dmDSfit-class: dmDSfit object

Description Usage Arguments Value Slots Author(s) See Also Examples

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

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## 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

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

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# --------------------------------------------------------------------------
# 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")

DRIMSeq documentation built on Nov. 17, 2017, 1:11 p.m.