DMC analysis

We will perform for the TCGA-BRCA data set DMC analysis using a two-tailed t-test and one-tailed t-test to compare the results.

library(ELMER)

# Reload TCGA BRCA dataset
load("~/paper_elmer/mae_BRCA_hg38_450K_no_ffpe.rda")

# one tailed test: probes hypo methylated in group1 vs group2
Hypo.probe <- get.diff.meth(mae, 
                            diff.dir="hypo",
                            minSubgroupFrac = 1,
                            group.col = "definition", 
                            group1 = "Primary solid Tumor", 
                            group2 = "Solid Tissue Normal",
                            sig.dif = 0.3) # get hypomethylated probes

# one tailed test: probes hyper methylated in group1 vs group2
Hyper.probe <- get.diff.meth(mae, 
                             diff.dir="hyper",
                             minSubgroupFrac = 1,
                             group.col = "definition", 
                             group1 = "Primary solid Tumor", 
                             group2 = "Solid Tissue Normal",
                             sig.dif = 0.3) # get hypermethylated probes

# two tailed test: probes differently methylated in group1 vs group2
diff.probe <- get.diff.meth(mae, 
                            diff.dir= "both",
                            minSubgroupFrac = 1,
                            group.col = "definition", 
                            group1 = "Primary solid Tumor", 
                            group2 = "Solid Tissue Normal",
                            sig.dif = 0.3) # get differentlly methylated probes

Results

Hypo.probe
Hyper.probe
diff.probe

Volcano plots

One tailed test

One tailed test - Hypermethylated probes

One tailed test - Hypermethylated probes

One tailed test - Hypomethylated probes

One tailed test - Hypomethylated probes

Two tailed test

Two tailed test - Differently methylated probes

Two tailed test - Differently methylated probes

Number of probes identified

df <- t(data.frame("Hypomethylated probes [one-tailed]" = length(Hypo.probe$probe),
           "Hypermethylated probes  [one-tailed]" = length(Hyper.probe$probe),
           "Differently methylated probes  [two-tailed]" = length(diff.probe$probe)))
colnames(df) <- "# probes"
as.data.frame(df)

Intersection of probes identified

Here we check that all our probes identified by two-tailed t-test the same of the two one-tailed t-test.

# Are all hypo methylated probes identified using a one-tailed test also found using the two-tailed test? 
table(Hypo.probe$probe %in% diff.probe$probe)

TRUE 
1446 
# Are all hypermethylated probes identified using a one-tailed test also found using the two-tailed test? 
table(Hyper.probe$probe %in% diff.probe$probe) 

TRUE 
1077 
# Are all differently methylated probes identified using two-tailed test also found using two one-tailed test? 
all(diff.probe$probe %in% c(Hyper.probe$probe,Hypo.probe$probe)) 
[1] TRUE

Check p-values dissimilarity of significant probes (Euclidian distnace - 0 means equal)

df <- t(data.frame(
        as.numeric(dist(rbind(diff.probe[Hypo.probe$probe,]$pvalue, Hypo.probe$pvalue))),
        as.numeric(dist(rbind(diff.probe[Hypo.probe$probe,]$adjust.p, Hypo.probe$adjust.p))),
        as.numeric(dist(rbind(diff.probe[Hyper.probe$probe,]$pvalue, Hyper.probe$pvalue))), 
        as.numeric(dist(rbind(diff.probe[Hyper.probe$probe,]$adjust.p, Hyper.probe$adjust.p)))
))
colnames(df) <- "distance"
rownames(df) <- c("eucledian distance pvalue hypomethylated probes [two-tailed vs one tailed test]",
                  "eucledian distance pvalue adjusted hypomethylated probes [two-tailed vs one tailed test]",
                  "eucledian distance pvalues hypermethylated probes [two-tailed vs one tailed test]",
                  "eucledian distance pvalues adjusted hypermethylated probes [two-tailed vs one tailed test]")
as.data.frame(df)

Check difference of all probes p-values

The plot below shows that the difference of the raw pvalues for the significant probes is really low and would not affect which were selected the significant probes.

