R/singelbracketsubset.R
In connorhknight/IBRAP: Integrated Benchmarking scRNA-seq Analytical Pipeline (IBRAP)
#' @title Method override for `'['` subset function regarding IBRAP S4 object
#'
#' @exportMethod `[`
setMethod(f = '[', signature = 'IBRAP',
function(x,
i,
j,
...,
drop = FALSE) {
if(!missing(i) & missing(j)) {
# Just features
if(!is.character(i)) {
ii <- .convert_subset_index(x = i, rownames(x))
} else {
ii <- i
}
.sample_metadata <- x@sample_metadata
list.methods <- list()
for(p in names(x@methods)) {
if(!is.na(x@methods[[p]]@counts[1])) {
genes <- ii[ii %in% rownames(x@methods[[p]]@counts)]
.counts <- x@methods[[p]]@counts[genes , , drop = FALSE]
} else {
.counts <- x@methods[[p]]@counts
}
if(!is.na(x@methods[[p]]@normalised[1])) {
genes <- ii[ii %in% rownames(x@methods[[p]]@normalised)]
.normalised <- x@methods[[p]]@normalised[genes, ,drop = FALSE]
} else {
.normalised <- x@methods[[p]]@normalised
}
if(!is.na(x@methods[[p]]@norm.scaled[1])) {
genes <- ii[ii %in% rownames(x@methods[[p]]@norm.scaled)]
.norm.scaled <- x@methods[[p]]@norm.scaled[genes, ,drop = FALSE]
} else {
.norm.scaled <- x@methods[[p]]@norm.scaled
}
if(!is.na(x@methods[[p]]@feature_metadata[1])) {
genes <- ii[ii %in% rownames(x@methods[[p]]@feature_metadata)]
.feature_metadata <- x@methods[[p]]@feature_metadata[genes, , drop = FALSE]
} else {
.feature_metadata <- x@methods[[p]]@feature_metadata
}
if(!is.null(x@methods[[p]]@highly.variable.genes)) {
.highly.variable.genes <- x@methods[[p]]@highly.variable.genes[x@methods[[p]]@highly.variable.genes %in% rownames(.counts)]
} else {
.highly.variable.genes <- x@methods[[p]]@highly.variable.genes
}
.neighbours <- x@methods[[p]]@neighbours
.computational_reductions <- x@methods[[p]]@computational_reductions
.integration_reductions <- x@methods[[p]]@integration_reductions
.visualisation_reductions <- x@methods[[p]]@visualisation_reductions
.cluster_assignments <- x@methods[[p]]@cluster_assignments
.benchmark_results <- x@methods[[p]]@benchmark_results
list.methods[[p]] <- new(Class = 'methods',
counts = .counts,
normalised = .normalised,
norm.scaled = .norm.scaled,
highly.variable.genes = .highly.variable.genes,
feature_metadata = .feature_metadata,
neighbours = .neighbours,
computational_reductions = .computational_reductions,
integration_reductions = .integration_reductions,
visualisation_reductions = .visualisation_reductions,
cluster_assignments = .cluster_assignments,
benchmark_results = .benchmark_results,
misc = x@methods[[p]]@misc)
}
return(new(Class = 'IBRAP',
methods = list.methods,
sample_metadata = .sample_metadata))
}
if(missing(i) & !missing(j)) {
# Just cells
if(!is.character(j)) {
jj <- .convert_subset_index(x = j, colnames(x))
} else {
jj <- j
}
jj <- j
.sample_metadata <- x@sample_metadata[jj, , drop = FALSE]
list.methods <- list()
for(p in names(x@methods)) {
if(!is.na(x@methods[[p]]@counts[1])) {
.counts <- x@methods[[p]]@counts[ , jj, drop = FALSE]
} else {
.counts <- x@methods[[p]]@counts
}
if(!is.na(x@methods[[p]]@normalised[1])) {
.normalised <- x@methods[[p]]@normalised[ , jj, drop = FALSE]
} else {
.normalised <- x@methods[[p]]@normalised
}
if(!is.na(x@methods[[p]]@norm.scaled[1])) {
.norm.scaled <- x@methods[[p]]@norm.scaled[ , jj, drop = FALSE]
} else {
.norm.scaled <- x@methods[[p]]@norm.scaled
}
