Last updated: 2020-10-15
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 6797660 | jgblanc | 2020-10-15 | Added GO terms |
| html | 1982147 | jgblanc | 2020-10-15 | Build site. |
| Rmd | 0e39940 | jgblanc | 2020-10-15 | Added GO terms |
| html | 9cc4fa0 | jgblanc | 2020-09-30 | Build site. |
| Rmd | 3294a81 | jgblanc | 2020-09-30 | new clusters |
| html | 9ede1d4 | jgblanc | 2020-04-24 | Build site. |
| Rmd | 69c5a29 | jgblanc | 2020-04-24 | Finished clusters |
| html | e26e754 | jgblanc | 2020-04-24 | Build site. |
| Rmd | 3a6666a | jgblanc | 2020-04-24 | Finished clusters |
| Rmd | 8298d4d | GitHub | 2020-04-16 | Merge branch ‘master’ into master |
| Rmd | 6b00f47 | em | 2020-03-27 | stuff |
| Rmd | a63fa86 | em | 2020-03-26 | stuff |
| html | 7591f0d | jgblanc | 2020-03-16 | Build site. |
| Rmd | df4fe40 | jgblanc | 2020-03-16 | added more pvals |
| Rmd | 2cb89cd | jgblanc | 2020-03-13 | adding coexpression stuff |
Here is the code to test for expression on coeexpression clusters from Walleey et al.
For this analysis we want to test for selection within specific coexpression modules. We used coexpression modules from Walley et al. (2016) who used weight gene coexpression network analysis (WGCNA) to to group together genes that were similarly expressed in at least 4 tissues in one maize inbred line. Their analysis resulted in 66 co-expression networks. Below we will load their co-expression networks and select all the clusters that have at least 100 genes in them.
modules <- read.delim("../data/Modules.txt",na.strings=c("","NA"), stringsAsFactors = F)
num_genes <- apply(modules, 2, function(x) length(which(!is.na(x))))
num_genes_100 <- which(num_genes >= 100)
modules <- modules[,num_genes_100]We now have 51 modules that have at least 100 genes. We will now get the median expression of all the genes that are present in our RNAseq dataset for each module and then test for selection on the median values in each cluster. The code below is similar code used to identify selected genes, we are simply conducting the test on median expression values in each line instead of a single gene expression in each line. Again we test for selection on the first 5 PCs and use the last half of PCs to estimate \(V_a\). Th function will return the p-values from the \(Q_{pc}\) test for all tissues.
cluster_analysis_func <- function(modules) {
alltissues = c('GRoot',"Kern","LMAD26","LMAN26", "L3Tip","GShoot","L3Base")
alltissuemodules = lapply(alltissues, function(mytissue){
##get the names of genes we have expression data for in each tissue
exp <- read.table(paste("../data/Raw_expression/",mytissue,".txt", sep="")) # read in expression level
geneNames = names(exp)
#get the median expression level for each module in this tissue
moduleExpression = apply(modules, 2, function(x){
olap = x[x %in% geneNames] #get genes in the module tha we have expression data for
moduleExp = dplyr::select(exp, all_of(olap)) #pull out the expression data for these genes
medianExp = apply(moduleExp, 1, median)
return(medianExp)
})
####identify selection in each of these modules
# Read in tissue specific kinship matrix
myF <- read.table(paste('../data/Kinship_matrices/F_',mytissue,'.txt',sep=""))
## Get Eigen Values and Vectors
myE <- eigen(myF)
E_vectors <- myE$vectors
E_values <- myE$values
## Testing for selection on first 5 PCs
myM = 1:5
## Using the last 1/2 of PCs to estimate Va
myL = 6:dim(myF)[1]
### test for selection on all modules
moduleSelection <- apply(moduleExpression, 2, function(x){
meanCenteredX = x[-length(x)] - mean(x)
myQpc = calcQpc(myZ = meanCenteredX, myU = E_vectors, myLambdas = E_values, myL = myL, myM = myM)
return(myQpc$pvals)
})
##make a dataframe with info to return
mydf = data.frame(t(moduleSelection))
names(mydf) = c('PC1','PC2','PC3','PC4','PC5')
mydf$module = row.names(mydf)
row.names(mydf) <- NULL
mydflong = tidyr::gather(mydf, 'PC','pval', PC1:PC5)
mydflong$tissue = mytissue
return(mydflong)})
return(alltissuemodules)
}We have the p-values, let’s calculate the FDR and look for significant p-values.
