Last updated: 2021-02-22

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Introduction

This code takes as input the filtered count data and two factors of unwanted variation fit by RUVs. It then uses limma and voom to perform differential expression analysis between treatment conditions using the data. After we have a set of differentially expressed genes, we look for enrichments amongst DE genes in GO pathways and previously published osteoarthritis datasets.

Load libraries and data

library("limma")
library("plyr")
library("dplyr")
library("edgeR")
library("tidyr")
library("ashr")
library("ggplot2")
library("RUVSeq")
library("topGO")

#load in filtered count data, RUVs output
filt_counts <- readRDS("data/filtered_counts.rds")
filt_counts <- filt_counts$counts
RUVsOut <- readRDS("data/RUVsOut.rds")

# load gene annotations
gene_anno <- read.delim("data/gene-annotation.txt",
                        sep = "\t")

# load in reordered sample information
sampleinfo <- readRDS("data/Sample.info.RNAseq.reordered.rds")

Specify design matrix for Linear mixed model

anno <- pData(RUVsOut)
x <- paste0(anno$Individual, anno$treatment)

anno$LibraryPrepBatch <- factor(anno$LibraryPrepBatch, levels = c("1", "2"))
anno$Replicate <- factor(anno$Replicate, levels = c("1", "2", "3"))

design <- model.matrix(~treatment + Individual + W_1 + W_2 + RIN,
                       data = anno)
colnames(design) <- gsub("treatment", "", colnames(design))
colnames(design)

[1] "(Intercept)"        "Unstrain"           "IndividualNA18856 "
[4] "IndividualNA19160 " "W_1"                "W_2"               
[7] "RIN"

Fit the model using limma and voom

# Model replicate as a random effect
# Recommended to run both voom and duplicateCorrelation twice.
# https://support.bioconductor.org/p/59700/#67620

#TMM Normalization
y <- DGEList(filt_counts)
y <- calcNormFactors(y, method = "TMM")
#Voom for differential expression
v1 <- voom(y, design)
corfit1 <- duplicateCorrelation(v1, design, block = x)

Warning in glmgam.fit(dx, dy, coef.start = start, tol = tol, maxit =
maxit, : Too much damping - convergence tolerance not achievable

corfit1$consensus.correlation

[1] 0.3550961

v2 <- voom(y, design, block = x, correlation = corfit1$consensus.correlation)
corfit2 <- duplicateCorrelation(v2, design, block = x)

Warning in glmgam.fit(dx, dy, coef.start = start, tol = tol, maxit =
maxit, : Too much damping - convergence tolerance not achievable

corfit2$consensus.correlation

[1] 0.3552816

fit <- lmFit(v2, design, block = x,
             correlation = corfit2$consensus.correlation)
fit <- eBayes(fit)

saveRDS(v2, "output/voom_results.rds")

Assess model results from voom

#this function extracts the results from the voom output for each of the variables (each "test")
get_results <- function(x, number = nrow(x$coefficients), sort.by = "none",
                        ...) {
  # x - object MArrayLM from eBayes output
  # ... - additional arguments passed to topTable
  stopifnot(class(x) == "MArrayLM")
  results <- topTable(x, number = number, sort.by = sort.by, ...)
  return(results)
}

results_treatment <- get_results(fit, coef = "Unstrain",
                              number = nrow(filt_counts), sort = "none")
ma_treatment <- ggplot(data.frame(Amean = fit$Amean, logFC = fit$coef[, "Unstrain"]),
                       aes(x = Amean, y = logFC)) +
  geom_point() +
  labs(x = "Average expression level", y = "Log fold change",
       title = "Treatment effect")
ma_treatment

Version	Author	Date
8542407	Anthony Hung	2021-01-21

hist_treatment <- ggplot(results_treatment, aes(x = P.Value)) +
  geom_histogram(binwidth = 0.01) +
  labs(x = "p-value", y = "Number of genes", title = "Treatment effect")
hist_treatment

Version	Author	Date
8542407	Anthony Hung	2021-01-21

Use ash for mutliple testing correction

Run ash on the treatment results.

run_ash <- function(x, coef) {
  # Perform multiple testing correction with adaptive shrinkage (ASH)
  #
  # x - object MArrayLM from eBayes output
  # coef - coefficient tested by eBayes
  stopifnot(class(x) == "MArrayLM", coef %in% colnames(x$coefficients))
  result <- ash(betahat = x$coefficients[, coef],
                sebetahat = x$stdev.unscaled[, coef] * sqrt(x$s2.post),
                df = x$df.total[1])
  return(result)
}

ash_treatment <- run_ash(fit, "Unstrain")
class(ash_treatment)

[1] "ash"

names(ash_treatment)

[1] "fitted_g" "loglik"   "logLR"    "data"     "result"   "call"

sum(ash_treatment$result$svalue < .05)

[1] 773

hist(ash_treatment$result$svalue)

Version	Author	Date
8542407	Anthony Hung	2021-01-21

Plots

Functions to make plots from the results

plot_ma <- function(x, qval) {
  # Create MA plot.
  #
  # x - data frame with topTable and ASH output
  #     (columns logFC, AveExpr, and qvalue)
  # qval - qvalue cutoff for calling a gene DE
  #
  stopifnot(is.data.frame(x), c("logFC", "AveExpr", "qvalue") %in% colnames(x),
            is.numeric(qval), qval <= 1, qval >= 0)
  x$highlight <- ifelse(x$qvalue < qval, "darkred", "gray75")
  x$highlight <- factor(x$highlight, levels = c("darkred", "gray75"))
  ggplot(x, aes(x = AveExpr, y = logFC, color = highlight, shape = highlight)) +
    geom_point() +
    labs(x = "Average expression level", y = "Log fold change") +
    scale_color_identity(drop = FALSE) +
    scale_shape_manual(values = c(16, 1), drop = FALSE) +
    theme(legend.position = "none")
#   scale_color_gradient(low = "red", high = "white", limits = c(0, 0.25))
}

plot_volcano <- function(x, qval) {
  # Create volcano plot.
  #
  # x - data frame with topTable and ASH output
  #     (columns logFC, P.Value, and qvalue)
  # qval - qvalue cutoff for calling a gene DE
  #
  stopifnot(is.data.frame(x), c("logFC", "P.Value", "qvalue") %in% colnames(x),
            is.numeric(qval), qval <= 1, qval >= 0)
  x$highlight <- ifelse(x$qvalue < qval, "darkred", "gray75")
  x$highlight <- factor(x$highlight, levels = c("darkred", "gray75"))
  ggplot(x, aes(x = logFC, y = -log10(P.Value), color = highlight)) +
    geom_point(shape = 1) +
    labs(x = "Log fold change",
         y = expression(-log[10] * " p-value")) +
    scale_color_identity(drop = FALSE) +
    theme(legend.position = "none")
}

plot_pval_hist <- function(x, qval) {
  # Create histogram of p-values.
  #
  # x - data frame with topTable and ash output (columns P.Value and qvalue)
  # qval - qvalue cutoff for calling a gene DE
  #
  stopifnot(is.data.frame(x), c("P.Value", "qvalue") %in% colnames(x))
  x$highlight <- ifelse(x$qvalue < qval, "darkred", "gray75")
  x$highlight <- factor(x$highlight, levels = c("darkred", "gray75"))
  ggplot(x, aes(x = P.Value, fill = highlight)) +
    geom_histogram(position = "stack", binwidth = 0.01) +
    scale_fill_identity(drop = FALSE) +
    labs(x = "p-value", y = "Number of genes")
}

Apply functions to build plots for FDR 0.05

tests <- colnames(fit$coefficients)
results <- vector(length = length(tests), mode = "list")
names(results) <- tests

for (test in tests[c(1:7)]) {
  # Extract limma results
     print(test)
  results[[test]] <- get_results(fit, coef = test)
  # Add mutliple testing correction with ASH
  output_ash <- run_ash(fit, coef = test)$result
  results[[test]] <- cbind(results[[test]], lfsr = output_ash$lfsr,
                           lfdr = output_ash$lfdr, qvalue = output_ash$qvalue,
                           svalue = output_ash$svalue)
}

[1] "(Intercept)"
[1] "Unstrain"
[1] "IndividualNA18856 "
[1] "IndividualNA19160 "
[1] "W_1"
[1] "W_2"
[1] "RIN"

#FDR 0.05
plot_ma(results[["Unstrain"]], 0.05)

Version	Author	Date
8542407	Anthony Hung	2021-01-21

plot_volcano(results[["Unstrain"]], 0.05)

Version	Author	Date
8542407	Anthony Hung	2021-01-21

plot_pval_hist(results[["Unstrain"]], 0.05)

Version	Author	Date
8542407	Anthony Hung	2021-01-21

table(results[["Unstrain"]]$qvalue < 0.05)


FALSE  TRUE 
 9499   987

significant_genes_05 <- row.names(results[["Unstrain"]])[results[["Unstrain"]]$qvalue < 0.05]
significant_symbols_05 <- gene_anno$external_gene_name[match(significant_genes_05, gene_anno$ensembl_gene_id)]
head(significant_symbols_05)

[1] DVL1   MRPL20 MMP23B PEX10  CEP104 ACOT7 
56858 Levels: A1BG A1BG-AS1 A1CF A2M A2M-AS1 A2ML1 A2ML1-AS1 ... ZZEF1

significant_anno_05 <- gene_anno[match(significant_genes_05, gene_anno$ensembl_gene_id),]

DE_results_original <- results

Gene ontology analysis with topGO

Use topGO for GO analysis. It accounts for the nested graph structure of GO terms to prune the number of GO categories tested (Alexa et al. 2006). Essentially, it decreases the redundancy of the results.

# k-s test making use of ranked based on p-values
genes <- results[["Unstrain"]]$qvalue
names(genes) <- rownames(results[["Unstrain"]])

selection <- function(allScore){ return(allScore < 0.05)} # function that returns TRUE/FALSE for p-values<0.05
allGO2genes <- annFUN.org(whichOnto="BP", feasibleGenes=NULL, mapping="org.Hs.eg.db", ID="ensembl")

Loading required package: org.Hs.eg.db

GOdata <- new("topGOdata",
  ontology="BP",
  allGenes=genes,
  annot=annFUN.GO2genes,
  GO2genes=allGO2genes,
  geneSel=selection,
  nodeSize=10)


Building most specific GOs .....

    ( 9780 GO terms found. )


Build GO DAG topology ..........

    ( 13861 GO terms and 32652 relations. )


Annotating nodes ...............

    ( 9204 genes annotated to the GO terms. )

results.ks <- runTest(GOdata, algorithm="classic", statistic="ks")


             -- Classic Algorithm -- 

         the algorithm is scoring 5178 nontrivial nodes
         parameters: 
             test statistic: ks
             score order: increasing

goEnrichment <- GenTable(GOdata, KS=results.ks, orderBy="KS", topNodes=20)
goEnrichment <- goEnrichment %>% 
     mutate(KS = ifelse(grepl("<", KS), 1e-30, KS))
goEnrichment$KS <- as.numeric(goEnrichment$KS)
goEnrichment <- goEnrichment[goEnrichment$KS<0.05,]
goEnrichment <- goEnrichment[,c("GO.ID","Term","KS")]
goEnrichment$Term <- gsub(" [a-z]*\\.\\.\\.$", "", goEnrichment$Term)
goEnrichment$Term <- gsub("\\.\\.\\.$", "", goEnrichment$Term)
goEnrichment$Term <- paste(goEnrichment$GO.ID, goEnrichment$Term, sep=", ")
goEnrichment$Term <- factor(goEnrichment$Term, levels=rev(goEnrichment$Term))
goEnrichment$KS <- as.numeric(goEnrichment$KS)

ggplot(goEnrichment, aes(x=Term, y=-log10(KS))) +
    stat_summary(geom = "bar", fun = mean, position = "dodge") +
    xlab("Biological process") +
    ylab("Enrichment") +
    ggtitle("Title") +
    scale_y_continuous(breaks = round(seq(0, max(-log10(goEnrichment$KS)), by = 2), 1)) +
    theme_bw(base_size=24) +
    theme(
        legend.position='none',
        legend.background=element_rect(),
        plot.title=element_text(angle=0, size=24, face="bold", vjust=1),
        axis.text.x=element_text(angle=0, size=18, face="bold", hjust=1.10),
        axis.text.y=element_text(angle=0, size=18, face="bold", vjust=0.5),
        axis.title=element_text(size=24, face="bold"),
        legend.key=element_blank(),     #removes the border
        legend.key.size=unit(1, "cm"),      #Sets overall area/size of the legend
        legend.text=element_text(size=18),  #Text size
        title=element_text(size=18)) +
    guides(colour=guide_legend(override.aes=list(size=2.5))) +
    coord_flip()

Version	Author	Date
8542407	Anthony Hung	2021-01-21

Additional analysis permuting samples:

Randomly shuffle the strain/unstrain labels 10 times and perform DE analysis on each, looking at the enriched GO terms to make sure the DE genes are not primarily driven by increased expression of certain genes in my cell type(s). Store the DE results to look at enrichments in OA gene sets later as well.

