Single-cell transcriptomes of human blastoids generated by Yanagida et al., 2021

Jialei Duan

2022-01-24

---

Yanagida, A., Spindlow, D., Nichols, J., Dattani, A., Smith, A., and Guo, G. (2021). Naive stem cell blastocyst model captures human embryo lineage segregation. Cell Stem Cell 28, 1016–1022.e4.

• BioProject Accession: PRJNA720968
• GEO Accession: GSE171820

---

Load required packages.

library(tidyverse)
library(Matrix)
library(patchwork)
library(extrafont)

Sys.time()

[1] "2022-01-24 16:29:12 CST"

Data preparation

Functions loading

source(
    file = file.path(
        SCRIPT_DIR,
        "utilities.R"
    )
)

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("PRJNA720968"), \(x) {
    file.path(
        PROJECT_DIR,
        "raw",
        "public",
        x,
        "matrix",
        "adata.h5ad"
    )
})
purrr::map_lgl(adata_files, file.exists)

[1] 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   495

Embedding

EMBEDDING_FILE <- "embedding_ncomponents15_ccc1_seed20210719.csv.gz"

embedding <- vroom::vroom(
    file = file.path(
        PROJECT_DIR,
        "raw/public/PRJNA720968",
        "clustering/PRJNA720968/exploring",
        "Scanpy_Harmony_2batches",
        EMBEDDING_FILE
    )
)

embedding |> head() |> knitr::kable()

cell	batch	louvain	leiden	x_tsne	y_tsne	x_fitsne	y_fitsne	x_umap_min_dist=0.5	y_umap_min_dist=0.5	x_umap_min_dist=0.1	y_umap_min_dist=0.1	x_phate	y_phate	x_pacmap	y_pacmap
GSM5234744	NA	0	2	7.242309	-12.512821	-1.952768	2.387407	6.3454080	7.973671	6.471455	6.6839929	-0.0221856	0.0038625	12.644343	-1.755934
GSM5234745	NA	1	7	16.029781	3.202589	4.077884	20.496506	3.3925710	4.460842	2.176941	5.6017890	-0.0019277	0.0039803	16.730646	-2.930145
GSM5234746	NA	1	2	1.688119	-9.728956	-4.528189	1.067349	6.7724795	6.335969	6.209920	5.6141768	-0.0243982	0.0049713	13.994924	-2.496070
GSM5234747	NA	1	2	22.322464	3.130961	5.642826	12.368830	3.8178000	5.929975	4.304469	6.4326148	0.0169830	0.0001892	15.122799	-2.661770
GSM5234748	NA	2	1	27.696249	21.690281	24.876318	28.604160	-0.6713522	4.234682	-1.068493	4.9413905	0.0385696	-0.0053330	-19.663921	-15.156654
GSM5234749	NA	3	0	-23.088488	-2.237011	3.598484	-27.333997	12.3851538	4.807675	8.015325	0.8049933	0.0243418	-0.0042199	-6.117634	12.861273

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() |>
    dplyr::select(-batch)

cell_metadata |> head() |> knitr::kable()

cell	num_umis	num_features	mt_percentage
GSM5234744	8204417	6873	0.0321321
GSM5234745	1022327	9561	0.0459491
GSM5234746	1588932	6761	0.1335784
GSM5234747	6359789	8078	0.2219017
GSM5234748	9677194	10090	0.0838952
GSM5234749	7426256	11928	0.0329500

cell_metadata_PRJNA720968 <- vroom::vroom(
    file = file.path(
        PROJECT_DIR,
        "raw",
        "public",
        "PRJNA720968",
        "matrix/cell_metadata.csv"
    )
) |>
    dplyr::mutate(
        lineage = factor(
            lineage,
        ),
        origin = factor(
            origin
        ),
        developmental_stage = factor(
            developmental_stage
        )
    )

cell_metadata_PRJNA720968 |>
    dplyr::count(origin, 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
    )

	origin	num_cells
	Blastocyst	228
	Blastoid	267
Sum	—	495

Check memory usage.

purrr::walk(list(matrix_readcount_use, cell_metadata_PRJNA720968), function(x) {
    print(object.size(x), units = "auto", standard = "SI")
})

