Tyser, R.C.V., Mahammadov, E., Nakanoh, S., Vallier, L., Scialdone, A., and Srinivas, S. (2021). Single-cell transcriptomic characterization of a gastrulating human embryo. Nature 600, 285–289.

  • BioProject Accession: PRJEB40781
  • AEArrayExpress Accession: E-MTAB-9388


Load required packages.

library(tidyverse)
library(Matrix)
library(patchwork)
library(extrafont)
Sys.time()
## [1] "2022-01-20 22:34:30 CST"

Data preparation

Functions loading

source(
    file = file.path(
        SCRIPT_DIR,
        "utilities.R"
    )
)
plot_embedding_highlight <- function(embedding, x, y, label, n_cols = 3) {
    cell_metadata_selected <- x
    selected_column <- y

    purrr::map(levels(cell_metadata_selected[[selected_column]]), \(x) {
        values <- embedding |>
            dplyr::left_join(cell_metadata_selected) |>
            dplyr::mutate(
                value = case_when(
                    .data[[selected_column]] == x ~ "1",
                    .data[[selected_column]] != x ~ "0"
                )
            ) |>
            dplyr::pull(value) |>
            as.integer() |>
            as.factor()

        plot_embedding(
            embedding = embedding[, c(x_column, y_column)],
            color_values = values,
            label = glue::glue(
                "{label}; ",
                "{x}: {sum(as.integer(as.character(values)), na.rm = TRUE)}"
            ),
            label_position = NULL,
            show_color_value_labels = FALSE,
            show_color_legend = FALSE,
            geom_point_size = GEOM_POINT_SIZE,
            sort_values = TRUE,
            shuffle_values = FALSE,
            rasterise = RASTERISED,
            legend_size = 2
        ) +
            theme_customized(
                legend_key_size = 2,
                legend_text_size = 5
            ) +
            scale_color_manual(
                na.translate = TRUE,
                values = c("grey70", "salmon"),
                na.value = "#7F7F7F"
            ) +
            ggplot2::annotate(
                geom = "text",
                x = Inf,
                y = Inf,
                label = sum(as.integer(as.character(values)), na.rm = TRUE),
                size = 5 / ggplot2::.pt,
                hjust = 1,
                vjust = 1,
                na.rm = FALSE
            )
    }) |>
        purrr::reduce(`+`) +
        patchwork::plot_layout(ncol = n_cols) +
        patchwork::plot_annotation(
            theme = ggplot2::theme(plot.margin = ggplot2::margin())
        )
}

Data loading

PROJECT_DIR <- "/Users/jialei/Dropbox/Data/Projects/UTSW/Peri-implantation"

Matrix

ad <- reticulate::import(module = "anndata", convert = TRUE)
print(ad$`__version__`)
## [1] "0.7.6"
adata_files <- purrr::map(c("", "_salmon", "_kallisto"), \(x) {
    file.path(
        PROJECT_DIR,
        "raw",
        "public",
        "PRJEB40781",
        paste0("matrix", x),
        "adata.h5ad"
    )
})
purrr::map_lgl(adata_files, file.exists)
## [1] TRUE TRUE TRUE
BACKED <- NULL
matrix_readcount_use <- purrr::map(adata_files, function(x) {
    ad$read_h5ad(
        filename = x, backed = BACKED
    ) |>
        convert_adata()
})
names(matrix_readcount_use) <- c("star", "salmon", "kallisto")

purrr::map_int(matrix_readcount_use, ncol)
##     star   salmon kallisto 
##     1195     1195     1195

Metadata

BACKED <- "r"
cell_metadata <- purrr::map(adata_files, function(x) {
    if (stringr::str_detect(x, pattern = "kallisto")) {
        y <- "kallisto"
    } else if (stringr::str_detect(x, pattern = "salmon")) {
        y <- "salmon"
    } else {
        y <- "star"
    }

    ad$read_h5ad(
        filename = x, backed = BACKED
    )$obs |>
        tibble::rownames_to_column(var = "cell") |>
        dplyr::select(cell, everything()) |>
        dplyr::mutate(method = y)
})
names(cell_metadata) <- c("star", "salmon", "kallisto")

purrr::map_int(cell_metadata, nrow)
##     star   salmon kallisto 
##     1195     1195     1195


