYBL2, CCNB2, NDC80, TOP2A, NCAPH, ASPM, DLGAP5, UBE2C, NUSAP1, TPX2, NEK2, RAD51AP1, UBE2S, CDK1, MAD2L1, KIF18A, CDT1, BIRC5, KIF11, TTK, NCAPG, AURKB, CHEK1, TRIP13, PRC1, KIFC1, KIF18B, AURKA, CCNB1, KIF4A, PTTG1, BMP4 |
|
organelle fission
GO:0048285
|
37 | 19.0 | 1.7e-16 | CDCA8, CDC20, KIF23, CENPE, MYBL2, CCNB2, NDC80, TOP2A, NCAPH, ASPM, DLGAP5, UBE2C, NUSAP1, TPX2, NEK2, RAD51AP1, UBE2S, CDK1, MAD2L1, KIF18A, CDT1, BIRC5, KIF11, TTK, NCAPG, AURKB, CHEK1, TRIP13, PRC1, KIFC1, KIF18B, AURKA, CCNB1, KIF4A, PTTG1, BMP4, MAPT |
|
microtubule cytoskeleton organization involved in mitosis
GO:1902850
|
23 | 12.0 | 1.9e-14 | CDCA8, CDC20, KIF23, CENPE, MYBL2, NDC80, DLGAP5, NUSAP1, TPX2, TACC3, NEK2, CDK1, MAD2L1, PAX6, BIRC5, KIF11, TTK, AURKB, PRC1, KIFC1, AURKA, CCNB1, KIF4A |
|
chromosome organization
GO:0051276
|
38 | 19.0 | 1.4e-13 | CDC45, CDCA8, CDC20, KIF23, CENPE, MYBL2, NDC80, TOP2A, NCAPH, DLGAP5, UBE2C, HJURP, NUSAP1, TPX2, TACC3, NEK2, CENPN, CDK1, MAD2L1, GINS1, KIF18A, CDT1, BIRC5, KIF11, EZH2, TTK, NCAPG, AURKB, GINS2, TRIP13, PRC1, KIFC1, KIF18B, CCNB1, KIF4A, MCM5, PTTG1, MAPT |
|
cell division
GO:0051301
|
37 | 19.0 | 2.5e-12 | CDCA8, CDC20, KIF23, CENPE, CCNB2, NDC80, TOP2A, NCAPH, E2F8, ASPM, CEP55, UBE2C, SKA1, NUSAP1, CDCA3, TPX2, TACC3, NEK2, UBE2S, CCNA2, CDK1, ERCC6L, MAD2L1, PAX6, CDT1, BIRC5, KIF11, NCAPG, AURKB, PRC1, KIFC1, KIF18B, KIF20A, AURKA, CCNB1, KIF4A, PTTG1 |
| Cluster_2 | ||||
|
female meiotic nuclear division
GO:0007143
|
7 | 3.6 | 1.0e-05 | CCNB2, NDC80, TOP2A, NCAPH, TTK, TRIP13, AURKA |
|
meiotic nuclear division
GO:0140013
|
12 | 6.1 | 5.4e-05 | CDC20, CCNB2, NDC80, TOP2A, NCAPH, ASPM, RAD51AP1, KIF18A, TTK, TRIP13, AURKA, PTTG1 |
|
meiotic cell cycle process
GO:1903046
|
12 | 6.1 | 1.8e-04 | CDC20, CCNB2, NDC80, TOP2A, NCAPH, ASPM, RAD51AP1, KIF18A, TTK, TRIP13, AURKA, PTTG1 |
|
meiotic chromosome segregation
GO:0045132
|
8 | 4.1 | 2.3e-04 | CCNB2, NDC80, NCAPH, ASPM, TTK, TRIP13, AURKA, PTTG1 |
|
cell cycle G2/M phase transition
GO:0044839
|
11 | 5.6 | 3.6e-04 | FOXM1, MELK, CCNB2, NDC80, CCNA2, CDK1, AURKB, CHEK1, DTL, AURKA, CCNB1 |
|
meiotic cell cycle
GO:0051321
|
13 | 6.6 | 5.8e-04 | CDC20, CCNB2, NDC80, TOP2A, NCAPH, ASPM, NEK2, RAD51AP1, KIF18A, TTK, TRIP13, AURKA, PTTG1 |
|
DNA replication
GO:0006260
|
14 | 7.1 | 8.8e-04 | CDC45, MCM10, E2F8, RRM2, CCNA2, CDK1, GINS1, CDT1, GINS2, CHAF1B, CHEK1, DTL, MCM5, DACH1 |
