Abstract base class for exploreDE's storage-agnostic dataset wrapper
Source:R/ExploreDataset.R
ExploreDataset.RdExploreDataset defines the accessor contract that every concrete
subclass must implement. It is not instantiable on its own — calling
ExploreDataset$new() raises an error. Use new_explore_dataset() to
build a concrete instance, or instantiate a subclass directly.
Methods implemented on the base class read only from
private$.config and are inherited unchanged by every subclass.
Methods that touch the storage backend are virtual
(stop("not implemented")) and must be overridden.
Value
An R6 class generator. $new() always errors; the concrete
subclasses return an object inheriting from ExploreDataset.
Methods
ExploreDataset$new()
Always errors: the base class is abstract. Use new_explore_dataset() or a concrete subclass.
Usage
ExploreDataset$new(...)ExploreDataset$data_type()
Dataset type from the config, e.g. "RNASeq", "CountQC", "CrisprCountQC" or "proteomics".
ExploreDataset$feature_id_kind()
What a feature ID denotes, from the config, e.g. "gene", "isoform", "protein",
"peptide", "phospho" or "guide".
ExploreDataset$background_expression()
Low-signal offset added to expression values before taking log2; from the config, default 10.
ExploreDataset$backend_kind()
Short tag naming the storage backend ("se", "h5ad" or "parquet"). Implemented by subclasses.
ExploreDataset$samples()
Sample metadata as a data.frame, one row per sample, rownames are sample IDs. Implemented by subclasses.
ExploreDataset$factor_names()
Names of the experimental factors from the config; empty when there are none.
ExploreDataset$primary_grouping()
Name of the factor used as the primary sample grouping, from the config.
ExploreDataset$secondary_grouping()
Name of the secondary grouping factor, or NA when there is none.
ExploreDataset$features()
Feature annotation as a data.frame, one row per feature, rownames are feature IDs, with a
display_name column. Implemented by subclasses.
ExploreDataset$assay_names()
Canonical names of the assays this dataset provides. Implemented by subclasses.
ExploreDataset$assay()
One assay as a features x samples matrix, optionally subset. Implemented by subclasses.
ExploreDataset$assay_label()
Human-readable label for an assay, from the config; name itself when no label is registered.
ExploreDataset$contrasts()
Names of the DE contrasts, from the config; empty when the dataset has no DE results.
ExploreDataset$primary_contrast()
Name of the primary contrast, from the config; NA when there are no contrasts.
ExploreDataset$de()
DE result table for one contrast, as a data.frame. Implemented by subclasses.
Arguments
contrastContrast name, one of
contrasts().
ExploreDataset$contrast_meta()
Metadata list of one contrast from the config: test and reference group, factor, method, thresholds. Errors on an unknown contrast.
Arguments
contrastContrast name, one of
contrasts().
ExploreDataset$contrast_label()
Display label of a contrast: currently the contrast name itself. Errors on an unknown contrast.
Arguments
contrastContrast name, one of
contrasts().
ExploreDataset$de_display_extras()
Names of extra DE columns shown for display only (e.g. SaintScore), from the config;
empty by default.
ExploreDataset$has_pvalue()
TRUE when the DE table of at least one contrast has a non-missing raw pValue;
FALSE for FDR-only producers such as SAINTexpress.
ExploreDataset$enrichment_kinds()
Enrichment kinds available, from the config: any of "ora" and "gsea".
ExploreDataset$enrichment()
Enrichment results of one contrast and kind, as a flat named list of clusterProfiler results. Implemented by subclasses.
Arguments
contrastContrast name, one of
contrasts().kind"ora"or"gsea".
ExploreDataset$enrichment_input()
Inputs for enrichment of one contrast: list(seq_anno, ranked_l2fc, selections).
Implemented by subclasses.
Arguments
contrastContrast name, one of
contrasts().
ExploreDataset$enrichment_table()
Enrichment results of one contrast and kind as one tidy data.frame, derived from
enrichment(); an empty data.frame when there are none.
Arguments
contrastContrast name, one of
contrasts().kind"ora"or"gsea".
ExploreDataset$enrichment_filter()
The p.adjust cut-off applied to the stored enrichment tables, from the config;
NA when none was applied.
ExploreDataset$sgrna_to_gene()
Named character vector mapping sgRNA IDs to gene names (CRISPR datasets); NULL otherwise.
ExploreDataset$proteomics_choices()
Choices and lookup for the proteomics feature picker, list(choices, lookup); NULL when the
dataset has none.
