.Rhistory

nrow(pca_plsda$data_PLSDA_VIP_filtered_features)
# take out the expression matrix, change the name to the major protein id, for downstream statistics
df_pg_exp <- df_pg_exp_GSEA
pvalues_anova <- matrix_PostHoc(df_pg_exp,meta$Experiment_group)
df_pg_exp_p <- cbind(pvalues_anova, df_pg_exp_GSEA)
df_pg_exp_p_filtered <- df_pg_exp_p[which(!is.na(df_pg_exp_p$p_PostHoc_pairs)),]
datatable(as.matrix(df_pg_exp_p_filtered))
meta$Experiment_group
matrix_fold_change<-function(data,group_info,group_name1,group_name2) {
group1<-which(group_info==group_name1)
group2<-which(group_info==group_name2)
fold_change<-apply(data,1,function(x) sum(x[group1])/sum(x[group2]))
return(fold_change)
}
matrix_fold_change<-function(data,group_info,group_name1,group_name2) {
group1<-which(group_info==group_name1)
group2<-which(group_info==group_name2)
fold_change<-apply(data,1,function(x) sum(x[group1])/sum(x[group2]))
return(fold_change)
}
fold_change_CK_PCK <- matrix_fold_change(log10(df_pg_exp),meta$Experiment_group,"CK","PCK")
p_CK_PCK <- matrix_ttest(df_pg_exp,meta$Experiment_group,"CK","PCK")
p_CK_PCK_adjusted <- p.adjust(p_CK_PCK,method = "fdr")
fold_change_CK_HC <- matrix_fold_change(df_pg_exp,meta$Experiment_group,"CK","HC")
p_CK_HC <- matrix_ttest(log10(df_pg_exp),meta$Experiment_group,"CK","HC")
p_CK_HC_adjusted <- p.adjust(p_CK_HC,method = "fdr")
fold_change_HC_PHC <- matrix_fold_change(df_pg_exp,meta$Experiment_group,"HC","PHC")
p_HC_PHC <- matrix_ttest(log10(df_pg_exp),meta$Experiment_group,"HC","PHC")
p_HC_PHC_adjusted <- p.adjust(p_HC_PHC,method = "fdr")
fold_change_PCK_PHC <- matrix_fold_change(df_pg_exp,meta$Experiment_group,"PCK","PHC")
p_PCK_PHC <- matrix_ttest(log10(df_pg_exp),meta$Experiment_group,"PCK","PHC")
p_PCK_PHC_adjusted <- p.adjust(p_PCK_PHC,method = "fdr")
df_pg_.exp_p_CK_PCK <- cbind(fold_change_CK_PCK,p_CK_PCK_adjusted,df_pg_exp_GSEA)
significant_CK_PCK <- df_pg_exp_p_CK_PCK[p_CK_PCK_adjusted < 0.05 ,]
df_pg_exp_p_CK_PCK <- cbind(fold_change_CK_PCK,p_CK_PCK_adjusted,df_pg_exp_GSEA)
significant_CK_PCK <- df_pg_exp_p_CK_PCK[p_CK_PCK_adjusted < 0.05 ,]
datatable(df_pg_exp_p_CK_PCK)
View(significant_CK_PCK)
p_CK_PCK_adjusted
range(p_CK_PCK_adjusted)
range(p_CK_HC_adjusted)
range(p_HC_PHC_adjusted)
range(p_PCK_PHC_adjusted)
reference_value <- fold_change_CK_PCK  # use the fold change of T1/T3
names(reference_value) <- df_pg_exp_GSEA$Proteins
names(reference_value) <- df_pg_ID_exp_GSEA$ID
reference_value
reference_value_ranked  <- sort(reference_value, decreasing = TRUE) # for gsea test
reference_value_filtered  <- reference_value_ranked[reference_value_ranked >1] #filtered value
# for classical erichment analysis
reference_genes <- names(reference_value_filtered)
length(reference_genes)
reference_genes
reference_genes_all <- df_pg_ID_exp_GSEA$ID
#human: "org.Hs.eg.db" ; mouse:"org.Mm.eg.db"
OrgDb         <- "org.Bt.eg.db"
keyType       <- "UNIPROT"
OrgDb         <- "org.Bt.eg.db"
keyType       <- "UNIPROT"
# human: "hsa"; mouse: "mmu"
# https://www.genome.jp/kegg/catalog/org_list.html   for full list
OrgDb_kegg <- "bta" # cow
keyType_kegg <-"uniprot"
?enrichPathway
enrichPathway
OrgDb         <- "org.Bt.eg.db"
keyType       <- "UNIPROT"
# human: "hsa"; mouse: "mmu"
# https://www.genome.jp/kegg/catalog/org_list.html   for full list
OrgDb_kegg <- "bta" # cow
keyType_kegg <-"uniprot"
# human: "human", mouse: "mouse"
organism_pathway <- "bovine" # or cow?