|
meiosis I cell cycle process
GO:0061982
|
8 | 4.1 | 3.2e-03 | CDC20, CCNB2, NDC80, TOP2A, RAD51AP1, TRIP13, AURKA, PTTG1 |
|
female gamete generation
GO:0007292
|
9 | 4.6 | 3.3e-03 | CCNB2, NDC80, TOP2A, NCAPH, ASPM, TTK, TRIP13, AURKA, PGR |
|
G2/M transition of mitotic cell cycle
GO:0000086
|
9 | 4.6 | 3.9e-03 | FOXM1, MELK, CCNA2, CDK1, AURKB, CHEK1, DTL, AURKA, CCNB1 |
| Cluster_3 | ||||
|
antimicrobial humoral immune response mediated by antimicrobial peptide
GO:0061844
|
9 | 4.6 | 4.4e-05 | S100A9, S100A7, CXCL10, CXCL13, CXCL11, CAMP, CXCL9, GNLY, CXCL14 |
|
antimicrobial humoral response
GO:0019730
|
10 | 5.1 | 1.2e-04 | S100A9, S100A7, CXCL10, CXCL13, CXCL11, CAMP, CXCL9, GNLY, RNASE4, CXCL14 |
|
chemokine-mediated signaling pathway
GO:0070098
|
8 | 4.1 | 2.3e-03 | CXCL10, CXCL13, CXCL11, CXCL9, CCL18, CCL8, ACKR1, CX3CR1 |
|
lymphocyte chemotaxis
GO:0048247
|
6 | 3.1 | 3.3e-03 | S100A7, CXCL10, CXCL13, CXCL11, CCL18, CCL8 |
|
granulocyte chemotaxis
GO:0071621
|
9 | 4.6 | 3.5e-03 | S100A9, S100A8, S100A7, CXCL10, CXCL13, CXCL11, CXCL9, CCL18, CCL8 |
|
response to chemokine
GO:1990868
|
8 | 4.1 | 3.8e-03 | CXCL10, CXCL13, CXCL11, CXCL9, CCL18, CCL8, ACKR1, CX3CR1 |
|
cellular response to chemokine
GO:1990869
|
8 | 4.1 | 3.8e-03 | CXCL10, CXCL13, CXCL11, CXCL9, CCL18, CCL8, ACKR1, CX3CR1 |
|
humoral immune response
GO:0006959
|
12 | 6.1 | 4.0e-03 | S100A9, S100A7, CXCL10, CXCL13, CXCL11, CAMP, CXCL9, GNLY, RNASE4, C7, CXCL14, GATA3 |
|
neutrophil chemotaxis
GO:0030593
|
8 | 4.1 | 4.2e-03 | S100A9, S100A8, CXCL10, CXCL13, CXCL11, CXCL9, CCL18, CCL8 |
|
response to molecule of bacterial origin
GO:0002237
|
15 | 7.7 | 4.6e-03 | S100A9, S100A8, S100A7, CXCL10, CXCL13, CXCL11, SLC7A5, CAMP, CXCL9, INAVA, IDO1, MAOB, PCK1, HMGCS2, CX3CR1 |
| Cluster_4 | ||||
|
lens development in camera-type eye
GO:0002088
|
5 | 2.6 | 1.5e-01 | PAX6, PLAAT1, BMP4, NDP, GATA3 |
| Cluster_6 | ||||
|
microtubule-based movement
GO:0007018
|
12 | 6.1 | 3.1e-02 | KIF23, CENPE, DLGAP5, KIF18A, KIF11, KIFC1, KIF18B, KIF20A, KIF4A, MAPT, TRIM58, CFAP69 |
| Cluster_7 | ||||
|
regulation of DNA biosynthetic process
GO:2000278
|
6 | 3.1 | 1.2e-01 | NEK2, CCNA2, AURKB, CHEK1, ADIPOQ, DACH1 |
| Cluster_9 | ||||
|
response to activity
GO:0014823
|
5 | 2.6 | 6.1e-02 | SLC7A5, CDK1, ADIPOQ, PCK1, CRY2 |
Full enriched enriched (enrich^2) result is in
obj@enriched_result.