alltissuemodules = cluster_analysis_func(modules)
#combine all into one list
bigdf <- do.call('rbind', alltissuemodules)
bigdf$fdr <- p.adjust(bigdf$pval, method='fdr') ##calculate an FDR
summary(bigdf$fdr) ##nothing is significant Min. 1st Qu. Median Mean 3rd Qu. Max.
0.4299 0.9996 0.9996 0.9990 0.9996 0.9996 kable(bigdf[which.min(bigdf[,3]),])| module | PC | pval | tissue | fdr | |
|---|---|---|---|---|---|
| 732 | RNA_Root_Meristem | PC5 | 0.0002409 | LMAD26 | 0.4299294 |
There is no significant selection on coexpression clusters. The lowest FDR is for the “RNA Root Meristem” cluster in adult leaf tissue on PC 5. We can also look at the clusters with with lowest bonferroni corrected p-values
t <- bigdf %>% group_by(PC, tissue) %>% mutate(bonferroni = p.adjust(pval, method='bonferroni')) %>% arrange(bonferroni)
kable(head(t))| module | PC | pval | tissue | fdr | bonferroni |
|---|---|---|---|---|---|
| RNA_Root_Meristem | PC5 | 0.0002409 | LMAD26 | 0.4299294 | 0.0122837 |
| RNA_FemaleSpikIt_._VegMeristem | PC1 | 0.0012924 | Kern | 0.7985099 | 0.0659101 |
| Protein_Germinating_Kernals | PC1 | 0.0017164 | Kern | 0.7985099 | 0.0875356 |
| RNA_Root_Cortex_2 | PC1 | 0.0017894 | Kern | 0.7985099 | 0.0912583 |
| Protein_EndoCrown.PericarpAl | PC1 | 0.0039731 | Kern | 0.9996316 | 0.2026281 |
| RNA_Root_Cortex | PC1 | 0.0053132 | Kern | 0.9996316 | 0.2709719 |
ZmRoot Clusters - get clustes with >= 100 genes
clusters <- fread("../data/schaefer_clusters.csv")
clusters <- clusters[,1:4]
dat <- clusters %>% group_by(ZmRoot) %>% mutate(num_genes = n()) %>% filter(num_genes >= 100)
names <- unique(dat$ZmRoot)
gene_list <- list()
max <- nrow(dat[dat$ZmRoot == 1,])
for(i in 1:length(names)) {
df <- clusters %>% filter(ZmRoot == names[i])
genes <- c(df$V1, rep(NA, max-nrow(df)))
gene_list[[i]] <- genes
}
modules <- do.call(cbind, gene_list)Run analysis and FDR correction
alltissuemodules = cluster_analysis_func(modules)
#combine all into one list
bigdf <- do.call('rbind', alltissuemodules)
bigdf$fdr <- p.adjust(bigdf$pval, method='fdr') ##calculate an FDR
summary(bigdf$fdr) ##nothing is significant Min. 1st Qu. Median Mean 3rd Qu. Max.
