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Merge pull request #298 from hms-dbmi-cellenics/biomage-changes-2
Biomage changes 2
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,115 @@ | ||
| run_fastmnn <- function(scdata_list, config, cells_id) { | ||
| settings <- config$dataIntegration$methodSettings[["fastmnn"]] | ||
| nfeatures <- settings$numGenes | ||
| normalization <- settings$normalisation | ||
| if (grepl("lognorm", normalization, ignore.case = TRUE)) { | ||
| normalization <- "LogNormalize" | ||
| } | ||
|
|
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| # calculate as many PCs for the PCA as possible, ideally 50, unless few cells | ||
| npcs_for_pca <- min(vapply(scdata_list, ncol, integer(1)) - 1, 50) | ||
| npcs <- config$dimensionalityReduction$numPCs | ||
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| # use the min of what the user wants and what can be calculated | ||
| if (!is.null(npcs)) { | ||
| npcs <- min(config$dimensionalityReduction$numPCs, npcs_for_pca) | ||
| } | ||
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| use_geosketch <- "downsampling" %in% names(config) && config$downsampling$method == "geosketch" | ||
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| scdata <- prepare_scdata_for_fastmnn(scdata_list, config, cells_id) | ||
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| # we need RNA assay to compute the integrated matrix | ||
| Seurat::DefaultAssay(scdata) <- "RNA" | ||
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| scdata <- scdata |> | ||
| Seurat::NormalizeData(normalization.method = normalization, verbose = FALSE) |> | ||
| Seurat::FindVariableFeatures(nfeatures = nfeatures, verbose = FALSE) |> | ||
| Seurat::ScaleData(verbose = FALSE) |> | ||
| Seurat::RunPCA(npcs = npcs_for_pca, verbose = FALSE) | ||
|
|
||
| # estimate number of PCs to be used downstream, for integration and clustering | ||
| if (is.null(npcs)) { | ||
| scdata@misc[["active.reduction"]] <- "pca" | ||
| npcs <- get_npcs(scdata) | ||
| message("Estimated number of PCs: ", npcs) | ||
| } | ||
|
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||
| scdata <- add_dispersions(scdata, normalization) | ||
| misc <- scdata@misc | ||
| # remove scale.data slot to avoid errors | ||
| # https://github.com/satijalab/seurat-wrappers/issues/126 | ||
| scdata@assays$RNA@scale.data <- as.matrix(0) | ||
|
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||
| if (use_geosketch) { | ||
| scdata <- | ||
| RunGeosketchFastMNN( | ||
| scdata, | ||
| split.by = "samples", | ||
| features = nfeatures, | ||
| dims = npcs, | ||
| config = config | ||
| ) | ||
| misc[["geosketch"]] <- TRUE | ||
| } else { | ||
| scdata_split <- Seurat::SplitObject(scdata, split.by = "samples") | ||
| scdata <- | ||
| SeuratWrappers::RunFastMNN( | ||
| scdata_split, | ||
| features = nfeatures, | ||
| d = npcs, | ||
| get.variance = TRUE | ||
| ) | ||
| } | ||
|
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| scdata@misc <- misc | ||
| scdata@misc[["numPCs"]] <- npcs | ||
| scdata@misc[["active.reduction"]] <- "mnn" | ||
|
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| return(scdata) | ||
| } | ||
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| prepare_scdata_for_fastmnn <- function(scdata_list, config, cells_id) { | ||
| # pre-process | ||
| scdata_list <- order_by_size(scdata_list) | ||
| scdata <- create_scdata(scdata_list, cells_id) | ||
|
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||
| exclude_groups <- config$dimensionalityReduction$excludeGeneCategories | ||
| # remove genes groups if required | ||
| if (length(exclude_groups) > 0) { | ||
| scdata <- remove_genes(scdata, exclude_groups) | ||
| } | ||
|
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| return(scdata) | ||
| } | ||
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| RunGeosketchFastMNN <- function(scdata, split.by, features, dims, config) { | ||
| set.seed(RANDOM_SEED) | ||
| perc_num_cells <- config$downsampling$methodSettings$geosketch$percentageToKeep | ||
| geosketch_list <- run_geosketch( | ||
| scdata, | ||
| dims = dims, | ||
| perc_num_cells = perc_num_cells | ||
| ) | ||
|
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| scdata_split <- Seurat::SplitObject(geosketch_list$sketch, split.by = split.by) | ||
| scdata_sketch_integrated <- | ||
| SeuratWrappers::RunFastMNN( | ||
| scdata_split, | ||
| features = nfeatures, | ||
| d = dims, | ||
| get.variance = TRUE | ||
| ) | ||
|
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| scdata <- learn_from_sketches( | ||
| geosketch_list$scdata, | ||
| geosketch_list$sketch, | ||
| scdata_sketch_integrated, | ||
| dims = dims | ||
| ) | ||
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| return(scdata) | ||
|
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| } |
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