#scramble treatment labels (x3) and store the DE genes (each time, randomly assign 9 samples to be strain)
permuted_results <- vector("list", 12)
for(time in 1:10){
     #set up DE and scramble labels
     anno <- pData(RUVsOut)
     x <- paste0(anno$Individual, anno$treatment)

     anno$LibraryPrepBatch <- factor(anno$LibraryPrepBatch, levels = c("1", "2"))
     anno$Replicate <- factor(anno$Replicate, levels = c("1", "2", "3"))

     strain_indices <- sample(nrow(anno), 9)
     anno$treatment <- "Unstrain"
     anno$treatment[strain_indices] <- "Strain"
     design <- model.matrix(~treatment + Individual + W_1 + W_2 + RIN,
                       data = anno)
     colnames(design) <- gsub("treatment", "", colnames(design))

     permuted_results[[time]] <- anno

     #perform DE
     #TMM Normalization
     y <- DGEList(filt_counts)
     y <- calcNormFactors(y, method = "TMM")
     #Voom for differential expression
     v1 <- voom(y, design)
     corfit1 <- duplicateCorrelation(v1, design, block = x)
     corfit1$consensus.correlation
     v2 <- voom(y, design, block = x, correlation = corfit1$consensus.correlation)
     corfit2 <- duplicateCorrelation(v2, design, block = x)
     corfit2$consensus.correlation
     fit <- lmFit(v2, design, block = x,
             correlation = corfit2$consensus.correlation)
     fit <- eBayes(fit)

     tests <- colnames(fit$coefficients)
     results <- vector(length = length(tests), mode = "list")
     names(results) <- tests

     for (test in tests[c(1:7)]) {
     # Extract limma results
      results[[test]] <- get_results(fit, coef = test)
     # Add mutliple testing correction with ASH
     output_ash <- run_ash(fit, coef = test)$result
     results[[test]] <- cbind(results[[test]], lfsr = output_ash$lfsr,
                              lfdr = output_ash$lfdr, qvalue = output_ash$qvalue,
                              svalue = output_ash$svalue)
     }

     #store results
     permuted_results[[10+time]] <- results[["Unstrain"]]
}

Warning in glmgam.fit(dx, dy, coef.start = start, tol = tol, maxit =
maxit, : Too much damping - convergence tolerance not achievable

Warning in glmgam.fit(dx, dy, coef.start = start, tol = tol, maxit =
maxit, : Too much damping - convergence tolerance not achievable

Warning in glmgam.fit(dx, dy, coef.start = start, tol = tol, maxit =
maxit, : Too much damping - convergence tolerance not achievable

Warning in glmgam.fit(dx, dy, coef.start = start, tol = tol, maxit =
maxit, : Too much damping - convergence tolerance not achievable

Warning in glmgam.fit(dx, dy, coef.start = start, tol = tol, maxit =
maxit, : Too much damping - convergence tolerance not achievable

Warning in glmgam.fit(dx, dy, coef.start = start, tol = tol, maxit =
maxit, : Too much damping - convergence tolerance not achievable

Warning in glmgam.fit(dx, dy, coef.start = start, tol = tol, maxit =
maxit, : Too much damping - convergence tolerance not achievable

Warning in glmgam.fit(dx, dy, coef.start = start, tol = tol, maxit =
maxit, : Too much damping - convergence tolerance not achievable

Warning in glmgam.fit(dx, dy, coef.start = start, tol = tol, maxit =
maxit, : Too much damping - convergence tolerance not achievable

Warning in glmgam.fit(dx, dy, coef.start = start, tol = tol, maxit =
maxit, : Too much damping - convergence tolerance not achievable

Warning in glmgam.fit(dx, dy, coef.start = start, tol = tol, maxit =
maxit, : Too much damping - convergence tolerance not achievable

Warning in glmgam.fit(dx, dy, coef.start = start, tol = tol, maxit =
maxit, : Too much damping - convergence tolerance not achievable

Warning in glmgam.fit(dx, dy, coef.start = start, tol = tol, maxit =
maxit, : Too much damping - convergence tolerance not achievable

Warning in glmgam.fit(dx, dy, coef.start = start, tol = tol, maxit =
maxit, : Too much damping - convergence tolerance not achievable

Warning in glmgam.fit(dx, dy, coef.start = start, tol = tol, maxit =
maxit, : Too much damping - convergence tolerance not achievable

Warning in glmgam.fit(dx, dy, coef.start = start, tol = tol, maxit =
maxit, : Too much damping - convergence tolerance not achievable

Warning in glmgam.fit(dx, dy, coef.start = start, tol = tol, maxit =
maxit, : Too much damping - convergence tolerance not achievable

Warning in glmgam.fit(dx, dy, coef.start = start, tol = tol, maxit =
maxit, : Too much damping - convergence tolerance not achievable

#visualize what the permutations look like (what the scrambled labels look like)
for(time in 1:10){
     print(permuted_results[[time]])
}