if(length(x@methods[[p]]@computational_reductions) != 0) {
.computational_reductions <- list()
for(g in names(x@methods[[p]]@computational_reductions)) {
.computational_reductions[[g]] <- x@methods[[p]]@computational_reductions[[g]][jj, , drop = FALSE]
}
} else {
.computational_reductions <-x@methods[[p]]@computational_reductions
}
if(length(x@methods[[p]]@integration_reductions) != 0) {
.integration_reductions <- list()
for(g in names(x@methods[[p]]@integration_reductions)) {
.integration_reductions[[g]] <- x@methods[[p]]@integration_reductions[[g]][jj, , drop = FALSE]
}
} else {
.integration_reductions <-x@methods[[p]]@integration_reductions
}
if(length(x@methods[[p]]@visualisation_reductions) != 0) {
.visualisation_reductions <- list()
for(g in names(x@methods[[p]]@visualisation_reductions)) {
.visualisation_reductions[[g]] <- x@methods[[p]]@visualisation_reductions[[g]][jj, , drop = FALSE]
}
} else {
.visualisation_reductions <- x@methods[[p]]@visualisation_reductions
}
if(length(x@methods[[p]]@cluster_assignments) != 0) {
.cluster_assignments <- list()
for(g in names(x@methods[[p]]@cluster_assignments)) {
.cluster_assignments[[g]] <- x@methods[[p]]@cluster_assignments[[g]][jj, , drop = FALSE]
}
} else {
.cluster_assignments <- x@methods[[p]]@cluster_assignments
}
.highly.variable.genes <- x@methods[[p]]@highly.variable.genes
.feature_metadata <- x@methods[[p]]@feature_metadata
if(length(x@methods[[p]]@neighbours) != 0) {
list.neighbours <- list()
for(l in names(x@methods[[p]]@neighbours)) {
.neighbours <- x@methods[[p]]@neighbours
list.neighbours.sub <- list()
for(t in names(x@methods[[p]]@neighbours[[l]])) {
list.neighbours.sub[[t]] <- x@methods[[p]]@neighbours[[l]][[t]][jj, jj, drop = FALSE]
}
list.neighbours[[l]] <- list.neighbours.sub
}
.neighbours <- list.neighbours
} else {
.neighbours <- x@methods[[p]]@neighbours
}
.benchmark_results <- x@methods[[p]]@benchmark_results
list.methods[[p]] <- new(Class = 'methods',
counts = .counts,
normalised = .normalised,
norm.scaled = .norm.scaled,
highly.variable.genes = .highly.variable.genes,
feature_metadata = .feature_metadata,
neighbours = .neighbours,
computational_reductions = .computational_reductions,
integration_reductions = .integration_reductions,
visualisation_reductions = .visualisation_reductions,
cluster_assignments = .cluster_assignments,
benchmark_results = .benchmark_results,
misc = x@methods[[p]]@misc)
}
return(new(Class = 'IBRAP',
methods = list.methods,
sample_metadata = .sample_metadata))
}
if(!missing(i) & !missing(j)) {
# features and cells
if(!is.character(i)) {
ii <- .convert_subset_index(x = i, rownames(x))
} else {
ii <- i
}
if(!is.character(j)) {
jj <- .convert_subset_index(x = j, colnames(x))
} else {
jj <- j
}
.sample_metadata <- x@sample_metadata[jj, , drop = FALSE]
list.methods <- list()
for(p in names(x@methods)) {
if(!is.na(x@methods[[p]]@counts[1])) {
cells <- jj[jj %in% colnames(x@methods[[p]]@counts)]
genes <- ii[ii %in% rownames(x@methods[[p]]@counts)]
.counts <- x@methods[[p]]@counts[genes , cells, drop = FALSE]
} else {
.counts <- x@methods[[p]]@counts
}
if(!is.na(x@methods[[p]]@normalised[1])) {
cells <- jj[jj %in% colnames(x@methods[[p]]@normalised)]
genes <- ii[ii %in% rownames(x@methods[[p]]@normalised)]
.normalised <- x@methods[[p]]@normalised[genes , cells, drop = FALSE]
