dmSQTLprecision-class {DRIMSeq} | R Documentation |
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
.
## S4 method for signature 'dmSQTLprecision' mean_expression(x) ## S4 method for signature 'dmSQTLprecision' common_precision(x) ## S4 method for signature 'dmSQTLprecision' genewise_precision(x)
x |
dmSQTLprecision object. |
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.
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.
Malgorzata Nowicka
dmSQTLdata
, dmSQTLfit
,
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)