Kagawa, H., Javali, A., Khoei, H.H., Sommer, T.M., Sestini, G., Novatchkova, M., Scholte Op Reimer, Y., Castel, G., Bruneau, A., Maenhoudt, N., et al. (2021). Human blastoids model blastocyst development and implantation. Nature 1–9.

  • BioProject Accession: PRJNA737139
  • GEO Accession: GSE177689


Load required packages.

library(tidyverse)
library(Matrix)
library(patchwork)
library(extrafont)
Sys.time()
## [1] "2022-01-18 00:33:31 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


Raw fastq files of these datasets were downloaded and re-processed as described in Yu et al. 2021 to minimize platform and processing differences.

ad <- reticulate::import(module = "anndata", convert = TRUE)
print(ad$`__version__`)
## [1] "0.7.6"
adata_files <- purrr::map(c("PRJNA737139", "PRJEB11202", "PRJNA431392", "PRJEB40781"), \(x) {
    file.path(
        PROJECT_DIR,
        "raw",
        "public",
        x,
        "matrix",
        "adata.h5ad"
    )
})
purrr::map_lgl(adata_files, file.exists)
## [1] TRUE TRUE TRUE TRUE
BACKED <- NULL
matrix_readcount_use <- purrr::map(adata_files, function(x) {
    ad$read_h5ad(
        filename = x, backed = BACKED
    ) |>
        convert_adata()
}) |>
    purrr::reduce(cbind)

matrix_readcount_use |> dim()
## [1] 33538 10702

Metadata

BACKED <- "r"
cell_metadata <- purrr::map(adata_files, function(x) {
    ad$read_h5ad(
        filename = x, backed = BACKED
    )$obs |>
        tibble::rownames_to_column(var = "cell") |>
        dplyr::select(cell, everything())
}) |>
    dplyr::bind_rows()

cell_metadata |> head()

Check memory usage.

purrr::walk(list(matrix_readcount_use, cell_metadata), \(x) {
    print(object.size(x), units = "auto", standard = "SI")
})
## 922.6 MB
## 1.1 MB

Clustering with PSCs and TSCs

Cell metadata of Kagawa et al. 2021

cell_metadata_PRJNA737139 <- vroom::vroom(
    file = file.path(
        PROJECT_DIR,
        "raw",
        "public",
        "PRJNA737139",
        "matrix/cell_metadata.csv"
    )
) |>
    dplyr::mutate(
        lineage = factor(
            lineage,
            levels = c(
                "Naive H9",
                "Primed H9",
                "TSCs",
                "Blastoid_24h",
                "Blastoid_60h",
                "Blastoid_96h"
            )
        ),
        source_name = factor(
            source_name
        )
    )

cell_metadata_PRJNA737139 |> head()


The expression matrix was re-generated from raw fastq files. To make this analysis comparable with the original publication, we retrieved the cell ids from the deposited dataset.


Retrieve the cell metadata from Gene Expression Omnibus.

cell_metadata_PRJNA737139_author <- vroom::vroom(
    file = "https://ftp.ncbi.nlm.nih.gov/geo/series/GSE177nnn/GSE177689/suppl/GSE177689_blastoid.H9.okae_bts5__scRNAseq.unfiltered__metadata.tsv.gz"
)
## Rows: 2715 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): samplename, treatment
## dbl (7): sampleid, nFeature_RNA, percent.mt, blastoid.Fig2b.lowres, blastoid...
## 
## 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.
cell_metadata_PRJNA737139_author |> head()
num_cells_used_in_Fig2b <- cell_metadata_PRJNA737139_author |>
    dplyr::filter(
        !is.na(blastoid.Fig2b.lowres)
    ) |>
    nrow()

print(
    glue::glue("Total number of cells deposited: {nrow(cell_metadata_PRJNA737139_author)}"),
    glue::glue("Number of cells used in Fig. 2b: {num_cells_used_in_Fig2b}")
)
## Total number of cells deposited: 2715
## Number of cells used in Fig. 2b: 2067
num_cells_deposited_blastoid <- cell_metadata_PRJNA737139_author |>
    dplyr::filter(
        stringr::str_starts(string = treatment, pattern = "PALLY"),
    ) |>
    nrow()

num_cells_used_in_Fig2b_blastoid <- cell_metadata_PRJNA737139_author |>
    dplyr::filter(
        stringr::str_starts(string = treatment, pattern = "PALLY"),
        !is.na(blastoid.Fig2b.lowres)
    ) |>
    nrow()

print(
    glue::glue("Total number of blastoid cells deposited: {num_cells_deposited_blastoid}"),
    glue::glue("Number of blastoid cells used in Fig. 2b: {num_cells_used_in_Fig2b_blastoid}")
)
## Total number of blastoid cells deposited: 2311
## Number of blastoid cells used in Fig. 2b: 1672

2067 out of total 2715 deposited cells (73.1%), and 1672 out of 2311 deposited blastoid cells (72.3%) are used in Fig. 2a-d.

Visualization

Embedding

EMBEDDING_FILE <- glue::glue(
    "embedding_ncomponents10_ccc1_seed20210719.csv.gz"
)

embedding_2067 <- vroom::vroom(
    file = file.path(
        PROJECT_DIR,
        "raw",
        "public",
        "PRJNA737139",
        "clustering",
        "PRJNA737139",
        "exploring",
        "Scanpy_Harmony",
        EMBEDDING_FILE
    )
)
## Rows: 2067 Columns: 16
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (2): cell, batch
## dbl (14): louvain, leiden, x_tsne, y_tsne, x_fitsne, y_fitsne, x_umap_min_di...
## 
## 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.

