dmDSfit-class: dmDSfit object

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

Description

dmDSfit extends the dmDSprecision class by adding the full model Dirichlet-multinomial (DM) and beta-binomial (BB) likelihoods, regression coefficients and feature proportion estimates. Result of calling the dmFit function.

Usage

## S4 method for signature 'dmDSfit'
design(object, type = "full_model")

proportions(x, ...)

## S4 method for signature 'dmDSfit'
proportions(x)

## S4 method for signature 'dmDSfit'
coefficients(object, level = "gene")

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.
level	Character specifying which type of results to return. Possible values "gene" or "feature".

Value

• design(object): Get a matrix with the full design.

• proportions(x): Get a data frame with estimated feature ratios for each sample.

• coefficients(x): Get the DM or BB regression coefficients.

Slots

design_fit_full

Numeric matrix of the design used to fit the full model.

fit_full

MatrixList containing estimated feature ratios in each sample based on the full Dirichlet-multinomial (DM) model.

lik_full

Numeric vector of the per gene DM full model likelihoods.

coef_full

MatrixList with the regression coefficients based on the DM model.

fit_full_bb

MatrixList containing estimated feature ratios in each sample based on the full beta-binomial (BB) model.

lik_full_bb

Numeric vector of the per gene BB full model likelihoods.

coef_full_bb

MatrixList with the regression coefficients based on the BB model.

Author(s)

Malgorzata Nowicka

See Also

dmDSdata, dmDSprecision, 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))

## 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")

gosianow/DRIMSeq documentation built on Aug. 8, 2020, 10:29 a.m.