dmDSprecision-class: dmDSprecision object

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

Description

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.

Usage

## 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

Arguments

type	Character indicating which design matrix should be returned. Possible values "precision", "full_model" or "null_model".
x, object	dmDSprecision object.
...	Other parameters that can be defined by methods using this generic.
value	Values that replace current attributes.

Details

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).

Value

• 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.

Slots

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.

Author(s)

Malgorzata Nowicka

See Also

dmDSdata, dmDSfit, dmDStest

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.