          Sample_ID Individual Sex Replicate treatment  RIN
18855_3_S 18855_3_S   NA18855    F         3  Unstrain 10.0
19160_3_S 19160_3_S   NA19160    M         3    Strain 10.0
18856_3_U 18856_3_U   NA18856    M         3    Strain 10.0
18856_1_U 18856_1_U   NA18856    M         1  Unstrain  9.9
18855_2_S 18855_2_S   NA18855    F         2  Unstrain 10.0
18856_2_S 18856_2_S   NA18856    M         2    Strain  9.6
19160_3_U 19160_3_U   NA19160    M         3    Strain 10.0
18855_2_U 18855_2_U   NA18855    F         2  Unstrain 10.0
19160_2_S 19160_2_S   NA19160    M         2  Unstrain 10.0
18855_1_S 18855_1_S   NA18855    F         1  Unstrain  9.9
18856_1_S 18856_1_S   NA18856    M         1  Unstrain  8.5
19160_1_S 19160_1_S   NA19160    M         1    Strain  9.9
19160_2_U 19160_2_U   NA19160    M         2    Strain 10.0
18855_1_U 18855_1_U   NA18855    F         1    Strain 10.0
18856_3_S 18856_3_S   NA18856    M         3    Strain  9.6
18856_2_U 18856_2_U   NA18856    M         2    Strain  9.5
18855_3_U 18855_3_U   NA18855    F         3  Unstrain 10.0
          LibraryPrepBatch  LibSize          W_1        W_2
18855_3_S                2 15419569  0.541963415 -1.1181740
19160_3_S                2 15177653  0.376349314 -1.4100700
18856_3_U                2 13722909  0.857248982 -0.8808562
18856_1_U                1 18647599  1.113674153 -1.5466409
18855_2_S                1 18256163  0.733375747 -0.7617020
18856_2_S                1 16782764 -0.051863231 -1.3484210
19160_3_U                2 19839604  0.224499121 -1.5560509
18855_2_U                1 18618691  0.665015743 -0.9464050
19160_2_S                1 16622407  0.308745015 -1.5149287
18855_1_S                1 19712744  0.626769726 -1.5366695
18856_1_S                1 13571439  1.038716806 -1.2664724
19160_1_S                1 20979958  0.461484490 -2.0300296
19160_2_U                1 20924281  0.002654243 -1.9184820
18855_1_U                1 20146697  0.649711618 -1.4110342
18856_3_S                2 15807863  0.728747075 -0.9909086
18856_2_U                1 15142152  0.359175096 -1.0809905
18855_3_U                2 18157918  0.487681866 -1.2290497
          Sample_ID Individual Sex Replicate treatment  RIN
18855_3_S 18855_3_S   NA18855    F         3    Strain 10.0
19160_3_S 19160_3_S   NA19160    M         3  Unstrain 10.0
18856_3_U 18856_3_U   NA18856    M         3    Strain 10.0
18856_1_U 18856_1_U   NA18856    M         1  Unstrain  9.9
18855_2_S 18855_2_S   NA18855    F         2    Strain 10.0
18856_2_S 18856_2_S   NA18856    M         2    Strain  9.6
19160_3_U 19160_3_U   NA19160    M         3  Unstrain 10.0
18855_2_U 18855_2_U   NA18855    F         2    Strain 10.0
19160_2_S 19160_2_S   NA19160    M         2    Strain 10.0
18855_1_S 18855_1_S   NA18855    F         1  Unstrain  9.9
18856_1_S 18856_1_S   NA18856    M         1    Strain  8.5
19160_1_S 19160_1_S   NA19160    M         1  Unstrain  9.9
19160_2_U 19160_2_U   NA19160    M         2  Unstrain 10.0
18855_1_U 18855_1_U   NA18855    F         1  Unstrain 10.0
18856_3_S 18856_3_S   NA18856    M         3  Unstrain  9.6
18856_2_U 18856_2_U   NA18856    M         2    Strain  9.5
18855_3_U 18855_3_U   NA18855    F         3    Strain 10.0
          LibraryPrepBatch  LibSize          W_1        W_2
18855_3_S                2 15419569  0.541963415 -1.1181740
19160_3_S                2 15177653  0.376349314 -1.4100700
18856_3_U                2 13722909  0.857248982 -0.8808562
18856_1_U                1 18647599  1.113674153 -1.5466409
18855_2_S                1 18256163  0.733375747 -0.7617020
18856_2_S                1 16782764 -0.051863231 -1.3484210
19160_3_U                2 19839604  0.224499121 -1.5560509
18855_2_U                1 18618691  0.665015743 -0.9464050
19160_2_S                1 16622407  0.308745015 -1.5149287
18855_1_S                1 19712744  0.626769726 -1.5366695
18856_1_S                1 13571439  1.038716806 -1.2664724
19160_1_S                1 20979958  0.461484490 -2.0300296
19160_2_U                1 20924281  0.002654243 -1.9184820
18855_1_U                1 20146697  0.649711618 -1.4110342
18856_3_S                2 15807863  0.728747075 -0.9909086
18856_2_U                1 15142152  0.359175096 -1.0809905
18855_3_U                2 18157918  0.487681866 -1.2290497
          Sample_ID Individual Sex Replicate treatment  RIN
18855_3_S 18855_3_S   NA18855    F         3  Unstrain 10.0
19160_3_S 19160_3_S   NA19160    M         3  Unstrain 10.0
18856_3_U 18856_3_U   NA18856    M         3    Strain 10.0
18856_1_U 18856_1_U   NA18856    M         1  Unstrain  9.9
18855_2_S 18855_2_S   NA18855    F         2    Strain 10.0
18856_2_S 18856_2_S   NA18856    M         2  Unstrain  9.6
19160_3_U 19160_3_U   NA19160    M         3    Strain 10.0
18855_2_U 18855_2_U   NA18855    F         2    Strain 10.0
19160_2_S 19160_2_S   NA19160    M         2    Strain 10.0
18855_1_S 18855_1_S   NA18855    F         1  Unstrain  9.9
18856_1_S 18856_1_S   NA18856    M         1  Unstrain  8.5
19160_1_S 19160_1_S   NA19160    M         1    Strain  9.9
19160_2_U 19160_2_U   NA19160    M         2    Strain 10.0
18855_1_U 18855_1_U   NA18855    F         1  Unstrain 10.0
18856_3_S 18856_3_S   NA18856    M         3    Strain  9.6
18856_2_U 18856_2_U   NA18856    M         2  Unstrain  9.5
18855_3_U 18855_3_U   NA18855    F         3    Strain 10.0
          LibraryPrepBatch  LibSize          W_1        W_2
18855_3_S                2 15419569  0.541963415 -1.1181740
19160_3_S                2 15177653  0.376349314 -1.4100700
18856_3_U                2 13722909  0.857248982 -0.8808562
18856_1_U                1 18647599  1.113674153 -1.5466409
18855_2_S                1 18256163  0.733375747 -0.7617020
18856_2_S                1 16782764 -0.051863231 -1.3484210
19160_3_U                2 19839604  0.224499121 -1.5560509
18855_2_U                1 18618691  0.665015743 -0.9464050
19160_2_S                1 16622407  0.308745015 -1.5149287
18855_1_S                1 19712744  0.626769726 -1.5366695
18856_1_S                1 13571439  1.038716806 -1.2664724
19160_1_S                1 20979958  0.461484490 -2.0300296
19160_2_U                1 20924281  0.002654243 -1.9184820
18855_1_U                1 20146697  0.649711618 -1.4110342
18856_3_S                2 15807863  0.728747075 -0.9909086
18856_2_U                1 15142152  0.359175096 -1.0809905
18855_3_U                2 18157918  0.487681866 -1.2290497
          Sample_ID Individual Sex Replicate treatment  RIN
18855_3_S 18855_3_S   NA18855    F         3  Unstrain 10.0
19160_3_S 19160_3_S   NA19160    M         3    Strain 10.0
18856_3_U 18856_3_U   NA18856    M         3  Unstrain 10.0
18856_1_U 18856_1_U   NA18856    M         1    Strain  9.9
18855_2_S 18855_2_S   NA18855    F         2  Unstrain 10.0
18856_2_S 18856_2_S   NA18856    M         2    Strain  9.6
19160_3_U 19160_3_U   NA19160    M         3    Strain 10.0
18855_2_U 18855_2_U   NA18855    F         2  Unstrain 10.0
19160_2_S 19160_2_S   NA19160    M         2    Strain 10.0
18855_1_S 18855_1_S   NA18855    F         1  Unstrain  9.9
18856_1_S 18856_1_S   NA18856    M         1    Strain  8.5
19160_1_S 19160_1_S   NA19160    M         1    Strain  9.9
19160_2_U 19160_2_U   NA19160    M         2    Strain 10.0
18855_1_U 18855_1_U   NA18855    F         1  Unstrain 10.0
18856_3_S 18856_3_S   NA18856    M         3  Unstrain  9.6
18856_2_U 18856_2_U   NA18856    M         2    Strain  9.5
18855_3_U 18855_3_U   NA18855    F         3  Unstrain 10.0
          LibraryPrepBatch  LibSize          W_1        W_2
18855_3_S                2 15419569  0.541963415 -1.1181740
19160_3_S                2 15177653  0.376349314 -1.4100700
18856_3_U                2 13722909  0.857248982 -0.8808562
18856_1_U                1 18647599  1.113674153 -1.5466409
18855_2_S                1 18256163  0.733375747 -0.7617020
18856_2_S                1 16782764 -0.051863231 -1.3484210
19160_3_U                2 19839604  0.224499121 -1.5560509
18855_2_U                1 18618691  0.665015743 -0.9464050
19160_2_S                1 16622407  0.308745015 -1.5149287
18855_1_S                1 19712744  0.626769726 -1.5366695
18856_1_S                1 13571439  1.038716806 -1.2664724
19160_1_S                1 20979958  0.461484490 -2.0300296
19160_2_U                1 20924281  0.002654243 -1.9184820
18855_1_U                1 20146697  0.649711618 -1.4110342
18856_3_S                2 15807863  0.728747075 -0.9909086
18856_2_U                1 15142152  0.359175096 -1.0809905
18855_3_U                2 18157918  0.487681866 -1.2290497
          Sample_ID Individual Sex Replicate treatment  RIN
18855_3_S 18855_3_S   NA18855    F         3  Unstrain 10.0
19160_3_S 19160_3_S   NA19160    M         3  Unstrain 10.0
18856_3_U 18856_3_U   NA18856    M         3    Strain 10.0
18856_1_U 18856_1_U   NA18856    M         1    Strain  9.9
18855_2_S 18855_2_S   NA18855    F         2  Unstrain 10.0
18856_2_S 18856_2_S   NA18856    M         2    Strain  9.6
19160_3_U 19160_3_U   NA19160    M         3  Unstrain 10.0
18855_2_U 18855_2_U   NA18855    F         2    Strain 10.0
19160_2_S 19160_2_S   NA19160    M         2    Strain 10.0
18855_1_S 18855_1_S   NA18855    F         1    Strain  9.9
18856_1_S 18856_1_S   NA18856    M         1    Strain  8.5
19160_1_S 19160_1_S   NA19160    M         1    Strain  9.9
19160_2_U 19160_2_U   NA19160    M         2  Unstrain 10.0
18855_1_U 18855_1_U   NA18855    F         1  Unstrain 10.0
18856_3_S 18856_3_S   NA18856    M         3  Unstrain  9.6
18856_2_U 18856_2_U   NA18856    M         2  Unstrain  9.5
18855_3_U 18855_3_U   NA18855    F         3    Strain 10.0
          LibraryPrepBatch  LibSize          W_1        W_2
18855_3_S                2 15419569  0.541963415 -1.1181740
19160_3_S                2 15177653  0.376349314 -1.4100700
18856_3_U                2 13722909  0.857248982 -0.8808562
18856_1_U                1 18647599  1.113674153 -1.5466409
18855_2_S                1 18256163  0.733375747 -0.7617020
18856_2_S                1 16782764 -0.051863231 -1.3484210
19160_3_U                2 19839604  0.224499121 -1.5560509
18855_2_U                1 18618691  0.665015743 -0.9464050
19160_2_S                1 16622407  0.308745015 -1.5149287
18855_1_S                1 19712744  0.626769726 -1.5366695
18856_1_S                1 13571439  1.038716806 -1.2664724
19160_1_S                1 20979958  0.461484490 -2.0300296
19160_2_U                1 20924281  0.002654243 -1.9184820
18855_1_U                1 20146697  0.649711618 -1.4110342
18856_3_S                2 15807863  0.728747075 -0.9909086
18856_2_U                1 15142152  0.359175096 -1.0809905
18855_3_U                2 18157918  0.487681866 -1.2290497
          Sample_ID Individual Sex Replicate treatment  RIN
18855_3_S 18855_3_S   NA18855    F         3  Unstrain 10.0
19160_3_S 19160_3_S   NA19160    M         3    Strain 10.0
18856_3_U 18856_3_U   NA18856    M         3    Strain 10.0
18856_1_U 18856_1_U   NA18856    M         1  Unstrain  9.9
18855_2_S 18855_2_S   NA18855    F         2  Unstrain 10.0
18856_2_S 18856_2_S   NA18856    M         2  Unstrain  9.6
19160_3_U 19160_3_U   NA19160    M         3  Unstrain 10.0
18855_2_U 18855_2_U   NA18855    F         2  Unstrain 10.0
19160_2_S 19160_2_S   NA19160    M         2    Strain 10.0
18855_1_S 18855_1_S   NA18855    F         1  Unstrain  9.9
18856_1_S 18856_1_S   NA18856    M         1    Strain  8.5
19160_1_S 19160_1_S   NA19160    M         1    Strain  9.9
19160_2_U 19160_2_U   NA19160    M         2    Strain 10.0
18855_1_U 18855_1_U   NA18855    F         1    Strain 10.0
18856_3_S 18856_3_S   NA18856    M         3    Strain  9.6
18856_2_U 18856_2_U   NA18856    M         2  Unstrain  9.5
18855_3_U 18855_3_U   NA18855    F         3    Strain 10.0
          LibraryPrepBatch  LibSize          W_1        W_2
18855_3_S                2 15419569  0.541963415 -1.1181740
19160_3_S                2 15177653  0.376349314 -1.4100700
18856_3_U                2 13722909  0.857248982 -0.8808562
18856_1_U                1 18647599  1.113674153 -1.5466409
18855_2_S                1 18256163  0.733375747 -0.7617020
18856_2_S                1 16782764 -0.051863231 -1.3484210
19160_3_U                2 19839604  0.224499121 -1.5560509
18855_2_U                1 18618691  0.665015743 -0.9464050
19160_2_S                1 16622407  0.308745015 -1.5149287
18855_1_S                1 19712744  0.626769726 -1.5366695
18856_1_S                1 13571439  1.038716806 -1.2664724
19160_1_S                1 20979958  0.461484490 -2.0300296
19160_2_U                1 20924281  0.002654243 -1.9184820
18855_1_U                1 20146697  0.649711618 -1.4110342
18856_3_S                2 15807863  0.728747075 -0.9909086
18856_2_U                1 15142152  0.359175096 -1.0809905
18855_3_U                2 18157918  0.487681866 -1.2290497
          Sample_ID Individual Sex Replicate treatment  RIN
18855_3_S 18855_3_S   NA18855    F         3  Unstrain 10.0
19160_3_S 19160_3_S   NA19160    M         3  Unstrain 10.0
18856_3_U 18856_3_U   NA18856    M         3    Strain 10.0
18856_1_U 18856_1_U   NA18856    M         1  Unstrain  9.9
18855_2_S 18855_2_S   NA18855    F         2    Strain 10.0
18856_2_S 18856_2_S   NA18856    M         2    Strain  9.6
19160_3_U 19160_3_U   NA19160    M         3    Strain 10.0
18855_2_U 18855_2_U   NA18855    F         2  Unstrain 10.0
19160_2_S 19160_2_S   NA19160    M         2  Unstrain 10.0
18855_1_S 18855_1_S   NA18855    F         1  Unstrain  9.9
18856_1_S 18856_1_S   NA18856    M         1    Strain  8.5
19160_1_S 19160_1_S   NA19160    M         1    Strain  9.9
19160_2_U 19160_2_U   NA19160    M         2  Unstrain 10.0
18855_1_U 18855_1_U   NA18855    F         1    Strain 10.0
18856_3_S 18856_3_S   NA18856    M         3  Unstrain  9.6
18856_2_U 18856_2_U   NA18856    M         2    Strain  9.5
18855_3_U 18855_3_U   NA18855    F         3    Strain 10.0
          LibraryPrepBatch  LibSize          W_1        W_2
18855_3_S                2 15419569  0.541963415 -1.1181740
19160_3_S                2 15177653  0.376349314 -1.4100700
18856_3_U                2 13722909  0.857248982 -0.8808562
18856_1_U                1 18647599  1.113674153 -1.5466409
18855_2_S                1 18256163  0.733375747 -0.7617020
18856_2_S                1 16782764 -0.051863231 -1.3484210
19160_3_U                2 19839604  0.224499121 -1.5560509
18855_2_U                1 18618691  0.665015743 -0.9464050
19160_2_S                1 16622407  0.308745015 -1.5149287
18855_1_S                1 19712744  0.626769726 -1.5366695
18856_1_S                1 13571439  1.038716806 -1.2664724
19160_1_S                1 20979958  0.461484490 -2.0300296
19160_2_U                1 20924281  0.002654243 -1.9184820
18855_1_U                1 20146697  0.649711618 -1.4110342
18856_3_S                2 15807863  0.728747075 -0.9909086
18856_2_U                1 15142152  0.359175096 -1.0809905
18855_3_U                2 18157918  0.487681866 -1.2290497
          Sample_ID Individual Sex Replicate treatment  RIN
18855_3_S 18855_3_S   NA18855    F         3  Unstrain 10.0
19160_3_S 19160_3_S   NA19160    M         3  Unstrain 10.0
18856_3_U 18856_3_U   NA18856    M         3    Strain 10.0
18856_1_U 18856_1_U   NA18856    M         1  Unstrain  9.9
18855_2_S 18855_2_S   NA18855    F         2    Strain 10.0
18856_2_S 18856_2_S   NA18856    M         2  Unstrain  9.6
19160_3_U 19160_3_U   NA19160    M         3  Unstrain 10.0
18855_2_U 18855_2_U   NA18855    F         2    Strain 10.0
19160_2_S 19160_2_S   NA19160    M         2  Unstrain 10.0
18855_1_S 18855_1_S   NA18855    F         1    Strain  9.9
18856_1_S 18856_1_S   NA18856    M         1    Strain  8.5
19160_1_S 19160_1_S   NA19160    M         1    Strain  9.9
19160_2_U 19160_2_U   NA19160    M         2    Strain 10.0
18855_1_U 18855_1_U   NA18855    F         1    Strain 10.0
18856_3_S 18856_3_S   NA18856    M         3  Unstrain  9.6
18856_2_U 18856_2_U   NA18856    M         2  Unstrain  9.5
18855_3_U 18855_3_U   NA18855    F         3    Strain 10.0
          LibraryPrepBatch  LibSize          W_1        W_2
18855_3_S                2 15419569  0.541963415 -1.1181740
19160_3_S                2 15177653  0.376349314 -1.4100700
18856_3_U                2 13722909  0.857248982 -0.8808562
18856_1_U                1 18647599  1.113674153 -1.5466409
18855_2_S                1 18256163  0.733375747 -0.7617020
18856_2_S                1 16782764 -0.051863231 -1.3484210
19160_3_U                2 19839604  0.224499121 -1.5560509
18855_2_U                1 18618691  0.665015743 -0.9464050
19160_2_S                1 16622407  0.308745015 -1.5149287
18855_1_S                1 19712744  0.626769726 -1.5366695
18856_1_S                1 13571439  1.038716806 -1.2664724
19160_1_S                1 20979958  0.461484490 -2.0300296
19160_2_U                1 20924281  0.002654243 -1.9184820
18855_1_U                1 20146697  0.649711618 -1.4110342
18856_3_S                2 15807863  0.728747075 -0.9909086
18856_2_U                1 15142152  0.359175096 -1.0809905
18855_3_U                2 18157918  0.487681866 -1.2290497
          Sample_ID Individual Sex Replicate treatment  RIN
18855_3_S 18855_3_S   NA18855    F         3  Unstrain 10.0
19160_3_S 19160_3_S   NA19160    M         3  Unstrain 10.0
18856_3_U 18856_3_U   NA18856    M         3  Unstrain 10.0
18856_1_U 18856_1_U   NA18856    M         1  Unstrain  9.9
18855_2_S 18855_2_S   NA18855    F         2    Strain 10.0
18856_2_S 18856_2_S   NA18856    M         2    Strain  9.6
19160_3_U 19160_3_U   NA19160    M         3    Strain 10.0
18855_2_U 18855_2_U   NA18855    F         2  Unstrain 10.0
19160_2_S 19160_2_S   NA19160    M         2    Strain 10.0
18855_1_S 18855_1_S   NA18855    F         1  Unstrain  9.9
18856_1_S 18856_1_S   NA18856    M         1    Strain  8.5
19160_1_S 19160_1_S   NA19160    M         1  Unstrain  9.9
19160_2_U 19160_2_U   NA19160    M         2    Strain 10.0
18855_1_U 18855_1_U   NA18855    F         1    Strain 10.0
18856_3_S 18856_3_S   NA18856    M         3    Strain  9.6
18856_2_U 18856_2_U   NA18856    M         2    Strain  9.5
18855_3_U 18855_3_U   NA18855    F         3  Unstrain 10.0
          LibraryPrepBatch  LibSize          W_1        W_2
18855_3_S                2 15419569  0.541963415 -1.1181740
19160_3_S                2 15177653  0.376349314 -1.4100700
18856_3_U                2 13722909  0.857248982 -0.8808562
18856_1_U                1 18647599  1.113674153 -1.5466409
18855_2_S                1 18256163  0.733375747 -0.7617020
18856_2_S                1 16782764 -0.051863231 -1.3484210
19160_3_U                2 19839604  0.224499121 -1.5560509
18855_2_U                1 18618691  0.665015743 -0.9464050
19160_2_S                1 16622407  0.308745015 -1.5149287
18855_1_S                1 19712744  0.626769726 -1.5366695
18856_1_S                1 13571439  1.038716806 -1.2664724
19160_1_S                1 20979958  0.461484490 -2.0300296
19160_2_U                1 20924281  0.002654243 -1.9184820
18855_1_U                1 20146697  0.649711618 -1.4110342
18856_3_S                2 15807863  0.728747075 -0.9909086
18856_2_U                1 15142152  0.359175096 -1.0809905
18855_3_U                2 18157918  0.487681866 -1.2290497
          Sample_ID Individual Sex Replicate treatment  RIN
18855_3_S 18855_3_S   NA18855    F         3    Strain 10.0
19160_3_S 19160_3_S   NA19160    M         3    Strain 10.0
18856_3_U 18856_3_U   NA18856    M         3  Unstrain 10.0
18856_1_U 18856_1_U   NA18856    M         1  Unstrain  9.9
18855_2_S 18855_2_S   NA18855    F         2    Strain 10.0
18856_2_S 18856_2_S   NA18856    M         2  Unstrain  9.6
19160_3_U 19160_3_U   NA19160    M         3    Strain 10.0
18855_2_U 18855_2_U   NA18855    F         2    Strain 10.0
19160_2_S 19160_2_S   NA19160    M         2  Unstrain 10.0
18855_1_S 18855_1_S   NA18855    F         1  Unstrain  9.9
18856_1_S 18856_1_S   NA18856    M         1  Unstrain  8.5
19160_1_S 19160_1_S   NA19160    M         1  Unstrain  9.9
19160_2_U 19160_2_U   NA19160    M         2  Unstrain 10.0
18855_1_U 18855_1_U   NA18855    F         1    Strain 10.0
18856_3_S 18856_3_S   NA18856    M         3    Strain  9.6
18856_2_U 18856_2_U   NA18856    M         2    Strain  9.5
18855_3_U 18855_3_U   NA18855    F         3    Strain 10.0
          LibraryPrepBatch  LibSize          W_1        W_2
18855_3_S                2 15419569  0.541963415 -1.1181740
19160_3_S                2 15177653  0.376349314 -1.4100700
18856_3_U                2 13722909  0.857248982 -0.8808562
18856_1_U                1 18647599  1.113674153 -1.5466409
18855_2_S                1 18256163  0.733375747 -0.7617020
18856_2_S                1 16782764 -0.051863231 -1.3484210
19160_3_U                2 19839604  0.224499121 -1.5560509
18855_2_U                1 18618691  0.665015743 -0.9464050
19160_2_S                1 16622407  0.308745015 -1.5149287
18855_1_S                1 19712744  0.626769726 -1.5366695
18856_1_S                1 13571439  1.038716806 -1.2664724
19160_1_S                1 20979958  0.461484490 -2.0300296
19160_2_U                1 20924281  0.002654243 -1.9184820
18855_1_U                1 20146697  0.649711618 -1.4110342
18856_3_S                2 15807863  0.728747075 -0.9909086
18856_2_U                1 15142152  0.359175096 -1.0809905
18855_3_U                2 18157918  0.487681866 -1.2290497