0.4377 0.9951 0.9951 0.9889 0.9951 0.9951 kable(bigdf[which.min(bigdf[,3]),])| module | PC | pval | tissue | fdr | |
|---|---|---|---|---|---|
| 158 | 4 | PC5 | 0.0011368 | LMAD26 | 0.4376829 |
Look at lowest p-value clusters
t <- bigdf %>% group_by(PC, tissue) %>% mutate(bonferroni = p.adjust(pval, method='bonferroni')) %>% arrange(bonferroni)
kable(head(t))| module | PC | pval | tissue | fdr | bonferroni |
|---|---|---|---|---|---|
| 4 | PC5 | 0.0011368 | LMAD26 | 0.4376829 | 0.0125052 |
| 3 | PC5 | 0.0023537 | LMAD26 | 0.4530808 | 0.0258903 |
| 9 | PC1 | 0.0053389 | Kern | 0.5741055 | 0.0587274 |
| 8 | PC2 | 0.0063947 | LMAD26 | 0.5741055 | 0.0703414 |
| 9 | PC3 | 0.0074559 | LMAN26 | 0.5741055 | 0.0820151 |
| 11 | PC1 | 0.0186208 | Kern | 0.9950821 | 0.2048286 |
Let’s look at the GO enrichment terms for the most significant clusters (bonferroni p < 0.05)
info <- fread("~/Downloads/GO_clusters.csv")
zmroot <- info %>% filter(Network == "ZmRoot") %>% select(Network, `MCL Cluster`, `GO Term`, `Num Genes common`, `Num GO genes`, `Num MCL Genes`, pval, `GO Term Name`)
# Module 4
zmroot %>% filter(`MCL Cluster` == "MCL4") Network MCL Cluster GO Term Num Genes common Num GO genes Num MCL Genes
1: ZmRoot MCL4 GO:0006558 4 18 745
2: ZmRoot MCL4 GO:1902221 4 18 745
3: ZmRoot MCL4 GO:0006559 3 10 745
4: ZmRoot MCL4 GO:0009074 3 10 745
5: ZmRoot MCL4 GO:1902222 3 10 745
6: ZmRoot MCL4 GO:0016841 3 11 745
pval
1: 0.00181
2: 0.00181
3: 0.00282
4: 0.00282
5: 0.00282
6: 0.00379
GO Term Name
1: L-phenylalanine metabolic process
2: erythrose 4-phosphate/phosphoenolpyruvate family amino acid metabolic process
3: L-phenylalanine catabolic process
4: aromatic amino acid family catabolic process
5: erythrose 4-phosphate/phosphoenolpyruvate family amino acid catabolic process
6: ammonia-lyase activity# Module 3
zmroot %>% filter(`MCL Cluster` == "MCL3") Network MCL Cluster GO Term Num Genes common Num GO genes Num MCL Genes
1: ZmRoot MCL3 GO:0006260 24 56 996
2: ZmRoot MCL3 GO:0000786 37 151 996
3: ZmRoot MCL3 GO:0032993 37 151 996
4: ZmRoot MCL3 GO:0044815 37 151 996
5: ZmRoot MCL3 GO:0006334 37 154 996
6: ZmRoot MCL3 GO:0034728 37 154 996
7: ZmRoot MCL3 GO:0065004 37 154 996
8: ZmRoot MCL3 GO:0071824 37 154 996
9: ZmRoot MCL3 GO:0044427 42 210 996
10: ZmRoot MCL3 GO:0003777 25 71 996
11: ZmRoot MCL3 GO:0005875 25 71 996
12: ZmRoot MCL3 GO:0006325 39 194 996
13: ZmRoot MCL3 GO:0007018 26 85 996
14: ZmRoot MCL3 GO:0007017 30 120 996
15: ZmRoot MCL3 GO:0044430 32 145 996
16: ZmRoot MCL3 GO:0034622 41 245 996
