dmSQTLtest object

Description

dmSQTLtest extends the dmSQTLfit class by adding the null model Dirichlet-multinomial likelihoods and the gene-level results of testing for differential transcript/exon usage QTLs. Result of dmTest.

Usage

## S4 method for signature 'dmSQTLtest'
results(x)

Arguments

Value

• results(x): Get a data frame with gene-level results.

Slots

lik_null

List of numeric vectors with the per gene-snp DM null model likelihoods.

results_gene

Data frame with the gene-level results including: gene_id - gene IDs, block_id - block IDs, snp_id - SNP IDs, lr - likelihood ratio statistics based on the DM model, df - degrees of freedom, pvalue - p-values estimated based on permutations and adj_pvalue - Benjamini & Hochberg adjusted p-values.

Author(s)

Malgorzata Nowicka

See Also

dmSQTLdata, dmSQTLprecision, dmSQTLfit

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)

## Fit full model proportions
d <- dmFit(d)

## Fit null model proportions, perform the LR test to detect tuQTLs 
## and use the permutation approach to adjust the p-values
d <- dmTest(d)

## Plot the gene-level p-values
plotPValues(d)

## Get the gene-level results
head(results(d))