Clustering

GEOM_POINT_SIZE <- 0.6
RASTERISED <- TRUE
x_column <- "x_umap_min_dist=0.1"
y_column <- "y_umap_min_dist=0.1"

EMBEDDING_TITLE_PREFIX <- "UMAP"

p_embedding_leiden <- plot_embedding(
    embedding = embedding_2067[, c(x_column, y_column)],
    color_values = embedding_2067$leiden |> as.factor(),
    label = glue::glue("{EMBEDDING_TITLE_PREFIX}; Leiden"),
    label_position = NULL,
    show_color_value_labels = TRUE,
    show_color_legend = FALSE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    shuffle_values = FALSE,
    rasterise = RASTERISED
) +
    theme_customized()

p_embedding_UMI <- plot_embedding(
    embedding = embedding_2067[, c(x_column, y_column)],
    color_values = log10(Matrix::colSums(matrix_readcount_use[, embedding_2067$cell])),
    label = glue::glue("{EMBEDDING_TITLE_PREFIX}; UMI"),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE * 1,
    geom_point_alpha = 1,
    sort_values = TRUE,
    shuffle_values = FALSE,
    label_size = 2.5,
    label_hjust = 0,
    label_vjust = 0,
    rasterise = RASTERISED,
    legend_size = 2,
    legend_ncol = 1
) +
    theme_customized(
        legend_key_size = 2,
        legend_text_size = 5
    )

p_embedding_MT <- plot_embedding(
    embedding = embedding_2067[, c(x_column, y_column)],
    color_values = embedding_2067 |>
        dplyr::left_join(
            cell_metadata # |> dplyr::select(-batch)
        ) |>
        dplyr::pull(mt_percentage),
    label = glue::glue("{EMBEDDING_TITLE_PREFIX}; MT %"),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE * 1,
    geom_point_alpha = 1,
    sort_values = TRUE,
    shuffle_values = FALSE,
    label_size = 2.5,
    label_hjust = 0,
    label_vjust = 0,
    rasterise = RASTERISED,
    legend_size = 2,
    legend_ncol = 1
) +
    theme_customized(
        legend_key_size = 2,
        legend_text_size = 5
    )

selected_feature <- "ENSG00000204531_POU5F1"
p_embedding_POU5F1 <- plot_embedding(
    embedding = embedding_2067[, c(x_column, y_column)],
    color_values = log10(calc_cpm(matrix_readcount_use)[selected_feature, embedding_2067$cell] + 1),
    label = glue::glue("{EMBEDDING_TITLE_PREFIX}; {selected_feature}"),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    geom_point_alpha = 1,
    sort_values = TRUE,
    shuffle_values = FALSE,
    label_size = 2.5,
    label_hjust = 0,
    label_vjust = 0,
    rasterise = FALSE,
    legend_size = 2,
    legend_ncol = 1
) +
    theme_customized(
        legend_key_size = 2,
        legend_text_size = 5
    )
purrr::reduce(
    list(
        p_embedding_leiden,
        p_embedding_UMI,
        p_embedding_MT,
        p_embedding_POU5F1
    ), `+`
) +
    patchwork::plot_layout(ncol = 2) +
    patchwork::plot_annotation(
        theme = ggplot2::theme(plot.margin = ggplot2::margin())
    )

Summary

embedding_2067 |>
    dplyr::left_join(
        cell_metadata
    ) |>
    dplyr::mutate(
        num_umis = Matrix::colSums(matrix_readcount_use[, cell]),
        num_genes = Matrix::colSums(matrix_readcount_use[, cell] > 0),
    ) |>
    dplyr::group_by(leiden) |>
    dplyr::summarise(
        num_cells = n(),
        median_umis = median(num_umis),
        median_genes = median(num_genes),
        meidan_MT = median(mt_percentage)
    ) |>
    gt::gt() |>
    gt::tab_options(table.font.size = "median")
leiden num_cells median_umis median_genes meidan_MT
0 205 2140756 5387.0 0.0529720842
1 182 2311908 5423.0 0.0404300979
2 173 1631691 4577.0 0.0488606864
3 156 2319072 6379.5 0.0413304999
4 152 1390714 3764.5 0.0543443623
5 149 855797 3103.0 0.0364863153
6 126 2646474 6842.5 0.0501413970
7 124 2202163 6414.5 0.0413531842
8 119 1673032 4618.0 0.0522041437
9 115 1849883 5760.0 0.0506782652
10 115 1738237 3665.0 0.0002466655
11 114 1201448 3621.0 0.0016033615
12 109 1327329 3660.0 0.0361322014
13 103 1507763 4685.0 0.0376887867
14 79 1519467 4506.0 0.0003062544
15 46 1677120 4610.0 0.0469316175
 


embedding_2067 |>
    dplyr::left_join(
        cell_metadata,
    ) |>
    dplyr::left_join(
        cell_metadata_PRJNA737139
    ) |>
    dplyr::mutate(
        num_umis = Matrix::colSums(matrix_readcount_use[, cell]),
        num_genes = Matrix::colSums(matrix_readcount_use[, cell] > 0),
    ) |>
    dplyr::group_by(lineage) |>
    dplyr::summarise(
        num_cells = n(),
        median_umis = median(num_umis),
        median_genes = median(num_genes),
        meidan_MT = median(mt_percentage)
    ) |>
    gt::gt() |>
    gt::tab_options(table.font.size = "median")
lineage num_cells median_umis median_genes meidan_MT
Naive H9 143 2322872 6187.0 0.03970047
Primed H9 124 2202163 6414.5 0.04135318
TSCs 128 2642517 6831.0 0.05028367
Blastoid_24h 223 2036212 5169.0 0.04553170
Blastoid_60h 460 1964516 4855.5 0.03979052
Blastoid_96h 989 1465353 4264.0 0.04480325