Usage
ExploreDataset$proteomics_choices(kind = c("gene", "protein"))ExploreDataset$all_pathways()
GO terms named in the GO BP, GO MF and GO CC columns of features(), as
"GO:ID term name" strings; empty when those columns are absent.
ExploreDataset$source()
Provenance list from the config: backend kind, schema version and build time.
ExploreDataset$format()
Summary of the dataset (type, dimensions, factors, contrasts, assays, enrichment), one
line per element. print() on the object shows these lines.
Examples
se <- readRDS(system.file("examples/proteomics/SummarizedExperiment.rds", package = "exploreDE"))
#> Loading required namespace: SummarizedExperiment
eds <- new_explore_dataset(se)
inherits(eds, "ExploreDataset")
#> [1] TRUE
eds$contrasts()
#> [1] "B_vs_A"
head(eds$de(eds$primary_contrast()))
#> nrPeptides IDcolumn fasta.id description
#> P0AFG3 25 P0AFG3 sp|P0AFG3|ODO1_ECOLI sp_P0AFG3_ODO1_ECOLI
#> P0AC41 15 P0AC41 sp|P0AC41|SDHA_ECOLI sp_P0AC41_SDHA_ECOLI
#> P0A7W1 5 P0A7W1 sp|P0A7W1|RS5_ECOLI sp_P0A7W1_RS5_ECOLI
#> P0A7V0 8 P0A7V0 sp|P0A7V0|RS2_ECOLI sp_P0A7V0_RS2_ECOLI
#> A1L429 1 A1L429 sp|A1L429|GG12C_HUMAN sp_A1L429_GG12C_HUMAN
#> P0AFG6 10 P0AFG6 sp|P0AFG6|ODO2_ECOLI sp_P0AFG6_ODO2_ECOLI
#> protein_length nr_tryptic_peptides CON REV modelName
#> P0AFG3 933 44 FALSE FALSE Linear_Model_moderated
#> P0AC41 588 31 FALSE FALSE Linear_Model_moderated
#> P0A7W1 167 11 FALSE FALSE Linear_Model_moderated
#> P0A7V0 241 14 FALSE FALSE Linear_Model_moderated
#> A1L429 117 2 FALSE FALSE Imputed_Mean_moderated
#> P0AFG6 405 20 FALSE FALSE Linear_Model_moderated
#> contrast log2Ratio std.error avgAbd statistic df pValue
#> P0AFG3 B_vs_A 2.076169 0.02592986 19.27265 26.95160 5.832013 2.407749e-07
#> P0AC41 B_vs_A 2.207672 0.05267450 19.16701 25.70541 5.832013 3.166968e-07
#> P0A7W1 B_vs_A 2.232960 0.05541211 21.40682 25.64938 5.832013 3.207217e-07
#> P0A7V0 B_vs_A 2.290956 0.06851577 20.84410 24.57190 5.832013 4.111082e-07
#> A1L429 B_vs_A -7.134870 0.06665272 21.11332 -68.04320 3.798723 5.272144e-07
#> P0AFG6 B_vs_A 2.690083 0.11613502 20.00267 22.16970 5.832013 7.449241e-07
#> conf.low conf.high sigma fdr feature_id effect_log2fc
#> P0AFG3 1.843691 2.308647 0.09434608 0.0001329935 P0AFG3 2.076169
#> P0AC41 1.948485 2.466860 0.10518546 0.0001329935 P0AC41 2.207672
#> P0A7W1 1.970231 2.495689 0.10662272 0.0001329935 P0A7W1 2.232960
#> P0A7V0 2.009583 2.572328 0.11418883 0.0001329935 P0A7V0 2.290956
#> A1L429 -7.499009 -6.770731 0.12842423 0.0001459329 A1L429 -7.134870
#> P0AFG6 2.323891 3.056276 0.14861122 0.0001562791 P0AFG6 2.690083
#> score_pvalue score_fdr usedInTest
#> P0AFG3 2.407749e-07 0.0001329935 TRUE
#> P0AC41 3.166968e-07 0.0001329935 TRUE
#> P0A7W1 3.207217e-07 0.0001329935 TRUE
#> P0A7V0 4.111082e-07 0.0001329935 TRUE
#> A1L429 5.272144e-07 0.0001459329 TRUE
#> P0AFG6 7.449241e-07 0.0001562791 TRUE
# The base class itself cannot be instantiated
try(ExploreDataset$new())
#> Error in initialize(...) :
#> ExploreDataset is abstract. Use new_explore_dataset() or one of the concrete subclasses.