dmSQTLfit-class {DRIMSeq} | R Documentation |
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
.
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.
Malgorzata Nowicka
dmSQTLdata
,
dmSQTLprecision
, dmSQTLtest
# -------------------------------------------------------------------------- # 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)