Cell type

plot_embedding_highlight(
    embedding = embedding_2067,
    x = cell_metadata_PRJNA737139,
    y = "lineage",
    label = "Kagawa et al. 2021",
    n_cols = 2
)

Expression

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

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

    cat(selected_feature, "\n")
    values <- log10(calc_cpm(matrix_readcount_use[, embedding_2067$cell])[selected_feature, ] + 1)

    plot_embedding(
        embedding = embedding_2067[, c(x_column, y_column)],
        color_values = values,
        label = paste(
            EMBEDDING_TITLE_PREFIX,
            selected_feature |> stringr::str_remove(pattern = "^E.+_"),
            sep = "; "
        ),
        label_position = NULL,
        show_color_value_labels = FALSE,
        show_color_legend = TRUE,
        geom_point_size = GEOM_POINT_SIZE,
        sort_values = TRUE,
        shuffle_values = FALSE,
        rasterise = RASTERISED,
        legend_size = 2
    ) +
        scale_color_viridis_c(
            na.value = "grey80"
        ) +
        theme_customized(
            legend_key_size = 2,
            legend_text_size = 5
        )
}) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 3, byrow = FALSE) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )
## ENSG00000171872_KLF17 
## ENSG00000204531_POU5F1 
## ENSG00000111704_NANOG 
## ENSG00000186103_ARGFX 
## ENSG00000164736_SOX17 
## ENSG00000125798_FOXA2 
## ENSG00000136574_GATA4 
## ENSG00000134853_PDGFRA 
## ENSG00000179348_GATA2 
## ENSG00000070915_SLC12A3 
## ENSG00000165556_CDX2 
## ENSG00000007866_TEAD3

Author-defined clusters


Visualize author-defined clusters on this embedding (Low resolution; Fig. 2b). Note: 1672 out of total 2311 GEO-deposited blastoid cells (72.3%) are used in Fig. 2a-d.


GEO-deposited cell metadata.

cell_metadata_PRJNA737139_author |>
    dplyr::count(treatment, name = "num_cells") |>
    gt::gt() |>
    gt::tab_options(table.font.size = "median") |>
    # gt::grand_summary_rows(
    gt::summary_rows(
        columns = c(num_cells),
        fns = list(
            Sum = ~ sum(.)
        ),
        decimals = 0
    )
treatment num_cells
2D_TSCM 128
E8 125
PALLY 248
PALLY_48_hours_LY_12_hours 744
PALLY_48_hours_LY_48_hours 1319
PXGL 151
Sum — 2,715


Cells included in Fig. 2a.

cell_metadata_PRJNA737139_author |>
    dplyr::filter(
        !is.na(blastoid.Fig2b.lowres)
    ) |>
    dplyr::count(treatment, name = "num_cells") |>
    gt::gt() |>
    gt::tab_options(table.font.size = "median") |>
    gt::summary_rows(
        columns = c(num_cells),
        fns = list(
            Sum = ~ sum(.)
        ),
        decimals = 0
    )
treatment num_cells
2D_TSCM 128
E8 124
PALLY 223
PALLY_48_hours_LY_12_hours 460
PALLY_48_hours_LY_48_hours 989
PXGL 143
Sum — 2,067


Match the names used in Fig. 2a.

cell_metadata_PRJNA737139 |>
    dplyr::filter(
        !is.na(blastoid.Fig2b.lowres)
    ) |>
    dplyr::count(lineage, name = "num_cells") |>
    dplyr::rename(group = lineage) |>
    gt::gt() |>
    gt::tab_options(table.font.size = "median") |>
    gt::summary_rows(
        columns = c(num_cells),
        fns = list(
            Sum = ~ sum(.)
        ),
        decimals = 0
    )
group num_cells
Naive H9 143
Primed H9 124
TSCs 128
Blastoid_24h 223
Blastoid_60h 460
Blastoid_96h 989
Sum — 2,067

Low resolution


Visualize author-defined clusters on this embedding (Low resolution; Fig. 2b).

plot_embedding(
    embedding = embedding_2067[, c(x_column, y_column)],
    color_values = embedding_2067 |>
        dplyr::left_join(
            cell_metadata_PRJNA737139
        ) |>
        dplyr::pull(blastoid.Fig2b.lowres)
        |> as.factor(),
    label = glue::glue("{EMBEDDING_TITLE_PREFIX}; Fig. 2b clusters"),
    label_position = NULL,
    show_color_value_labels = TRUE,
    show_color_legend = FALSE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    shuffle_values = FALSE,
    rasterise = RASTERISED
) +
    theme_customized()

calc_group_composition(
    data = embedding_2067 |>
        dplyr::left_join(
            cell_metadata_PRJNA737139
        ),
    x = "blastoid.Fig2b.lowres",
    group = "lineage"
) |>
    dplyr::mutate(
        blastoid.Fig2b.lowres = as.factor(blastoid.Fig2b.lowres)
    ) |>
    plot_barplot(
        x = "blastoid.Fig2b.lowres",
        y = "percentage",
        z = "lineage",
        legend_ncol = 1
    )

Visualize selected four non-blastocyst genes on this embedding (Extended Data Fig. 3b).