Check memory usage.

purrr::walk(list(matrix_readcount_use, cell_metadata), function(x) {
    print(object.size(x), units = "auto", standard = "SI")
})
## 203.6 MB
## 381.5 kB

STAR, Salmon, Kallisto

Cell metadata & embedding

cell_metadata_PRJEB40781 <- vroom::vroom(
    file = file.path(
        PROJECT_DIR,
        "raw",
        "public",
        "PRJEB40781",
        "matrix",
        "cell_metadata.csv"
    )
) |>
    dplyr::mutate(
        lineage = factor(lineage)
    )
## Rows: 1195 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): cell, run, source_name, developmental_stage, individual, sex, sampl...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
EMBEDDING_FILE <- "embedding_ncomponents15_ccc1_seed20210719.csv.gz"

embedding_1195 <- purrr::map(c("star", "salmon", "kallisto"), \(x) {
    vroom::vroom(
        file = file.path(
            PROJECT_DIR,
            "raw/public/PRJEB40781",
            "clustering/PRJEB40781/",
            x,
            "/exploring",
            "Scanpy_Harmony",
            EMBEDDING_FILE
        )
    ) |>
        dplyr::mutate(method = x)
}) |>
    dplyr::bind_rows()

embedding_1195 |> head()

Visualization

x_column <- "x_umap_min_dist=0.1"
y_column <- "y_umap_min_dist=0.1"

GEOM_POINT_SIZE <- 1.25
EMBEDDING_TITLE_PREFIX <- "UMAP"
RASTERISED <- TRUE

Clustering

cell_metadata |>
    dplyr::bind_rows() |>
    dplyr::group_by(method) |>
    dplyr::summarise(
        num_cells = dplyr::n(),
        median_umis = median(num_umis),
        median_features = median(num_features),
        median_mt_percentage = median(mt_percentage)
    ) |>
    dplyr::mutate(
        method = factor(
            method,
            levels = c("star", "salmon", "kallisto")
        )
    ) |>
    dplyr::arrange(method) |>
    gt::gt()
method num_cells median_umis median_features median_mt_percentage
star 1195 899241 4379 8.164051e-03
salmon 1195 531492 4500 7.394771e-03
kallisto 1195 553929 4949 4.271283e-06
 
purrr::map(c("star", "salmon", "kallisto"), \(x) {
    plot_embedding(
        embedding = embedding_1195[, c(x_column, y_column, "method")] |>
            dplyr::filter(method == x),
        color_values = embedding_1195$leiden[embedding_1195$method == x]
        |> as.factor(),
        label = paste(
            EMBEDDING_TITLE_PREFIX,
            paste(x, "embedding"),
            paste(x, "clustering"),
            sep = "; "
        ),
        label_position = NULL,
        show_color_value_labels = TRUE,
        show_color_legend = FALSE,
        geom_point_size = GEOM_POINT_SIZE,
        sort_values = FALSE,
        shuffle_values = TRUE,
        rasterise = RASTERISED
    ) +
        theme_customized()
}) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 3) +
    patchwork::plot_annotation(
        theme = ggplot2::theme(plot.margin = ggplot2::margin())
    )

purrr::map(c("star", "salmon", "kallisto"), \(x) {
    embedding_star <- embedding_1195[, c(
        x_column, y_column,
        "method", "cell", "leiden"
    )] |>
        dplyr::filter(method == "star")

    values <- embedding_1195[, c(
        x_column, y_column,
        "method", "leiden", "cell"
    )] |>
        dplyr::filter(method == x) |>
        dplyr::left_join(
            embedding_star |> dplyr::select(cell, leiden_star = leiden)
        ) |>
        dplyr::pull(leiden_star)

