Concrete subclass for CRISPR Mageck CountQC outputs

Identical to ExploreDatasetSE_CountQC plus a sgRNA → gene mapping parsed from the SE feature IDs, which are assumed to follow the GENE_<n>_<SEQUENCE> convention.

Value

An R6 class generator; $new() returns an ExploreDatasetSE_CrisprCountQC object.

Details

Inherits .prep() from ExploreDatasetSE_CountQC unchanged.

Super classes

ExploreDataset -> ExploreDatasetSE -> ExploreDatasetSE_CountQC -> ExploreDatasetSE_CrisprCountQC

Methods

Public methods

• ExploreDatasetSE_CrisprCountQC$clone()

Inherited methods

• ExploreDataset$all_pathways()
• ExploreDataset$assay_label()
• ExploreDataset$background_expression()
• ExploreDataset$contrast_label()
• ExploreDataset$contrast_meta()
• ExploreDataset$contrasts()
• ExploreDataset$data_type()
• ExploreDataset$de_all()
• ExploreDataset$de_display_extras()
• ExploreDataset$enrichment_filter()
• ExploreDataset$enrichment_kinds()
• ExploreDataset$enrichment_table()
• ExploreDataset$factor_levels()
• ExploreDataset$factor_names()
• ExploreDataset$feature_display()
• ExploreDataset$feature_id_kind()
• ExploreDataset$feature_ids()
• ExploreDataset$format()
• ExploreDataset$has_assay()
• ExploreDataset$has_de()
• ExploreDataset$has_enrichment()
• ExploreDataset$has_pvalue()
• ExploreDataset$n_features()
• ExploreDataset$n_samples()
• ExploreDataset$omics_class()
• ExploreDataset$organism()
• ExploreDataset$primary_assay()
• ExploreDataset$primary_assay_name()
• ExploreDataset$primary_contrast()
• ExploreDataset$primary_grouping()
• ExploreDataset$proteomics_choices()
• ExploreDataset$raw_assay()
• ExploreDataset$raw_assay_name()
• ExploreDataset$ref_build()
• ExploreDataset$sample_factor()
• ExploreDataset$sample_ids()
• ExploreDataset$secondary_grouping()
• ExploreDataset$sgrna_to_gene()
• ExploreDataset$source()
• ExploreDatasetSE$assay()
• ExploreDatasetSE$assay_names()
• ExploreDatasetSE$backend()
• ExploreDatasetSE$backend_kind()
• ExploreDatasetSE$de()
• ExploreDatasetSE$enrichment()
• ExploreDatasetSE$enrichment_input()
• ExploreDatasetSE$features()
• ExploreDatasetSE$initialize()
• ExploreDatasetSE$samples()

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ExploreDatasetSE_CrisprCountQC$clone()

The objects of this class are cloneable with this method.

Usage

ExploreDatasetSE_CrisprCountQC$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(SummarizedExperiment)
# Guide counts: 4 guides for each of 10 genes, named GENE_<n>_<SEQUENCE>
set.seed(1)
guides <- sprintf("GENE%d_%d_ACGTACGTACGT", rep(1:10, each = 4), rep(1:4, 10))
counts <- matrix(rpois(40 * 4, 50), nrow = 40, dimnames = list(guides, paste0("S", 1:4)))
samples <- S4Vectors::DataFrame(
  `Condition [Factor]` = rep(c("ctrl", "treated"), each = 2),
  row.names = colnames(counts),
  check.names = FALSE
)
se <- SummarizedExperiment(assays = list(counts = counts), colData = samples)
eds <- ExploreDatasetSE_CrisprCountQC$new(se)
eds
#> <ExploreDatasetSE_CrisprCountQC  (se)>
#>   data_type:        CrisprCountQC
#>   feature_id_kind:  guide
#>   organism:         NA
#>   ref_build:        NA
#>   samples x features: 4 x 40
#>   factors (1):
#>     Condition  (primary): ctrl, treated
#>   contrasts (0): 
#>   primary_contrast: NA
#>   assays:           counts, vst
#>   enrichment:       (none)
head(eds$sgrna_to_gene())
#> GENE1_1_ACGTACGTACGT GENE1_2_ACGTACGTACGT GENE1_3_ACGTACGTACGT 
#>              "GENE1"              "GENE1"              "GENE1" 
#> GENE1_4_ACGTACGTACGT GENE2_1_ACGTACGTACGT GENE2_2_ACGTACGTACGT 
#>              "GENE1"              "GENE2"              "GENE2"