Perform ks test for enrichments of GO terms amongst the permuted DE genes

for (permutation in 11:20){
     results <- permuted_results[[permutation]]
     
     # k-s test making use of ranked based on p-values
     genes <- results$qvalue
     names(genes) <- rownames(results)

     selection <- function(allScore){ return(allScore < 0.05)} # function that returns TRUE/FALSE for p-values<0.05
     allGO2genes <- annFUN.org(whichOnto="BP", feasibleGenes=NULL, mapping="org.Hs.eg.db", ID="ensembl")
     GOdata <- new("topGOdata",
       ontology="BP",
       allGenes=genes,
       annot=annFUN.GO2genes,
       GO2genes=allGO2genes,
       geneSel=selection,
       nodeSize=10)

     results.ks <- runTest(GOdata, algorithm="classic", statistic="ks")
     goEnrichment <- GenTable(GOdata, KS=results.ks, orderBy="KS", topNodes=20)
     goEnrichment <- goEnrichment %>% 
          mutate(KS = ifelse(grepl("<", KS), 1e-30, KS))
     goEnrichment$KS <- as.numeric(goEnrichment$KS)
     goEnrichment <- goEnrichment[goEnrichment$KS<0.05,]
     goEnrichment <- goEnrichment[,c("GO.ID","Term","KS")]
     goEnrichment$Term <- gsub(" [a-z]*\\.\\.\\.$", "", goEnrichment$Term)
     goEnrichment$Term <- gsub("\\.\\.\\.$", "", goEnrichment$Term)
     goEnrichment$Term <- paste(goEnrichment$GO.ID, goEnrichment$Term, sep=", ")
     goEnrichment$Term <- factor(goEnrichment$Term, levels=rev(goEnrichment$Term))
     goEnrichment$KS <- as.numeric(goEnrichment$KS)

     print(goEnrichment$Term)
}


Building most specific GOs .....

    ( 9780 GO terms found. )


Build GO DAG topology ..........

    ( 13861 GO terms and 32652 relations. )


Annotating nodes ...............

    ( 9204 genes annotated to the GO terms. )


             -- Classic Algorithm -- 

         the algorithm is scoring 5178 nontrivial nodes
         parameters: 
             test statistic: ks
             score order: increasing

 [1] GO:0010591, regulation of lamellipodium assembly  
 [2] GO:0043038, amino acid activation                 
 [3] GO:0043039, tRNA aminoacylation                   
 [4] GO:0003230, cardiac atrium development            
 [5] GO:0006418, tRNA aminoacylation for protein       
 [6] GO:0010592, positive regulation of lamellipodium  
 [7] GO:0032635, interleukin-6 production              
 [8] GO:0043489, RNA stabilization                     
 [9] GO:0060021, roof of mouth development             
[10] GO:0021536, diencephalon development              
[11] GO:0003209, cardiac atrium morphogenesis          
[12] GO:0003283, atrial septum development             
[13] GO:0002820, negative regulation of adaptive immune
[14] GO:0050919, negative chemotaxis                   
[15] GO:0006928, movement of cell or subcellular       
[16] GO:0048255, mRNA stabilization                    
[17] GO:0045669, positive regulation of osteoblast     
[18] GO:0061029, eyelid development in camera-type eye 
[19] GO:0043486, histone exchange                      
[20] GO:0010950, positive regulation of endopeptidase  
20 Levels: GO:0010950, positive regulation of endopeptidase ...


Building most specific GOs .....

    ( 9780 GO terms found. )


Build GO DAG topology ..........

    ( 13861 GO terms and 32652 relations. )


Annotating nodes ...............