df1<-obj@enriched_result |> as_tibble()
df1
#> # A tibble: 34 × 7
#> Description ID Count Cluster PCT Padj geneID
#> <chr> <chr> <int> <chr> <dbl> <dbl> <chr>
#> 1 nuclear chromosome segregation GO:0… 33 Cluste… 17 6.70e-18 CDCA8…
#> 2 chromosome segregation GO:0… 37 Cluste… 19 6.70e-18 CDCA8…
#> 3 mitotic sister chromatid segregati… GO:0… 27 Cluste… 14 2.10e-17 CDCA8…
#> 4 sister chromatid segregation GO:0… 29 Cluste… 15 2.20e-17 CDCA8…
#> 5 mitotic nuclear division GO:0… 31 Cluste… 16 4.10e-17 CDCA8…
#> 6 nuclear division GO:0… 36 Cluste… 18 5.30e-17 CDCA8…
#> 7 organelle fission GO:0… 37 Cluste… 19 1.7 e-16 CDCA8…
#> 8 microtubule cytoskeleton organizat… GO:1… 23 Cluste… 12 1.9 e-14 CDCA8…
#> 9 chromosome organization GO:0… 38 Cluste… 19 1.4 e-13 CDC45…
#> 10 cell division GO:0… 37 Cluste… 19 2.5 e-12 CDCA8…
#> # ℹ 24 more rowsWe also generated gene modules and pathway clusters based on
clustering results. You can assess them by obj@gene_modules
and obj@pathway_clusters.
head(obj@gene_modules)
#> $Cluster_1
#> [1] "ASPM" "AURKA" "AURKB" "BIRC5" "BMP4" "CCNA2"
#> [7] "CCNB1" "CCNB2" "CDC20" "CDC45" "CDCA3" "CDCA8"
#> [13] "CDK1" "CDT1" "CENPE" "CENPM" "CENPN" "CEP55"
#> [19] "CHEK1" "DLGAP5" "E2F8" "ERCC6L" "EZH2" "GINS1"
#> [25] "GINS2" "HJURP" "KIF11" "KIF18A" "KIF18B" "KIF20A"
#> [31] "KIF23" "KIF4A" "KIFC1" "MAD2L1" "MAPT" "MCM5"
#> [37] "MYBL2" "NCAPG" "NCAPH" "NDC80" "NEK2" "NUSAP1"
#> [43] "PAX6" "PRC1" "PTTG1" "RAD51AP1" "SKA1" "TACC3"
#> [49] "TOP2A" "TPX2" "TRIP13" "TTK" "UBE2C" "UBE2S"
#>
#> $Cluster_2
#> [1] "ASPM" "AURKA" "AURKB" "CCNA2" "CCNB1" "CCNB2"
#> [7] "CDC20" "CDC45" "CDK1" "CDT1" "CHAF1B" "CHEK1"
#> [13] "DACH1" "DTL" "E2F8" "FOXM1" "GINS1" "GINS2"
#> [19] "KIF18A" "MCM10" "MCM5" "MELK" "NCAPH" "NDC80"
#> [25] "NEK2" "PGR" "PTTG1" "RAD51AP1" "RRM2" "TOP2A"
#> [31] "TRIP13" "TTK"
#>
#> $Cluster_3
#> [1] "ACKR1" "C7" "CAMP" "CCL18" "CCL8" "CX3CR1" "CXCL10" "CXCL11"
#> [9] "CXCL13" "CXCL14" "CXCL9" "GATA3" "GNLY" "HMGCS2" "IDO1" "INAVA"
#> [17] "MAOB" "PCK1" "RNASE4" "S100A7" "S100A8" "S100A9" "SLC7A5"
#>
#> $Cluster_4
#> [1] "BMP4" "GATA3" "NDP" "PAX6" "PLAAT1"
#>
