plotPrecision: Precision versus mean expression plot

In DRIMSeq: Differential transcript usage and tuQTL analyses with Dirichlet-multinomial model in RNA-seq

Description

Precision versus mean expression plot

Usage

plotPrecision(x, ...)

## S4 method for signature 'dmDSprecision'
plotPrecision(x)

## S4 method for signature 'dmSQTLprecision'
plotPrecision(x)

Arguments

x	dmDSprecision or dmSQTLprecision object.
...	Other parameters that can be defined by methods using this generic.

Value

Normally in the differential analysis based on RNA-seq data, such plot has dispersion parameter plotted on the y-axis. Here, the y-axis represents precision since in the Dirichlet-multinomial model this is the parameter that is directly estimated. It is important to keep in mind that the precision parameter (gamma0) is inverse proportional to dispersion (theta): theta = 1 / (1 + gamma0). In RNA-seq data, we can typically observe a trend where the dispersion decreases (here, precision increases) for genes with higher mean expression.

Author(s)

Malgorzata Nowicka

See Also

plotData, plotProportions, plotPValues

Examples

# --------------------------------------------------------------------------
# Create dmDSdata object 
# --------------------------------------------------------------------------
## Get kallisto transcript counts from the 'PasillaTranscriptExpr' package

library(PasillaTranscriptExpr)

data_dir  <- system.file("extdata", package = "PasillaTranscriptExpr")

## Load metadata
pasilla_metadata <- read.table(file.path(data_dir, "metadata.txt"), 
header = TRUE, as.is = TRUE)

## Load counts
pasilla_counts <- read.table(file.path(data_dir, "counts.txt"), 
header = TRUE, as.is = TRUE)

## Create a pasilla_samples data frame
pasilla_samples <- data.frame(sample_id = pasilla_metadata$SampleName, 
  group = pasilla_metadata$condition)
levels(pasilla_samples$group)

## Create a dmDSdata object
d <- dmDSdata(counts = pasilla_counts, samples = pasilla_samples)

## Use a subset of genes, which is defined in the following file
gene_id_subset <- readLines(file.path(data_dir, "gene_id_subset.txt"))

d <- d[names(d) %in% gene_id_subset, ]

# --------------------------------------------------------------------------
# Differential transcript usage analysis - simple two group comparison 
# --------------------------------------------------------------------------

## 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_gene_expr = 10, min_feature_expr = 10)

plotData(d)

## Create the design matrix
design_full <- model.matrix(~ group, data = samples(d))

## To make the analysis reproducible
set.seed(123)
## Calculate precision
d <- dmPrecision(d, design = design_full)

plotPrecision(d)

head(mean_expression(d))
common_precision(d)
head(genewise_precision(d))

DRIMSeq documentation built on Nov. 8, 2020, 8:25 p.m.