p.adjust_cutoff <- 0.01
# GO enichment analysis/ over reprentative analysis
result_enrichGO_CC <- enrichGO(gene          = reference_genes,
keyType       = keyType,
#universe      = names(geneList),
OrgDb         = OrgDb,
ont           = "CC",
pAdjustMethod = "BH",
pvalueCutoff  = p.adjust_cutoff,
qvalueCutoff  = 0.05,
readable      = TRUE)
# take over the over-reprentation /enrichment result
result_enrichment <- result_enrichGO_CC
# convert the entrezid to gene name for better visualization
result_enrichment <- setReadable(result_enrichment, OrgDb, keyType)
datatable(result_enrichment@result)
if(any(result_enrichment@result$p.adjust < p.adjust_cutoff)){
barplot(result_enrichment)
}
#datatable(pca_plsda$data_PLSDA_VIP_filtered_features)
datatable(df_pg_ID_exp_GSEA[as.numeric(row.names(pca_plsda$data_PLSDA_VIP_filtered_features)),])
#datatable(pca_plsda$data_PLSDA_VIP_filtered_features)
datatable(df_pg[as.numeric(row.names(pca_plsda$data_PLSDA_VIP_filtered_features)),])
pca_plsda$data_PLSDA_VIP_filtered_features
p.adjust_cutoff <- 0.5
result_GO_GSEA_CC <- gseGO(geneList     = reference_value_ranked,
OrgDb        = OrgDb,
keyType      = keyType,
ont          = "CC",
nPerm        = 1000,
minGSSize    = 5,
maxGSSize    = 500,
pvalueCutoff = p.adjust_cutoff,
verbose      = TRUE)
p.adjust_cutoff
reference_value_ranked
OrgDb
keyType
result_GSEA <- result_GO_GSEA_CC
length(which(p_CK_PCK_adjusted
)
# setup the two groups to compare
groups_VS <- c("CK", "PCK")
# setup the two groups to compare
groups_VS <- c("CK", "PCK")
# setup the two groups to compare
groups_VS <- c("CK", "PCK")
groups_VS
fold_change_VS <- matrix_fold_change(log10(df_pg_exp),meta$Experiment_group,groups_VS[1],groups_VS[2])
p_VS <- matrix_ttest(df_pg_exp,meta$Experiment_group,groups_VS[1],groups_VS[2])
p_VS_adjusted <- p.adjust(p_VS,method = "fdr")
df_pg_exp_p_VS <- cbind(fold_change_VS,p_VS_adjusted,df_pg_exp_GSEA)
View(df_pg_exp_p_VS)
significant_VS <- df_pg_exp_p_VS[p_VS_adjusted < 0.05 ,]
datatable(df_pg_exp_p_VS)
reference_value <- fold_change_VS  # use the fold change of T1/T3
groups_VS[1]
groups_VS[2]
datatable(significant_VS)
dim(df_pg)
dim(df_pg_exp)
fold_change_VS <- matrix_fold_change(df_pg_exp,meta$Experiment_group,groups_VS[1],groups_VS[2])
meta$Experiment_group
which(meta$Experiment_group==groups_VS[1])
which(meta$Experiment_group==groups_VS[1]), which(meta$Experiment_group==groups_VS[2])
c(which(meta$Experiment_group==groups_VS[1]), which(meta$Experiment_group==groups_VS[2]))
df_pg_exp_p_VS <- cbind(fold_change,p,p_adjusted,df_pg_exp_GSEA[,c(which(meta$Experiment_group==groups_VS[1]), which(meta$Experiment_group==groups_VS[2]))])
fold_change <- matrix_fold_change(df_pg_exp,meta$Experiment_group,groups_VS[1],groups_VS[2])
p <- matrix_ttest(df_pg_exp,meta$Experiment_group,groups_VS[1],groups_VS[2])
p_adjusted <- p.adjust(p,method = "fdr")