FEATURES_SELECTED <- c(
    "ENSG00000094755_GABRP",
    "ENSG00000134817_APLNR",
    "ENSG00000016082_ISL1",
    "ENSG00000143320_CRABP2"
)

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

    cat(selected_feature, "\n")
    values <- log10(calc_cpm(matrix_readcount_use[, embedding_2067$cell])[selected_feature, ] + 1)

    plot_embedding(
        embedding = embedding_2067[, c(x_column, y_column)],
        color_values = values,
        label = paste(
            EMBEDDING_TITLE_PREFIX,
            selected_feature |> stringr::str_remove(pattern = "^E.+_"),
            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
    ) +
        scale_color_viridis_c(
            na.value = "grey80"
        ) +
        theme_customized(
            legend_key_size = 2,
            legend_text_size = 5
        )
}) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 2, byrow = FALSE) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )
## ENSG00000094755_GABRP 
## ENSG00000134817_APLNR 
## ENSG00000016082_ISL1 
## ENSG00000143320_CRABP2

High resolution


Visualize author-defined clusters on this embedding (High resolution; Extended Data Fig. 3a).

plot_embedding(
    embedding = embedding_2067[, c(x_column, y_column)],
    color_values = embedding_2067 |>
        dplyr::left_join(
            cell_metadata_PRJNA737139
        ) |>
        dplyr::pull(blastoid.FigS3a.highres)
        |> as.factor(),
    label = glue::glue("{EMBEDDING_TITLE_PREFIX}; Fig. S3a clusters"),
    label_position = NULL,
    show_color_value_labels = TRUE,
    show_color_legend = FALSE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    shuffle_values = FALSE,
    rasterise = RASTERISED
) +
    theme_customized()

p_barplot_highres_1 <- calc_group_composition(
    data = embedding_2067 |>
        dplyr::left_join(
            cell_metadata_PRJNA737139
        ),
    x = "blastoid.FigS3a.highres",
    group = "lineage"
) |>
    dplyr::mutate(
        lineage = stringr::str_remove(string = lineage, pattern = "_.+$"),
        lineage = as.factor(
            lineage
        ),
        blastoid.FigS3a.highres = as.factor(blastoid.FigS3a.highres)
    ) |>
    plot_barplot(
        x = "blastoid.FigS3a.highres",
        y = "percentage",
        z = "lineage",
        legend_ncol = 1
    )

p_barplot_highres_2 <- calc_group_composition(
    data = embedding_2067 |>
        dplyr::left_join(
            cell_metadata_PRJNA737139
        ),
    x = "blastoid.FigS3a.highres",
    group = "lineage"
) |>
    dplyr::mutate(
        blastoid.FigS3a.highres = as.factor(blastoid.FigS3a.highres)
    ) |>
    plot_barplot(
        x = "blastoid.FigS3a.highres",
        y = "percentage",
        z = "lineage",
        legend_ncol = 1
    )

list(p_barplot_highres_1, p_barplot_highres_2) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 1, byrow = FALSE, guides = "collect") +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

Subset of blastoid cells


According to the cell metadata deposited at GEO, only 96h-blastoid cells were included for comparison with other datasets.

cell_metadata_PRJNA737139_author |>
    dplyr::filter(
        !is.na(blastoid.FigS2g.PZT.integration)
    ) |>
    dplyr::count(treatment, name = "num_cells") |>
    gt::gt() |>
    gt::tab_options(table.font.size = "median") |>
    gt::summary_rows(
        columns = c(num_cells),
        fns = list(
            Sum = ~ sum(.)
        ),
        decimals = 0
    )
treatment num_cells
E8 124
PALLY_48_hours_LY_48_hours 989
PXGL 143
Sum — 1,256


Match the names used in Fig. 2a.

cell_metadata_PRJNA737139 |>
    dplyr::filter(
        !is.na(blastoid.FigS2g.PZT.integration)
    ) |>
    dplyr::count(lineage, name = "num_cells") |>
    dplyr::rename(group = lineage) |>
    gt::gt() |>
    gt::tab_options(table.font.size = "median") |>
    gt::summary_rows(
        columns = c(num_cells),
        fns = list(
            Sum = ~ sum(.)
        ),
        decimals = 0
    )
group num_cells
Naive H9 143
Primed H9 124
Blastoid_96h 989
Sum — 1,256


Highlight blastoid cells used in Fig. 2e-f.

plot_embedding(
    embedding = embedding_2067[, c(x_column, y_column, "cell")] |>
        dplyr::left_join(cell_metadata_PRJNA737139),
    color_values = embedding_2067[, c(x_column, y_column, "cell")] |>
        dplyr::left_join(cell_metadata_PRJNA737139) |>
        dplyr::mutate(
            value = case_when(
                (
                    stringr::str_detect(string = lineage, pattern = "Blast") & is.na(blastoid.FigS2g.PZT.integration)
                ) ~ "1",
                TRUE ~ "0"
            )
        ) |>
        dplyr::pull(value) |>
        as.factor(),
    label = glue::glue("{EMBEDDING_TITLE_PREFIX}; 96h blastoid cells"),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = FALSE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    shuffle_values = FALSE,
    rasterise = RASTERISED
) +
    theme_customized() +
    ggplot2::scale_color_manual(
        values = c("grey70", "salmon")
    )

Integration with other datasets


Instead of only including a subset of each dataset, this embedding includes all the cells mentioned in their respective publications to cover the full spectrum of cellular diversity.