Raw p-values

data <- data.frame(probe = one_tailed_hypo$probe,
                  x = one_tailed_hypo$pvalue,
                  significant = as.factor(ifelse(one_tailed_hypo$probe %in% Hypo.probe$probe, "Significant","Insignificant")) ,
                  y = abs(two_tailed[match(one_tailed_hypo$probe,two_tailed$probe),]$pvalue-one_tailed_hypo$pvalue))
data <- subset(data, x < 0.5)
data$y <- -log10(data$y)
data$x <- -log10(data$x)
ggplot(data,aes(x=x,
                y=y,
                shape = significant,
                color = significant)) + 
  geom_point() + 
  geom_point(data = subset(data, significant == 'Significant'),   
             aes(x = x, 
                 y = y, 
                 color = significant)
             ) +
  theme_bw() + 
  scale_color_manual(values = c("Significant" = '#ff0000',
                                'Insignificant' = '#000000'),
                     name = "Probe identified as:") + 
  scale_shape_manual(values = c('Significant' = 17, 
                                'Insignificant' = 16),
                     name="Probe identified as:") +
  labs(title = "Comparing one-tailed  (hypo direction) and two-tailed raw-pvalues",
       subtitle = "Showing only probes raw p-value < 0.5.",
       y = "-log10(Difference of raw p-values Two-tailed t-test - One tailed t-test)", 
       x = "-log10(Raw P-value - One tailed t-test)") 

data <- data.frame(probe = one_tailed_hyper$probe,
                  x = one_tailed_hyper$pvalue,
                  significant = as.factor(ifelse(one_tailed_hyper$probe %in% Hyper.probe$probe, "Significant","Insignificant")) ,
                  y = abs(two_tailed[match(one_tailed_hyper$probe,two_tailed$probe),]$pvalue-one_tailed_hyper$pvalue))
data <- subset(data, x < 0.5)
data$y <- -log10(data$y)
data$x <- -log10(data$x)
ggplot(data,aes(x=x,
                y=y,
                shape = significant,
                color = significant)) + 
  geom_point() + 
  geom_point(data = subset(data, significant == 'Significant'),   
             aes(x = x, 
                 y = y, 
                 color = significant)
             ) +
  theme_bw() + 
  scale_color_manual(values = c("Significant" = '#ff0000',
                                'Insignificant' = '#000000'),
                     name = "Probe identified as:") + 
  scale_shape_manual(values = c('Significant' = 17, 
                                'Insignificant' = 16),
                     name="Probe identified as:") +
  labs(title = "Comparing one-tailed  (hyper direction) and two-tailed raw-pvalues",
       subtitle = "Showing only probes raw p-value < 0.5.",
       y = "-log10(Difference of raw p-values Two-tailed t-test - One tailed t-test)", 
       x = "-log10(Raw P-value - One tailed t-test)")

Adjusted p-values

data <- data.frame(probe = one_tailed_hypo$probe,
                  x = one_tailed_hypo$adjust.p,
                  significant = as.factor(ifelse(one_tailed_hypo$probe %in% Hypo.probe$probe, "Significant","Insignificant")) ,
                  y = abs(two_tailed[match(one_tailed_hypo$probe,two_tailed$probe),]$adjust.p-one_tailed_hypo$adjust.p))
data <- subset(data, x < 0.5)
data$y <- -log10(data$y)
data$x <- -log10(data$x)
ggplot(data,aes(x=x,
                y=y,
                shape = significant,
                color = significant)) + 
  geom_point() + 
  geom_point(data = subset(data, significant == 'Significant'),   
             aes(x = x, 
                 y = y, 
                 color = significant)
             ) +
  theme_bw() + 
  scale_color_manual(values = c("Significant" = '#ff0000',
                                'Insignificant' = '#000000'),
                     name = "Probe identified as:") + 
  scale_shape_manual(values = c('Significant' = 17, 
                                'Insignificant' = 16),
                     name="Probe identified as:") +
  geom_vline(xintercept=-log10(0.01), linetype="dashed", color = "blue") +
  labs(title = "Comparing one-tailed  (hypo direction) and two-tailed adjusted-pvalues",
       subtitle = "Showing only probes adjusted p-value < 0.5. Dashed blue line: 0.01 cut-off",
       y = "-log10(|Difference of raw p-values Two-tailed t-test - One tailed t-test|)", 
       x = "-log10(Adjusted P-value - One tailed t-test)") 

