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# Gene set analysis

## Overview

Teaching: XX min
Exercises: XX min
Questions
• How do we find differentially expressed pathways?

Objectives
• Explain how to find differentially expressed pathways with gene set analysis in R.

• Understand how differentially expressed genes can enrich a gene set.

• Explain how to perform a gene set analysis in R, using clusterProfiler.

## Contribute!

This episode is intended to introduce the concept of how to carry out a functional analysis of a subset of differentially expressed (DE) genes, by means of assessing how significantly DE genes enrich gene sets of our interest.

First, we are going to explore the basic concept of enriching a gene set with differentially expressed (DE) genes. Recall the differential expression analysis.

``````library(SummarizedExperiment)
library(DESeq2)
``````
``````se <- readRDS("data/GSE96870_se.rds")
``````
``````dds <- DESeq2::DESeqDataSet(se[, se\$tissue == "Cerebellum"],
design = ~ sex + time)
``````
``````Warning in DESeq2::DESeqDataSet(se[, se\$tissue == "Cerebellum"], design = ~sex + : some variables in design formula are
characters, converting to factors
``````
``````dds <- DESeq2::DESeq(dds)
``````

Fetch results for the contrast between male and female mice.

``````resSex <- DESeq2::results(dds, contrast = c("sex", "Male", "Female"))
summary(resSex)
``````
``````
out of 32652 with nonzero total read count
LFC > 0 (up)       : 53, 0.16%
LFC < 0 (down)     : 71, 0.22%
outliers [1]       : 10, 0.031%
low counts [2]     : 13717, 42%
(mean count < 6)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
``````

Select DE genes between males and females with FDR < 5%.

``````sexDE <- as.data.frame(subset(resSex, padj < 0.05))
dim(sexDE)
``````
``````[1] 54  6
``````
``````sexDE <- sexDE[order(abs(sexDE\$log2FoldChange), decreasing=TRUE), ]
``````
``````               baseMean log2FoldChange     lfcSE      stat        pvalue          padj
Eif2s3y       1410.8750       12.62514 0.5652155  22.33685 1.620659e-110 1.022366e-106
Kdm5d          692.1672       12.55386 0.5936267  21.14773  2.895664e-99  1.370011e-95
Uty            667.4375       12.01728 0.5935911  20.24505  3.927797e-91  1.486671e-87
Ddx3y         2072.9436       11.87241 0.3974927  29.86825 5.087220e-196 4.813782e-192
Xist         22603.0359      -11.60429 0.3362822 -34.50761 6.168523e-261 1.167393e-256
LOC105243748    52.9669        9.08325 0.5976242  15.19893  3.594320e-52  1.133708e-48
``````
``````sexDEgenes <- rownames(sexDE)
``````
``````[1] "Eif2s3y"      "Kdm5d"        "Uty"          "Ddx3y"        "Xist"         "LOC105243748"
``````
``````length(sexDEgenes)
``````
``````[1] 54
``````

# Enrichment of a curated gene set

## Contribute!

Here we illustrate how to assess the enrichment of one gene set we curate ourselves with our subset of DE genes with sex-specific expression. Here we form such a gene set with genes from sex chromosomes. Could you think of another more accurate gene set formed by genes with sex-specific expression?

Build a gene set formed by genes located in the sex chromosomes X and Y.

``````xygenes <- rownames(se)[decode(seqnames(rowRanges(se)) %in% c("X", "Y"))]
length(xygenes)
``````
``````[1] 2123
``````

Build a contingency table and conduct a one-tailed Fisher’s exact test that verifies the association between genes being DE between male and female mice and being located in a sex chromosome.

``````N <- nrow(se)
n <- length(sexDEgenes)
m <- length(xygenes)
k <- length(intersect(xygenes, sexDEgenes))
dnames <- list(GS=c("inside", "outside"), DE=c("yes", "no"))
t <- matrix(c(k, n-k, m-k, N+k-n-m),
nrow=2, ncol=2, dimnames=dnames)
t
``````
``````         DE
GS        yes    no
inside   18  2105
outside  36 39627
``````
``````fisher.test(t, alternative="greater")
``````
``````
Fisher's Exact Test for Count Data

data:  t
p-value = 7.944e-11
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
5.541517      Inf
sample estimates:
odds ratio
9.411737
``````

# Gene ontology analysis with clusterProfiler

## Contribute!

Here we illustrate how to assess the enrichment on the entire collection of Gene Ontology (GO) gene sets using the package clusterProfiler. Could we illustrate any missing important feature of this package for this analysis objective? Could we briefly mention other packages that may be useful for this task?