    ( 9204 genes annotated to the GO terms. )


             -- Classic Algorithm -- 

         the algorithm is scoring 5178 nontrivial nodes
         parameters: 
             test statistic: ks
             score order: increasing

 [1] GO:0007186, G protein-coupled receptor signaling   
 [2] GO:0032501, multicellular organismal process       
 [3] GO:0003008, system process                         
 [4] GO:0048856, anatomical structure development       
 [5] GO:0061138, morphogenesis of a branching epithelium
 [6] GO:0009888, tissue development                     
 [7] GO:0001763, morphogenesis of a branching structure 
 [8] GO:0048513, animal organ development               
 [9] GO:0032502, developmental process                  
[10] GO:0009653, anatomical structure morphogenesis     
[11] GO:0001934, positive regulation of protein         
[12] GO:0007275, multicellular organism development     
[13] GO:0051239, regulation of multicellular organismal 
[14] GO:0048731, system development                     
[15] GO:0022612, gland morphogenesis                    
[16] GO:0042327, positive regulation of phosphorylation 
[17] GO:0048645, animal organ formation                 
[18] GO:0051241, negative regulation of multicellular   
[19] GO:0030154, cell differentiation                   
[20] GO:0048646, anatomical structure formation involved
20 Levels: GO:0048646, anatomical structure formation involved ...


Building most specific GOs .....

    ( 9780 GO terms found. )


Build GO DAG topology ..........

    ( 13861 GO terms and 32652 relations. )


Annotating nodes ...............

    ( 9204 genes annotated to the GO terms. )


             -- Classic Algorithm -- 

         the algorithm is scoring 5178 nontrivial nodes
         parameters: 
             test statistic: ks
             score order: increasing

 [1] GO:0010591, regulation of lamellipodium assembly  
 [2] GO:0043038, amino acid activation                 
 [3] GO:0043039, tRNA aminoacylation                   
 [4] GO:0003230, cardiac atrium development            
 [5] GO:0006418, tRNA aminoacylation for protein       
 [6] GO:0010592, positive regulation of lamellipodium  
 [7] GO:0032635, interleukin-6 production              
 [8] GO:0043489, RNA stabilization                     
 [9] GO:0060021, roof of mouth development             
[10] GO:0021536, diencephalon development              
[11] GO:0003209, cardiac atrium morphogenesis          
[12] GO:0003283, atrial septum development             
[13] GO:0002820, negative regulation of adaptive immune
[14] GO:0050919, negative chemotaxis                   
[15] GO:0006928, movement of cell or subcellular       
[16] GO:0048255, mRNA stabilization                    
[17] GO:0045669, positive regulation of osteoblast     
[18] GO:0061029, eyelid development in camera-type eye 
[19] GO:0043486, histone exchange                      
[20] GO:0010950, positive regulation of endopeptidase  
20 Levels: GO:0010950, positive regulation of endopeptidase ...


Building most specific GOs .....

    ( 9780 GO terms found. )


Build GO DAG topology ..........

    ( 13861 GO terms and 32652 relations. )


Annotating nodes ...............

    ( 9204 genes annotated to the GO terms. )


             -- Classic Algorithm -- 

         the algorithm is scoring 5178 nontrivial nodes
         parameters: 
             test statistic: ks
             score order: increasing

 [1] GO:0006928, movement of cell or subcellular        
 [2] GO:0007166, cell surface receptor signaling pathway
 [3] GO:0040011, locomotion                             
 [4] GO:0042127, regulation of cell proliferation       
 [5] GO:0007165, signal transduction                    
 [6] GO:0048731, system development                     
 [7] GO:0023052, signaling                              
 [8] GO:0009653, anatomical structure morphogenesis     
 [9] GO:0030154, cell differentiation                   
[10] GO:0007154, cell communication                     
[11] GO:0048869, cellular developmental process         
[12] GO:0050896, response to stimulus                   
[13] GO:0009605, response to external stimulus          
[14] GO:0051270, regulation of cellular component       
[15] GO:0030334, regulation of cell migration           
[16] GO:0016477, cell migration                         
[17] GO:0051239, regulation of multicellular organismal 
[18] GO:0048870, cell motility                          
[19] GO:0051674, localization of cell                   
[20] GO:0051716, cellular response to stimulus          
20 Levels: GO:0051716, cellular response to stimulus ...


Building most specific GOs .....

    ( 9780 GO terms found. )


Build GO DAG topology ..........

    ( 13861 GO terms and 32652 relations. )


Annotating nodes ...............

    ( 9204 genes annotated to the GO terms. )


             -- Classic Algorithm -- 

         the algorithm is scoring 5178 nontrivial nodes
         parameters: 
             test statistic: ks
             score order: increasing

 [1] GO:0010591, regulation of lamellipodium assembly  
 [2] GO:0043038, amino acid activation                 
 [3] GO:0043039, tRNA aminoacylation                   
 [4] GO:0003230, cardiac atrium development            
 [5] GO:0006418, tRNA aminoacylation for protein       
 [6] GO:0010592, positive regulation of lamellipodium  
 [7] GO:0032635, interleukin-6 production              
 [8] GO:0043489, RNA stabilization                     
 [9] GO:0060021, roof of mouth development             
[10] GO:0021536, diencephalon development              
[11] GO:0003209, cardiac atrium morphogenesis          
[12] GO:0003283, atrial septum development             
[13] GO:0002820, negative regulation of adaptive immune
[14] GO:0050919, negative chemotaxis                   
[15] GO:0006928, movement of cell or subcellular       
[16] GO:0048255, mRNA stabilization                    
[17] GO:0045669, positive regulation of osteoblast     
[18] GO:0061029, eyelid development in camera-type eye 
[19] GO:0043486, histone exchange                      
[20] GO:0010950, positive regulation of endopeptidase  
20 Levels: GO:0010950, positive regulation of endopeptidase ...


Building most specific GOs .....

    ( 9780 GO terms found. )


Build GO DAG topology ..........

    ( 13861 GO terms and 32652 relations. )


Annotating nodes ...............

    ( 9204 genes annotated to the GO terms. )


             -- Classic Algorithm -- 

         the algorithm is scoring 5178 nontrivial nodes
         parameters: 
             test statistic: ks
             score order: increasing

 [1] GO:0010591, regulation of lamellipodium assembly  
 [2] GO:0043038, amino acid activation                 
 [3] GO:0043039, tRNA aminoacylation                   
 [4] GO:0003230, cardiac atrium development            
 [5] GO:0006418, tRNA aminoacylation for protein       
 [6] GO:0010592, positive regulation of lamellipodium  
 [7] GO:0032635, interleukin-6 production              
 [8] GO:0043489, RNA stabilization                     
 [9] GO:0060021, roof of mouth development             
[10] GO:0021536, diencephalon development              
[11] GO:0003209, cardiac atrium morphogenesis          
[12] GO:0003283, atrial septum development             
[13] GO:0002820, negative regulation of adaptive immune
[14] GO:0050919, negative chemotaxis                   
[15] GO:0006928, movement of cell or subcellular       
[16] GO:0048255, mRNA stabilization                    
[17] GO:0045669, positive regulation of osteoblast     
[18] GO:0061029, eyelid development in camera-type eye 
[19] GO:0043486, histone exchange                      
[20] GO:0010950, positive regulation of endopeptidase  
20 Levels: GO:0010950, positive regulation of endopeptidase ...


Building most specific GOs .....

    ( 9780 GO terms found. )


Build GO DAG topology ..........

    ( 13861 GO terms and 32652 relations. )


Annotating nodes ...............

    ( 9204 genes annotated to the GO terms. )


             -- Classic Algorithm -- 

         the algorithm is scoring 5178 nontrivial nodes
         parameters: 
             test statistic: ks
             score order: increasing

 [1] GO:0006614, SRP-dependent cotranslational protein
 [2] GO:0045047, protein targeting to ER              
 [3] GO:0072599, establishment of protein localization
 [4] GO:0006613, cotranslational protein targeting to 
 [5] GO:0000184, nuclear-transcribed mRNA catabolic   
 [6] GO:0070972, protein localization to endoplasmic  
 [7] GO:0019083, viral transcription                  
 [8] GO:0019080, viral gene expression                
 [9] GO:0006612, protein targeting to membrane        
[10] GO:0006413, translational initiation             
[11] GO:0000956, nuclear-transcribed mRNA catabolic   
[12] GO:0006605, protein targeting                    
[13] GO:0006412, translation                          
[14] GO:0043043, peptide biosynthetic process         
[15] GO:0010469, regulation of signaling receptor     
[16] GO:0030198, extracellular matrix organization    
[17] GO:0006518, peptide metabolic process            
[18] GO:0035295, tube development                     
[19] GO:0043062, extracellular structure organization 
[20] GO:1901342, regulation of vasculature development
20 Levels: GO:1901342, regulation of vasculature development ...


Building most specific GOs .....

    ( 9780 GO terms found. )


Build GO DAG topology ..........

    ( 13861 GO terms and 32652 relations. )


Annotating nodes ...............

    ( 9204 genes annotated to the GO terms. )


             -- Classic Algorithm -- 

         the algorithm is scoring 5178 nontrivial nodes
         parameters: 
             test statistic: ks
             score order: increasing

 [1] GO:0010591, regulation of lamellipodium assembly  
 [2] GO:0043038, amino acid activation                 
 [3] GO:0043039, tRNA aminoacylation                   
 [4] GO:0003230, cardiac atrium development            
 [5] GO:0006418, tRNA aminoacylation for protein       
 [6] GO:0010592, positive regulation of lamellipodium  
 [7] GO:0032635, interleukin-6 production              
 [8] GO:0043489, RNA stabilization                     
 [9] GO:0060021, roof of mouth development             
[10] GO:0021536, diencephalon development              
[11] GO:0003209, cardiac atrium morphogenesis          
[12] GO:0003283, atrial septum development             
[13] GO:0002820, negative regulation of adaptive immune
[14] GO:0050919, negative chemotaxis                   
[15] GO:0006928, movement of cell or subcellular       
[16] GO:0048255, mRNA stabilization                    
[17] GO:0045669, positive regulation of osteoblast     
[18] GO:0061029, eyelid development in camera-type eye 
[19] GO:0043486, histone exchange                      
[20] GO:0010950, positive regulation of endopeptidase  
20 Levels: GO:0010950, positive regulation of endopeptidase ...


Building most specific GOs .....

    ( 9780 GO terms found. )


Build GO DAG topology ..........

    ( 13861 GO terms and 32652 relations. )


Annotating nodes ...............

    ( 9204 genes annotated to the GO terms. )


             -- Classic Algorithm -- 

         the algorithm is scoring 5178 nontrivial nodes
         parameters: 
             test statistic: ks
             score order: increasing

 [1] GO:0010591, regulation of lamellipodium assembly  
 [2] GO:0043038, amino acid activation                 
 [3] GO:0043039, tRNA aminoacylation                   
 [4] GO:0003230, cardiac atrium development            
 [5] GO:0006418, tRNA aminoacylation for protein       
 [6] GO:0010592, positive regulation of lamellipodium  
 [7] GO:0032635, interleukin-6 production              
 [8] GO:0043489, RNA stabilization                     
 [9] GO:0060021, roof of mouth development             
[10] GO:0021536, diencephalon development              
[11] GO:0003209, cardiac atrium morphogenesis          
[12] GO:0003283, atrial septum development             
[13] GO:0002820, negative regulation of adaptive immune
[14] GO:0050919, negative chemotaxis                   
[15] GO:0006928, movement of cell or subcellular       
[16] GO:0048255, mRNA stabilization                    
[17] GO:0045669, positive regulation of osteoblast     
[18] GO:0061029, eyelid development in camera-type eye 
[19] GO:0043486, histone exchange                      
[20] GO:0010950, positive regulation of endopeptidase  
20 Levels: GO:0010950, positive regulation of endopeptidase ...


Building most specific GOs .....

    ( 9780 GO terms found. )


Build GO DAG topology ..........

    ( 13861 GO terms and 32652 relations. )


Annotating nodes ...............

