dmDSfit-class {DRIMSeq} | R Documentation |
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.
## 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")
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. |
level |
Character specifying which type of results to return. Possible
values |
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.
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.
Malgorzata Nowicka
dmDSdata
, dmDSprecision
,
dmDStest
# -------------------------------------------------------------------------- # 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")