} else {
.normalised <- x@methods[[p]]@normalised
}
if(!is.na(x@methods[[p]]@norm.scaled[1])) {
cells <- jj[jj %in% colnames(x@methods[[p]]@norm.scaled)]
genes <- ii[ii %in% rownames(x@methods[[p]]@norm.scaled)]
.norm.scaled <- x@methods[[p]]@norm.scaled[genes , cells, drop = FALSE]
} else {
.norm.scaled <- x@methods[[p]]@norm.scaled
}
if(!is.na(x@methods[[p]]@feature_metadata[1])) {
genes <- ii[ii %in% rownames(x@methods[[p]]@feature_metadata)]
.feature_metadata <- x@methods[[p]]@feature_metadata[genes, , drop = FALSE]
} else {
.feature_metadata <- x@methods[[p]]@feature_metadata
}
if(length(x@methods[[p]]@computational_reductions) != 0) {
.computational_reductions <- list()
for(g in names(x@methods[[p]]@computational_reductions)) {
.computational_reductions[[g]] <- x@methods[[p]]@computational_reductions[[g]][jj, , drop = FALSE]
}
} else {
.computational_reductions <-x@methods[[p]]@computational_reductions
}
if(length(x@methods[[p]]@integration_reductions) != 0) {
.integration_reductions <- list()
for(g in names(x@methods[[p]]@integration_reductions)) {
.integration_reductions[[g]] <- x@methods[[p]]@integration_reductions[[g]][jj, , drop = FALSE]
}
} else {
.integration_reductions <-x@methods[[p]]@integration_reductions
}
if(length(x@methods[[p]]@visualisation_reductions) != 0) {
.visualisation_reductions <- list()
for(g in names(x@methods[[p]]@visualisation_reductions)) {
.visualisation_reductions[[g]] <- x@methods[[p]]@visualisation_reductions[[g]][jj, , drop = FALSE]
}
} else {
.visualisation_reductions <-x@methods[[p]]@visualisation_reductions
}
if(length(x@methods[[p]]@cluster_assignments) != 0) {
.cluster_assignments <- list()
for(g in names(x@methods[[p]]@cluster_assignments)) {
.cluster_assignments[[g]] <- x@methods[[p]]@cluster_assignments[[g]][jj, , drop = FALSE]
}
} else {
.cluster_assignments <- x@methods[[p]]@cluster_assignments
}
if(!is.null(x@methods[[p]]@highly.variable.genes)) {
.highly.variable.genes <- x@methods[[p]]@highly.variable.genes[x@methods[[p]]@highly.variable.genes %in% rownames(.counts)]
} else {
.highly.variable.genes <- x@methods[[p]]@highly.variable.genes
}
if(length(x@methods[[p]]@neighbours) != 0) {
list.neighbours <- list()
for(l in names(x@methods[[p]]@neighbours)) {
.neighbours <- x@methods[[p]]@neighbours
list.neighbours.sub <- list()
for(t in names(x@methods[[p]]@neighbours[[l]])) {
list.neighbours.sub[[t]] <- x@methods[[p]]@neighbours[[l]][[t]][jj, jj, drop = FALSE]
}
list.neighbours[[l]] <- list.neighbours.sub
}
.neighbours <- list.neighbours
} else {
.neighbours <- x@methods[[p]]@neighbours
}
.benchmark_results <- x@methods[[p]]@benchmark_results
list.methods[[p]] <- new(Class = 'methods',
counts = .counts,
normalised = .normalised,
norm.scaled = .norm.scaled,
highly.variable.genes = .highly.variable.genes,
feature_metadata = .feature_metadata,
neighbours = .neighbours,
computational_reductions = .computational_reductions,
integration_reductions = .integration_reductions,
visualisation_reductions = .visualisation_reductions,
cluster_assignments = .cluster_assignments,
benchmark_results = .benchmark_results,
misc = x@methods[[p]]@misc)
}
return(new(Class = 'IBRAP',
methods = list.methods,
sample_metadata = .sample_metadata))
}
})
connorhknight/IBRAP documentation built on March 9, 2023, 7:01 p.m.
