Model DESeq2

RNA-Seq s Bioconductorem v R

Mary Piper

Bioinformatics Consultant and Trainer

Model DESeq2

Teorie DE

Model DESeq2 – vztah průměr–rozptyl

# Syntax for apply()
apply(data, rows/columns, function_to_apply)

# Calculating mean for each gene (each row)
mean_counts <- apply(wt_rawcounts[, 1:3], 1, mean)

# Calculating variance for each gene (each row)
variance_counts <- apply(wt_rawcounts[, 1:3], 1, var)

Model DESeq2 – disperze

Vizualizace vztahu mezi průměrem a rozptylem:

# Creating data frame with mean and variance for every gene
df <- data.frame(mean_counts, variance_counts)

ggplot(df) +
        geom_point(aes(x=mean_counts, y=variance_counts)) + 
        scale_y_log10() +
        scale_x_log10() +
        xlab("Mean counts per gene") +
        ylab("Variance per gene")

Model DESeq2 – disperze

Vztah průměr–rozptyl

Model DESeq2 – disperze

$Var$: rozptyl

$\mu$: průměr

$\alpha$: disperze

Vzorec pro disperzi: $Var = \mu + \alpha * \mu^{2}$

Vztah mezi průměrem, rozptylem a disperzí:

$$\uparrow variance \Rightarrow \uparrow dispersion$$

$$\uparrow mean \Rightarrow \downarrow dispersion$$

Model DESeq2 – disperze

# Plot dispersion estimates
plotDispEsts(dds_wt)

Graf disperze

Model DESeq2 – disperze

bad_dispersion_plots

Lass uns üben!

RNA-Seq s Bioconductorem v R