Second, let’s perform a gene set analysis for an entire collection of gene sets using the Bioconductor package clusterProfiler. For this purpose, we will fetch the results for the contrast between two time points.

``````resTime <- DESeq2::results(dds, contrast = c("time", "Day8", "Day0"))
summary(resTime)
``````
``````
out of 32652 with nonzero total read count
LFC > 0 (up)       : 4472, 14%
LFC < 0 (down)     : 4276, 13%
outliers [1]       : 10, 0.031%
low counts [2]     : 8732, 27%
(mean count < 1)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
``````

Select DE genes between `Day0` and `Day8` with FDR < 5% and minimum 1.5-fold change.

``````timeDE <- as.data.frame(subset(resTime, padj < 0.05 & abs(log2FoldChange) > log2(1.5)))
dim(timeDE)
``````
``````[1] 2110    6
``````
``````timeDE <- timeDE[order(abs(timeDE\$log2FoldChange), decreasing=TRUE), ]
``````
``````              baseMean log2FoldChange     lfcSE      stat       pvalue         padj
LOC105245444  2.441873       4.768938 0.9013067  5.291138 1.215573e-07 1.800765e-06
LOC105246405  9.728219       4.601505 0.6101832  7.541186 4.657174e-14 2.507951e-12
4933427D06Rik 1.480365       4.556126 1.0318402  4.415535 1.007607e-05 9.169093e-05
A930006I01Rik 2.312732      -4.353155 0.9176026 -4.744053 2.094837e-06 2.252139e-05
LOC105245223  3.272536       4.337202 0.8611255  5.036666 4.737099e-07 6.047199e-06
A530053G22Rik 1.554735       4.243903 1.0248977  4.140806 3.460875e-05 2.720142e-04
``````
``````timeDEgenes <- rownames(timeDE)
``````
``````[1] "LOC105245444"  "LOC105246405"  "4933427D06Rik" "A930006I01Rik" "LOC105245223"  "A530053G22Rik"
``````
``````length(timeDEgenes)
``````
``````[1] 2110
``````

Call the `enrichGO()` function from clusterProfiler as follows.

``````library(clusterProfiler)
library(org.Mm.eg.db)
library(enrichplot)

resTimeGO <- enrichGO(gene = timeDEgenes,
keyType = "SYMBOL",
universe = rownames(se),
OrgDb = org.Mm.eg.db,
ont = "BP",
pvalueCutoff = 0.01,
qvalueCutoff = 0.01)
dim(resTimeGO)
``````
``````[1] 32  9
``````
``````head(resTimeGO)
``````
``````                   ID                                           Description GeneRatio   BgRatio       pvalue
GO:0071674 GO:0071674                            mononuclear cell migration   32/1142 166/20469 6.296646e-10
GO:0035456 GO:0035456                           response to interferon-beta   16/1142  48/20469 3.291098e-09
GO:0050900 GO:0050900                                   leukocyte migration   49/1142 355/20469 4.355926e-09
GO:0030595 GO:0030595                                  leukocyte chemotaxis   34/1142 214/20469 3.265849e-08
GO:0035458 GO:0035458                  cellular response to interferon-beta   13/1142  38/20469 6.940867e-08
GO:0002523 GO:0002523 leukocyte migration involved in inflammatory response   10/1142  23/20469 1.649290e-07
GO:0071674 3.080319e-06 2.852712e-06
GO:0035456 7.103063e-06 6.578212e-06
GO:0050900 7.103063e-06 6.578212e-06
GO:0030595 3.994133e-05 3.699003e-05
GO:0035458 6.790944e-05 6.289156e-05
GO:0002523 1.344721e-04 1.245358e-04
geneID
GO:0035456                                                                                                                                                                          Tgtp1/Tgtp2/F830016B08Rik/Iigp1/Ifitm6/Igtp/Gm4951/Bst2/Irgm1/Gbp6/Ifi47/Aim2/Ifitm7/Irgm2/Ifit1/Ifi204
GO:0035458                                                                                                                                                                                             Tgtp1/Tgtp2/F830016B08Rik/Iigp1/Igtp/Gm4951/Irgm1/Gbp6/Ifi47/Aim2/Irgm2/Ifit1/Ifi204
Count
GO:0071674    32
GO:0035456    16
GO:0050900    49
GO:0030595    34
GO:0035458    13
GO:0002523    10
``````

