dmDSdispersion object

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Description

dmDSdispersion extends the dmDSdata by adding the dispersion estimates of Dirichlet-multinomial distribution used to model the feature (e.g., transcript, exon, exonic bin) coiunts for each gene in the differential splicing analysis. Result of dmDispersion.

Usage

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mean_expression(x, ...)

## S4 method for signature 'dmDSdispersion'
mean_expression(x)

common_dispersion(x, ...)

## S4 method for signature 'dmDSdispersion'
common_dispersion(x)

common_dispersion(x) <- value

## S4 replacement method for signature 'dmDSdispersion'
common_dispersion(x) <- value

genewise_dispersion(x, ...)

## S4 method for signature 'dmDSdispersion'
genewise_dispersion(x)

genewise_dispersion(x) <- value

## S4 replacement method for signature 'dmDSdispersion'
genewise_dispersion(x) <- value

Arguments

x

dmDSdispersion object.

...

Other parameters that can be defined by methods using this generic.

value

Values that replace current attributes.

Value

  • mean_expression(x): Get a data frame with mean gene expression.

  • common_dispersion(x), common_dispersion(x) <- value: Get or set common dispersion. value must be numeric of length 1.

  • genewise_dispersion(x), genewise_dispersion(x) <- value: Get a data frame with gene-wise dispersion or set new gene-wise dispersion. value must be a data frame with "gene_id" and "genewise_dispersion" columns.

Slots

mean_expression

Numeric vector of mean gene expression.

common_dispersion

Numeric value of estimated common dispersion.

genewise_dispersion

Numeric vector of estimated gene-wise dispersions.

Author(s)

Malgorzata Nowicka

See Also

data_dmDSdata, dmDSdata, dmDSfit, dmDStest

Examples

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###################################
### Differential splicing analysis
###################################
# If possible, use BPPARAM = BiocParallel::MulticoreParam() with more workers

d <- data_dmDSdata

### 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_samps_feature_prop = 0)

### Calculate dispersion
d <- dmDispersion(d, BPPARAM = BiocParallel::SerialParam())
plotDispersion(d)

head(mean_expression(d))
common_dispersion(d)
head(genewise_dispersion(d))