    ( 9204 genes annotated to the GO terms. )


             -- Classic Algorithm -- 

         the algorithm is scoring 5178 nontrivial nodes
         parameters: 
             test statistic: ks
             score order: increasing

 [1] GO:0010591, regulation of lamellipodium assembly  
 [2] GO:0043038, amino acid activation                 
 [3] GO:0043039, tRNA aminoacylation                   
 [4] GO:0003230, cardiac atrium development            
 [5] GO:0006418, tRNA aminoacylation for protein       
 [6] GO:0010592, positive regulation of lamellipodium  
 [7] GO:0032635, interleukin-6 production              
 [8] GO:0043489, RNA stabilization                     
 [9] GO:0060021, roof of mouth development             
[10] GO:0021536, diencephalon development              
[11] GO:0003209, cardiac atrium morphogenesis          
[12] GO:0003283, atrial septum development             
[13] GO:0002820, negative regulation of adaptive immune
[14] GO:0050919, negative chemotaxis                   
[15] GO:0006928, movement of cell or subcellular       
[16] GO:0048255, mRNA stabilization                    
[17] GO:0045669, positive regulation of osteoblast     
[18] GO:0061029, eyelid development in camera-type eye 
[19] GO:0043486, histone exchange                      
[20] GO:0010950, positive regulation of endopeptidase  
20 Levels: GO:0010950, positive regulation of endopeptidase ...

Look at enrichment of DE genes amongst previously published datasets of OA related genes

Return back first to the original DE results to evaluate enrichment.

library(dplyr)
# load gene annotations
gene_anno <- read.delim("data/gene-annotation.txt",
                        sep = "\t")

threshold <- 0.05
DE_genes_ensg <- rownames(DE_results_original[["Unstrain"]])[DE_results_original[["Unstrain"]]$qvalue < threshold]
DE_genes <- gene_anno$external_gene_name[match(DE_genes_ensg, gene_anno$ensembl_gene_id)]
other_genes_ensg <- rownames(DE_results_original[["Unstrain"]])[DE_results_original[["Unstrain"]]$qvalue >= threshold]
other_genes <- gene_anno$external_gene_name[match(other_genes_ensg, gene_anno$ensembl_gene_id)]

#Genes from https://www.nature.com/articles/s41588-018-0327-1
#Table S4: Functional genomics annotations for all genes within 500kb of the osteoarthritis-associated variants. (subset to those with any evidence)
fungenomic <- c("ENSG00000060718", "ENSG00000270962", "ENSG00000150337", "ENSG00000264522", "ENSG00000136631", "ENSG00000023902", "ENSG00000117362", "ENSG00000159208", "ENSG00000163125", "ENSG00000143374", "ENSG00000143369", "ENSG00000143420", "ENSG00000163131", "ENSG00000143387", "ENSG00000143412", "ENSG00000143409", "ENSG00000213190", "ENSG00000120334", "ENSG00000117593", "ENSG00000135870", "ENSG00000227373", "ENSG00000152061", "ENSG00000135862", "ENSG00000058085", "ENSG00000116701", "ENSG00000198756", "ENSG00000198860", "ENSG00000143353", "ENSG00000228536", "ENSG00000116141", "ENSG00000203667", "ENSG00000185420", "ENSG00000018699", "ENSG00000049323", "ENSG00000119812", "ENSG00000059728", "ENSG00000244617", "ENSG00000116001", "ENSG00000116005", "ENSG00000143977", "ENSG00000035141", "ENSG00000163235", "ENSG00000116039", "ENSG00000124357", "ENSG00000135636", "ENSG00000115419", "ENSG00000115415", "ENSG00000138378", "ENSG00000204217", "ENSG00000138439", "ENSG00000163596", "ENSG00000138380", "ENSG00000138442", "ENSG00000173166", "ENSG00000163599", "ENSG00000178467", "ENSG00000178252", "ENSG00000178035", "ENSG00000172037", "ENSG00000145020", "ENSG00000145029", "ENSG00000173402", "ENSG00000164068", "ENSG00000173540", "ENSG00000176095", "ENSG00000185614", "ENSG00000182179", "ENSG00000164076", "ENSG00000234667", "ENSG00000164078", "ENSG00000228008", "ENSG00000004534", "ENSG00000003756", "ENSG00000001617", "ENSG00000114353", "ENSG00000214706", "ENSG00000114378", "ENSG00000068001", "ENSG00000126062", "ENSG00000007402", "ENSG00000114737", "ENSG00000114767", "ENSG00000090097", "ENSG00000248487", "ENSG00000114779", "ENSG00000114786", "ENSG00000162244", "ENSG00000164087", "ENSG00000247596", "ENSG00000164088", "ENSG00000164091", "ENSG00000168237", "ENSG00000239557", "ENSG00000010318", "ENSG00000010322", "ENSG00000168268", "ENSG00000163939", "ENSG00000163938", "ENSG00000016864", "ENSG00000114902", "ENSG00000270941", "ENSG00000114904", "ENSG00000055957", "ENSG00000162267", "ENSG00000055955", "ENSG00000272573", "ENSG00000163935", "ENSG00000163932", "ENSG00000163931", "ENSG00000127415", "ENSG00000127418", "ENSG00000178222", "ENSG00000159692", "ENSG00000163945", "ENSG00000244459", "ENSG00000174137", "ENSG00000163950", "ENSG00000270195", "ENSG00000168936", "ENSG00000013810", "ENSG00000068078", "ENSG00000168924", "ENSG00000109685", "ENSG00000130997", "ENSG00000159733", "ENSG00000157869", "ENSG00000109705", "ENSG00000109606", "ENSG00000248444", "ENSG00000109610", "ENSG00000181982", "ENSG00000168228", "ENSG00000053900", "ENSG00000091490", "ENSG00000138814", "ENSG00000138821", "ENSG00000109320", "ENSG00000109332", "ENSG00000145354", "ENSG00000164039", "ENSG00000253558", "ENSG00000132842", "ENSG00000113273", "ENSG00000132840", "ENSG00000145692", "ENSG00000132837", "ENSG00000156427", "ENSG00000174705", "ENSG00000079691", "ENSG00000272462", "ENSG00000272810", "ENSG00000187837", "ENSG00000010704", "ENSG00000218281", "ENSG00000124508", "ENSG00000111801", "ENSG00000112763", "ENSG00000124635", "ENSG00000196787", "ENSG00000197903", "ENSG00000204305", "ENSG00000204304", "ENSG00000213654", "ENSG00000198502", "ENSG00000224796", "ENSG00000223865", "ENSG00000204248", "ENSG00000204227", "ENSG00000030110", "ENSG00000096433", "ENSG00000161896", "ENSG00000161904", "ENSG00000137207", "ENSG00000171453", "ENSG00000112715", "ENSG00000181577", "ENSG00000137216", "ENSG00000112759", "ENSG00000146232", "ENSG00000196284", "ENSG00000271857", "ENSG00000124813", "ENSG00000196284")
fungenomic1 <- c("ENSG00000124813", "ENSG00000271857", "ENSG00000112782", "ENSG00000172348", "ENSG00000112175", "ENSG00000111799", "ENSG00000112697", "ENSG00000118407", "ENSG00000196586", "ENSG00000105852", "ENSG00000004799", "ENSG00000158560", "ENSG00000004864", "ENSG00000006377", "ENSG00000105880", "ENSG00000253981", "ENSG00000173295", "ENSG00000104626", "ENSG00000173281", "ENSG00000248538", "ENSG00000175806", "ENSG00000147697", "ENSG00000153310", "ENSG00000106688", "ENSG00000096968", "ENSG00000136867", "ENSG00000119321", "ENSG00000176386", "ENSG00000148229", "ENSG00000157653", "ENSG00000157657", "ENSG00000196739", "ENSG00000229314", "ENSG00000228278", "ENSG00000181634", "ENSG00000106952", "ENSG00000041982", "ENSG00000196739", "ENSG00000229314", "ENSG00000228278", "ENSG00000181634", "ENSG00000106952", "ENSG00000041982", "ENSG00000182752", "ENSG00000136869", "ENSG00000167081", "ENSG00000136944", "ENSG00000169155", "ENSG00000136859", "ENSG00000136830", "ENSG00000066382", "ENSG00000110047", "ENSG00000068971", "ENSG00000110025", "ENSG00000168060", "ENSG00000149809", "ENSG00000162298", "ENSG00000014216", "ENSG00000162241", "ENSG00000126391", "ENSG00000245532", "ENSG00000251562", "ENSG00000142186", "ENSG00000168056", "ENSG00000260233", "ENSG00000176973", "ENSG00000173338", "ENSG00000173327", "ENSG00000173039", "ENSG00000172922", "ENSG00000172638", "ENSG00000175592", "ENSG00000175550", "ENSG00000175334", "ENSG00000179292", "ENSG00000174684", "ENSG00000174576", "ENSG00000174547", "ENSG00000174516", "ENSG00000174165", "ENSG00000172638", "ENSG00000175592", "ENSG00000175550", "ENSG00000175334", "ENSG00000179292", "ENSG00000174684", "ENSG00000174576", "ENSG00000174547", "ENSG00000174516", "ENSG00000174165", "ENSG00000173914", "ENSG00000173898", "ENSG00000173599", "ENSG00000173237", "ENSG00000173156", "ENSG00000172932", "ENSG00000172613", "ENSG00000175514", "ENSG00000167800", "ENSG00000137492", "ENSG00000204529", "ENSG00000254632", "ENSG00000255100", "ENSG00000182704", "ENSG00000261578", "ENSG00000078124", "ENSG00000149269", "ENSG00000227376", "ENSG00000048649", "ENSG00000064102", "ENSG00000111790", "ENSG00000110841", "ENSG00000174236", "ENSG00000230519", "ENSG00000061794", "ENSG00000087494", "ENSG00000139263", "ENSG00000118596", "ENSG00000237176", "ENSG00000111581", "ENSG00000175782", "ENSG00000135679", "ENSG00000111605", "ENSG00000090382", "ENSG00000166225", "ENSG00000166226", "ENSG00000127328", "ENSG00000166268", "ENSG00000139318", "ENSG00000070961", "ENSG00000177889", "ENSG00000120833", "ENSG00000258274", "ENSG00000169372", "ENSG00000173588", "ENSG00000089041", "ENSG00000135124", "ENSG00000188735", "ENSG00000256546", "ENSG00000184047", "ENSG00000284934", "ENSG00000256304", "ENSG00000139726", "ENSG00000130787", "ENSG00000139722", "ENSG00000090975", "ENSG00000139726", "ENSG00000130787", "ENSG00000139722", "ENSG00000150967", "ENSG00000090975", "ENSG00000051825", "ENSG00000130921", "ENSG00000235423", "ENSG00000111328", "ENSG00000139697", "ENSG00000150977")
fungenomic2 <- c("ENSG00000184209", "ENSG00000111364", "ENSG00000250091", "ENSG00000178882", "ENSG00000196498", "ENSG00000140285", "ENSG00000138592", "ENSG00000242737", "ENSG00000259618", "ENSG00000081014", "ENSG00000183578", "ENSG00000137869", "ENSG00000128849", "ENSG00000128918", "ENSG00000137845", "ENSG00000169032", "ENSG00000188501", "ENSG00000166949", "ENSG00000033800", "ENSG00000129038", "ENSG00000167178", "ENSG00000129009", "ENSG00000137868", "ENSG00000140481", "ENSG00000138623", "ENSG00000179151", "ENSG00000140465", "ENSG00000103653", "ENSG00000140506", "ENSG00000140474", "ENSG00000140497", "ENSG00000178802", "ENSG00000178741", "ENSG00000178718", "ENSG00000140398", "ENSG00000140400", "ENSG00000169410", "ENSG00000177971", "ENSG00000173548", "ENSG00000173546", "ENSG00000277639", "ENSG00000103479", "ENSG00000166971", "ENSG00000103494", "ENSG00000140718", "ENSG00000177508", "ENSG00000062038", "ENSG00000103047", "ENSG00000258429", "ENSG00000102908", "ENSG00000198373", "ENSG00000157322", "ENSG00000255185", "ENSG00000157353", "ENSG00000103051", "ENSG00000189091", "ENSG00000157368", "ENSG00000051523", "ENSG00000167513", "ENSG00000141012", "ENSG00000167522", "ENSG00000197912", "ENSG00000015413", "ENSG00000131165", "ENSG00000158792", "ENSG00000158805", "ENSG00000187741", "ENSG00000258839", "ENSG00000258947", "ENSG00000223959", "ENSG00000197879", "ENSG00000132386", "ENSG00000132383", "ENSG00000108963", "ENSG00000177374", "ENSG00000070366", "ENSG00000262333", "ENSG00000167721", "ENSG00000070444", "ENSG00000127804", "ENSG00000108578", "ENSG00000263603", "ENSG00000266490", "ENSG00000181481", "ENSG00000265798", "ENSG00000196712", "ENSG00000126861", "ENSG00000185862", "ENSG00000126860", "ENSG00000131242", "ENSG00000108651", "ENSG00000178691", "ENSG00000126858", "ENSG00000172992", "ENSG00000184922", "ENSG00000159314", "ENSG00000225190", "ENSG00000266918", "ENSG00000214425", "ENSG00000264070", "ENSG00000204652", "ENSG00000120088", "ENSG00000185294", "ENSG00000186868", "ENSG00000262539", "ENSG00000261575", "ENSG00000266497", "ENSG00000238083", "ENSG00000185829", "ENSG00000073969", "ENSG00000108379", "ENSG00000108433", "ENSG00000172992", "ENSG00000136448", "ENSG00000184922", "ENSG00000225190", "ENSG00000108433", "ENSG00000141376", "ENSG00000121068", "ENSG00000121075", "ENSG00000256124", "ENSG00000125398", "ENSG00000134490", "ENSG00000101782", "ENSG00000141458", "ENSG00000053747", "ENSG00000141447", "ENSG00000105088", "ENSG00000080573", "ENSG00000130812", "ENSG00000130810", "ENSG00000244165", "ENSG00000130816", "ENSG00000267534", "ENSG00000090339", "ENSG00000105376", "ENSG00000220201", "ENSG00000267303", "ENSG00000105397", "ENSG00000065989", "ENSG00000129353", "ENSG00000267100", "ENSG00000129351", "ENSG00000079805", "ENSG00000099203", "ENSG00000127616", "ENSG00000197256", "ENSG00000105518", "ENSG00000205517", "ENSG00000198003", "ENSG00000130176", "ENSG00000102575", "ENSG00000105223", "ENSG00000268366", "ENSG00000090013", "ENSG00000090006", "ENSG00000105245", "ENSG00000086544", "ENSG00000077312", "ENSG00000261857", "ENSG00000233622", "ENSG00000167601", "ENSG00000105329", "ENSG00000105388", "ENSG00000160570", "ENSG00000105723", "ENSG00000105722", "ENSG00000079462", "ENSG00000167619", "ENSG00000105429", "ENSG00000167646", "ENSG00000080031", "ENSG00000160471", "ENSG00000095752", "ENSG00000187902", "ENSG00000090971", "ENSG00000179954", "ENSG00000171443", "ENSG00000131848", "ENSG00000078747", "ENSG00000101460", "ENSG00000101464", "ENSG00000131069", "ENSG00000078814", "ENSG00000100991", "ENSG00000088298", "ENSG00000101000", "ENSG00000242372", "ENSG00000101019", "ENSG00000125965", "ENSG00000126001", "ENSG00000125991", "ENSG00000088340", "ENSG00000244462", "ENSG00000025293", "ENSG00000171222", "ENSG00000231123", "ENSG00000157551", "ENSG00000157554", "ENSG00000183527", "ENSG00000205581", "ENSG00000189306", "ENSG00000182841", "ENSG00000100243", "ENSG00000128274", "ENSG00000274717", "ENSG00000100266", "ENSG00000100294", "ENSG00000100300", "ENSG00000100304", "ENSG00000159307", "ENSG00000100347", "ENSG00000188677")
fungenomic <- c(fungenomic, fungenomic1, fungenomic2)