Let’s build a more readable table of results.

``````library(kableExtra)

resTimeGOtab <- as.data.frame(resTimeGO)
resTimeGOtab\$ID <- NULL
resTimeGOtab\$geneID <- sapply(strsplit(resTimeGO\$geneID, "/"), paste, collapse=", ")
ktab <- kable(resTimeGOtab, row.names=TRUE, caption="GO results for DE genes between time points.")
``````
GO results for DE genes between time points.
Description GeneRatio BgRatio pvalue p.adjust qvalue geneID Count
GO:0071674 mononuclear cell migration 32/1142 166/20469 0.00e+00 0.0000031 0.0000029 Tnfsf18, Aire, Ccl17, Ccr7, Nlrp12, Ccl2, Retnlg, Apod, Il12a, Ccl5, Fpr2, Fut7, Ccl7, Spn, Itgb3, Grem1, Ptk2b, Lgals3, Adam8, Dusp1, Ch25h, Nbl1, Alox5, Padi2, Plg, Calr, Ager, Ccl6, Mdk, Itga4, Hsd3b7, Trpm4 32
GO:0035456 response to interferon-beta 16/1142 48/20469 0.00e+00 0.0000071 0.0000066 Tgtp1, Tgtp2, F830016B08Rik, Iigp1, Ifitm6, Igtp, Gm4951, Bst2, Irgm1, Gbp6, Ifi47, Aim2, Ifitm7, Irgm2, Ifit1, Ifi204 16
GO:0050900 leukocyte migration 49/1142 355/20469 0.00e+00 0.0000071 0.0000066 Tnfsf18, Aire, Ccl17, Ccr7, Nlrp12, Bst1, Ccl2, Retnlg, Ppbp, Cxcl5, Apod, Il12a, Ccl5, Fpr2, Umodl1, Fut7, Ccl7, Ccl28, Spn, Sell, Itgb3, Grem1, Cxcl1, Ptk2b, Lgals3, Adam8, Pf4, Dusp1, Ch25h, S100a8, Nbl1, Alox5, Padi2, Plg, Edn3, Il33, Ptn, Ada, Calr, Ager, Ccl6, Prex1, Aoc3, Itgam, Mdk, Itga4, Hsd3b7, P2ry12, Trpm4 49
GO:0030595 leukocyte chemotaxis 34/1142 214/20469 0.00e+00 0.0000399 0.0000370 Tnfsf18, Ccl17, Ccr7, Bst1, Ccl2, Retnlg, Ppbp, Cxcl5, Il12a, Ccl5, Fpr2, Ccl7, Sell, Grem1, Cxcl1, Ptk2b, Lgals3, Adam8, Pf4, Dusp1, Ch25h, S100a8, Nbl1, Alox5, Padi2, Edn3, Ptn, Calr, Ccl6, Prex1, Itgam, Mdk, Hsd3b7, Trpm4 34
GO:0035458 cellular response to interferon-beta 13/1142 38/20469 1.00e-07 0.0000679 0.0000629 Tgtp1, Tgtp2, F830016B08Rik, Iigp1, Igtp, Gm4951, Irgm1, Gbp6, Ifi47, Aim2, Irgm2, Ifit1, Ifi204 13
GO:0002523 leukocyte migration involved in inflammatory response 10/1142 23/20469 2.00e-07 0.0001345 0.0001245 Ccl2, Ppbp, Fut7, Adam8, S100a8, Alox5, Ptn, Aoc3, Itgam, Mdk 10
GO:0071675 regulation of mononuclear cell migration 21/1142 107/20469 4.00e-07 0.0002770 0.0002565 Tnfsf18, Aire, Ccr7, Ccl2, Apod, Il12a, Ccl5, Fpr2, Spn, Itgb3, Grem1, Ptk2b, Lgals3, Adam8, Dusp1, Nbl1, Padi2, Calr, Ager, Mdk, Itga4 21