#> $Cluster_6
#> [1] "CENPE" "CFAP69" "DLGAP5" "KIF11" "KIF18A" "KIF18B" "KIF20A" "KIF23"
#> [9] "KIF4A" "KIFC1" "MAPT" "TRIM58"
#>
#> $Cluster_7
#> [1] "ADIPOQ" "AURKB" "CCNA2" "CHEK1" "DACH1" "NEK2"From saved data frame
We can parse any data frame saved from clusterProfiler on disk, for re-producing results. Like this:
Save data frame
write.csv(ego,"ego_enriched.csv")
res0<-read_csv("ego_enriched.csv")
#> New names:
#> Rows: 470 Columns: 10
#> ── Column specification
#> ──────────────────────────────────────────────────────── Delimiter: "," chr
#> (6): ...1, ID, Description, GeneRatio, BgRatio, geneID dbl (4): pvalue,
#> p.adjust, qvalue, Count
#> ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
#> Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> • `` -> `...1`
res0
#> # A tibble: 470 × 10
#> ...1 ID Description GeneRatio BgRatio pvalue p.adjust qvalue geneID
#> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr>
#> 1 GO:009… GO:0… nuclear ch… 33/196 236/11… 2.78e-21 6.69e-18 6.16e-18 CDCA8…
#> 2 GO:000… GO:0… chromosome… 37/196 316/11… 4.45e-21 6.69e-18 6.16e-18 CDCA8…
#> 3 GO:000… GO:0… mitotic si… 27/196 151/11… 2.10e-20 2.10e-17 1.94e-17 CDCA8…
#> 4 GO:000… GO:0… sister chr… 29/196 184/11… 2.92e-20 2.20e-17 2.02e-17 CDCA8…
#> 5 GO:014… GO:0… mitotic nu… 31/196 224/11… 6.84e-20 4.12e-17 3.79e-17 CDCA8…
#> 6 GO:000… GO:0… nuclear di… 36/196 325/11… 1.05e-19 5.29e-17 4.87e-17 CDCA8…
#> 7 GO:004… GO:0… organelle … 37/196 360/11… 3.98e-19 1.71e-16 1.57e-16 CDCA8…
#> 8 GO:190… GO:1… microtubul… 23/196 135/11… 5.01e-17 1.88e-14 1.73e-14 CDCA8…
#> 9 GO:005… GO:0… chromosome… 38/196 470/11… 4.33e-16 1.45e-13 1.33e-13 CDC45…
#> 10 GO:005… GO:0… cell divis… 37/196 487/11… 8.15e-15 2.45e-12 2.26e-12 CDCA8…
#> # ℹ 460 more rows
#> # ℹ 1 more variable: Count <dbl>do EnrichGT
obj1<-res0|> EnrichGT(P.adj = 0.2,ClusterNum = 10,nTop = 10)
obj@gt_object| Parse form: ego | ||||
| Split into 10 Clusters. Generated by github@zhimingye/EnrichGT | ||||
| Description | Count | PCT | Padj | geneID |
|---|---|---|---|---|
| Cluster_1 | ||||
|
nuclear chromosome segregation
GO:0098813
|
