Dispersion versus mean expression plot

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Description

Dispersion versus mean expression plot

Usage

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plotDispersion(x, ...)

## S4 method for signature 'dmDSdispersion'
plotDispersion(x, out_dir = NULL)

## S4 method for signature 'dmSQTLdispersion'
plotDispersion(x, out_dir = NULL)

Arguments

x

dmDSdispersion or dmSQTLdispersion object.

...

Other parameters that can be defined by methods using this generic.

out_dir

Character string that is used to save the plot in paste0(out_dir, plot_name, ".pdf") file. plot_name depends on type of a plot produced, for example, plot_name = "hist_features" for histogram with number of features per gene. If NULL, the plot is returned as ggplot object and can be further modified, for example, using theme().

Value

Scatterplot of Dirichlet-multinomial gene-wise dispersion versus mean gene expression. Both variables are scaled with log10. One dot in the plot corresponds to a gene.

Author(s)

Malgorzata Nowicka

See Also

data_dmDSdata, data_dmSQTLdata, plotData, plotFit, plotTest

Examples

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###################################
### Differential splicing analysis
###################################
# If possible, use BPPARAM = BiocParallel::MulticoreParam() with more workers

d <- data_dmDSdata

### Filtering
# Check what is the minimal number of replicates per condition 
table(samples(d)$group)
d <- dmFilter(d, min_samps_gene_expr = 7, min_samps_feature_expr = 3, 
 min_samps_feature_prop = 0)

### Calculate dispersion
d <- dmDispersion(d, BPPARAM = BiocParallel::SerialParam())

plotDispersion(d)