Included cells and their lineage annotations are the same as in their respective publications. See here for details.


EMBEDDING_FILE <- glue::glue(
    "embedding_ncomponents18_ccc1_seed20210719.csv.gz"
)

embedding <- vroom::vroom(
    file = file.path(
        PROJECT_DIR,
        "raw",
        "public",
        "PRJNA737139",
        "clustering",
        "PRJNA737139_PRJEB11202_PRJNA431392_PRJEB40781",
        "exploring",
        "Scanpy_Harmony_variable",
        EMBEDDING_FILE
    )
)

embedding |> head()
studies <- tibble::tribble(
    ~bioproject, ~citation,
    "PRJEB11202", "Petropoulos et al. 2016",
    "PRJEB40781", "Tyser et al. 2021",
    "PRJNA431392", "Zhou et al. 2019",
    "PRJNA737139", "Kagawa et al. 2021",
    "PRJNA632839", "Yu et al. 2021",
    "PRJNA658478", "Liu et al. 2021",
    "PRJNA720968", "Yanagida et al. 2021",
    "PRJNA667174", "Fan et al. 2021",
    "PRJNA738498", "Sozen et al. 2021",
    "PRJNA737139", "Kagawa et al. 2021"
)

studies <- setNames(
    object = studies$citation,
    nm = studies$bioproject
)
embedding |>
    dplyr::count(batch, name = "num_cells") |>
    dplyr::mutate(
        study = studies[batch]
    ) |>
    gt::gt() |>
    gt::tab_options(table.font.size = "median") |>
    gt::summary_rows(
        columns = c(num_cells),
        fns = list(
            Sum = ~ sum(.)
        ),
        decimals = 0
    )
batch num_cells study
PRJEB11202 1529 Petropoulos et al. 2016
PRJEB40781 1195 Tyser et al. 2021
PRJNA431392 5911 Zhou et al. 2019
PRJNA737139 2067 Kagawa et al. 2021
Sum — 10,702 —


Metadata

# PRJNA431392; Zhou et al. 2019
embryos_selected_PRJNA431392 <- c(
    "ha_D6_E2",
    "hm_D6_E1",
    "hm_D6_E2",
    "hm_D8_E2",
    "hm_D8_E3",
    "hm_D8_E5",
    "ha_D8_E1",
    "hm_D8_E1",
    "hv_D8_E1",
    "hv_D8_E2",
    "hv_D8_E3",
    "hv_D10_E6",
    "ha_D10_E1",
    "ha_D10_E2",
    "hm_D10_E4",
    "hm_D10_E9",
    "hv_D10_E7",
    "hv_D10_E8",
    "ha_D12_E1",
    "hv_D12_E1",
    "hv_D12_E2"
)

cell_metadata_PRJNA431392 <- vroom::vroom(
    file = file.path(
        PROJECT_DIR,
        "raw",
        "public",
        "PRJNA431392",
        "matrix/cell_metadata.csv"
    )
) |>
    dplyr::mutate(
        developmental_stage = factor(
            Day,
            levels = stringr::str_sort(
                unique(Day),
                numeric = TRUE
            )
        ),
        lineage = factor(
            Lineage,
            levels = c(
                "EPI",
                "PE",
                "TE",
                "MIX"
            )
        ),
        `3184` = case_when(
            Ori_Day_Emb %in% embryos_selected_PRJNA431392 ~ "1",
            TRUE ~ "0"
        )
    ) |>
    dplyr::rename(cell = Sample)


# PRJEB40781; Tyser et al. 2021
cell_metadata_PRJEB40781 <- vroom::vroom(
    file = file.path(
        PROJECT_DIR,
        "raw",
        "public",
        "PRJEB40781",
        "matrix/cell_metadata.csv"
    )
) |>
    dplyr::mutate(
        lineage = factor(lineage),
        lineage_ontology = factor(lineage_ontology),
        sampling_site = factor(sampling_site)
    )


# PRJEB11202; Petropoulos et al. 2016
cell_metadata_PRJEB11202 <- vroom::vroom(
    file = file.path(
        PROJECT_DIR,
        "raw",
        "public",
        "PRJEB11202",
        "matrix/cell_metadata.csv"
    )
) |>
    dplyr::mutate(
        developmental_stage = stringr::str_remove(
            string = individual,
            pattern = "\\..+$"
        ),
        developmental_stage = factor(
            developmental_stage,
            levels = stringr::str_sort(
                unique(developmental_stage),
                numeric = TRUE
            )
        ),
        #
        lineage = factor(
            inferred_lineage,
            levels = c(
                "epiblast",
                "primitive_endoderm",
                "trophectoderm",
                "not_applicable"
            )
        )
    ) |>
    dplyr::select(
        cell, lineage, developmental_stage
    )