17: ZmRoot MCL3 GO:0006928 26 99 996
18: ZmRoot MCL3 GO:0003774 26 101 996
19: ZmRoot MCL3 GO:0006259 41 282 996
20: ZmRoot MCL3 GO:0016986 12 20 996
21: ZmRoot MCL3 GO:0006270 11 22 996
22: ZmRoot MCL3 GO:0034061 9 25 996
23: ZmRoot MCL3 GO:0003887 8 23 996
24: ZmRoot MCL3 GO:0006352 14 83 996
25: ZmRoot MCL3 GO:0000280 5 9 996
26: ZmRoot MCL3 GO:0007067 5 9 996
27: ZmRoot MCL3 GO:1903047 5 9 996
28: ZmRoot MCL3 GO:0009360 4 7 996
29: ZmRoot MCL3 GO:0042575 4 7 996
30: ZmRoot MCL3 GO:0048285 5 13 996
31: ZmRoot MCL3 GO:0032774 14 108 996
32: ZmRoot MCL3 GO:0071554 14 108 996
33: ZmRoot MCL3 GO:0022402 6 21 996
34: ZmRoot MCL3 GO:0016779 16 138 996
35: ZmRoot MCL3 GO:0008107 4 14 996
36: ZmRoot MCL3 GO:0008417 4 14 996
37: ZmRoot MCL3 GO:0031127 4 14 996
38: ZmRoot MCL3 GO:0042546 4 15 996
39: ZmRoot MCL3 GO:0004990 3 8 996
Network MCL Cluster GO Term Num Genes common Num GO genes Num MCL Genes
pval GO Term Name
1: 3.54e-19 DNA replication
2: 4.40e-19 nucleosome
3: 4.40e-19 protein-DNA complex
4: 4.40e-19 DNA packaging complex
5: 9.00e-19 nucleosome assembly
6: 9.00e-19 nucleosome organization
7: 9.00e-19 protein-DNA complex assembly
8: 9.00e-19 protein-DNA complex subunit organization
9: 5.92e-18 chromosomal part
10: 1.86e-17 microtubule motor activity
11: 1.86e-17 microtubule associated complex
12: 7.56e-17 chromatin organization
13: 2.24e-16 microtubule-based movement
14: 5.45e-16 microtubule-based process
15: 2.78e-15 cytoskeletal part
16: 9.82e-15 cellular macromolecular complex assembly
17: 1.34e-14 movement of cell or subcellular component
18: 2.26e-14 motor activity
19: 1.22e-12 DNA metabolic process
20: 1.67e-12 obsolete transcription initiation factor activity
21: 2.10e-10 DNA replication initiation
22: 3.18e-07 DNA polymerase activity
23: 1.98e-06 DNA-directed DNA polymerase activity
24: 5.60e-06 DNA-templated transcription, initiation
25: 1.18e-05 nuclear division
26: 1.18e-05 mitotic nuclear division
27: 1.18e-05 mitotic cell cycle process
28: 8.44e-05 DNA polymerase III complex
29: 8.44e-05 DNA polymerase complex
30: 1.05e-04 organelle fission
31: 1.16e-04 RNA biosynthetic process
32: 1.16e-04 cell wall organization or biogenesis
33: 1.39e-04 cell cycle process
34: 1.50e-04 nucleotidyltransferase activity
35: 1.92e-03 galactoside 2-alpha-L-fucosyltransferase activity
36: 1.92e-03 fucosyltransferase activity
37: 1.92e-03 alpha-(1,2)-fucosyltransferase activity
38: 2.54e-03 cell wall biogenesis
39: 3.17e-03 oxytocin receptor activity
pval GO Term Name# Module 9
