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05_chow_hfd_minimal.md

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Download data

wget https://github.com/lcdb/genomics-workshop-2018/raw/master/extras/bedtools-and-deseq.tsv

Load and inspect

# if downloaded separately:
# df <- read.table('bedtools-and-deseq.tsv')

# For the purposes of reproductibility:
df <- read.table('https://github.com/lcdb/genomics-workshop-2018/raw/master/extras/bedtools-and-deseq.tsv')
head(df)
##           baseMean log2FoldChange      lfcSE        pvalue          padj
## Serpina6  5895.825       2.489289 0.05453799  0.000000e+00  0.000000e+00
## Rhobtb1   3291.547       1.952765 0.06116129 1.087320e-223 9.723899e-220
## Saa4     21111.122       2.960472 0.12378740 2.099070e-126 1.251466e-122
## Asl      42410.548      -1.721420 0.07739541 1.351328e-109 6.042464e-106
## Bhlhe40   2310.291       1.996435 0.09101069 1.171360e-106 4.190189e-103
## Aacs      1422.679       3.272415 0.15590378  8.100041e-98  2.414622e-94
##          direction gained_ac     gene
## Serpina6        up     FALSE Serpina6
## Rhobtb1         up     FALSE  Rhobtb1
## Saa4            up     FALSE     Saa4
## Asl             dn     FALSE      Asl
## Bhlhe40         up     FALSE  Bhlhe40
## Aacs            up     FALSE     Aacs

nrow(df)
## [1] 23227

ncol(df)
## [1] 8

dim(df)
## [1] 23227     8

Inspection

How many up/down/unchanged genes?

head(df$direction == 'up')
## [1]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE

sum(df$direction == 'up')
## [1] 3223

sum(df$direction == 'dn')
## [1] 2691

Percent up/down:

sum(df$direction == 'up') / nrow(df)
## [1] 0.1387609

sum(df$direction == 'dn') / nrow(df)
## [1] 0.1158565

How to get number of genes with gained H3K27ac?

Plotting

library(ggplot2)
ggplot(df) + geom_point(mapping=aes(x=baseMean, y=log2FoldChange))
## Warning: Removed 5273 rows containing missing values (geom_point).

We should put baseMean on a log scale:

ggplot(df) + geom_point(mapping=aes(x=log10(baseMean), y=log2FoldChange))
## Warning: Removed 5273 rows containing missing values (geom_point).

Let's color by direction:

ggplot(df) + geom_point(mapping=aes(x=log10(baseMean), y=log2FoldChange, color=direction))
## Warning: Removed 5273 rows containing missing values (geom_point).

Heres a nicer way of coloring:

ggplot(df) + geom_point(mapping=aes(x=log10(baseMean), y=log2FoldChange, color=direction)) +
    scale_color_manual(
        values=c('un'='#888888', 'up'='firebrick', 'dn'='dodgerblue4')
    )
## Warning: Removed 5273 rows containing missing values (geom_point).

Now let's color by H3k27ac instead of direction.

ggplot(df) +
    geom_point(mapping=aes(x=log10(baseMean), y=log2FoldChange, color=gained_ac))
## Warning: Removed 5273 rows containing missing values (geom_point).

Hmm, it looks like the TRUE ones are being hidden. Here is how we can plot layers in ggplot, to make sure the TRUE gets plotted after the FALSE. The key is in the data=subset(...) part for the geom:

ggplot(df, mapping=aes(x=log10(baseMean), y=log2FoldChange)) +
    geom_point(data=subset(df, !df$gained_ac)) +
    geom_point(data=subset(df, df$gained_ac), color='red')
## Warning: Removed 5267 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

It might be nice to combine the previous plots together:

ggplot(df, mapping=aes(x=log10(baseMean), y=log2FoldChange)) +
    geom_point(mapping=aes(color=direction)) +
    scale_color_manual(
        values=c('un'='#888888', 'up'='firebrick', 'dn'='dodgerblue4')) +
    geom_point(
      data=subset(df, df$gained_ac),
      color='orange', size=.5)
## Warning: Removed 5273 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

Or facet it:

ggplot(df, mapping=aes(x=log10(baseMean), y=log2FoldChange)) +
    geom_point(mapping=aes(color=direction)) +
    scale_color_manual(
        values=c('un'='#888888', 'up'='firebrick', 'dn'='dodgerblue4')) +
    facet_wrap(~gained_ac)
## Warning: Removed 5273 rows containing missing values (geom_point).

Or a volcano plot:

ggplot(df, mapping=aes(x=log2FoldChange, y=-log10(padj))) +
    geom_point(mapping=aes(color=direction)) +
    scale_color_manual(
        values=c('un'='#888888', 'up'='firebrick', 'dn'='dodgerblue4')) +
    geom_point(
      data=subset(df, df$gained_ac),
      color='orange', size=.5)
## Warning: Removed 5341 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).


ggplot(df, mapping=aes(x=log2FoldChange, y=-log10(padj))) +
    geom_point(mapping=aes(color=direction)) +
    scale_color_manual(
        values=c('un'='#888888', 'up'='firebrick', 'dn'='dodgerblue4')) +
    facet_wrap(~gained_ac)
## Warning: Removed 5341 rows containing missing values (geom_point).