knitr::opts_chunk$set(echo = TRUE)
library(CellBench) library(dplyr) library(ggplot2) set_cellbench_threads(4) cellbench_mrna_mix_data <- load_mrna_mix_data() data <- list( mrna_mix_celseq = cellbench_mrna_mix_data$mrna_mix_celseq ) norm_method <- list( scran = scran_norm_expr ) impute_method <- fn_arg_seq( impute_knn_smooth, k = c(4, 8, 16, 32) ) res <- data %>% apply_methods(norm_method) res <- res %>% apply_methods(impute_method) dim_red <- list( pca = compute_pca ) res <- res %>% apply_methods(dim_red) append_anno <- function(data_key, result) { mRNA_amount <- colData(cellbench_mrna_mix_data$mrna_mix_celseq)$mRNA_amount truth <- with( colData(cellbench_mrna_mix_data$mrna_mix_celseq), paste(H2228_prop, H1975_prop, HCC827_prop) ) result %>% tibble::add_column(mRNA_amount, .before = TRUE) %>% tibble::add_column(truth, .before = TRUE) } annotated_res <- res %>% dplyr::mutate(data_key = paste(data)) %>% dplyr::mutate(result = map2(data_key, result, append_anno)) %>% dplyr::select(-data_key) plot_df <- tidyr::unnest(annotated_res) plot_df %>% ggplot(aes(x = Dim1, y = Dim2, col = truth)) + geom_point() + facet_wrap(~impute_method, nrow = 2) + ggtitle("KNN Smooth Imputation")
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