dmSQTLtest-class {DRIMSeq} | R Documentation |
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
.
## S4 method for signature 'dmSQTLtest' results(x)
x |
dmSQTLtest object. |
... |
Other parameters that can be defined by methods using this generic. |
results(x)
: Get a data frame with gene-level results.
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.
Malgorzata Nowicka
dmSQTLdata
,
dmSQTLprecision
, dmSQTLfit
# -------------------------------------------------------------------------- # 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))