    plot_embedding(
        embedding = embedding_1195[, c(x_column, y_column, "method")] |>
            dplyr::filter(method == x),
        color_values = values
        |> as.factor(),
        label = paste(
            EMBEDDING_TITLE_PREFIX,
            paste(x, "embedding"),
            "star clustering",
            sep = "; "
        ),
        label_position = NULL,
        show_color_value_labels = TRUE,
        show_color_legend = FALSE,
        geom_point_size = GEOM_POINT_SIZE,
        sort_values = FALSE,
        shuffle_values = TRUE,
        rasterise = RASTERISED
    ) +
        theme_customized()
}) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 3) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

Expression

purrr::map(c("star", "salmon", "kallisto"), \(x) {
    plot_embedding(
        embedding = embedding_1195[, c(x_column, y_column, "method")] |>
            dplyr::filter(method == x),
        color_values = embedding_1195[, c("method", "cell")] |>
            dplyr::filter(method == x) |>
            dplyr::left_join(
                cell_metadata[[x]]
            ) |>
            dplyr::pull(num_umis) |>
            {
                \(x) log10(x)
            }(),
        label = paste(EMBEDDING_TITLE_PREFIX, paste(x, "embedding"), "UMI", sep = "; "),
        label_position = NULL,
        show_color_value_labels = FALSE,
        show_color_legend = TRUE,
        geom_point_size = GEOM_POINT_SIZE / 1.5,
        sort_values = TRUE,
        shuffle_values = FALSE,
        rasterise = RASTERISED,
        legend_size = 2
    ) +
        theme_customized(
            legend_key_size = 2,
            legend_text_size = 5
        )
}) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 3) +
    patchwork::plot_annotation(
        theme = ggplot2::theme(plot.margin = ggplot2::margin())
    )

FEATURES_SELECTED <- c(
    "ENSG00000204531_POU5F1",
    #
    "ENSG00000164736_SOX17",
    #
    "ENSG00000179348_GATA2"
)
purrr::map(FEATURES_SELECTED, \(y) {
    selected_feature <- y

    purrr::map(c("star", "salmon", "kallisto"), \(x) {
        values <- log10(
            calc_cpm(
                matrix_readcount_use[[x]][, embedding_1195$cell[embedding_1195$method == x]]
            )[selected_feature, ] + 1
        )

        plot_embedding(
            embedding = embedding_1195[, c(x_column, y_column, "method")] |>
                dplyr::filter(method == x),
            color_values = values,
            label = paste(
                EMBEDDING_TITLE_PREFIX,
                paste(x, "embedding"),
                x,
                selected_feature |> stringr::str_remove(pattern = "^.+_"),
                sep = "; "
            ),
            label_position = NULL,
            show_color_value_labels = FALSE,
            show_color_legend = TRUE,
            geom_point_size = GEOM_POINT_SIZE / 1.5,
            sort_values = TRUE,
            shuffle_values = FALSE,
            rasterise = RASTERISED,
            legend_size = 2
        ) +
            theme_customized(
                legend_key_size = 2,
                legend_text_size = 5
            )
    })
}) |>
    unlist(recursive = FALSE) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 3) +
    patchwork::plot_annotation(
        theme = ggplot2::theme(plot.margin = ggplot2::margin())
    )

purrr::map(FEATURES_SELECTED, \(y) {
    selected_feature <- y

    embedding_star <- embedding_1195[, c(
        x_column, y_column,
        "method", "cell", "leiden"
    )] |>
        dplyr::filter(method == "star")

    purrr::map(c("star", "salmon", "kallisto"), \(x) {
        values <- log10(
            calc_cpm(
                matrix_readcount_use[[x]][, embedding_star$cell]
            )[selected_feature, ] + 1
        )

