| plotPrecision {DRIMSeq} | R Documentation |
Precision versus mean expression plot
plotPrecision(x, ...) ## S4 method for signature 'dmDSprecision' plotPrecision(x) ## S4 method for signature 'dmSQTLprecision' plotPrecision(x)
x |
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... |
Other parameters that can be defined by methods using this generic. |
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.
Malgorzata Nowicka
plotData, plotProportions,
plotPValues
# --------------------------------------------------------------------------
# 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))