zmroot %>% filter(`MCL Cluster` == "MCL9") Network MCL Cluster GO Term Num Genes common Num GO genes Num MCL Genes
1: ZmRoot MCL9 GO:0016706 3 9 204
2: ZmRoot MCL9 GO:0000003 5 64 204
3: ZmRoot MCL9 GO:0019953 5 64 204
4: ZmRoot MCL9 GO:0044703 5 64 204
5: ZmRoot MCL9 GO:0022414 5 66 204
6: ZmRoot MCL9 GO:0051213 3 28 204
7: ZmRoot MCL9 GO:0004470 2 11 204
pval
1: 4.53e-05
2: 1.90e-04
3: 1.90e-04
4: 1.90e-04
5: 2.20e-04
6: 1.57e-03
7: 3.57e-03
GO Term Name
1: oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen, 2-oxoglutarate as one donor, and incorporation of one atom each of oxygen into both donors
2: reproduction
3: sexual reproduction
4: multi-organism reproductive process
5: reproductive process
6: dioxygenase activity
7: malic enzyme activity# Module 8
zmroot %>% filter(`MCL Cluster` == "MCL8") Network MCL Cluster GO Term Num Genes common Num GO genes Num MCL Genes
1: ZmRoot MCL8 GO:0006869 7 60 206
2: ZmRoot MCL8 GO:0008289 7 102 206
3: ZmRoot MCL8 GO:0019439 4 35 206
4: ZmRoot MCL8 GO:1901361 4 43 206
5: ZmRoot MCL8 GO:0004367 2 6 206
6: ZmRoot MCL8 GO:0046434 2 7 206
7: ZmRoot MCL8 GO:0004966 2 9 206
8: ZmRoot MCL8 GO:0006559 2 10 206
9: ZmRoot MCL8 GO:0009074 2 10 206
10: ZmRoot MCL8 GO:1902222 2 10 206
11: ZmRoot MCL8 GO:0016841 2 11 206
pval
1: 6.86e-07
2: 2.44e-05
3: 2.03e-04
4: 4.54e-04
5: 1.02e-03
6: 1.42e-03
7: 2.41e-03
8: 3.00e-03
9: 3.00e-03
10: 3.00e-03
11: 3.64e-03
GO Term Name
1: lipid transport
2: lipid binding
3: aromatic compound catabolic process
4: organic cyclic compound catabolic process
5: glycerol-3-phosphate dehydrogenase [NAD+] activity
6: organophosphate catabolic process
7: galanin receptor activity
8: L-phenylalanine catabolic process
9: aromatic amino acid family catabolic process
10: erythrose 4-phosphate/phosphoenolpyruvate family amino acid catabolic process
11: ammonia-lyase activityZmSAM Clusters - get clustes with >= 100 genes
dat <- clusters %>% group_by(ZmSAM) %>% mutate(num_genes = n()) %>% filter(num_genes >= 100)
names <- unique(dat$ZmSAM)
gene_list <- list()
max <- nrow(dat[dat$ZmSAM == 0,])
for(i in 1:length(names)) {
df <- clusters %>% filter(ZmSAM == names[i])
genes <- c(df$V1, rep(NA, max-nrow(df)))
gene_list[[i]] <- genes
}
modules <- do.call(cbind, gene_list)Run analysis and FDR correction
alltissuemodules = cluster_analysis_func(modules)
#combine all into one list
bigdf <- do.call('rbind', alltissuemodules)
bigdf$fdr <- p.adjust(bigdf$pval, method='fdr') ##calculate an FDR
summary(bigdf$fdr) ##nothing is significant Min. 1st Qu. Median Mean 3rd Qu. Max.