        plot_embedding(
            embedding = embedding_star,
            color_values = values,
            label = paste(
                EMBEDDING_TITLE_PREFIX,
                "star embedding",
                x,
                selected_feature |> stringr::str_remove(pattern = "^.+_"),
                sep = "; "
            ),
            label_position = NULL,
            show_color_value_labels = FALSE,
            show_color_legend = TRUE,
            geom_point_size = GEOM_POINT_SIZE / 1.5,
            sort_values = TRUE,
            shuffle_values = FALSE,
            rasterise = RASTERISED,
            legend_size = 2
        ) +
            theme_customized(
                legend_key_size = 2,
                legend_text_size = 5
            )
    })
}) |>
    unlist(recursive = FALSE) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 3) +
    patchwork::plot_annotation(
        theme = ggplot2::theme(plot.margin = ggplot2::margin())
    )

FEATURES_SELECTED <- c(
    "ENSG00000204531_POU5F1",
    "ENSG00000111704_NANOG",
    "ENSG00000171872_KLF17",
    "ENSG00000186103_ARGFX",
    #
    "ENSG00000164736_SOX17",
    "ENSG00000125798_FOXA2",
    "ENSG00000136574_GATA4",
    "ENSG00000134853_PDGFRA",
    #
    "ENSG00000179348_GATA2",
    "ENSG00000070915_SLC12A3",
    "ENSG00000165556_CDX2",
    "ENSG00000007866_TEAD3"
)

plot_violin(
    cells = embedding_1195 |>
        split(~method) |>
        purrr::map(
            \(x) {
                x |> dplyr::mutate(cell = paste(method, cell, sep = "_"))
            } |>
                dplyr::pull(cell)
        ) |>
        {
            \(x) x[c("star", "salmon", "kallisto")]
        }(),
    features = FEATURES_SELECTED,
    matrix_cpm = calc_cpm(purrr::map(names(matrix_readcount_use), \(x) {
        colnames(matrix_readcount_use[[x]]) <-
            paste(x,
                colnames(matrix_readcount_use[[x]]),
                sep = "_"
            )
        return(matrix_readcount_use[[x]])
    }) |>
        purrr::reduce(cbind)),
    y_title = "Aligner/Mapper",
    strip_text_size = 6
) +
    theme_grey(base_size = 6) %+replace%
    ggplot2::theme(
        axis.title.y = ggplot2::element_text(
            family = "Arial",
            size = 6,
            angle = 90,
            margin = ggplot2::margin(
                t = 0, r = 1, b = 0, l = 0,
                unit = "mm"
            )
        ),
    )

pairs <- combn(c("star", "salmon", "kallisto"), 2)

purrr::map(seq_len(ncol(pairs)), \(x) {
    aa <- pairs[, x, drop = TRUE][1]
    bb <- pairs[, x, drop = TRUE][2]

    a <- rowMeans(calc_cpm(matrix_readcount_use[[aa]]))
    b <- rowMeans(calc_cpm(matrix_readcount_use[[bb]]))

    a <- log10(a + 1)
    b <- log10(b + 1)
    corr <- cor(a, b, method = "spearman")