df_pg_exp_p_VS <- cbind(fold_change,p,p_adjusted,df_pg_exp_GSEA[,c(which(meta$Experiment_group==groups_VS[1]), which(meta$Experiment_group==groups_VS[2]))])
significant_VS <- df_pg_exp_p_VS[p_VS_adjusted < 0.05 ,]
datatable(significant_VS)
p <- matrix_ttest(df_pg_exp,meta$Experiment_group,groups_VS[1],groups_VS[2])
p_adjusted <- p.adjust(p,method = "fdr")
range(p_adjusted)
datatable(df_pg_exp_p_VS)
df_pg_exp_p_VS <- cbind(fold_change,p,p_adjusted,df_pg_ID_exp_GSEA[,c(which(meta$Experiment_group==groups_VS[1]), which(meta$Experiment_group==groups_VS[2]))])
df_pg_exp_p_VS <- cbind(fold_change,p,p_adjusted,df_pg_ID_exp_GSEA[,c(1,which(meta$Experiment_group==groups_VS[1]), which(meta$Experiment_group==groups_VS[2]))])
datatable(df_pg_exp_p_VS)
# rank the table based on the p value
t <- df_pg_exp_p_VS[order(df_pg_exp_p_VS$p_adjusted),]
data.table(t)
datatable(t)
df_pg_exp_p_VS <- cbind(fold_change,p,p_adjusted,df_pg_exp_GSEA[,c(which(meta$Experiment_group==groups_VS[1]), which(meta$Experiment_group==groups_VS[2]))])
rownames(df_pg_exp_p_VS) <- df_pg_ID_exp_GSEA[1,]
dim(df_pg_exp_p_VS)
dim(df_pg_ID_exp_GSEA)
rownames(df_pg_exp_p_VS)
rownames(df_pg_exp_p_VS) <- df_pg_ID_exp_GSEA[,1]
row.names(df_pg_exp_p_VS) <- df_pg_ID_exp_GSEA[,1]
row.names(df_pg_exp_p_VS)
dim(df_pg_exp_p_VS)
df_pg_ID_exp_GSEA[,1]
row.names(df_pg_exp_p_VS) <- as.data.frame(df_pg_ID_exp_GSEA)[,1]
datatable(df_pg_exp_p_VS)
# rank the table based on the p value
t <- df_pg_exp_p_VS[order(df_pg_exp_p_VS$p_adjusted),]
datatable(t)
significant_VS
head(T)
head(t)
View(t)
significant_VS <- df_pg_exp_p_VS[df_pg_exp_p_VS$p_adjusted < 0.05,]
significant_VS
# rank the table based on the p value
df_pg_exp_p_VS <- df_pg_exp_p_VS[order(df_pg_exp_p_VS$p_adjusted),]
significant_VS <- df_pg_exp_p_VS[df_pg_exp_p_VS$p_adjusted < 0.05,]
significant_VS
View(df_pg_exp_p_VS)
range(p_adjusted)
fold_change <- matrix_fold_change(df_pg_exp,meta$Experiment_group,groups_VS[1],groups_VS[2])
p <- matrix_ttest(df_pg_exp,meta$Experiment_group,groups_VS[1],groups_VS[2])
p_adjusted <- p.adjust(p,method = "fdr")
df_pg_exp_ttest <- cbind(fold_change,p,p_adjusted,df_pg_exp_GSEA[,c(which(meta$Experiment_group==groups_VS[1]), which(meta$Experiment_group==groups_VS[2]))])
View(df_pg_exp_ttest)
row.names(df_pg_exp_ttest) <- as.data.frame(df_pg_ID_exp_GSEA)[,1]
reference_genes
reference_value <- fold_change  # use the fold change of
names(reference_value) <- df_pg_ID_exp_GSEA$ID
reference_value_ranked  <- sort(reference_value, decreasing = TRUE) # for gsea test
reference_value_filtered  <- reference_value_ranked[reference_value_ranked >1] #filtered value
length(reference_value_filtered)
View(df_pg_exp_ttest_p_001)
df_pg_exp_ttest_p_001 <- df_pg_exp_ttest[df_pg_exp_ttest$p < 0.01,]
View(df_pg_exp_ttest_p_001)
df_pg_exp_ttest_p_05 <- df_pg_exp_ttest[df_pg_exp_ttest$p < 0.05,]
View(df_pg_exp_ttest_p_05)
View(df_pg_exp_ttest_p_05)
df_pg_exp_ttest <- df_pg_exp_ttest[order(df_pg_exp_ttest$p_adjusted),]
df_pg_exp_ttest_p_05 <- df_pg_exp_ttest[df_pg_exp_ttest$p < 0.05,]