0.9999 0.9999 0.9999 0.9999 0.9999 0.9999 kable(bigdf[which.min(bigdf[,3]),])| module | PC | pval | tissue | fdr | |
|---|---|---|---|---|---|
| 184 | 2 | PC5 | 0.0033344 | LMAD26 | 0.9999109 |
Look at lowest p-value clusters
t <- bigdf %>% group_by(PC, tissue) %>% mutate(bonferroni = p.adjust(pval, method='bonferroni')) %>% arrange(bonferroni)
kable(head(t))| module | PC | pval | tissue | fdr | bonferroni |
|---|---|---|---|---|---|
| 2 | PC5 | 0.0033344 | LMAD26 | 0.9999109 | 0.0433477 |
| 7 | PC3 | 0.0315067 | LMAN26 | 0.9999109 | 0.4095866 |
| 10 | PC1 | 0.0320505 | Kern | 0.9999109 | 0.4166559 |
| 4 | PC5 | 0.0436062 | LMAD26 | 0.9999109 | 0.5668812 |
| 2 | PC1 | 0.0536048 | Kern | 0.9999109 | 0.6968619 |
| 5 | PC5 | 0.0560459 | LMAN26 | 0.9999109 | 0.7285967 |
Let’s look at the GO enrichment terms for the most significant clusters (bonferroni p < 0.05)
zmsam <- info %>% filter(Network == "ZmSAM") %>% select(Network, `MCL Cluster`, `GO Term`, `Num Genes common`, `Num GO genes`, `Num MCL Genes`, pval, `GO Term Name`)
# Module 2
zmsam %>% filter(`MCL Cluster` == "MCL2") Network MCL Cluster GO Term Num Genes common Num GO genes Num MCL Genes
1: ZmSAM MCL2 GO:0033180 8 10 942
2: ZmSAM MCL2 GO:0004347 3 5 942
3: ZmSAM MCL2 GO:0030120 6 24 942
4: ZmSAM MCL2 GO:0000502 3 7 942
5: ZmSAM MCL2 GO:0006094 3 7 942
pval GO Term Name
1: 4.62e-09 proton-transporting V-type ATPase, V1 domain
2: 1.72e-03 glucose-6-phosphate isomerase activity
3: 1.93e-03 vesicle coat
4: 5.53e-03 proteasome complex
5: 5.53e-03 gluconeogenesisZmPAN Clusters - get clustes with >= 100 genes
dat <- clusters %>% group_by(ZmPAN) %>% mutate(num_genes = n()) %>% filter(num_genes >= 100)
names <- unique(dat$ZmPAN)
gene_list <- list()
max <- nrow(dat[dat$ZmPAN == 0,])
for(i in 1:length(names)) {
df <- clusters %>% filter(ZmPAN == names[i])
genes <- c(df$V1, rep(NA, max-nrow(df)))
gene_list[[i]] <- genes
}
modules <- do.call(cbind, gene_list)Run analysis and FDR correction
alltissuemodules = cluster_analysis_func(modules)
#combine all into one list
bigdf <- do.call('rbind', alltissuemodules)
bigdf$fdr <- p.adjust(bigdf$pval, method='fdr') ##calculate an FDR
summary(bigdf$fdr) ##nothing is significant Min. 1st Qu. Median Mean 3rd Qu. Max.
0.9992 0.9992 0.9992 0.9992 0.9992 0.9992 kable(bigdf[which.min(bigdf[,3]),])| module | PC | pval | tissue | fdr | |
|---|---|---|---|---|---|
| 241 | 3 | PC5 | 0.0045783 | LMAD26 | 0.9991676 |
Look at lowest p-value clusters
t <- bigdf %>% group_by(PC, tissue) %>% mutate(bonferroni = p.adjust(pval, method='bonferroni')) %>% arrange(bonferroni)
kable(head(t))| module | PC | pval | tissue | fdr | bonferroni |
|---|---|---|---|---|---|
| 3 | PC5 | 0.0045783 | LMAD26 | 0.9991676 | 0.0778318 |
