In Shians/CellBench: Construct Benchmarks for Single Cell Analysis Methods

library(CellBench)
library(dplyr)
library(ggplot2)
library(purrr)
knitr::opts_chunk$set(echo = TRUE)

CellBench:::load_mrna_mix_data()

set_cellbench_threads(4)

data <- list(
    mrna_mix_celseq = cellbench_mrna_mix_data$mrna_mix_celseq
        %>% sample_cells(n = 150)
        %>% sample_genes(n = 2000)
)

impute_method <- list(
    scran_drimpute = impute_drimpute,
    basics = impute_basics,
    knn_smooth = impute_knn_smooth
)

res1 <- apply_methods(data, impute_method)

res1

dim_red <- list(
    pca = compute_pca,
    pca_500_most_var = partial(compute_pca_most_var, ngenes = 500),
    pca_300_most_var = partial(compute_pca_most_var, ngenes = 300),
    pca_100_most_var = partial(compute_pca_most_var, ngenes = 100)
)

res2 <- res1 %>%
    apply_methods(dim_red)

res2

mrna_mix_data <- data$mrna_mix_celseq
append_anno <- function(data, result) {

    mRNA_amount <- colData(mrna_mix_data)$mRNA_amount
    truth <- with(
        colData(mrna_mix_data),
        paste(H2228_prop, H1975_prop, HCC827_prop)
    )

    result %>%
        tibble::add_column(mRNA_amount, .before = TRUE) %>%
        tibble::add_column(truth, .before = TRUE)
}

annotated_res <- res2 %>%
   mutate(result = map2(data, result, append_anno))

annotated_res

plot_df <- tidyr::unnest(annotated_res)

plot_df %>%
    ggplot(aes(x = Dim1, y = Dim2, col = truth)) +
    geom_point() +
    facet_grid(dim_red~impute_method, scales = "free") + 
    theme_bw()

Shians/CellBench documentation built on Oct. 16, 2023, 7:54 a.m.