#doi: https://doi.org/10.1101/835850
#Supplementary Table 2. Genes with significant cross-omics differences between high-grade and low-grade cartilage. (transcriptomic, proteomic both were significant)
s2 <- c("ENSG00000164163", "ENSG00000100997", "ENSG00000154175", "ENSG00000131473", "ENSG00000068366", "ENSG00000133627", "ENSG00000148700", "ENSG00000144843", "ENSG00000035687", "ENSG00000106624", "ENSG00000173744", "ENSG00000177674", "ENSG00000101444", "ENSG00000004455", "ENSG00000085662", "ENSG00000187134", "ENSG00000151632", "ENSG00000059573", "ENSG00000128918", "ENSG00000184254", "ENSG00000116337", "ENSG00000196510", "ENSG00000151150", "ENSG00000166825", "ENSG00000189058", "ENSG00000142192", "ENSG00000140750", "ENSG00000135931", "ENSG00000100299", "ENSG00000104763", "ENSG00000160072", "ENSG00000071553", "ENSG00000006453", "ENSG00000107949", "ENSG00000099968", "ENSG00000104164", "ENSG00000183336", "ENSG00000113460", "ENSG00000186665", "ENSG00000106392", "ENSG00000223953", "ENSG00000159403", "ENSG00000123838", "ENSG00000169239", "ENSG00000042493", "ENSG00000105519", "ENSG00000153048", "ENSG00000164305", "ENSG00000196954", "ENSG00000007080", "ENSG00000152492", "ENSG00000055813", "ENSG00000110104", "ENSG00000115009", "ENSG00000260916", "ENSG00000000971", "ENSG00000016391", "ENSG00000255112", "ENSG00000130724", "ENSG00000054938", "ENSG00000163815", "ENSG00000169583", "ENSG00000159212", "ENSG00000111726", "ENSG00000164237", "ENSG00000064666", "ENSG00000117519", "ENSG00000168434", "ENSG00000204291", "ENSG00000182871", "ENSG00000164692", "ENSG00000168542", "ENSG00000187498", "ENSG00000134871", "ENSG00000130635", "ENSG00000112280", "ENSG00000184432", "ENSG00000181789", "ENSG00000160917", "ENSG00000110090", "ENSG00000157184", "ENSG00000121005", "ENSG00000006016", "ENSG00000095713", "ENSG00000160213", "ENSG00000159692", "ENSG00000117151", "ENSG00000064601", "ENSG00000117984", "ENSG00000083799", "ENSG00000138061", "ENSG00000135929", "ENSG00000110063", "ENSG00000153904", "ENSG00000162733", "ENSG00000198171", "ENSG00000168872", "ENSG00000165732", "ENSG00000145833", "ENSG00000213782", "ENSG00000023697", "ENSG00000105928", "ENSG00000065357", "ENSG00000102967", "ENSG00000105821", "ENSG00000197635") 
s2.1<- c("ENSG00000176978", "ENSG00000188641", "ENSG00000134755", "ENSG00000103356", "ENSG00000115380", "ENSG00000113790", "ENSG00000115211", "ENSG00000145191", "ENSG00000155849", "ENSG00000074800", "ENSG00000159023", "ENSG00000082397", "ENSG00000177106", "ENSG00000157554", "ENSG00000023318", "ENSG00000081177", "ENSG00000178896", "ENSG00000120699", "ENSG00000164687", "ENSG00000119812", "ENSG00000078098", "ENSG00000116120", "ENSG00000077942", "ENSG00000079459", "ENSG00000160752", "ENSG00000154783", "ENSG00000113578", "ENSG00000132589", "ENSG00000115414", "ENSG00000162998", "ENSG00000134363", "ENSG00000163430", "ENSG00000087086", "ENSG00000179271", "ENSG00000117308", "ENSG00000164574", "ENSG00000131386", "ENSG00000100626", "ENSG00000023909", "ENSG00000131459", "ENSG00000006625", "ENSG00000137563", "ENSG00000170266", "ENSG00000124767", "ENSG00000173221", "ENSG00000108010", "ENSG00000182512", "ENSG00000163938", "ENSG00000113552", "ENSG00000100522", "ENSG00000135677", "ENSG00000171723", "ENSG00000166123", "ENSG00000164294", "ENSG00000030582", "ENSG00000105447", "ENSG00000082701", "ENSG00000100577", "ENSG00000188342", "ENSG00000144366", "ENSG00000206172", "ENSG00000244734", "ENSG00000111911", "ENSG00000170144", "ENSG00000138668", "ENSG00000164070", "ENSG00000152137", "ENSG00000166033", "ENSG00000170801", "ENSG00000196305", "ENSG00000163453", "ENSG00000172349", "ENSG00000172458", "ENSG00000106348", "ENSG00000104613", "ENSG00000198001", "ENSG00000169047", "ENSG00000172183", "ENSG00000136156", "ENSG00000205726", "ENSG00000197256", "ENSG00000102781", "ENSG00000156113", "ENSG00000116685", "ENSG00000102753", "ENSG00000135862", "ENSG00000185896", "ENSG00000111716", "ENSG00000166816", "ENSG00000116977", "ENSG00000005156", "ENSG00000050405", "ENSG00000176619", "ENSG00000161654", "ENSG00000130332", "ENSG00000119681", "ENSG00000168056", "ENSG00000139329", "ENSG00000011009", "ENSG00000166963", "ENSG00000131711", "ENSG00000108984", "ENSG00000277443", "ENSG00000124159", "ENSG00000185432", "ENSG00000166482", "ENSG00000131446", "ENSG00000008394", "ENSG00000123342", "ENSG00000107186", "ENSG00000105926", "ENSG00000133030", "ENSG00000011028", "ENSG00000125901", "ENSG00000113048", "ENSG00000053372", "ENSG00000099810", "ENSG00000150712", "ENSG00000132382", "ENSG00000105357", "ENSG00000196586", "ENSG00000138119", "ENSG00000105835", "ENSG00000095380", "ENSG00000124479", "ENSG00000104419", "ENSG00000166136", "ENSG00000049759", "ENSG00000162599", "ENSG00000239672")
s2.2 <- c("ENSG00000243678", "ENSG00000123609", "ENSG00000152465", "ENSG00000055044", "ENSG00000139910", "ENSG00000119655", "ENSG00000215440", "ENSG00000182446", "ENSG00000159899", "ENSG00000181019", "ENSG00000184162", "ENSG00000148572", "ENSG00000065320", "ENSG00000090273", "ENSG00000136159", "ENSG00000132661", "ENSG00000136811", "ENSG00000127083", "ENSG00000178814", "ENSG00000141447", "ENSG00000070882", "ENSG00000117859", "ENSG00000149380", "ENSG00000170515", "ENSG00000111845", "ENSG00000163346", "ENSG00000183570", "ENSG00000148843", "ENSG00000067992", "ENSG00000107438", "ENSG00000131435", "ENSG00000139197", "ENSG00000178921", "ENSG00000141959", "ENSG00000247077", "ENSG00000101856", "ENSG00000067177", "ENSG00000139289", "ENSG00000173868", "ENSG00000153823", "ENSG00000087842", "ENSG00000123143", "ENSG00000188257", "ENSG00000124181", "ENSG00000198805", "ENSG00000102978", "ENSG00000105854", "ENSG00000133110", "ENSG00000131626", "ENSG00000196262", "ENSG00000066027", "ENSG00000188783", "ENSG00000126457", "ENSG00000100462", "ENSG00000136875", "ENSG00000147224", "ENSG00000157778", "ENSG00000117569", "ENSG00000148344", "ENSG00000134247", "ENSG00000124212", "ENSG00000196396", "ENSG00000132670", "ENSG00000173482", "ENSG00000105426", "ENSG00000183255", "ENSG00000177192", "ENSG00000089159", "ENSG00000183010", "ENSG00000100504", "ENSG00000116260", "ENSG00000112210", "ENSG00000132341", "ENSG00000099901", "ENSG00000106538", "ENSG00000113643", "ENSG00000159200", "ENSG00000133119", "ENSG00000155366", "ENSG00000177189", "ENSG00000116954", "ENSG00000133818", "ENSG00000114767", "ENSG00000147655", "ENSG00000133318", "ENSG00000196154", "ENSG00000177409", "ENSG00000184178", "ENSG00000006747", "ENSG00000132581", "ENSG00000117118", "ENSG00000100445", "ENSG00000157020", "ENSG00000138674", "ENSG00000075223", "ENSG00000138758", "ENSG00000166401", "ENSG00000135919", "ENSG00000109686", "ENSG00000147010", "ENSG00000174705", "ENSG00000104635", "ENSG00000102172", "ENSG00000135632", "ENSG00000145335", "ENSG00000077312", "ENSG00000172164", "ENSG00000169371", "ENSG00000124104", "ENSG00000089006", "ENSG00000095637", "ENSG00000128487", "ENSG00000115306", "ENSG00000075142", "ENSG00000100883", "ENSG00000102359", "ENSG00000111786", "ENSG00000114850", "ENSG00000118804", "ENSG00000101109", "ENSG00000165283", "ENSG00000023734", "ENSG00000125755", "ENSG00000159082", "ENSG00000136111", "ENSG00000106638", "ENSG00000184905", "ENSG00000204065", "ENSG00000204071", "ENSG00000120708", "ENSG00000169231", "ENSG00000051596", "ENSG00000100296", "ENSG00000134809", "ENSG00000105197", "ENSG00000035862", "ENSG00000142910", "ENSG00000137462", "ENSG00000125304", "ENSG00000135048", "ENSG00000136842", "ENSG00000138594", "ENSG00000123610", "ENSG00000078902", "ENSG00000025772", "ENSG00000067369", "ENSG00000136527", "ENSG00000135148", "ENSG00000126602", "ENSG00000132481", "ENSG00000162222", "ENSG00000123416", "ENSG00000183785", "ENSG00000025708", "ENSG00000154277", "ENSG00000118939", "ENSG00000132478", "ENSG00000183696", "ENSG00000090686", "ENSG00000160293", "ENSG00000162692", "ENSG00000213585", "ENSG00000136100", "ENSG00000139722", "ENSG00000138442", "ENSG00000065183", "ENSG00000196363", "ENSG00000196419", "ENSG00000139131", "ENSG00000065978", "ENSG00000140836", "ENSG00000100711", "ENSG00000109917")
cross_omic <- c(s2, s2.1, s2.2)