33 | 17.0 | 6.7e-18 | CDCA8, CDC20, KIF23, CENPE, MYBL2, CCNB2, NDC80, TOP2A, NCAPH, ASPM, DLGAP5, UBE2C, NUSAP1, TPX2, TACC3, NEK2, CDK1, MAD2L1, KIF18A, CDT1, BIRC5, KIF11, TTK, NCAPG, AURKB, TRIP13, PRC1, KIFC1, KIF18B, AURKA, CCNB1, KIF4A, PTTG1 |
|
chromosome segregation
GO:0007059
|
37 | 19.0 | 6.7e-18 | CDCA8, CDC20, KIF23, CENPE, MYBL2, CCNB2, NDC80, TOP2A, NCAPH, ASPM, DLGAP5, UBE2C, HJURP, SKA1, NUSAP1, TPX2, TACC3, NEK2, CENPM, CENPN, CDK1, MAD2L1, KIF18A, CDT1, BIRC5, KIF11, TTK, NCAPG, AURKB, TRIP13, PRC1, KIFC1, KIF18B, AURKA, CCNB1, KIF4A, PTTG1 |
|
mitotic sister chromatid segregation
GO:0000070
|
27 | 14.0 | 2.1e-17 | CDCA8, CDC20, KIF23, CENPE, MYBL2, NDC80, NCAPH, DLGAP5, UBE2C, NUSAP1, TPX2, NEK2, CDK1, MAD2L1, KIF18A, CDT1, BIRC5, KIF11, TTK, NCAPG, AURKB, TRIP13, PRC1, KIFC1, KIF18B, CCNB1, KIF4A |
|
sister chromatid segregation
GO:0000819
|
29 | 15.0 | 2.2e-17 | CDCA8, CDC20, KIF23, CENPE, MYBL2, NDC80, TOP2A, NCAPH, DLGAP5, UBE2C, NUSAP1, TPX2, TACC3, NEK2, CDK1, MAD2L1, KIF18A, CDT1, BIRC5, KIF11, TTK, NCAPG, AURKB, TRIP13, PRC1, KIFC1, KIF18B, CCNB1, KIF4A |
|
mitotic nuclear division
GO:0140014
|
31 | 16.0 | 4.1e-17 | CDCA8, CDC20, KIF23, CENPE, MYBL2, NDC80, NCAPH, DLGAP5, UBE2C, NUSAP1, TPX2, NEK2, UBE2S, CDK1, MAD2L1, KIF18A, CDT1, BIRC5, KIF11, TTK, NCAPG, AURKB, CHEK1, TRIP13, PRC1, KIFC1, KIF18B, AURKA, CCNB1, KIF4A, BMP4 |
|
nuclear division
GO:0000280
|
36 | 18.0 | 5.3e-17 | CDCA8, CDC20, KIF23, CENPE, MYBL2, CCNB2, NDC80, TOP2A, NCAPH, ASPM, DLGAP5, UBE2C, NUSAP1, TPX2, NEK2, RAD51AP1, UBE2S, CDK1, MAD2L1, KIF18A, CDT1, BIRC5, KIF11, TTK, NCAPG, AURKB, CHEK1, TRIP13, PRC1, KIFC1, KIF18B, AURKA, CCNB1, KIF4A, PTTG1, BMP4 |
|
organelle fission
GO:0048285
|
37 | 19.0 | 1.7e-16 | CDCA8, CDC20, KIF23, CENPE, MYBL2, CCNB2, NDC80, TOP2A, NCAPH, ASPM, DLGAP5, UBE2C, NUSAP1, TPX2, NEK2, RAD51AP1, UBE2S, CDK1, MAD2L1, KIF18A, CDT1, BIRC5, KIF11, TTK, NCAPG, AURKB, CHEK1, TRIP13, PRC1, KIFC1, KIF18B, AURKA, CCNB1, KIF4A, PTTG1, BMP4, MAPT |
|
microtubule cytoskeleton organization involved in mitosis
GO:1902850
|