Visualization

Clustering

p_embedding_leiden <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding$leiden |> as.factor(),
    label = glue::glue("{EMBEDDING_TITLE_PREFIX}; Leiden"),
    label_position = NULL,
    show_color_value_labels = TRUE,
    show_color_legend = FALSE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    shuffle_values = FALSE,
    rasterise = RASTERISED
) +
    theme_customized()

p_embedding_UMI <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = log10(Matrix::colSums(matrix_readcount_use[, embedding$cell])),
    label = glue::glue("{EMBEDDING_TITLE_PREFIX}; UMI"),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE * 1,
    geom_point_alpha = 1,
    sort_values = TRUE,
    shuffle_values = FALSE,
    label_size = 2.5,
    label_hjust = 0,
    label_vjust = 0,
    rasterise = RASTERISED,
    legend_size = 2,
    legend_ncol = 1
) +
    theme_customized(
        legend_key_size = 2,
        legend_text_size = 5
    )

p_embedding_MT <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding |>
        dplyr::left_join(
            cell_metadata # |> dplyr::select(-batch)
        ) |>
        dplyr::pull(mt_percentage),
    label = glue::glue("{EMBEDDING_TITLE_PREFIX}; MT %"),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE * 1,
    geom_point_alpha = 1,
    sort_values = TRUE,
    shuffle_values = FALSE,
    label_size = 2.5,
    label_hjust = 0,
    label_vjust = 0,
    rasterise = RASTERISED,
    legend_size = 2,
    legend_ncol = 1
) +
    theme_customized(
        legend_key_size = 2,
        legend_text_size = 5
    )

p_embedding_batch <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding$batch |> as.factor(),
    label = glue::glue("{EMBEDDING_TITLE_PREFIX}; Batch"),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE / 2,
    sort_values = FALSE,
    shuffle_values = TRUE,
    rasterise = RASTERISED,
    legend_size = 2
) +
    theme_customized(
        legend_key_size = 2,
        legend_text_size = 5
    ) +
    scale_color_manual(
        values = scales::hue_pal()(n = length(unique(embedding$batch))),
        labels = studies
    )
purrr::reduce(
    list(
        p_embedding_leiden,
        p_embedding_batch,
        p_embedding_UMI,
        p_embedding_MT
    ), `+`
) +
    patchwork::plot_layout(ncol = 2) +
    patchwork::plot_annotation(
        theme = ggplot2::theme(plot.margin = ggplot2::margin())
    )

purrr::map(unique(embedding$batch), \(x) {
    plot_embedding(
        embedding = embedding[, c(x_column, y_column)],
        color_values = as.integer(embedding$batch == x) |> as.factor(),
        label = glue::glue("{EMBEDDING_TITLE_PREFIX}; {studies[x]}, {sum(as.integer(embedding$batch == x), 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(
            values = c("grey70", "salmon")
        )
}) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 2) +
    patchwork::plot_annotation(
        theme = ggplot2::theme(plot.margin = ggplot2::margin())
    )

Cell annotation

Petropoulos et al. 2016

bioproject <- "PRJEB11202"
cell_metadata_selected <- cell_metadata_PRJEB11202

selected_column <- "developmental_stage"
p_embedding_developmental_stage <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding |>
        dplyr::left_join(cell_metadata_selected) |>
        dplyr::pull(.data[[selected_column]]),
    label = glue::glue(
        "{EMBEDDING_TITLE_PREFIX}; {studies[bioproject]}; ",
        "{selected_column |>
        stringr::str_to_title() |>
        stringr::str_replace(pattern = \"_\", replacement = \" \")}"
    ),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    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 = scales::hue_pal()(n = length(unique(cell_metadata_selected[[selected_column]]))),
        na.value = "#7F7F7F"
    )

selected_column <- "lineage"
p_embedding_lineage <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding |>
        dplyr::left_join(cell_metadata_selected) |>
        dplyr::pull(.data[[selected_column]]),
    label = glue::glue(
        "{EMBEDDING_TITLE_PREFIX}; {studies[bioproject]}; ",
        "{selected_column |>
        stringr::str_to_title() |>
        stringr::str_replace(pattern = \"_\", replacement = \" \")}"
    ),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    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 = scales::hue_pal()(n = length(unique(cell_metadata_selected[[selected_column]]))),
        na.value = "#7F7F7F"
    )

list(
    p_embedding_lineage,
    p_embedding_developmental_stage
) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 2) +
    patchwork::plot_annotation(
        theme = ggplot2::theme(plot.margin = ggplot2::margin())
    )


Salmon: highlighted group of cells; Light grey: cells belonging to this dataset but not the highlighted group; Dark grey: cells belonging to other datasets.

plot_embedding_highlight(
    embedding = embedding,
    x = cell_metadata_selected,
    y = "lineage",
    label = studies[bioproject],
    n_cols = 2
)

plot_embedding_highlight(
    embedding = embedding,
    x = cell_metadata_selected,
    y = "developmental_stage",
    label = studies[bioproject],
    n_cols = 2
)

Zhou et al. 2019

bioproject <- "PRJNA431392"
cell_metadata_selected <- cell_metadata_PRJNA431392
selected_column <- "developmental_stage"

p_embedding_developmental_stage <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding |>
        dplyr::left_join(cell_metadata_selected) |>
        dplyr::pull(.data[[selected_column]]),
    label = glue::glue(
        "{EMBEDDING_TITLE_PREFIX}; {studies[bioproject]}; ",
        "{selected_column |>
        stringr::str_to_title() |>
        stringr::str_replace(pattern = \"_\", replacement = \" \")}"
    ),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    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 = scales::hue_pal()(n = length(unique(cell_metadata_selected[[selected_column]]))),
        na.value = "#7F7F7F"
    )