datatable(df_pg_exp_ttest_p_05)
# for classical erichment analysis
reference_genes <- names(df_pg_exp_ttest_p_05)
reference_genes
# for classical erichment analysis
reference_genes <- names(df_pg_exp_ttest_p_05)
reference_genes
df_pg_exp_ttest_p_05
# for classical erichment analysis
reference_genes <- row.names(df_pg_exp_ttest_p_05)
reference_genes
p.adjust_cutoff <- 0.5
result_KEGG_module_enrich <- enrichMKEGG(gene = reference_genes,
organism     = OrgDb_kegg,
keyType      = keyType_kegg,
pvalueCutoff = p.adjust_cutoff,
pAdjustMethod = "BH",
minGSSize = 10,
maxGSSize = 500,
qvalueCutoff = 0.2)
# take over the over-reprentation /enrichment result
result_enrichment <- result_KEGG_module_enrich
# read-in the expression file
df_pg <- read_tsv("Sample20-ProteinGroups(2)(1).txt", col_names  =  TRUE)
# for consistency,change the name
df_pg_exp_GSEA <- df_pg[,c(-1,-2,-3)]
df_pg_ID_exp_GSEA <- df_pg[,c(-2,-3)]
#setup the metadata
meta <- read.delim("Experiment_group.txt",header = FALSE)
colnames(meta) <- c("sample_Name", "Experiment_group")
# setup the two groups to compare
groups_VS <- c("CK", "HC")
matrix_fold_change<-function(data,group_info,group_name1,group_name2) {
group1<-which(group_info==group_name1)
group2<-which(group_info==group_name2)
fold_change<-apply(data,1,function(x) sum(x[group1])/sum(x[group2]))
return(fold_change)
}
fold_change_CK_PCK <- matrix_fold_change(log10(df_pg_exp),meta$Experiment_group,"CK","PCK")
p_CK_PCK <- matrix_ttest(df_pg_exp,meta$Experiment_group,"CK","PCK")
p_CK_PCK_adjusted <- p.adjust(p_CK_PCK,method = "fdr")
fold_change_CK_HC <- matrix_fold_change(df_pg_exp,meta$Experiment_group,"CK","HC")
p_CK_HC <- matrix_ttest(log10(df_pg_exp),meta$Experiment_group,"CK","HC")
p_CK_HC_adjusted <- p.adjust(p_CK_HC,method = "fdr")
fold_change_HC_PHC <- matrix_fold_change(df_pg_exp,meta$Experiment_group,"HC","PHC")
p_HC_PHC <- matrix_ttest(log10(df_pg_exp),meta$Experiment_group,"HC","PHC")
p_HC_PHC_adjusted <- p.adjust(p_HC_PHC,method = "fdr")
fold_change_PCK_PHC <- matrix_fold_change(df_pg_exp,meta$Experiment_group,"PCK","PHC")
p_PCK_PHC <- matrix_ttest(log10(df_pg_exp),meta$Experiment_group,"PCK","PHC")
p_PCK_PHC_adjusted <- p.adjust(p_PCK_PHC,method = "fdr")
# calculate the statistics
fold_change <- matrix_fold_change(df_pg_exp,meta$Experiment_group,groups_VS[1],groups_VS[2])
p <- matrix_ttest(df_pg_exp,meta$Experiment_group,groups_VS[1],groups_VS[2])
p_adjusted <- p.adjust(p,method = "fdr")
# combine all p value and selected columns together
df_pg_exp_ttest <- cbind(fold_change,p,p_adjusted,df_pg_exp_GSEA[,c(which(meta$Experiment_group==groups_VS[1]), which(meta$Experiment_group==groups_VS[2]))])
row.names(df_pg_exp_ttest) <- as.data.frame(df_pg_ID_exp_GSEA)[,1]
# rank the table based on the p value
df_pg_exp_ttest <- df_pg_exp_ttest[order(df_pg_exp_ttest$p_adjusted),]
df_pg_exp_ttest_p_05 <- df_pg_exp_ttest[df_pg_exp_ttest$p < 0.05,]
