plotPValues: Plot p-value distribution

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

Description

Plot p-value distribution

Usage

plotPValues(x, ...)

## S4 method for signature 'dmDStest'
plotPValues(x, level = "gene")

## S4 method for signature 'dmSQTLtest'
plotPValues(x)

Arguments

x	dmDStest or dmSQTLtest object.
...	Other parameters that can be defined by methods using this generic.
level	Character specifying which type of results to return. Possible values "gene" or "feature".

Value

Plot a histogram of p-values.

Author(s)

Malgorzata Nowicka

See Also

plotData, plotPrecision, plotProportions

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))

## Fit full model proportions
d <- dmFit(d, design = design_full)

## Get fitted proportions
head(proportions(d))
## Get the DM regression coefficients (gene-level) 
head(coefficients(d))
## Get the BB regression coefficients (feature-level) 
head(coefficients(d), level = "feature")

## Fit null model proportions and perform the LR test to detect DTU
d <- dmTest(d, coef = "groupKD")

## Plot the gene-level p-values
plotPValues(d)

## Get the gene-level results
head(results(d))

## Plot feature proportions for a top DTU gene
res <- results(d)
res <- res[order(res$pvalue, decreasing = FALSE), ]

top_gene_id <- res$gene_id[1]

plotProportions(d, gene_id = top_gene_id, group_variable = "group")

plotProportions(d, gene_id = top_gene_id, group_variable = "group", 
  plot_type = "lineplot")

plotProportions(d, gene_id = top_gene_id, group_variable = "group", 
  plot_type = "ribbonplot")

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