selected_column <- "lineage"
p_embedding_lineage <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding |>
        dplyr::left_join(cell_metadata_selected) |>
        dplyr::pull(.data[[selected_column]]),
    label = glue::glue(
        "{EMBEDDING_TITLE_PREFIX}; {studies[bioproject]}; ",
        "{selected_column |>
        stringr::str_to_title() |>
        stringr::str_replace(pattern = \"_\", replacement = \" \")}"
    ),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    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 = scales::hue_pal()(n = length(unique(cell_metadata_selected[[selected_column]]))),
        na.value = "#7F7F7F"
    )

selected_column <- "3184"
p_embedding_chosen <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding |>
        dplyr::left_join(cell_metadata_selected) |>
        dplyr::pull(.data[[selected_column]]),
    label = glue::glue(
        "{EMBEDDING_TITLE_PREFIX}; {studies[bioproject]}; ",
        "{selected_column |>
        stringr::str_to_title() |>
        stringr::str_replace(pattern = \"_\", replacement = \" \")}"
    ),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    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"
    )

list(
    p_embedding_lineage,
    p_embedding_developmental_stage,
    p_embedding_chosen
) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 2) +
    patchwork::plot_annotation(
        theme = ggplot2::theme(plot.margin = ggplot2::margin())
    )

plot_embedding_highlight(
    embedding = embedding,
    x = cell_metadata_selected,
    y = "developmental_stage",
    label = studies[bioproject],
    n_cols = 2
)

Tyser et al. 2021

bioproject <- "PRJEB40781"
cell_metadata_selected <- cell_metadata_PRJEB40781

selected_column <- "sampling_site"
p_embedding_sampling_site <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding |>
        dplyr::left_join(cell_metadata_selected) |>
        dplyr::pull(.data[[selected_column]]),
    label = glue::glue(
        "{EMBEDDING_TITLE_PREFIX}; {studies[bioproject]}; ",
        "{selected_column |>
        stringr::str_to_title() |>
        stringr::str_replace(pattern = \"_\", replacement = \" \")}"
    ),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    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 = scales::hue_pal()(n = length(unique(cell_metadata_selected[[selected_column]]))),
        na.value = "grey70"
    )

selected_column <- "lineage"
p_embedding_lineage <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding |>
        dplyr::left_join(cell_metadata_selected) |>
        dplyr::pull(.data[[selected_column]]),
    label = glue::glue(
        "{EMBEDDING_TITLE_PREFIX}; {studies[bioproject]}; ",
        "{selected_column |>
        stringr::str_to_title() |>
        stringr::str_replace(pattern = \"_\", replacement = \" \")}"
    ),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    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 = scales::hue_pal()(n = length(unique(cell_metadata_selected[[selected_column]]))),
        na.value = "#7F7F7F"
    )

list(
    p_embedding_lineage,
    p_embedding_sampling_site
) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 2) +
    patchwork::plot_annotation(
        theme = ggplot2::theme(plot.margin = ggplot2::margin())
    )

plot_embedding_highlight(
    embedding = embedding,
    x = cell_metadata_selected,
    y = "lineage",
    label = studies[bioproject],
    n_cols = 2
)

Kagawa et al. 2021

bioproject <- "PRJNA737139"
cell_metadata_selected <- cell_metadata_PRJNA737139

selected_column <- "source_name"
p_embedding_source <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding |>
        dplyr::left_join(cell_metadata_selected) |>
        dplyr::pull(.data[[selected_column]]),
    label = glue::glue(
        "{EMBEDDING_TITLE_PREFIX}; {studies[bioproject]}; ",
        "{selected_column |>
        stringr::str_to_title() |>
        stringr::str_replace(pattern = \"_\", replacement = \" \")}"
    ),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    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 = scales::hue_pal()(n = length(unique(cell_metadata_selected[[selected_column]]))),
        na.value = "#7F7F7F"
    )

selected_column <- "lineage"
p_embedding_lineage <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding |>
        dplyr::left_join(cell_metadata_selected) |>
        dplyr::pull(.data[[selected_column]]),
    label = glue::glue(
        "{EMBEDDING_TITLE_PREFIX}; {studies[bioproject]}; ",
        "{selected_column |>
        stringr::str_to_title() |>
        stringr::str_replace(pattern = \"_\", replacement = \" \")}"
    ),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    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 = scales::hue_pal()(n = length(unique(cell_metadata_selected[[selected_column]]))),
        na.value = "#7F7F7F"
    )

list(
    p_embedding_source,
    p_embedding_lineage
) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 2) +
    patchwork::plot_annotation(
        theme = ggplot2::theme(plot.margin = ggplot2::margin())
    )

plot_embedding_highlight(
    embedding = embedding,
    x = cell_metadata_selected,
    y = "lineage",
    label = studies[bioproject],
    n_cols = 2
)