datatable(df_pg_exp_ttest_p_05)
reference_value <- fold_change  # use the fold change of
names(reference_value) <- df_pg_ID_exp_GSEA$ID
reference_value_ranked  <- sort(reference_value, decreasing = TRUE) # for gsea test
reference_value_filtered  <- reference_value_ranked[reference_value_ranked >1] #filtered value
# for classical erichment analysis
reference_genes <- row.names(df_pg_exp_ttest_p_05)
reference_genes_all <- df_pg_ID_exp_GSEA$ID
length(reference_genes)
reference_genes
p.adjust_cutoff <- 0.5
result_KEGG_module_enrich <- enrichMKEGG(gene = reference_genes,
organism     = OrgDb_kegg,
keyType      = keyType_kegg,
pvalueCutoff = p.adjust_cutoff,
pAdjustMethod = "BH",
minGSSize = 10,
maxGSSize = 500,
qvalueCutoff = 0.2)
# take over the over-reprentation /enrichment result
result_enrichment <- result_KEGG_module_enrich
result_enrichment
# convert the entrezid to gene name for better visualization
result_enrichment <- setReadable(result_enrichment,  OrgDb, keyType)
null(result_enrichment)
is.null(result_enrichment)
if(! is.null(result_enrichment)){
result_enrichment <- setReadable(result_enrichment,  OrgDb, keyType)
}
pvalueCutoff:  `r result_enrichment@pvalueCutoff`
pAdjustMethod: `r result_enrichment@pAdjustMethod`
qvalueCutoff: `r result_enrichment@qvalueCutoff`
organism: `r result_enrichment@organism`
ontology: `r result_enrichment@ontology`
length(reference_genes)
reference_genes
# take over the over-reprentation /enrichment result
result_enrichment <- result_KEGG_module_enrich
result_enrichment
if(! is.null(result_enrichment)){
result_enrichment <- setReadable(result_enrichment,  OrgDb, keyType)
}
if(! is.null(result_enrichment)){
datatable(result_enrichment@result)
}
if(! is.null(result_enrichment)){
if(any(result_enrichment@result$p.adjust < p.adjust_cutoff)){
barplot(result_enrichment)
}
}
if(!is.null(result_enrichment)){
if(any(result_enrichment@result$p.adjust < p.adjust_cutoff)){
enrichplot::dotplot(result_enrichment)
}
}
result_enrichment@result
if(!is.null(result_enrichment)){
if(any(result_enrichment@result$p.adjust < p.adjust_cutoff)){
cnetplot(result_enrichment,categorySize="pvalue", foldChange=reference_value_ranked)
}
}
if(any(result_enrichment@result$p.adjust < p.adjust_cutoff)){
upsetplot(result_enrichment)
heatplot(result_enrichment,foldChange=reference_value_ranked)
}
if(!is.null(result_enrichment)){
if(any(result_enrichment@result$p.adjust < p.adjust_cutoff)){
upsetplot(result_enrichment)
heatplot(result_enrichment,foldChange=reference_value_ranked)
}
}
result_enrichGO_CC_local <- enrichGO(gene          = reference_genes,
keyType       = keyType,
universe      = reference_genes_all,
OrgDb         = OrgDb,
ont           = "CC",
pAdjustMethod = "BH",
pvalueCutoff  = p.adjust_cutoff,
qvalueCutoff  = 0.5,
readable      = TRUE)
# take over the over-reprentation /enrichment result
result_enrichment <- result_enrichGO_CC_local