23 | 12.0 | 1.9e-14 | CDCA8, CDC20, KIF23, CENPE, MYBL2, NDC80, DLGAP5, NUSAP1, TPX2, TACC3, NEK2, CDK1, MAD2L1, PAX6, BIRC5, KIF11, TTK, AURKB, PRC1, KIFC1, AURKA, CCNB1, KIF4A |
|
chromosome organization
GO:0051276
|
38 | 19.0 | 1.4e-13 | CDC45, CDCA8, CDC20, KIF23, CENPE, MYBL2, NDC80, TOP2A, NCAPH, DLGAP5, UBE2C, HJURP, NUSAP1, TPX2, TACC3, NEK2, CENPN, CDK1, MAD2L1, GINS1, KIF18A, CDT1, BIRC5, KIF11, EZH2, TTK, NCAPG, AURKB, GINS2, TRIP13, PRC1, KIFC1, KIF18B, CCNB1, KIF4A, MCM5, PTTG1, MAPT |
|
cell division
GO:0051301
|
37 | 19.0 | 2.5e-12 | CDCA8, CDC20, KIF23, CENPE, CCNB2, NDC80, TOP2A, NCAPH, E2F8, ASPM, CEP55, UBE2C, SKA1, NUSAP1, CDCA3, TPX2, TACC3, NEK2, UBE2S, CCNA2, CDK1, ERCC6L, MAD2L1, PAX6, CDT1, BIRC5, KIF11, NCAPG, AURKB, PRC1, KIFC1, KIF18B, KIF20A, AURKA, CCNB1, KIF4A, PTTG1 |
| Cluster_2 | ||||
|
female meiotic nuclear division
GO:0007143
|
7 | 3.6 | 1.0e-05 | CCNB2, NDC80, TOP2A, NCAPH, TTK, TRIP13, AURKA |
|
meiotic nuclear division
GO:0140013
|
12 | 6.1 | 5.4e-05 | CDC20, CCNB2, NDC80, TOP2A, NCAPH, ASPM, RAD51AP1, KIF18A, TTK, TRIP13, AURKA, PTTG1 |
|
meiotic cell cycle process
GO:1903046
|
12 | 6.1 | 1.8e-04 | CDC20, CCNB2, NDC80, TOP2A, NCAPH, ASPM, RAD51AP1, KIF18A, TTK, TRIP13, AURKA, PTTG1 |
|
meiotic chromosome segregation
GO:0045132
|
8 | 4.1 | 2.3e-04 | CCNB2, NDC80, NCAPH, ASPM, TTK, TRIP13, AURKA, PTTG1 |
|
cell cycle G2/M phase transition
GO:0044839
|
11 | 5.6 | 3.6e-04 | FOXM1, MELK, CCNB2, NDC80, CCNA2, CDK1, AURKB, CHEK1, DTL, AURKA, CCNB1 |
|
meiotic cell cycle
GO:0051321
|
13 | 6.6 | 5.8e-04 | CDC20, CCNB2, NDC80, TOP2A, NCAPH, ASPM, NEK2, RAD51AP1, KIF18A, TTK, TRIP13, AURKA, PTTG1 |
|
DNA replication
GO:0006260
|
14 | 7.1 | 8.8e-04 | CDC45, MCM10, E2F8, RRM2, CCNA2, CDK1, GINS1, CDT1, GINS2, CHAF1B, CHEK1, DTL, MCM5, DACH1 |
|
meiosis I cell cycle process
GO:0061982
|
8 | 4.1 | 3.2e-03 | CDC20, CCNB2, NDC80, TOP2A, RAD51AP1, TRIP13, AURKA, PTTG1 |
|
female gamete generation
GO:0007292
|
9 | 4.6 | 3.3e-03 | CCNB2, NDC80, TOP2A, NCAPH, ASPM, TTK, TRIP13, AURKA, PGR |
|
G2/M transition of mitotic cell cycle
GO:0000086
|
9 | 4.6 | 3.9e-03 | FOXM1, MELK, CCNA2, CDK1, AURKB, CHEK1, DTL, AURKA, CCNB1 |
| Cluster_3 | ||||