R session info

devtools::session_info()$platform
##  setting  value
##  version  R version 4.1.2 (2021-11-01)
##  os       macOS Monterey 12.1
##  system   aarch64, darwin20.6.0
##  ui       unknown
##  language (EN)
##  collate  en_US.UTF-8
##  ctype    en_US.UTF-8
##  tz       America/Chicago
##  date     2022-01-18
##  pandoc   2.14.0.3 @ /Applications/RStudio.app/Contents/MacOS/pandoc/ (via rmarkdown)
devtools::session_info()$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)
beeswarm 0.4.0 2021-06-01 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 3.1.0 2021-10-27 CRAN (R 4.1.1)
colorspace 2.0-2 2021-06-24 CRAN (R 4.1.1)
crayon 1.4.2 2021-10-29 CRAN (R 4.1.1)
curl 4.3.2 2021-06-23 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 1.2.0 2021-12-05 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)
ggbeeswarm 0.6.0 2017-08-07 CRAN (R 4.1.2)
ggplot2 3.3.5 2021-06-25 CRAN (R 4.1.1)
ggrastr 1.0.1 2021-12-08 Github (VPetukhov/ggrastr@7aed9af2b9cffabda86e6d2af2fa10d4e60cc63d)
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 2022-01-15 Github (tidyverse/lubridate@53e5892a548b3425d6c3bf887542aa105341ab73)
magrittr 2.0.1 2020-11-17 CRAN (R 4.1.1)
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)
pkgconfig 2.0.3 2019-09-22 CRAN (R 4.1.1)
pkgload 1.2.4 2021-11-30 CRAN (R 4.1.2)
png 0.1-7 2013-12-03 CRAN (R 4.1.1)
prettyunits 1.1.1 2020-01-24 CRAN (R 4.1.1)
processx 3.5.2 2021-04-30 CRAN (R 4.1.1)
ps 1.6.0 2021-02-28 CRAN (R 4.1.1)
purrr 0.3.4 2020-04-17 CRAN (R 4.1.1)
R.cache 0.15.0 2021-04-30 CRAN (R 4.1.1)
R.methodsS3 1.8.1 2020-08-26 CRAN (R 4.1.1)
R.oo 1.24.0 2020-08-26 CRAN (R 4.1.1)
R.utils 2.11.0 2021-09-26 CRAN (R 4.1.1)
R6 2.5.1.9000 2021-12-09 Github (r-lib/R6@1b05b89f30fe6713cb9ff51d91fc56bd3016e4b2)
ragg 1.2.1.9000 2021-12-08 Github (r-lib/ragg@c68c6665ef894f16c006333658b32bf25d2e9d19)
Rcpp 1.0.8 2022-01-13 CRAN (R 4.1.2)
readr 2.1.1 2021-11-30 CRAN (R 4.1.2)
readxl 1.3.1.9000 2022-01-06 Github (tidyverse/readxl@1059a768436df38fb7b33f8e0415c18585c11ac5)
remotes 2.4.2 2021-12-02 Github (r-lib/remotes@fcad17b68b7a19d5363d64adfb0a0426a3a5b3db)
reprex 2.0.1 2021-08-05 CRAN (R 4.1.1)
reticulate 1.23 2022-01-14 CRAN (R 4.1.2)
rlang 0.99.0.9003 2022-01-17 Github (r-lib/rlang@a165f49e841dc848fb01133dd265a83c5a671013)
rmarkdown 2.11.8 2022-01-15 Github (rstudio/rmarkdown@52a65394a6e1ab84b10feccf893e2a16ee25aeb3)
rprojroot 2.0.2 2020-11-15 CRAN (R 4.1.1)
rstudioapi 0.13.0-9000 2022-01-15 Github (rstudio/rstudioapi@5d0f0873dc160779c71bf4b00d8b016b898f6fb5)
Rttf2pt1 1.3.9 2021-07-22 CRAN (R 4.1.1)
rvest 1.0.2 2021-10-16 CRAN (R 4.1.1)
sass 0.4.0 2021-05-12 CRAN (R 4.1.1)
scales 1.1.1 2020-05-11 CRAN (R 4.1.1)
sessioninfo 1.2.2 2021-12-06 CRAN (R 4.1.2)
stringi 1.7.6 2021-11-29 CRAN (R 4.1.2)
stringr 1.4.0.9000 2022-01-17 Github (tidyverse/stringr@3848cd70b1e331e6c20401e4da518ff4c3725324)
styler 1.6.2.9000 2022-01-17 Github (r-lib/styler@9274aed613282eca01909ae8c341224055d9c928)
systemfonts 1.0.3.9000 2021-12-07 Github (r-lib/systemfonts@414114e645efb316def3d8de1056d855f92d588e)
testthat 3.1.1.9000 2022-01-13 Github (r-lib/testthat@f09df60dd881530332b252474e9f35c97f8640be)
textshaping 0.3.6 2021-10-13 CRAN (R 4.1.1)
tibble 3.1.6.9000 2021-12-30 Github (tidyverse/tibble@2d8d3fddda2b879a82cb58086f2107da4f04befa)
tidyr 1.1.4 2021-09-27 CRAN (R 4.1.1)
tidyselect 1.1.1 2021-04-30 CRAN (R 4.1.1)
tidyverse 1.3.1.9000 2021-12-08 Github (tidyverse/tidyverse@6186fbf09bf359110f8800ff989cbbdd40485eb0)
tzdb 0.2.0 2021-10-27 CRAN (R 4.1.1)
usethis 2.1.5.9000 2022-01-17 Github (r-lib/usethis@16d14b148cca803e15be4b33a7453a69e8d6fc29)
utf8 1.2.2 2021-07-24 CRAN (R 4.1.1)
vctrs 0.3.8 2021-04-29 CRAN (R 4.1.1)
vipor 0.4.5 2017-03-22 CRAN (R 4.1.2)
viridisLite 0.4.0 2021-04-13 CRAN (R 4.1.1)
vroom 1.5.7 2021-11-30 CRAN (R 4.1.2)
withr 2.4.3 2021-11-30 CRAN (R 4.1.2)
xfun 0.29 2021-12-14 CRAN (R 4.1.2)
xml2 1.3.3 2021-11-30 CRAN (R 4.1.2)
yaml 2.2.1 2020-02-01 CRAN (R 4.1.1)