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)
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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))