# convert the entrezid to gene name for better visualization
result_enrichment <- setReadable(result_enrichment, OrgDb, keyType)
result_enrichment
if(!is.null(result_enrichment)){
datatable(result_enrichment@result)
}
if(!is.null(result_enrichment)){
if(any(result_enrichment@result$p.adjust < p.adjust_cutoff)){
barplot(result_enrichment)
}
}
if(!is.null(result_enrichment)){
if(any(result_enrichment@result$p.adjust < p.adjust_cutoff)){
barplot(result_enrichment)
}
}
if(any(result_enrichment@result$p.adjust < p.adjust_cutoff)){
barplot(result_enrichment)
}
barplot(result_enrichment)
result_enrichment@result
is.null(result_enrichment)
!is.null(result_enrichment)
any(result_enrichment@result$p.adjust
)
result_enrichment@result$p.adjust
result_enrichment@result$p.adjust < p.adjust_cutoff
p.adjust_cutoff
if(!is.null(result_enrichment)){
if(any(result_enrichment@result$p.adjust < p.adjust_cutoff)){
cnetplot(result_enrichment,categorySize="pvalue", foldChange=reference_value_ranked)
}
}
if(!is.null(result_enrichment)){
if(any(result_enrichment@result$p.adjust < p.adjust_cutoff)){
upsetplot(result_enrichment)
heatplot(result_enrichment,foldChange=reference_value_ranked)
}
}
groups_VS <- c("PCK", "PHC")
groups_VS <- c("PCK", "PHC")
matrix_fold_change<-function(data,group_info,group_name1,group_name2) {
group1<-which(group_info==group_name1)
group2<-which(group_info==group_name2)
fold_change<-apply(data,1,function(x) sum(x[group1])/sum(x[group2]))
return(fold_change)
}
# calculate the statistics
fold_change <- matrix_fold_change(df_pg_exp,meta$Experiment_group,groups_VS[1],groups_VS[2])
p <- matrix_ttest(df_pg_exp,meta$Experiment_group,groups_VS[1],groups_VS[2])
p_adjusted <- p.adjust(p,method = "fdr")
# combine all p value and selected columns together
df_pg_exp_ttest <- cbind(fold_change,p,p_adjusted,df_pg_exp_GSEA[,c(which(meta$Experiment_group==groups_VS[1]), which(meta$Experiment_group==groups_VS[2]))])
row.names(df_pg_exp_ttest) <- as.data.frame(df_pg_ID_exp_GSEA)[,1]
# rank the table based on the p value
df_pg_exp_ttest <- df_pg_exp_ttest[order(df_pg_exp_ttest$p_adjusted),]
df_pg_exp_ttest_p_05 <- df_pg_exp_ttest[df_pg_exp_ttest$p < 0.05,]
reference_value <- fold_change  # use the fold change of
names(reference_value) <- df_pg_ID_exp_GSEA$ID
reference_value_ranked  <- sort(reference_value, decreasing = TRUE) # for gsea test
reference_value_filtered  <- reference_value_ranked[reference_value_ranked >1] #filtered value
# for classical erichment analysis
reference_genes <- row.names(df_pg_exp_ttest_p_05)
reference_genes_all <- df_pg_ID_exp_GSEA$ID
#  setup the parameter for the enrichment analysis
#human: "org.Hs.eg.db" ; mouse:"org.Mm.eg.db", bovine/cow "org.Bt.eg.db"
OrgDb         <- "org.Bt.eg.db"
keyType       <- "UNIPROT"
# human: "hsa"; mouse: "mmu"
# https://www.genome.jp/kegg/catalog/org_list.html   for full list
OrgDb_kegg <- "bta" # cow
keyType_kegg <-"uniprot"
# human: "human", mouse: "mouse"
organism_pathway <- "bovine" # or cow?
