Concrete ExploreDataset backend over a canonical AnnData (.h5ad)

Reads a canonical exploreDE/1.0 AnnData and implements the accessor contract inherited from ExploreDataset. Accepts a path to a .h5ad file or a live anndataR object. This is the backend for user-supplied data ("bring your own data") and the eventual single backend once producers emit canonical .h5ad directly.

Value

An R6 class generator; $new() returns an ExploreDatasetH5AD object.

Super class

ExploreDataset -> ExploreDatasetH5AD

Methods

Public methods

• ExploreDatasetH5AD$new()

• ExploreDatasetH5AD$backend()

• ExploreDatasetH5AD$backend_kind()

• ExploreDatasetH5AD$samples()

• ExploreDatasetH5AD$features()

• ExploreDatasetH5AD$de()

• ExploreDatasetH5AD$assay_names()

• ExploreDatasetH5AD$assay()

• ExploreDatasetH5AD$enrichment()

• ExploreDatasetH5AD$enrichment_table()

• ExploreDatasetH5AD$enrichment_input()

• ExploreDatasetH5AD$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$enrichment_kinds()
• ExploreDataset$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()

---

ExploreDatasetH5AD$new()

Read a canonical .h5ad or take a live AnnData, build the config from it, and prepare the feature and DE tables.

Usage

ExploreDatasetH5AD$new(input)

Arguments

input

An anndataR AnnData object, or a path to an existing .h5ad file.

---

ExploreDatasetH5AD$backend()

The AnnData parts read at construction, as list(obs, var, layers, varm, uns) of plain R objects.

Usage

ExploreDatasetH5AD$backend()

---

ExploreDatasetH5AD$backend_kind()

Returns "h5ad".

Usage

ExploreDatasetH5AD$backend_kind()

---

ExploreDatasetH5AD$samples()

Sample metadata from obs, with the factor_ prefix stripped from column names and a Name column added when missing.

Usage

ExploreDatasetH5AD$samples()

---

ExploreDatasetH5AD$features()

Feature annotation from var, with feature_id and display_name columns, ordered by the p-value of the primary contrast.

Usage

ExploreDatasetH5AD$features()

---

ExploreDatasetH5AD$de()

DE table of one contrast from varm, with the legacy aliases log2Ratio, pValue and fdr added; an empty data.frame when the slot is missing. Errors on an unknown contrast.

Usage

ExploreDatasetH5AD$de(contrast)

Arguments

contrast

Contrast name, one of contrasts().

---

ExploreDatasetH5AD$assay_names()

Canonical assay names from the config's assay map whose layer exists; computed assays are always included.

Usage

ExploreDatasetH5AD$assay_names()

---

ExploreDatasetH5AD$assay()

One assay as a features x samples matrix (the layer transposed); normalised, normalised_log2, vst, fpkm and tpm are computed when the config maps them to no layer.

Usage

ExploreDatasetH5AD$assay(name, features = NULL, samples = NULL)

Arguments

name

Canonical assay name, one of assay_names().

features

Feature IDs (rows) to keep; NULL keeps all.

samples

Sample IDs (columns) to keep; NULL keeps all.

---

ExploreDatasetH5AD$enrichment()

Enrichment results of one contrast and kind, rebuilt from uns$enrichment and uns$enrichment_support.

Usage

ExploreDatasetH5AD$enrichment(contrast, kind)

Arguments

contrast

Contrast name, one of contrasts().

kind

"ora" or "gsea".

---

ExploreDatasetH5AD$enrichment_table()

Tidy enrichment table of one contrast and kind: the table stored in uns$enrichment when present, otherwise derived from enrichment() as in the base class.

Usage

ExploreDatasetH5AD$enrichment_table(contrast, kind)

Arguments

contrast

Contrast name, one of contrasts().

kind

"ora" or "gsea".

---

ExploreDatasetH5AD$enrichment_input()

Enrichment inputs rebuilt from the DE table: features(), log2 fold changes sorted decreasing, and up/down feature selections at the contrast's FDR and log2FC thresholds.

Usage

ExploreDatasetH5AD$enrichment_input(contrast)

Arguments

contrast

Contrast name, one of contrasts().

---

ExploreDatasetH5AD$clone()

The objects of this class are cloneable with this method.

