dmDSprecision-class {DRIMSeq} | R Documentation |
dmDSprecision extends the dmDSdata
by adding the
precision estimates of the Dirichlet-multinomial distribution used to model
the feature (e.g., transcript, exon, exonic bin) counts for each gene in the
differential usage analysis. Result of calling the dmPrecision
function.
## S4 method for signature 'dmDSprecision' design(object, type = "precision") mean_expression(x, ...) ## S4 method for signature 'dmDSprecision' mean_expression(x) common_precision(x, ...) ## S4 method for signature 'dmDSprecision' common_precision(x) common_precision(x) <- value ## S4 replacement method for signature 'dmDSprecision' common_precision(x) <- value genewise_precision(x, ...) ## S4 method for signature 'dmDSprecision' genewise_precision(x) genewise_precision(x) <- value ## S4 replacement method for signature 'dmDSprecision' genewise_precision(x) <- value
type |
Character indicating which design matrix should be returned.
Possible values |
x, object |
dmDSprecision object. |
... |
Other parameters that can be defined by methods using this generic. |
value |
Values that replace current attributes. |
Normally, in the differential analysis based on RNA-seq data, such as, for example, differential gene expression, dispersion (of negative-binomial model) is estimated. Here, we estimate precision of the Dirichlet-multinomial model as it is more convenient computationally. To obtain dispersion estimates, one can use a formula: dispersion = 1 / (1 + precision).
mean_expression(x)
: Get a data frame with mean gene
expression.
common_precision(x), common_precision(x) <- value
:
Get or set common precision. value
must be numeric of length 1.
genewise_precision(x), genewise_precision(x) <- value
: Get a data
frame with gene-wise precision or set new gene-wise precision. value
must be a data frame with "gene_id" and "genewise_precision" columns.
mean_expression
Numeric vector of mean gene expression.
common_precision
Numeric value of estimated common precision.
genewise_precision
Numeric vector of estimated gene-wise precisions.
design_precision
Numeric matrix of the design used to estimate precision.
Malgorzata Nowicka
# -------------------------------------------------------------------------- # 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))