p.adjust_cutoff <- 0.5
# GO enichment analysis/ over reprentative analysis
result_enrichGO_CC_local <- enrichGO(gene          = reference_genes,
keyType       = keyType,
universe      = reference_genes_all,
OrgDb         = OrgDb,
ont           = "CC",
pAdjustMethod = "BH",
pvalueCutoff  = p.adjust_cutoff,
qvalueCutoff  = 0.5,
readable      = TRUE)
# take over the over-reprentation /enrichment result
result_enrichment <- result_enrichGO_CC_local
result_enrichment
# convert the entrezid to gene name for better visualization
result_enrichment <- setReadable(result_enrichment, OrgDb, keyType)
result_enrichment
if(!is.null(result_enrichment)){
if(any(result_enrichment@result$p.adjust < p.adjust_cutoff)){
upsetplot(result_enrichment)
heatplot(result_enrichment,foldChange=reference_value_ranked)
}
}
!is.null(result_enrichment
)
result_enrichment@result$p.adjust
p.adjust_cutoff
result_enrichment@result$p.adjust < p.adjust_cutoff
sum(result_enrichment@result$p.adjust < p.adjust_cutoff)
if(!is.null(result_enrichment)){
if(sum(result_enrichment@result$p.adjust < p.adjust_cutoff)>1){
upsetplot(result_enrichment)
heatplot(result_enrichment,foldChange=reference_value_ranked)
}
}
setwd("C:/Users/Figeys Lab/Dropbox/Project_R_packages/metalab")
library(metalab)
library(metalab)
library(metalab)
devtools::build()
devtools::install()
knit_with_parameters('C:/Users/Figeys Lab/Dropbox/Project_R_packages/metalab/inst/rmd/input.Rmd', encoding = 'UTF-8')
library(devtools)
devtools::install_github("hrbrmstr/streamgraph")
library(streamgraph)
library(dplyr)
remove.packages("rlang", lib="C:/Program Files/R/R-3.5.1/library")
library("rlang", lib.loc="~/R/win-library/3.5")
library(rlang)
?invisible
library(metalab)
library(metalab)
rmdtext
rmdtext()
?knit2html
?knit2html
library(knitr)
?find_lipid_combo
devtools::load_all(".")
library(metalab)
library(metalab)
?find_lipid_combo
library(metalab)
library(metalab)
?find_lipid_combo
library(metalab)
library(roxygen2)
?find_lipid_combo
?find_lipid_combo
library(metalab)
library(metalab)
?
?find_lipid_combo
setwd("C:/Users/Figeys Lab/Dropbox/Project_MetaLab/openCPU_apps/markdownapp-master/inst/www")
remove_allNA_rows <- function(df){
if(any(apply(df,1,function(x)all(is.na(x))))){ # if there is any all NA rows
df[-which(apply(df,1,function(x)all(is.na(x)))),]
}else{
df
}
# this function can deal with data.frame and data matrix
# note that NA has to be NA, otherwise, precess the data first
}
remove_allNA_columns <- function(df){
if(any(apply(df,2,function(x)all(is.na(x))))){ # if there is any all NA columns
df[,-which(apply(df,2,function(x)all(is.na(x))))]
}else{
df
}
# this function can deal with data.frame and data matrix
# note that NA has to be NA, otherwise, precess the data first
}