data <- data.frame(probe = one_tailed_hyper$probe,
                  x = one_tailed_hyper$adjust.p,
                  significant = as.factor(ifelse(one_tailed_hyper$probe %in% Hyper.probe$probe, "Significant","Insignificant")) ,
                  y = abs(two_tailed[match(one_tailed_hyper$probe,two_tailed$probe),]$adjust.p-one_tailed_hyper$adjust.p))
data <- subset(data, x < 0.5)
data$y <- -log10(data$y)
data$x <- -log10(data$x)
ggplot(data,aes(x=x,
                y=y,
                shape = significant,
                color = significant)) + 
  geom_point() + 
  geom_point(data = subset(data, significant == 'Significant'),   
             aes(x = x, 
                 y = y, 
                 color = significant)
             ) +
  theme_bw() + 
  scale_color_manual(values = c("Significant" = '#ff0000',
                                'Insignificant' = '#000000'),
                     name = "Probe identified as:") + 
  scale_shape_manual(values = c('Significant' = 17, 
                                'Insignificant' = 16),
                     name="Probe identified as:") +
  geom_vline(xintercept=-log10(0.01), linetype="dashed", color = "blue") +
  labs(title = "Comparing one-tailed  (hyper direction) and two-tailed adjusted-pvalues",
       subtitle = "Showing only probes adjusted p-value < 0.5. Dashed blue line: 0.01 cut-off",
       y = "-log10(|Difference of raw p-values Two-tailed t-test - One tailed t-test|)", 
       x = "-log10(Adjusted P-value - One tailed t-test)")

Check difference of mean DNA methylation

range(diff.probe$Primary.solid.Tumor_Minus_Solid.Tissue.Normal)
[1] -0.4839175  0.5459320
range(Hyper.probe$Primary.solid.Tumor_Minus_Solid.Tissue.Normal)
[1] 0.3000309 0.5459320
range(Hypo.probe$Primary.solid.Tumor_Minus_Solid.Tissue.Normal)
[1] -0.4839175 -0.3000173
---
title: "ELMER - two-tailed vs one-tailed test"
output: html_notebook
---

# DMC analysis

We will perform for the TCGA-BRCA data set DMC analysis using a two-tailed t-test and one-tailed t-test to compare the results.

```{r, eval=FALSE}
library(ELMER)

# Reload TCGA BRCA dataset
load("~/paper_elmer/mae_BRCA_hg38_450K_no_ffpe.rda")

# one tailed test: probes hypo methylated in group1 vs group2
Hypo.probe <- get.diff.meth(mae, 
                            diff.dir="hypo",
                            minSubgroupFrac = 1,
                            group.col = "definition", 
                            group1 = "Primary solid Tumor", 
                            group2 = "Solid Tissue Normal",
                            sig.dif = 0.3) # get hypomethylated probes

# one tailed test: probes hyper methylated in group1 vs group2
Hyper.probe <- get.diff.meth(mae, 
                             diff.dir="hyper",
                             minSubgroupFrac = 1,
                             group.col = "definition", 
                             group1 = "Primary solid Tumor", 
                             group2 = "Solid Tissue Normal",
                             sig.dif = 0.3) # get hypermethylated probes

# two tailed test: probes differently methylated in group1 vs group2
diff.probe <- get.diff.meth(mae, 
                            diff.dir= "both",
                            minSubgroupFrac = 1,
                            group.col = "definition", 
                            group1 = "Primary solid Tumor", 
                            group2 = "Solid Tissue Normal",
                            sig.dif = 0.3) # get differentlly methylated probes
```

```{r, include=FALSE}
load(file = "test.rda")
library(readr)
two_tailed <- read_csv("getMethdiff.both.probes.csv")
one_tailed_hyper <- read_csv("getMethdiff.hyper.probes.csv")
one_tailed_hypo <- read_csv("getMethdiff.hypo.probes.csv")
```

## Results
```{r}
Hypo.probe
Hyper.probe
diff.probe
```

## Volcano plots
### One tailed test
![One tailed test - Hypermethylated probes](volcanoPlot.probes.hyper.png)

![One tailed test - Hypomethylated probes](volcanoPlot.probes.hypo.png)

### Two tailed test 

![Two tailed test - Differently methylated probes](volcanoPlot.probes.two_tailed.png)