|
antimicrobial humoral immune response mediated by antimicrobial peptide
GO:0061844
|
9 | 4.6 | 4.4e-05 | S100A9, S100A7, CXCL10, CXCL13, CXCL11, CAMP, CXCL9, GNLY, CXCL14 |
|
antimicrobial humoral response
GO:0019730
|
10 | 5.1 | 1.2e-04 | S100A9, S100A7, CXCL10, CXCL13, CXCL11, CAMP, CXCL9, GNLY, RNASE4, CXCL14 |
|
chemokine-mediated signaling pathway
GO:0070098
|
8 | 4.1 | 2.3e-03 | CXCL10, CXCL13, CXCL11, CXCL9, CCL18, CCL8, ACKR1, CX3CR1 |
|
lymphocyte chemotaxis
GO:0048247
|
6 | 3.1 | 3.3e-03 | S100A7, CXCL10, CXCL13, CXCL11, CCL18, CCL8 |
|
granulocyte chemotaxis
GO:0071621
|
9 | 4.6 | 3.5e-03 | S100A9, S100A8, S100A7, CXCL10, CXCL13, CXCL11, CXCL9, CCL18, CCL8 |
|
response to chemokine
GO:1990868
|
8 | 4.1 | 3.8e-03 | CXCL10, CXCL13, CXCL11, CXCL9, CCL18, CCL8, ACKR1, CX3CR1 |
|
cellular response to chemokine
GO:1990869
|
8 | 4.1 | 3.8e-03 | CXCL10, CXCL13, CXCL11, CXCL9, CCL18, CCL8, ACKR1, CX3CR1 |
|
humoral immune response
GO:0006959
|
12 | 6.1 | 4.0e-03 | S100A9, S100A7, CXCL10, CXCL13, CXCL11, CAMP, CXCL9, GNLY, RNASE4, C7, CXCL14, GATA3 |
|
neutrophil chemotaxis
GO:0030593
|
8 | 4.1 | 4.2e-03 | S100A9, S100A8, CXCL10, CXCL13, CXCL11, CXCL9, CCL18, CCL8 |
|
response to molecule of bacterial origin
GO:0002237
|
15 | 7.7 | 4.6e-03 | S100A9, S100A8, S100A7, CXCL10, CXCL13, CXCL11, SLC7A5, CAMP, CXCL9, INAVA, IDO1, MAOB, PCK1, HMGCS2, CX3CR1 |
| Cluster_4 | ||||
|
lens development in camera-type eye
GO:0002088
|
5 | 2.6 | 1.5e-01 | PAX6, PLAAT1, BMP4, NDP, GATA3 |
| Cluster_6 | ||||
|
microtubule-based movement
GO:0007018
|
12 | 6.1 | 3.1e-02 | KIF23, CENPE, DLGAP5, KIF18A, KIF11, KIFC1, KIF18B, KIF20A, KIF4A, MAPT, TRIM58, CFAP69 |
| Cluster_7 | ||||
|
regulation of DNA biosynthetic process
GO:2000278
|
6 | 3.1 | 1.2e-01 | NEK2, CCNA2, AURKB, CHEK1, ADIPOQ, DACH1 |
| Cluster_9 | ||||
|
response to activity
GO:0014823
|
5 | 2.6 | 6.1e-02 | SLC7A5, CDK1, ADIPOQ, PCK1, CRY2 |
Session info
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
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#> ──────────────────────────────────────────────────────────────────────────────