GO:0002685 regulation of leukocyte migration 31/1142 213/20469 9.00e-07 0.0005212 0.0004827 Tnfsf18, Aire, Ccr7, Bst1, Ccl2, Apod, Il12a, Ccl5, Fpr2, Fut7, Ccl28, Spn, Sell, Itgb3, Grem1, Ptk2b, Lgals3, Adam8, Dusp1, Nbl1, Padi2, Edn3, Il33, Ptn, Ada, Calr, Ager, Aoc3, Mdk, Itga4, P2ry12 31
GO:0050953 sensory perception of light stimulus 26/1142 161/20469 1.00e-06 0.0005212 0.0004827 Aipl1, Vsx2, Nxnl2, Lrit3, Cryba2, Bfsp2, Lrat, Gabrr2, Lum, Rlbp1, Pde6g, Gpr179, Col1a1, Cplx3, Best1, Ush1g, Rs1, Rdh5, Guca1b, Th, Ppef2, Rbp4, Olfm2, Rom1, Vsx1, Rpe65 26
GO:0060326 cell chemotaxis 38/1142 298/20469 1.70e-06 0.0008479 0.0007853 Tnfsf18, Ccl17, Ccr7, Bst1, Ccl2, Retnlg, Ppbp, Cxcl5, Nr4a1, Il12a, Ccl5, Fpr2, Ccl7, Ccl28, Sell, Grem1, Cxcl1, Ptk2b, Lgals3, Adam8, Pf4, Dusp1, Ch25h, S100a8, Nbl1, Alox5, Padi2, Edn3, Ptn, Plxnb3, Calr, Lpar1, Ccl6, Prex1, Itgam, Mdk, Hsd3b7, Trpm4 38
GO:0007601 visual perception 25/1142 157/20469 2.00e-06 0.0008935 0.0008275 Aipl1, Vsx2, Nxnl2, Lrit3, Cryba2, Bfsp2, Lrat, Gabrr2, Lum, Rlbp1, Pde6g, Gpr179, Col1a1, Cplx3, Best1, Rs1, Rdh5, Guca1b, Th, Ppef2, Rbp4, Olfm2, Rom1, Vsx1, Rpe65 25
GO:0097529 myeloid leukocyte migration 30/1142 214/20469 3.10e-06 0.0012605 0.0011674 Tnfsf18, Ccl17, Ccr7, Bst1, Ccl2, Retnlg, Ppbp, Cxcl5, Ccl5, Fpr2, Umodl1, Fut7, Ccl7, Sell, Grem1, Cxcl1, Ptk2b, Lgals3, Adam8, Pf4, Dusp1, S100a8, Nbl1, Edn3, Ager, Ccl6, Prex1, Itgam, Mdk, P2ry12 30
GO:1990266 neutrophil migration 21/1142 122/20469 3.70e-06 0.0013884 0.0012858 Ccl17, Ccr7, Bst1, Ccl2, Ppbp, Cxcl5, Ccl5, Umodl1, Fut7, Ccl7, Sell, Cxcl1, Lgals3, Adam8, Pf4, S100a8, Edn3, Ccl6, Prex1, Itgam, Mdk 21
GO:0030593 neutrophil chemotaxis 18/1142 98/20469 7.10e-06 0.0024639 0.0022819 Ccl17, Ccr7, Bst1, Ccl2, Ppbp, Cxcl5, Ccl5, Ccl7, Sell, Cxcl1, Lgals3, Pf4, S100a8, Edn3, Ccl6, Prex1, Itgam, Mdk 18
GO:0071677 positive regulation of mononuclear cell migration 14/1142 64/20469 9.10e-06 0.0029614 0.0027426 Tnfsf18, Ccr7, Ccl2, Il12a, Ccl5, Fpr2, Spn, Itgb3, Ptk2b, Lgals3, Adam8, Calr, Ager, Itga4 14
GO:0030198 extracellular matrix organization 36/1142 297/20469 1.02e-05 0.0029937 0.0027725 Nepn, Has2, Fbln5, Adamts14, Nox1, Adamtsl2, Mmp8, Lum, Itgb3, Nid1, Grem1, Elf3, Col5a3, Lgals3, Col1a1, Serpinh1, Col27a1, Loxl4, Agt, Kazald1, Colq, Pxdn, Plg, Col11a2, Col15a1, P4ha1, Mpzl3, Mmp15, Has3, Cav1, Ccdc80, Spint1, Abi3bp, Adamts16, Col14a1, Cyp1b1 36