## Number of probes identified
```{r, cols.print=20}
df <- t(data.frame("Hypomethylated probes [one-tailed]" = length(Hypo.probe$probe),
           "Hypermethylated probes  [one-tailed]" = length(Hyper.probe$probe),
           "Differently methylated probes  [two-tailed]" = length(diff.probe$probe)))
colnames(df) <- "# probes"
as.data.frame(df)
```

## Intersection of probes identified

Here we check that all our probes identified by two-tailed t-test the same of the two one-tailed t-test. 

```{r, cols.print=20}
# Are all hypo methylated probes identified using a one-tailed test also found using the two-tailed test? 
table(Hypo.probe$probe %in% diff.probe$probe)
# Are all hypermethylated probes identified using a one-tailed test also found using the two-tailed test? 
table(Hyper.probe$probe %in% diff.probe$probe) 
# Are all differently methylated probes identified using two-tailed test also found using two one-tailed test? 
all(diff.probe$probe %in% c(Hyper.probe$probe,Hypo.probe$probe)) 
```

## Check p-values  dissimilarity of significant probes (Euclidian distnace - 0 means equal)
```{r, cols.print=20}
df <- t(data.frame(
        as.numeric(dist(rbind(diff.probe[Hypo.probe$probe,]$pvalue, Hypo.probe$pvalue))),
        as.numeric(dist(rbind(diff.probe[Hypo.probe$probe,]$adjust.p, Hypo.probe$adjust.p))),
        as.numeric(dist(rbind(diff.probe[Hyper.probe$probe,]$pvalue, Hyper.probe$pvalue))), 
        as.numeric(dist(rbind(diff.probe[Hyper.probe$probe,]$adjust.p, Hyper.probe$adjust.p)))
))
colnames(df) <- "distance"
rownames(df) <- c("eucledian distance pvalue hypomethylated probes [two-tailed vs one tailed test]",
                  "eucledian distance pvalue adjusted hypomethylated probes [two-tailed vs one tailed test]",
                  "eucledian distance pvalues hypermethylated probes [two-tailed vs one tailed test]",
                  "eucledian distance pvalues adjusted hypermethylated probes [two-tailed vs one tailed test]")
as.data.frame(df)
```

##  Check difference of all probes p-values

The plot below shows that the difference of the raw pvalues for the significant probes is really low and would not affect which were selected the significant probes.

### Raw p-values
```{r,fig.height=7}
data <- data.frame(probe = one_tailed_hypo$probe,
                  x = one_tailed_hypo$pvalue,
                  significant = as.factor(ifelse(one_tailed_hypo$probe %in% Hypo.probe$probe, "Significant","Insignificant")) ,
                  y = abs(two_tailed[match(one_tailed_hypo$probe,two_tailed$probe),]$pvalue-one_tailed_hypo$pvalue))

data <- subset(data, x < 0.5)
data$y <- -log10(data$y)
data$x <- -log10(data$x)

ggplot(data,aes(x=x,
                y=y,
                shape = significant,
                color = significant)) + 
  geom_point() + 
  geom_point(data = subset(data, significant == 'Significant'),   
             aes(x = x, 
                 y = y, 
                 color = significant)
             ) +
  theme_bw() + 
  scale_color_manual(values = c("Significant" = '#ff0000',
                                'Insignificant' = '#000000'),
                     name = "Probe identified as:") + 
  scale_shape_manual(values = c('Significant' = 17, 
                                'Insignificant' = 16),
                     name="Probe identified as:") +
  labs(title = "Comparing one-tailed  (hypo direction) and two-tailed raw-pvalues",
       subtitle = "Showing only probes raw p-value < 0.5.",
       y = "-log10(Difference of raw p-values Two-tailed t-test - One tailed t-test)", 
       x = "-log10(Raw P-value - One tailed t-test)") 

data <- data.frame(probe = one_tailed_hyper$probe,
                  x = one_tailed_hyper$pvalue,
                  significant = as.factor(ifelse(one_tailed_hyper$probe %in% Hyper.probe$probe, "Significant","Insignificant")) ,
                  y = abs(two_tailed[match(one_tailed_hyper$probe,two_tailed$probe),]$pvalue-one_tailed_hyper$pvalue))

data <- subset(data, x < 0.5)
data$y <- -log10(data$y)
data$x <- -log10(data$x)

ggplot(data,aes(x=x,
                y=y,
                shape = significant,
                color = significant)) + 
  geom_point() + 
  geom_point(data = subset(data, significant == 'Significant'),   
             aes(x = x, 
                 y = y, 
                 color = significant)
             ) +
  theme_bw() + 
  scale_color_manual(values = c("Significant" = '#ff0000',
                                'Insignificant' = '#000000'),
                     name = "Probe identified as:") + 
  scale_shape_manual(values = c('Significant' = 17, 
                                'Insignificant' = 16),
                     name="Probe identified as:") +
  labs(title = "Comparing one-tailed  (hyper direction) and two-tailed raw-pvalues",
       subtitle = "Showing only probes raw p-value < 0.5.",
       y = "-log10(Difference of raw p-values Two-tailed t-test - One tailed t-test)", 
       x = "-log10(Raw P-value - One tailed t-test)") 