R Package Documentation
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#' @title Method override for `'['` subset function regarding IBRAP S4 object
#'
#' @exportMethod `[`
setMethod(f = '[', signature = 'IBRAP',
function(x,
i,
j,
...,
drop = FALSE) {
if(!missing(i) & missing(j)) {
# Just features
if(!is.character(i)) {
ii <- .convert_subset_index(x = i, rownames(x))
} else {
ii <- i
}
.sample_metadata <- x@sample_metadata
list.methods <- list()
for(p in names(x@methods)) {
if(!is.na(x@methods[[p]]@counts[1])) {
genes <- ii[ii %in% rownames(x@methods[[p]]@counts)]
.counts <- x@methods[[p]]@counts[genes , , drop = FALSE]
} else {
.counts <- x@methods[[p]]@counts
}
if(!is.na(x@methods[[p]]@normalised[1])) {
genes <- ii[ii %in% rownames(x@methods[[p]]@normalised)]
.normalised <- x@methods[[p]]@normalised[genes, ,drop = FALSE]
} else {
.normalised <- x@methods[[p]]@normalised
}
if(!is.na(x@methods[[p]]@norm.scaled[1])) {
genes <- ii[ii %in% rownames(x@methods[[p]]@norm.scaled)]
.norm.scaled <- x@methods[[p]]@norm.scaled[genes, ,drop = FALSE]
} else {
.norm.scaled <- x@methods[[p]]@norm.scaled
}
if(!is.na(x@methods[[p]]@feature_metadata[1])) {
genes <- ii[ii %in% rownames(x@methods[[p]]@feature_metadata)]
.feature_metadata <- x@methods[[p]]@feature_metadata[genes, , drop = FALSE]
} else {
.feature_metadata <- x@methods[[p]]@feature_metadata
}
if(!is.null(x@methods[[p]]@highly.variable.genes)) {
.highly.variable.genes <- x@methods[[p]]@highly.variable.genes[x@methods[[p]]@highly.variable.genes %in% rownames(.counts)]
} else {
.highly.variable.genes <- x@methods[[p]]@highly.variable.genes
}
.neighbours <- x@methods[[p]]@neighbours
.computational_reductions <- x@methods[[p]]@computational_reductions
.integration_reductions <- x@methods[[p]]@integration_reductions
.visualisation_reductions <- x@methods[[p]]@visualisation_reductions
.cluster_assignments <- x@methods[[p]]@cluster_assignments
.benchmark_results <- x@methods[[p]]@benchmark_results
list.methods[[p]] <- new(Class = 'methods',
counts = .counts,
normalised = .normalised,
norm.scaled = .norm.scaled,
highly.variable.genes = .highly.variable.genes,
feature_metadata = .feature_metadata,
neighbours = .neighbours,
computational_reductions = .computational_reductions,
integration_reductions = .integration_reductions,
visualisation_reductions = .visualisation_reductions,
cluster_assignments = .cluster_assignments,
benchmark_results = .benchmark_results,
misc = x@methods[[p]]@misc)
}
return(new(Class = 'IBRAP',
methods = list.methods,
sample_metadata = .sample_metadata))
}
if(missing(i) & !missing(j)) {
# Just cells
if(!is.character(j)) {
jj <- .convert_subset_index(x = j, colnames(x))
} else {
jj <- j
}
jj <- j
.sample_metadata <- x@sample_metadata[jj, , drop = FALSE]
list.methods <- list()
for(p in names(x@methods)) {
if(!is.na(x@methods[[p]]@counts[1])) {
.counts <- x@methods[[p]]@counts[ , jj, drop = FALSE]
} else {
.counts <- x@methods[[p]]@counts
}
if(!is.na(x@methods[[p]]@normalised[1])) {
.normalised <- x@methods[[p]]@normalised[ , jj, drop = FALSE]
} else {
.normalised <- x@methods[[p]]@normalised
}
if(!is.na(x@methods[[p]]@norm.scaled[1])) {
.norm.scaled <- x@methods[[p]]@norm.scaled[ , jj, drop = FALSE]
} else {
.norm.scaled <- x@methods[[p]]@norm.scaled
}