Usage

ExploreDatasetH5AD$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(SummarizedExperiment)
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#> 
#> Attaching package: ‘MatrixGenerics’
#> The following objects are masked from ‘package:matrixStats’:
#> 
#>     colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#>     colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#>     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#>     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#>     colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#>     colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#>     colWeightedMeans, colWeightedMedians, colWeightedSds,
#>     colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#>     rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#>     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#>     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#>     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#>     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#>     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#>     rowWeightedSds, rowWeightedVars
#> Loading required package: GenomicRanges
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#> Loading required package: generics
#> 
#> Attaching package: ‘generics’
#> The following objects are masked from ‘package:base’:
#> 
#>     as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
#>     setequal, union
#> 
#> Attaching package: ‘BiocGenerics’
#> The following objects are masked from ‘package:stats’:
#> 
#>     IQR, mad, sd, var, xtabs
#> The following object is masked from ‘package:utils’:
#> 
#>     data
#> The following objects are masked from ‘package:base’:
#> 
#>     Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
#>     as.data.frame, basename, cbind, colnames, dirname, do.call,
#>     duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
#>     mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
#>     rank, rbind, rownames, sapply, saveRDS, scale, sequence, table,
#>     tapply, transform, unique, unsplit, which.max, which.min
#> Loading required package: S4Vectors
#> 
#> Attaching package: ‘S4Vectors’
#> The following object is masked from ‘package:utils’:
#> 
#>     findMatches
#> The following objects are masked from ‘package:base’:
#> 
#>     I, expand.grid, unname
#> Loading required package: IRanges
#> Loading required package: Seqinfo
#> Loading required package: Biobase
#> Welcome to Bioconductor
#> 
#>     Vignettes contain introductory material; view with
#>     'browseVignettes()'. To cite Bioconductor, see
#>     'citation("Biobase")', and for packages 'citation("pkgname")'.
#> 
#> Attaching package: ‘Biobase’
#> The following object is masked from ‘package:MatrixGenerics’:
#> 
#>     rowMedians
#> The following objects are masked from ‘package:matrixStats’:
#> 
#>     anyMissing, rowMedians
se <- readRDS(system.file("examples/proteomics/SummarizedExperiment.rds", package = "exploreDE"))
de <- as.data.frame(rowData(se)$constrast_B_vs_A)
path <- tempfile(fileext = ".h5ad")
build_explore_h5ad(
  x = list(raw = assay(se, "rawData"), transformed = assay(se, "transformedData")),
  out = path,
  omics = "proteomics",
  coldata = colData(se),
  rowdata = de,
  factors = "G_",
  de = list(B_vs_A = de)
)
eds <- ExploreDatasetH5AD$new(path)
eds
#> <ExploreDatasetH5AD  (h5ad)>
#>   data_type:        proteomics
#>   feature_id_kind:  protein
#>   organism:         NA
#>   ref_build:        NA
#>   samples x features: 6 x 1384
#>   factors (1):
#>     g_  (primary): A, B
#>   contrasts (1): B_vs_A
#>   primary_contrast: B_vs_A
#>   assays:           intensities, intensities_raw
#>   enrichment:       (none)
head(eds$de("B_vs_A"))
#>        effect_log2fc score_pvalue    score_fdr feature_id gene_name gene_id
#> P0AFG3      2.076169 2.407749e-07 0.0001329935     P0AFG3    P0AFG3  P0AFG3
#> P0AC41      2.207672 3.166968e-07 0.0001329935     P0AC41    P0AC41  P0AC41
#> P0A7W1      2.232960 3.207217e-07 0.0001329935     P0A7W1    P0A7W1  P0A7W1
#> P0A7V0      2.290956 4.111082e-07 0.0001329935     P0A7V0    P0A7V0  P0A7V0
#> A1L429     -7.134870 5.272144e-07 0.0001459329     A1L429    A1L429  A1L429
#> P0AFG6      2.690083 7.449241e-07 0.0001562791     P0AFG6    P0AFG6  P0AFG6
#>                  description log2Ratio       pValue          fdr usedInTest
#> P0AFG3  sp_P0AFG3_ODO1_ECOLI  2.076169 2.407749e-07 0.0001329935       TRUE
#> P0AC41  sp_P0AC41_SDHA_ECOLI  2.207672 3.166968e-07 0.0001329935       TRUE
#> P0A7W1   sp_P0A7W1_RS5_ECOLI  2.232960 3.207217e-07 0.0001329935       TRUE
#> P0A7V0   sp_P0A7V0_RS2_ECOLI  2.290956 4.111082e-07 0.0001329935       TRUE
#> A1L429 sp_A1L429_GG12C_HUMAN -7.134870 5.272144e-07 0.0001459329       TRUE
#> P0AFG6  sp_P0AFG6_ODO2_ECOLI  2.690083 7.449241e-07 0.0001562791       TRUE