```

### Adjusted p-values
```{r,fig.height=7}
data <- data.frame(probe = one_tailed_hypo$probe,
                  x = one_tailed_hypo$adjust.p,
                  significant = as.factor(ifelse(one_tailed_hypo$probe %in% Hypo.probe$probe, "Significant","Insignificant")) ,
                  y = abs(two_tailed[match(one_tailed_hypo$probe,two_tailed$probe),]$adjust.p-one_tailed_hypo$adjust.p))

data <- subset(data, x < 0.5)
data$y <- -log10(data$y)
data$x <- -log10(data$x)

ggplot(data,aes(x=x,
                y=y,
                shape = significant,
                color = significant)) + 
  geom_point() + 
  geom_point(data = subset(data, significant == 'Significant'),   
             aes(x = x, 
                 y = y, 
                 color = significant)
             ) +
  theme_bw() + 
  scale_color_manual(values = c("Significant" = '#ff0000',
                                'Insignificant' = '#000000'),
                     name = "Probe identified as:") + 
  scale_shape_manual(values = c('Significant' = 17, 
                                'Insignificant' = 16),
                     name="Probe identified as:") +
  geom_vline(xintercept=-log10(0.01), linetype="dashed", color = "blue") +
  labs(title = "Comparing one-tailed  (hypo direction) and two-tailed adjusted-pvalues",
       subtitle = "Showing only probes adjusted p-value < 0.5. Dashed blue line: 0.01 cut-off",
       y = "-log10(|Difference of raw p-values Two-tailed t-test - One tailed t-test|)", 
       x = "-log10(Adjusted P-value - One tailed t-test)") 

data <- data.frame(probe = one_tailed_hyper$probe,
                  x = one_tailed_hyper$adjust.p,
                  significant = as.factor(ifelse(one_tailed_hyper$probe %in% Hyper.probe$probe, "Significant","Insignificant")) ,
                  y = abs(two_tailed[match(one_tailed_hyper$probe,two_tailed$probe),]$adjust.p-one_tailed_hyper$adjust.p))

data <- subset(data, x < 0.5)
data$y <- -log10(data$y)
data$x <- -log10(data$x)

ggplot(data,aes(x=x,
                y=y,
                shape = significant,
                color = significant)) + 
  geom_point() + 
  geom_point(data = subset(data, significant == 'Significant'),   
             aes(x = x, 
                 y = y, 
                 color = significant)
             ) +
  theme_bw() + 
  scale_color_manual(values = c("Significant" = '#ff0000',
                                'Insignificant' = '#000000'),
                     name = "Probe identified as:") + 
  scale_shape_manual(values = c('Significant' = 17, 
                                'Insignificant' = 16),
                     name="Probe identified as:") +
  geom_vline(xintercept=-log10(0.01), linetype="dashed", color = "blue") +
  labs(title = "Comparing one-tailed  (hyper direction) and two-tailed adjusted-pvalues",
       subtitle = "Showing only probes adjusted p-value < 0.5. Dashed blue line: 0.01 cut-off",
       y = "-log10(|Difference of raw p-values Two-tailed t-test - One tailed t-test|)", 
       x = "-log10(Adjusted P-value - One tailed t-test)") 

```

## Check difference of mean DNA methylation 
```{r, cols.print=20}
range(diff.probe$Primary.solid.Tumor_Minus_Solid.Tissue.Normal)
range(Hyper.probe$Primary.solid.Tumor_Minus_Solid.Tissue.Normal)
range(Hypo.probe$Primary.solid.Tumor_Minus_Solid.Tissue.Normal)
```

