dmSQTLfit-class: dmSQTLfit object

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

Description

dmSQTLfit extends the dmSQTLprecision class by adding the full model Dirichlet-multinomial (DM) likelihoods, regression coefficients and feature proportion estimates needed for the transcript/exon usage QTL analysis. Full model is defined by the genotype of a SNP associated with a gene. Estimation takes place for all the genes and all the SNPs/blocks assigned to the genes. Result of dmFit.

Slots

fit_full

List of MatrixList objects containing estimated feature ratios in each sample based on the full Dirichlet-multinomial (DM) model.

lik_full

List of numeric vectors of the per gene DM full model likelihoods.

coef_full

MatrixList with the regression coefficients based on the DM model.

Author(s)

Malgorzata Nowicka

See Also

dmSQTLdata, dmSQTLprecision, 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)

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

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