dmSQTLprecision-class: dmSQTLprecision object

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

Description

dmSQTLprecision extends the dmSQTLdata by adding the precision estimates of Dirichlet-multinomial distribution used to model the feature (e.g., transcript, exon, exonic bin) counts for each gene-SNP pair in the QTL analysis. Result of dmPrecision.

Usage

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

## S4 method for signature 'dmSQTLprecision'
common_precision(x)

## S4 method for signature 'dmSQTLprecision'
genewise_precision(x)

Arguments

x	dmSQTLprecision object.

Value

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

• common_precision(x): Get common precision.

• genewise_precision(x): Get a data frame with gene-wise precision.

Slots

mean_expression

Numeric vector of mean gene expression.

common_precision

Numeric value of estimated common precision.

genewise_precision

List of estimated gene-wise precisions. Each element of this list is a vector of precisions estimated for all the genotype blocks assigned to a given gene.

Author(s)

Malgorzata Nowicka

See Also

dmSQTLdata, dmSQTLfit, dmSQTLtest

Examples

# --------------------------------------------------------------------------
# Create dmSQTLdata object
# --------------------------------------------------------------------------
# Use subsets of data defined in the GeuvadisTranscriptExpr package

library(GeuvadisTranscriptExpr)

geuv_counts <- GeuvadisTranscriptExpr::counts
geuv_genotypes <- GeuvadisTranscriptExpr::genotypes
geuv_gene_ranges <- GeuvadisTranscriptExpr::gene_ranges
geuv_snp_ranges <- GeuvadisTranscriptExpr::snp_ranges

colnames(geuv_counts)[c(1,2)] <- c("feature_id", "gene_id")
colnames(geuv_genotypes)[4] <- "snp_id"
geuv_samples <- data.frame(sample_id = colnames(geuv_counts)[-c(1,2)])

d <- dmSQTLdata(counts = geuv_counts, gene_ranges = geuv_gene_ranges,  
  genotypes = geuv_genotypes, snp_ranges = geuv_snp_ranges, 
  samples = geuv_samples, window = 5e3)

# --------------------------------------------------------------------------
# sQTL analysis - simple group comparison
# --------------------------------------------------------------------------

## Filtering
d <- dmFilter(d, min_samps_gene_expr = 70, min_samps_feature_expr = 5,
  minor_allele_freq = 5, min_gene_expr = 10, min_feature_expr = 10)
  
plotData(d)

## To make the analysis reproducible
set.seed(123)
## Calculate precision
d <- dmPrecision(d)

plotPrecision(d)

markrobinsonuzh/DRIMSeq documentation built on May 21, 2019, 12:23 p.m.