Plot the information on the tumor immune contexture

Plot the information on the tumor immune contexture, as extracted with run_quantiseqr()

quantiplot(obj)

Arguments

obj

An object, either

• a SummarizedExperiment where the quantifications are stored

• a simple data.frame object, as obtained by run_quantiseqr()

Value

A ggplot object

Examples

data(dataset_racle)
dim(dataset_racle$expr_mat)
#> [1] 32467     4
res_quantiseq_run <- quantiseqr::run_quantiseq(
  expression_data = dataset_racle$expr_mat,
  signature_matrix = "TIL10",
  is_arraydata = FALSE,
  is_tumordata = TRUE,
  scale_mRNA = TRUE
)
#> 
#> Running quanTIseq deconvolution module
#> Gene expression normalization and re-annotation (arrays: FALSE)
#> Removing 17 noisy genes
#> Removing 15 genes with high expression in tumors
#> Signature genes found in data set: 135/138 (97.83%)
#> Mixture deconvolution (method: lsei)
#> Deconvolution successful!

# using a SummarizedExperiment object
library("SummarizedExperiment")
se_racle <- SummarizedExperiment(
  assays = List(
    abundance = dataset_racle$expr_mat
  ),
  colData = DataFrame(
    SampleName = colnames(dataset_racle$expr_mat)
  )
)

res_run_SE <- quantiseqr::run_quantiseq(
    expression_data = se_racle,
    signature_matrix = "TIL10",
    is_arraydata = FALSE,
    is_tumordata = TRUE,
    scale_mRNA = TRUE
)
#> 
#> Running quanTIseq deconvolution module
#> Gene expression normalization and re-annotation (arrays: FALSE)
#> Removing 17 noisy genes
#> Removing 15 genes with high expression in tumors
#> Signature genes found in data set: 135/138 (97.83%)
#> Mixture deconvolution (method: lsei)
#> Deconvolution successful!

quantiplot(res_quantiseq_run)

# equivalent to...
quantiplot(res_run_SE)
#> Found quantifications for the TIL10 signature...