| 2 | PC5 | 0.0090431 | GShoot | 0.9991676 | 0.1537334 |
| 1 | PC5 | 0.0092036 | LMAN26 | 0.9991676 | 0.1564620 |
| 13 | PC1 | 0.0129806 | Kern | 0.9991676 | 0.2206705 |
| 13 | PC5 | 0.0142503 | LMAD26 | 0.9991676 | 0.2422548 |
| 10 | PC2 | 0.0157494 | LMAN26 | 0.9991676 | 0.2677405 |
Let’s look at the GO enrichment terms for the most significant clusters (bonferroni p < 0.05)
zmpan <- info %>% filter(Network == "ZmPAN") %>% select(Network, `MCL Cluster`, `GO Term`, `Num Genes common`, `Num GO genes`, `Num MCL Genes`, pval, `GO Term Name`)
# Module 3
zmpan %>% filter(`MCL Cluster` == "MCL3") Network MCL Cluster GO Term Num Genes common Num GO genes Num MCL Genes
1: ZmPAN MCL3 GO:0006414 20 37 793
2: ZmPAN MCL3 GO:0044391 27 84 793
3: ZmPAN MCL3 GO:0015935 17 49 793
4: ZmPAN MCL3 GO:0022613 9 12 793
5: ZmPAN MCL3 GO:0042254 9 12 793
6: ZmPAN MCL3 GO:0003746 12 25 793
7: ZmPAN MCL3 GO:0004298 12 27 793
8: ZmPAN MCL3 GO:0070003 12 27 793
9: ZmPAN MCL3 GO:0008135 20 90 793
10: ZmPAN MCL3 GO:0005839 12 29 793
11: ZmPAN MCL3 GO:0044085 9 27 793
12: ZmPAN MCL3 GO:0015934 10 36 793
13: ZmPAN MCL3 GO:0006334 20 154 793
14: ZmPAN MCL3 GO:0034728 20 154 793
15: ZmPAN MCL3 GO:0065004 20 154 793
16: ZmPAN MCL3 GO:0071824 20 154 793
17: ZmPAN MCL3 GO:0044429 18 141 793
18: ZmPAN MCL3 GO:0019843 5 10 793
19: ZmPAN MCL3 GO:0006626 4 6 793
20: ZmPAN MCL3 GO:0007007 4 6 793
21: ZmPAN MCL3 GO:0042719 4 6 793
22: ZmPAN MCL3 GO:0045039 4 6 793
23: ZmPAN MCL3 GO:0007005 4 7 793
24: ZmPAN MCL3 GO:0007006 4 7 793
25: ZmPAN MCL3 GO:0044743 4 7 793
26: ZmPAN MCL3 GO:0065002 4 7 793
27: ZmPAN MCL3 GO:0070585 4 7 793
28: ZmPAN MCL3 GO:0071806 4 7 793
29: ZmPAN MCL3 GO:0072655 4 7 793
30: ZmPAN MCL3 GO:0090151 4 7 793
31: ZmPAN MCL3 GO:1990542 4 7 793
32: ZmPAN MCL3 GO:0098798 6 20 793
33: ZmPAN MCL3 GO:0006325 20 194 793
34: ZmPAN MCL3 GO:0000786 17 151 793
35: ZmPAN MCL3 GO:0032993 17 151 793
36: ZmPAN MCL3 GO:0044815 17 151 793
37: ZmPAN MCL3 GO:0034622 23 245 793
38: ZmPAN MCL3 GO:0005853 4 8 793
39: ZmPAN MCL3 GO:0016272 4 10 793
40: ZmPAN MCL3 GO:0006413 8 48 793
41: ZmPAN MCL3 GO:0003743 8 50 793
42: ZmPAN MCL3 GO:0016986 5 20 793
43: ZmPAN MCL3 GO:0017038 4 13 793
44: ZmPAN MCL3 GO:0015929 3 7 793
45: ZmPAN MCL3 GO:0006465 3 8 793
Network MCL Cluster GO Term Num Genes common Num GO genes Num MCL Genes
pval GO Term Name
1: 8.91e-19 translational elongation
2: 1.30e-17 ribosomal subunit
3: 3.34e-12 small ribosomal subunit
4: 5.43e-11 ribonucleoprotein complex biogenesis
5: 5.43e-11 ribosome biogenesis
6: 5.61e-11 translation elongation factor activity
7: 1.74e-10 threonine-type endopeptidase activity
8: 1.74e-10 threonine-type peptidase activity
9: 3.77e-10 translation factor activity, RNA binding
10: 4.82e-10 proteasome core complex
