dmSQTLdata-class {DRIMSeq} | R Documentation |
dmSQTLdata contains genomic feature expression (counts), genotypes and sample
information needed for the transcript/exon usage QTL analysis. It can be
created with function dmSQTLdata
.
## S4 method for signature 'dmSQTLdata' counts(object) ## S4 method for signature 'dmSQTLdata' samples(x) ## S4 method for signature 'dmSQTLdata' names(x) ## S4 method for signature 'dmSQTLdata' length(x) ## S4 method for signature 'dmSQTLdata,ANY' x[i, j]
x, object |
dmSQTLdata object. |
i, j |
Parameters used for subsetting. |
names(x)
: Get the gene names.
length(x)
:
Get the number of genes.
x[i, j]
: Get a subset of dmDSdata
object that consists of counts, genotypes and blocks corresponding to genes i
and samples j.
counts
MatrixList
of expression, in counts, of
genomic features. Rows correspond to genomic features, such as exons or
transcripts. Columns correspond to samples. MatrixList is partitioned in a
way that each of the matrices in a list contains counts for a single gene.
genotypes
MatrixList of unique genotypes. Rows correspond to blocks, columns to samples. Each matrix in this list is a collection of unique genotypes that are matched with a given gene.
blocks
MatrixList with two columns block_id
and snp_id
.
For each gene, it identifies SNPs with identical genotypes across the
samples and assigns them to blocks.
samples
Data frame with information about samples. It must contain
variable sample_id
with unique sample names.
Malgorzata Nowicka
dmSQTLprecision
,
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)