Enrichment Function

Use fisher’s exact test to compute enrichment of DE genes amongst the OA related gene sets.

enrich <- function(genes_of_interest, DEgenes, backgroundgenes){
     #fill contingency table
     DE_interest <- length(intersect(genes_of_interest, DEgenes))
     background_interest <- length(intersect(genes_of_interest, backgroundgenes))
     DE_non_interest <- length(DEgenes) - DE_interest
     background_non_interest <- length(backgroundgenes) - background_interest
     contingency_table <- matrix(c(DE_interest, background_interest, DE_non_interest, background_non_interest), nrow = 2, ncol = 2, byrow = TRUE)
     
     #perform test
     print(contingency_table)
     return(fisher.test(contingency_table, alternative="greater"))
}


enrich(fungenomic, DE_genes_ensg, other_genes_ensg)

     [,1] [,2]
[1,]   49  293
[2,]  938 9206


    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 0.001826
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
 1.242775      Inf
sample estimates:
odds ratio 
  1.641232

enrich(cross_omic, DE_genes_ensg, other_genes_ensg)

     [,1] [,2]
[1,]   42  296
[2,]  945 9203


    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 0.03712
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
 1.025732      Inf
sample estimates:
odds ratio 
  1.381779

Look at enrichments from permuted DE analysis (scrambled strain labels to make sure the FET results aren’t just because some genes are more likely to be detected as DE in these cells)

threshold <- 0.05
for(permutation in 11:20){
     print(permutation)
     DE_results <- permuted_results[[permutation]]
     DE_genes_ensg <- rownames(DE_results)[DE_results$qvalue < threshold]
     DE_genes <- gene_anno$external_gene_name[match(DE_genes_ensg, gene_anno$ensembl_gene_id)]
     other_genes_ensg <- rownames(DE_results)[DE_results$qvalue >= threshold]
     other_genes <- gene_anno$external_gene_name[match(other_genes_ensg, gene_anno$ensembl_gene_id)]

     print(enrich(fungenomic, DE_genes_ensg, other_genes_ensg))
     print(enrich(cross_omic, DE_genes_ensg, other_genes_ensg))
}

[1] 11
     [,1]  [,2]
[1,]    0   342
[2,]    0 10144

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 1
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
   0 Inf
sample estimates:
odds ratio 
         0 

     [,1]  [,2]
[1,]    0   338
[2,]    0 10148

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 1
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
   0 Inf
sample estimates:
odds ratio 
         0 

[1] 12
     [,1]  [,2]
[1,]    0   342
[2,]    0 10144

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 1
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
   0 Inf
sample estimates:
odds ratio 
         0 

     [,1]  [,2]
[1,]    0   338
[2,]    0 10148

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 1
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
   0 Inf
sample estimates:
odds ratio 
         0 

[1] 13
     [,1]  [,2]
[1,]    0   342
[2,]    0 10144

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 1
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
   0 Inf
sample estimates:
odds ratio 
         0 

     [,1]  [,2]
[1,]    0   338
[2,]    0 10148

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 1
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
   0 Inf
sample estimates:
odds ratio 
         0 

[1] 14
     [,1]  [,2]
[1,]    0   342
[2,]    7 10137

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 1
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
   0 Inf
sample estimates:
odds ratio 
         0 

     [,1]  [,2]
[1,]    0   338
[2,]    7 10141

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 1
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
   0 Inf
sample estimates:
odds ratio 
         0 

[1] 15
     [,1]  [,2]
[1,]    0   342
[2,]    0 10144

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 1
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
   0 Inf
sample estimates:
odds ratio 
         0 

     [,1]  [,2]
[1,]    0   338
[2,]    0 10148

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 1
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
   0 Inf
sample estimates:
odds ratio 
         0 

[1] 16
     [,1]  [,2]
[1,]    0   342
[2,]    0 10144

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 1
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
   0 Inf
sample estimates:
odds ratio 
         0 

     [,1]  [,2]
[1,]    0   338
[2,]    0 10148

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 1
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
   0 Inf
sample estimates:
odds ratio 
         0 

[1] 17
     [,1]  [,2]
[1,]    0   342
[2,]    0 10144

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 1
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
   0 Inf
sample estimates:
odds ratio 
         0 

     [,1]  [,2]
[1,]    0   338
[2,]    0 10148

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 1
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
   0 Inf
sample estimates:
odds ratio 
         0 

[1] 18
     [,1]  [,2]
[1,]    0   342
[2,]    0 10144

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 1
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
   0 Inf
sample estimates:
odds ratio 
         0 

     [,1]  [,2]
[1,]    0   338
[2,]    0 10148

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 1
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
   0 Inf
sample estimates:
odds ratio 
         0 

[1] 19
     [,1]  [,2]
[1,]    0   342
[2,]    0 10144

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 1
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
   0 Inf
sample estimates:
odds ratio 
         0 

     [,1]  [,2]
[1,]    0   338
[2,]    0 10148

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 1
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
   0 Inf
sample estimates:
odds ratio 
         0 

[1] 20
     [,1]  [,2]
[1,]    0   342
[2,]    0 10144

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 1
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
   0 Inf
sample estimates:
odds ratio 
         0 

     [,1]  [,2]
[1,]    0   338
[2,]    0 10148

    Fisher's Exact Test for Count Data

data:  contingency_table
p-value = 1
alternative hypothesis: true odds ratio is greater than 1
95 percent confidence interval:
   0 Inf
sample estimates:
odds ratio 
         0

sessionInfo()

R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats4    parallel  stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] org.Hs.eg.db_3.8.2          topGO_2.36.0               
 [3] SparseM_1.78                GO.db_3.8.2                
 [5] AnnotationDbi_1.46.0        graph_1.62.0               
 [7] RUVSeq_1.18.0               EDASeq_2.18.0              
 [9] ShortRead_1.42.0            GenomicAlignments_1.20.1   
[11] SummarizedExperiment_1.14.1 DelayedArray_0.10.0        
[13] matrixStats_0.57.0          Rsamtools_2.0.0            
[15] GenomicRanges_1.36.1        GenomeInfoDb_1.20.0        
[17] Biostrings_2.52.0           XVector_0.24.0             
[19] IRanges_2.18.3              S4Vectors_0.22.1           
[21] BiocParallel_1.18.1         Biobase_2.44.0             
[23] BiocGenerics_0.30.0         ggplot2_3.3.3              
[25] ashr_2.2-47                 tidyr_1.1.2                
[27] edgeR_3.26.5                dplyr_1.0.2                
[29] plyr_1.8.6                  limma_3.40.6               

loaded via a namespace (and not attached):
 [1] bitops_1.0-6           fs_1.3.1               bit64_0.9-7           
 [4] httr_1.4.2             progress_1.2.2         RColorBrewer_1.1-2    
 [7] rprojroot_2.0.2        tools_3.6.1            R6_2.5.0              
[10] irlba_2.3.3            DBI_1.1.0              colorspace_2.0-0      
[13] withr_2.3.0            prettyunits_1.1.1      tidyselect_1.1.0      
[16] bit_4.0.4              compiler_3.6.1         git2r_0.26.1          
[19] labeling_0.4.2         rtracklayer_1.44.0     scales_1.1.1          
[22] SQUAREM_2020.4         genefilter_1.66.0      DESeq_1.36.0          
[25] mixsqp_0.3-43          stringr_1.4.0          digest_0.6.27         
[28] rmarkdown_1.13         R.utils_2.9.0          pkgconfig_2.0.3       
[31] htmltools_0.5.0        invgamma_1.1           rlang_0.4.10          
[34] RSQLite_2.1.1          farver_2.0.3           generics_0.0.2        
[37] hwriter_1.3.2          R.oo_1.24.0            RCurl_1.98-1.1        
[40] magrittr_2.0.1         GenomeInfoDbData_1.2.1 Matrix_1.2-18         
[43] Rcpp_1.0.5             munsell_0.5.0          etrunct_0.1           
[46] lifecycle_0.2.0        R.methodsS3_1.8.1      stringi_1.4.6         
[49] whisker_0.3-2          yaml_2.2.1             MASS_7.3-52           
[52] zlibbioc_1.30.0        grid_3.6.1             blob_1.2.0            
[55] promises_1.1.1         crayon_1.3.4           lattice_0.20-41       
[58] splines_3.6.1          GenomicFeatures_1.36.3 annotate_1.62.0       
[61] hms_0.5.3              locfit_1.5-9.1         knitr_1.23            
[64] pillar_1.4.7           biomaRt_2.40.1         geneplotter_1.62.0    
[67] XML_3.98-1.20          glue_1.4.2             evaluate_0.14         
[70] latticeExtra_0.6-28    vctrs_0.3.6            httpuv_1.5.1          
[73] gtable_0.3.0           purrr_0.3.4            xfun_0.8              
[76] aroma.light_3.14.0     xtable_1.8-4           later_1.1.0.1         
[79] survival_2.44-1.1      truncnorm_1.0-8        tibble_3.0.4          
[82] memoise_1.1.0          workflowr_1.6.2        statmod_1.4.32        
[85] ellipsis_0.3.1

DEanalysis_bulkRNA

Anthony Hung

2021-01-19