11: 6.69e-07 cellular component biogenesis
12: 1.07e-06 large ribosomal subunit
13: 4.13e-06 nucleosome assembly
14: 4.13e-06 nucleosome organization
15: 4.13e-06 protein-DNA complex assembly
16: 4.13e-06 protein-DNA complex subunit organization
17: 1.57e-05 mitochondrial part
18: 2.27e-05 rRNA binding
19: 3.72e-05 protein targeting to mitochondrion
20: 3.72e-05 inner mitochondrial membrane organization
21: 3.72e-05 mitochondrial intermembrane space protein transporter complex
22: 3.72e-05 protein import into mitochondrial inner membrane
23: 8.41e-05 mitochondrion organization
24: 8.41e-05 mitochondrial membrane organization
25: 8.41e-05 intracellular protein transmembrane import
26: 8.41e-05 intracellular protein transmembrane transport
27: 8.41e-05 protein localization to mitochondrion
28: 8.41e-05 protein transmembrane transport
29: 8.41e-05 establishment of protein localization to mitochondrion
30: 8.41e-05 establishment of protein localization to mitochondrial membrane
31: 8.41e-05 mitochondrial transmembrane transport
32: 1.02e-04 mitochondrial protein complex
33: 1.21e-04 chromatin organization
34: 1.32e-04 nucleosome
35: 1.32e-04 protein-DNA complex
36: 1.32e-04 DNA packaging complex
37: 1.61e-04 cellular macromolecular complex assembly
38: 1.63e-04 eukaryotic translation elongation factor 1 complex
39: 4.58e-04 prefoldin complex
40: 6.18e-04 translational initiation
41: 8.19e-04 translation initiation factor activity
42: 9.95e-04 obsolete transcription initiation factor activity
43: 1.41e-03 protein import
44: 2.04e-03 hexosaminidase activity
45: 3.16e-03 signal peptide processing
pval GO Term Name
sessionInfo()R version 3.6.2 (2019-12-12)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] knitr_1.29 data.table_1.12.8 dplyr_1.0.0 quaint_0.0.0.9000
[5] ggpubr_0.3.0 ggplot2_3.3.2 reshape2_1.4.4 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 xfun_0.15 purrr_0.3.4 haven_2.3.1
[5] lattice_0.20-41 carData_3.0-4 colorspace_1.4-1 vctrs_0.3.1
[9] generics_0.0.2 htmltools_0.5.0 yaml_2.2.1 rlang_0.4.6
[13] later_1.1.0.1 pillar_1.4.4 foreign_0.8-72 glue_1.4.1
[17] withr_2.2.0 readxl_1.3.1 lifecycle_0.2.0 plyr_1.8.6
[21] stringr_1.4.0 cellranger_1.1.0 munsell_0.5.0 ggsignif_0.6.0
[25] gtable_0.3.0 zip_2.0.4 evaluate_0.14 rio_0.5.16
[29] forcats_0.5.0 httpuv_1.5.4 curl_4.3 highr_0.8
[33] broom_0.5.6 Rcpp_1.0.4.6 promises_1.1.1 scales_1.1.1
[37] backports_1.1.8 abind_1.4-5 fs_1.4.1 hms_0.5.3
[41] digest_0.6.25 stringi_1.4.6 openxlsx_4.1.5 rstatix_0.6.0
[45] grid_3.6.2 rprojroot_1.3-2 tools_3.6.2 magrittr_1.5
[49] tibble_3.0.1 crayon_1.3.4 whisker_0.4 tidyr_1.1.0
[53] car_3.0-8 pkgconfig_2.0.3 ellipsis_0.3.1 rmarkdown_2.3
[57] R6_2.4.1 nlme_3.1-148 git2r_0.27.1 compiler_3.6.2