if(length(x@methods[[p]]@computational_reductions) != 0) {
.computational_reductions <- list()
for(g in names(x@methods[[p]]@computational_reductions)) {
.computational_reductions[[g]] <- x@methods[[p]]@computational_reductions[[g]][jj, , drop = FALSE]
}
} else {
.computational_reductions <-x@methods[[p]]@computational_reductions
}
if(length(x@methods[[p]]@integration_reductions) != 0) {
.integration_reductions <- list()
for(g in names(x@methods[[p]]@integration_reductions)) {
.integration_reductions[[g]] <- x@methods[[p]]@integration_reductions[[g]][jj, , drop = FALSE]
}
} else {
.integration_reductions <-x@methods[[p]]@integration_reductions
}
if(length(x@methods[[p]]@visualisation_reductions) != 0) {
.visualisation_reductions <- list()
for(g in names(x@methods[[p]]@visualisation_reductions)) {
.visualisation_reductions[[g]] <- x@methods[[p]]@visualisation_reductions[[g]][jj, , drop = FALSE]
}
} else {
.visualisation_reductions <- x@methods[[p]]@visualisation_reductions
}
if(length(x@methods[[p]]@cluster_assignments) != 0) {
.cluster_assignments <- list()
for(g in names(x@methods[[p]]@cluster_assignments)) {
.cluster_assignments[[g]] <- x@methods[[p]]@cluster_assignments[[g]][jj, , drop = FALSE]
}
} else {
.cluster_assignments <- x@methods[[p]]@cluster_assignments
}
.highly.variable.genes <- x@methods[[p]]@highly.variable.genes
.feature_metadata <- x@methods[[p]]@feature_metadata
if(length(x@methods[[p]]@neighbours) != 0) {
list.neighbours <- list()
for(l in names(x@methods[[p]]@neighbours)) {
.neighbours <- x@methods[[p]]@neighbours
list.neighbours.sub <- list()
for(t in names(x@methods[[p]]@neighbours[[l]])) {
list.neighbours.sub[[t]] <- x@methods[[p]]@neighbours[[l]][[t]][jj, jj, drop = FALSE]
}
list.neighbours[[l]] <- list.neighbours.sub
}
.neighbours <- list.neighbours
} else {
.neighbours <- x@methods[[p]]@neighbours
}
.benchmark_results <- x@methods[[p]]@benchmark_results
list.methods[[p]] <- new(Class = 'methods',
counts = .counts,
normalised = .normalised,
norm.scaled = .norm.scaled,
highly.variable.genes = .highly.variable.genes,
feature_metadata = .feature_metadata,
neighbours = .neighbours,
computational_reductions = .computational_reductions,
integration_reductions = .integration_reductions,
visualisation_reductions = .visualisation_reductions,
cluster_assignments = .cluster_assignments,
benchmark_results = .benchmark_results,
misc = x@methods[[p]]@misc)
}
return(new(Class = 'IBRAP',
methods = list.methods,
sample_metadata = .sample_metadata))
}
if(!missing(i) & !missing(j)) {
# features and cells
if(!is.character(i)) {
ii <- .convert_subset_index(x = i, rownames(x))
} else {
ii <- i
}
if(!is.character(j)) {
jj <- .convert_subset_index(x = j, colnames(x))
} else {
jj <- j
}
.sample_metadata <- x@sample_metadata[jj, , drop = FALSE]
list.methods <- list()
for(p in names(x@methods)) {
if(!is.na(x@methods[[p]]@counts[1])) {
cells <- jj[jj %in% colnames(x@methods[[p]]@counts)]
genes <- ii[ii %in% rownames(x@methods[[p]]@counts)]
.counts <- x@methods[[p]]@counts[genes , cells, drop = FALSE]
} else {
.counts <- x@methods[[p]]@counts
}
if(!is.na(x@methods[[p]]@normalised[1])) {
cells <- jj[jj %in% colnames(x@methods[[p]]@normalised)]
genes <- ii[ii %in% rownames(x@methods[[p]]@normalised)]
.normalised <- x@methods[[p]]@normalised[genes , cells, drop = FALSE]