GO:0043062 extracellular structure organization 36/1142 298/20469 1.10e-05 0.0029937 0.0027725 Nepn, Has2, Fbln5, Adamts14, Nox1, Adamtsl2, Mmp8, Lum, Itgb3, Nid1, Grem1, Elf3, Col5a3, Lgals3, Col1a1, Serpinh1, Col27a1, Loxl4, Agt, Kazald1, Colq, Pxdn, Plg, Col11a2, Col15a1, P4ha1, Mpzl3, Mmp15, Has3, Cav1, Ccdc80, Spint1, Abi3bp, Adamts16, Col14a1, Cyp1b1 36
GO:0045229 external encapsulating structure organization 36/1142 298/20469 1.10e-05 0.0029937 0.0027725 Nepn, Has2, Fbln5, Adamts14, Nox1, Adamtsl2, Mmp8, Lum, Itgb3, Nid1, Grem1, Elf3, Col5a3, Lgals3, Col1a1, Serpinh1, Col27a1, Loxl4, Agt, Kazald1, Colq, Pxdn, Plg, Col11a2, Col15a1, P4ha1, Mpzl3, Mmp15, Has3, Cav1, Ccdc80, Spint1, Abi3bp, Adamts16, Col14a1, Cyp1b1 36
GO:0034341 response to interferon-gamma 21/1142 133/20469 1.48e-05 0.0038065 0.0035252 Ccl17, Gbp4, Ccl2, Tgtp1, H2-Q7, Il12rb1, Ifitm6, Ccl5, Ccl7, Nos2, Nlrc5, Bst2, Irgm1, Gbp6, Capg, Ifitm7, Gbp9, Gbp5, Irgm2, Ccl6, Aqp4 21
GO:0036336 dendritic cell migration 8/1142 23/20469 2.12e-05 0.0051736 0.0047913 Tnfsf18, Ccr7, Nlrp12, Retnlg, Il12a, Alox5, Calr, Trpm4 8
GO:0002819 regulation of adaptive immune response 27/1142 203/20469 2.49e-05 0.0058002 0.0053716 Tnfsf18, Ccr7, H2-Q6, H2-Q7, Alox15, Il12a, Il12rb1, H2-Q4, Fut7, Spn, H2-Q2, Irf7, Cd274, Tnfrsf13c, Il33, H2-Q1, Ada, C3, Tfrc, H2-K1, Ager, H2-T23, Tap2, Tnfsf13b, Pla2g4a, Trpm4, Parp3 27
GO:0001906 cell killing 26/1142 193/20469 2.79e-05 0.0060788 0.0056296 Ccl17, Gzmb, Ccl2, Cxcl5, H2-Q6, H2-Q7, Il12a, H2-Q4, Il18rap, Ccl28, Nos2, Cxcl1, H2-Q2, Lgals3, Pf4, Tap1, H2-Q1, Emp2, Scnn1b, C3, H2-K1, Ager, H2-T23, Itgam, Tap2, Clec7a 26
GO:1903039 positive regulation of leukocyte cell-cell adhesion 28/1142 216/20469 2.86e-05 0.0060788 0.0056296 Ccr7, Nr4a3, Has2, Ccl2, Il12a, Il12rb1, Ccl5, Fut7, Spn, Cd5, Icosl, Il2rg, Adam8, Cd274, Tnfrsf13c, Hsph1, Alox5, Btn2a2, Ada, Tfrc, Cd46, Ager, Xbp1, H2-T23, Tnfsf13b, Mdk, Itga4, Cav1 28
GO:1901615 organic hydroxy compound metabolic process 50/1142 492/20469 3.17e-05 0.0064643 0.0059866 Hao1, Dio3, Cyp1a1, Nr4a2, Sult1a1, Cyp4f18, Lrat, Ddc, Apoc1, Alox15, Lhcgr, Drd4, Cyp4f15, Acer2, Lcat, Hsd17b1, Ptk2b, Actn3, Akr1c14, Ch25h, Cyp11a1, Dct, Cyp2d22, Dkkl1, Cyp51, Srebf1, Scnn1b, Npc1l1, Moxd1, Msmo1, Hmgcs1, Ctsk, Ebp, Th, Qdpr, Abca1, Itgam, Kcnj6, Pla2g4a, Pmel, Cyb5r2, Cdh3, Rbp4, Hsd3b7, Idh2, Slc5a5, Tg, Rpe65, Gpr37, Cyp1b1 50