    ggplot2::ggplot(
        data = NULL,
        ggplot2::aes(
            x = a,
            y = b
        )
    ) +
        # geom_point() +
        ggpointdensity::geom_pointdensity(alpha = 0.3, na.rm = TRUE) +
        ggplot2::scale_x_continuous(
            name = glue::glue(
                "<span style='color:red'>**{aa}**</span>; ",
                "log<sub>10</sub> CPM + 1"
            )
        ) +
        ggplot2::scale_y_continuous(
            name = glue::glue(
                "<span style='color:red'>**{bb}**</span>; ",
                "log<sub>10</sub> CPM + 1"
            )
        ) +
        ggplot2::coord_fixed() +
        ggplot2::geom_abline(color = "salmon") +
        ggplot2::labs(color = "Density") +
        ggplot2::theme_bw(base_size = 6) %+replace%
        ggplot2::theme(
            axis.title.x = ggtext::element_markdown(
                # axis.title = ggtext::element_textbox(
                family = "Arial",
                size = 7,
                margin = ggplot2::margin(
                    t = 1, r = 0, b = 0, l = 0,
                    unit = "mm"
                )
            ),
            axis.title.y = ggtext::element_markdown(
                family = "Arial",
                size = 7,
                angle = 90,
                margin = ggplot2::margin(
                    t = 0, r = 1, b = 0, l = 0,
                    unit = "mm"
                )
            ),
            #
            legend.background = ggplot2::element_blank(),
            legend.margin = ggplot2::margin(
                t = 0, r = 0, b = 0, l = 0, unit = "mm"
            ),
            legend.key = ggplot2::element_blank(),
            legend.key.size = grid::unit(2, "mm"),
            legend.text = ggplot2::element_text(
                family = "Arial",
                size = 5,
                margin = ggplot2::margin(
                    t = 0, r = 0, b = 0, l = -0.5,
                    unit = "mm"
                )
            ),
            #
            legend.position = c(0.825, 0.3),
            legend.justification = c(0, 1)
        ) +
        ggtext::geom_richtext(
            ggplot2::aes(
                x = 0,
                y = 4
            ),
            label = paste("***ρ*** =", round(corr, 3)),
            fill = NA,
            family = "Arial",
            size = 5 / ggplot2::.pt,
            hjust = 0,
            vjust = 1
        )
}) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(nrow = 1) +
    patchwork::plot_annotation(
        theme = ggplot2::theme(plot.margin = ggplot2::margin())
    )