} else {
.normalised <- x@methods[[p]]@normalised
}
if(!is.na(x@methods[[p]]@norm.scaled[1])) {
cells <- jj[jj %in% colnames(x@methods[[p]]@norm.scaled)]
genes <- ii[ii %in% rownames(x@methods[[p]]@norm.scaled)]
.norm.scaled <- x@methods[[p]]@norm.scaled[genes , cells, drop = FALSE]
} else {
.norm.scaled <- x@methods[[p]]@norm.scaled
}
if(!is.na(x@methods[[p]]@feature_metadata[1])) {
genes <- ii[ii %in% rownames(x@methods[[p]]@feature_metadata)]
.feature_metadata <- x@methods[[p]]@feature_metadata[genes, , drop = FALSE]
} else {
.feature_metadata <- x@methods[[p]]@feature_metadata
}
if(length(x@methods[[p]]@computational_reductions) != 0) {
.computational_reductions <- list()
for(g in names(x@methods[[p]]@computational_reductions)) {
.computational_reductions[[g]] <- x@methods[[p]]@computational_reductions[[g]][jj, , drop = FALSE]
}
} else {
.computational_reductions <-x@methods[[p]]@computational_reductions
}
if(length(x@methods[[p]]@integration_reductions) != 0) {
.integration_reductions <- list()
for(g in names(x@methods[[p]]@integration_reductions)) {
.integration_reductions[[g]] <- x@methods[[p]]@integration_reductions[[g]][jj, , drop = FALSE]
}
} else {
.integration_reductions <-x@methods[[p]]@integration_reductions
}
if(length(x@methods[[p]]@visualisation_reductions) != 0) {
.visualisation_reductions <- list()
for(g in names(x@methods[[p]]@visualisation_reductions)) {
.visualisation_reductions[[g]] <- x@methods[[p]]@visualisation_reductions[[g]][jj, , drop = FALSE]
}
} else {
.visualisation_reductions <-x@methods[[p]]@visualisation_reductions
}
if(length(x@methods[[p]]@cluster_assignments) != 0) {
.cluster_assignments <- list()
for(g in names(x@methods[[p]]@cluster_assignments)) {
.cluster_assignments[[g]] <- x@methods[[p]]@cluster_assignments[[g]][jj, , drop = FALSE]
}
} else {
.cluster_assignments <- x@methods[[p]]@cluster_assignments
}
if(!is.null(x@methods[[p]]@highly.variable.genes)) {
.highly.variable.genes <- x@methods[[p]]@highly.variable.genes[x@methods[[p]]@highly.variable.genes %in% rownames(.counts)]
} else {
.highly.variable.genes <- x@methods[[p]]@highly.variable.genes
}
if(length(x@methods[[p]]@neighbours) != 0) {
list.neighbours <- list()
for(l in names(x@methods[[p]]@neighbours)) {
.neighbours <- x@methods[[p]]@neighbours
list.neighbours.sub <- list()
for(t in names(x@methods[[p]]@neighbours[[l]])) {
list.neighbours.sub[[t]] <- x@methods[[p]]@neighbours[[l]][[t]][jj, jj, drop = FALSE]
}
list.neighbours[[l]] <- list.neighbours.sub
}
.neighbours <- list.neighbours
} else {
.neighbours <- x@methods[[p]]@neighbours
}
.benchmark_results <- x@methods[[p]]@benchmark_results
list.methods[[p]] <- new(Class = 'methods',
counts = .counts,
normalised = .normalised,
norm.scaled = .norm.scaled,
highly.variable.genes = .highly.variable.genes,
feature_metadata = .feature_metadata,
neighbours = .neighbours,
computational_reductions = .computational_reductions,
integration_reductions = .integration_reductions,
visualisation_reductions = .visualisation_reductions,
cluster_assignments = .cluster_assignments,
benchmark_results = .benchmark_results,
misc = x@methods[[p]]@misc)
}
return(new(Class = 'IBRAP',
methods = list.methods,
sample_metadata = .sample_metadata))
}
})
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