GO:0097530 granulocyte migration 22/1142 151/20469 3.39e-05 0.0066403 0.0061496 Tnfsf18, Ccl17, Ccr7, Bst1, Ccl2, Ppbp, Cxcl5, Ccl5, Umodl1, Fut7, Ccl7, Sell, Cxcl1, Lgals3, Adam8, Pf4, S100a8, Edn3, Ccl6, Prex1, Itgam, Mdk 22
GO:0007159 leukocyte cell-cell adhesion 38/1142 340/20469 3.71e-05 0.0069751 0.0064597 Tnfsf18, Ccr7, Nr4a3, Has2, Slfn1, Ccl2, Il12a, Il12rb1, Ccl5, Fut7, Ccl28, Spn, Cd5, Sell, Icosl, Il2rg, Btn1a1, Lgals3, Adam8, Cd274, Tnfrsf13c, Hsph1, S100a8, Btla, Alox5, Btn2a2, Ada, Tfrc, Cd46, Ass1, Ager, Xbp1, H2-T23, Itgam, Tnfsf13b, Mdk, Itga4, Cav1 38
GO:1903037 regulation of leukocyte cell-cell adhesion 35/1142 304/20469 4.03e-05 0.0072725 0.0067351 Tnfsf18, Ccr7, Nr4a3, Has2, Slfn1, Ccl2, Il12a, Il12rb1, Ccl5, Fut7, Ccl28, Spn, Cd5, Icosl, Il2rg, Btn1a1, Lgals3, Adam8, Cd274, Tnfrsf13c, Hsph1, Btla, Alox5, Btn2a2, Ada, Tfrc, Cd46, Ass1, Ager, Xbp1, H2-T23, Tnfsf13b, Mdk, Itga4, Cav1 35
GO:0031349 positive regulation of defense response 30/1142 244/20469 4.16e-05 0.0072725 0.0067351 Tnfsf18, Ccr7, Zbp1, Pla2g3, Il12a, Ccl5, Mmp8, Fpr2, Il18rap, Nlrc5, Cxcl1, Adam8, Irf7, Irgm1, S100a8, Pla2g5, Aim2, Srebf1, Il33, C3, Npas2, Gbp5, Irgm2, Ager, Aoc3, H2-T23, Pla2g4a, Mdk, Ace, Ifi204 30
GO:0002822 regulation of adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains 25/1142 187/20469 4.50e-05 0.0075878 0.0070271 Tnfsf18, Ccr7, H2-Q6, H2-Q7, Il12a, Il12rb1, H2-Q4, Fut7, Spn, H2-Q2, Cd274, Tnfrsf13c, Il33, H2-Q1, Ada, C3, Tfrc, H2-K1, Ager, H2-T23, Tap2, Tnfsf13b, Pla2g4a, Trpm4, Parp3 25
GO:0072676 lymphocyte migration 17/1142 102/20469 4.66e-05 0.0075967 0.0070354 Aire, Ccl17, Ccr7, Ccl2, Apod, Ccl5, Fut7, Ccl7, Spn, Itgb3, Ptk2b, Adam8, Ch25h, Padi2, Ccl6, Itga4, Hsd3b7 17
GO:0071621 granulocyte chemotaxis 19/1142 123/20469 5.07e-05 0.0079992 0.0074081 Tnfsf18, Ccl17, Ccr7, Bst1, Ccl2, Ppbp, Cxcl5, Ccl5, Ccl7, Sell, Cxcl1, Lgals3, Pf4, S100a8, Edn3, Ccl6, Prex1, Itgam, Mdk 19
GO:0032103 positive regulation of response to external stimulus 41/1142 387/20469 6.37e-05 0.0097332 0.0090140 Tnfsf18, Ccr7, Ccl2, Zbp1, Pla2g3, Ntf3, Il12a, Ccl5, Mmp8, Fpr2, Il18rap, Sell, Casr, Nlrc5, Cxcl1, Ptk2b, Adam8, Irf7, Irgm1, S100a8, Pla2g5, Aim2, Plg, Srebf1, Edn3, Il33, Ptn, C3, Calr, Lpar1, Gbp5, Irgm2, Ager, Aoc3, H2-T23, Pla2g4a, Mdk, Ace, Stx3, P2ry12, Ifi204 41

• Key point 1