R session info

devtools::session_info()$platform
##  setting  value
##  version  R version 4.1.2 (2021-11-01)
##  os       macOS Monterey 12.1
##  system   aarch64, darwin20.6.0
##  ui       unknown
##  language (EN)
##  collate  en_US.UTF-8
##  ctype    en_US.UTF-8
##  tz       America/Chicago
##  date     2022-01-20
##  pandoc   2.14.0.3 @ /Applications/RStudio.app/Contents/MacOS/pandoc/ (via rmarkdown)
devtools::session_info()$pack |>
    as_tibble() |>
    dplyr::select(
        package,
        loadedversion,
        date,
        `source`
    ) |>
    # print(n = nrow(.))
    gt::gt() |>
    gt::tab_options(table.font.size = "median")
package loadedversion date source
assertthat 0.2.1 2019-03-21 CRAN (R 4.1.1)
backports 1.4.1 2021-12-13 CRAN (R 4.1.2)
bit 4.0.4 2020-08-04 CRAN (R 4.1.1)
bit64 4.0.5 2020-08-30 CRAN (R 4.1.1)
brio 1.1.3 2021-11-30 CRAN (R 4.1.2)
broom 0.7.11 2022-01-03 CRAN (R 4.1.2)
bslib 0.3.1 2021-10-06 CRAN (R 4.1.1)
cachem 1.0.6 2021-08-19 CRAN (R 4.1.1)
callr 3.7.0 2021-04-20 CRAN (R 4.1.1)
cellranger 1.1.0 2016-07-27 CRAN (R 4.1.1)
checkmate 2.0.0 2020-02-06 CRAN (R 4.1.1)
cli 3.1.1 2022-01-20 CRAN (R 4.1.2)
codetools 0.2-18 2020-11-04 CRAN (R 4.1.2)
colorspace 2.0-2 2021-06-24 CRAN (R 4.1.1)
crayon 1.4.2 2021-10-29 CRAN (R 4.1.1)
data.table 1.14.2 2021-09-27 CRAN (R 4.1.1)
DBI 1.1.2 2021-12-20 CRAN (R 4.1.2)
dbplyr 2.1.1 2021-04-06 CRAN (R 4.1.1)
desc 1.4.0 2021-09-28 CRAN (R 4.1.1)
devtools 2.4.3.9000 2022-01-15 Github (r-lib/devtools@e2f25cd69031c8d2099106baed894df4109cb7a4)
digest 0.6.29 2021-12-01 CRAN (R 4.1.2)
dplyr 1.0.7.9000 2022-01-12 Github (tidyverse/dplyr@05013358ace44fe17a51395d49d384232d18d6c1)
dtplyr 1.2.1 2022-01-19 CRAN (R 4.1.2)
ellipsis 0.3.2 2021-04-29 CRAN (R 4.1.1)
evaluate 0.14 2019-05-28 CRAN (R 4.1.1)
extrafont 0.17 2014-12-08 CRAN (R 4.1.1)
extrafontdb 1.0 2012-06-11 CRAN (R 4.1.1)
fansi 1.0.2 2022-01-14 CRAN (R 4.1.2)
farver 2.1.0 2021-02-28 CRAN (R 4.1.1)
fastmap 1.1.0 2021-01-25 CRAN (R 4.1.1)
forcats 0.5.1.9000 2021-11-29 Github (tidyverse/forcats@b4dade0636a46543c30b0b647d97c3ce697c0ada)
fs 1.5.2.9000 2021-12-09 Github (r-lib/fs@6d1182fea7e1c1ddbef3b0bba37c0b0a2e09749c)
gargle 1.2.0 2021-07-02 CRAN (R 4.1.1)
generics 0.1.1 2021-10-25 CRAN (R 4.1.1)
ggplot2 3.3.5 2021-06-25 CRAN (R 4.1.1)
ggpointdensity 0.1.0 2021-11-14 Github (LKremer/ggpointdensity@02f3ab24eb22e3e34294baeca23a8998db43be70)
glue 1.6.0.9000 2021-12-21 Github (tidyverse/glue@76793ef2c376140350c0e1909e66fd404a52b1ef)
googledrive 2.0.0 2021-07-08 CRAN (R 4.1.1)
googlesheets4 1.0.0 2021-07-21 CRAN (R 4.1.1)
gt 0.3.1.9000 2022-01-17 Github (rstudio/gt@fcabb414c55b70c9e445fbedfb24d52fe394ba61)
gtable 0.3.0.9000 2021-10-28 Github (r-lib/gtable@a0bd2721a0a31c8b4391b84aabe98f8c85881140)
haven 2.4.3 2021-08-04 CRAN (R 4.1.1)
highr 0.9 2021-04-16 CRAN (R 4.1.1)
hms 1.1.1 2021-09-26 CRAN (R 4.1.1)
htmltools 0.5.2 2021-08-25 CRAN (R 4.1.1)
httr 1.4.2 2020-07-20 CRAN (R 4.1.1)
jquerylib 0.1.4 2021-04-26 CRAN (R 4.1.1)
jsonlite 1.7.3 2022-01-17 CRAN (R 4.1.2)
knitr 1.37.1 2021-12-21 https://yihui.r-universe.dev (R 4.1.2)
labeling 0.4.2 2020-10-20 CRAN (R 4.1.1)
lattice 0.20-45 2021-09-22 CRAN (R 4.1.2)
lifecycle 1.0.1 2021-09-24 CRAN (R 4.1.1)
lubridate 1.8.0 2022-01-20 Github (tidyverse/lubridate@566590f51364e6c42251cc1721f37c314ddf7e5f)
magrittr 2.0.1 2020-11-17 CRAN (R 4.1.1)
MASS 7.3-55 2022-01-13 CRAN (R 4.1.2)
Matrix 1.4-0 2021-12-08 CRAN (R 4.1.2)
memoise 2.0.1 2021-11-26 CRAN (R 4.1.2)
modelr 0.1.8.9000 2021-10-27 Github (tidyverse/modelr@16168e0624215d9d1a008f3a85de30aeb75302f6)
munsell 0.5.0 2018-06-12 CRAN (R 4.1.1)
patchwork 1.1.0.9000 2021-10-27 Github (thomasp85/patchwork@79223d3002e7bd7e715a270685c6507d684b2622)
pillar 1.6.4 2021-10-18 CRAN (R 4.1.1)
pkgbuild 1.3.1 2021-12-20 CRAN (R 4.1.2)
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