57.9 MB
86.8 kB

Clustering of human blastoids

cell_metadata_PRJNA720968 <- cell_metadata_PRJNA720968 |>
    dplyr::mutate(
        annotated = paste(origin, lineage, sep = ": "),
        annotated = factor(
            annotated,
            levels = c(
                "Blastocyst: Epiblast",
                "Blastocyst: Hypoblast",
                "Blastocyst: Inner Cell Mass",
                "Blastocyst: Inner Cell Mass-Trophectoderm Transition",
                "Blastocyst: Early Trophectoderm",
                "Blastocyst: Trophectoderm",
                "Blastocyst: Unknown",
                "Blastoid: Epiblast",
                "Blastoid: Hypoblast",
                "Blastoid: Transitioning",
                "Blastoid: Trophectoderm"
            )
        )
    )

embedding |>
    dplyr::left_join(
        cell_metadata |>
            dplyr::select(cell, num_umis:mt_percentage)
    ) |>
    dplyr::left_join(
        cell_metadata_PRJNA720968
    ) |>
    dplyr::group_by(
        origin
    ) |>
    dplyr::summarise(
        num_cells = dplyr::n(),
        median_umis = median(num_umis),
        median_features = median(num_features),
        median_mt_percentage = median(mt_percentage)
    ) |>
    gt::gt() |>
    gt::tab_options(table.font.size = "median") |>
    gt::summary_rows(
        columns = c(num_cells),
        fns = list(
            Sum = ~ sum(.)
        ),
        decimals = 0
    ) |>
    gt::summary_rows(
        columns = c("median_umis", "median_features", "median_mt_percentage"),
        fns = list(
            Median = ~ median(.)
        ),
        decimals = 2
    )

	origin	num_cells	median_umis	median_features	median_mt_percentage
	Blastocyst	228	7781726	9003	0.06319315
	Blastoid	267	7579396	9576	0.03904364
Sum	—	495	—	—	—
Median	—	—	7,680,561.25	9,289.50	0.05

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

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

# CB_POSITION <- c(0.8, 0.995)
p_embedding_UMI <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding |>
        dplyr::left_join(
            cell_metadata
        ) |>
        dplyr::pull(num_umis) |>
        {
            \(x) log10(x)
        }(),
    label = paste(EMBEDDING_TITLE_PREFIX, "UMI", 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
) +
    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::pull(mt_percentage),
    label = paste(EMBEDDING_TITLE_PREFIX, "MT %", 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
) +
    theme_customized(
        legend_key_size = 2,
        legend_text_size = 5
    )

selected_feature <- "ENSG00000204531_POU5F1"
p_embedding_POU5F1 <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = log10(calc_cpm(matrix_readcount_use)[selected_feature, embedding$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
    )

Clustering of 495 Smart-seq2 single cells.

purrr::reduce(
    list(
        p_embedding_leiden,
        p_embedding_UMI,
        p_embedding_MT,
        p_embedding_POU5F1
    ),
    `+`
) +
    patchwork::plot_layout(ncol = 2) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

Origin & developmental stage

p_embedding_origin <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding |>
        dplyr::left_join(cell_metadata_PRJNA720968) |>
        dplyr::pull(origin),
    label = glue::glue("{EMBEDDING_TITLE_PREFIX}; Origin"),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    shuffle_values = TRUE,
    rasterise = RASTERISED,
    legend_size = 2
) +
    theme_customized(
        legend_key_size = 2,
        legend_text_size = 5
    )

p_embedding_developmental_stage <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding |>
        dplyr::left_join(cell_metadata_PRJNA720968) |>
        dplyr::pull(developmental_stage),
    label = glue::glue("{EMBEDDING_TITLE_PREFIX}; Developmental stage"),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    shuffle_values = TRUE,
    rasterise = RASTERISED,
    legend_size = 2
) +
    theme_customized(
        legend_key_size = 2,
        legend_text_size = 5
    )

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

Developmental stage

embedding |>
    dplyr::left_join(
        cell_metadata
    ) |>
    dplyr::left_join(cell_metadata_PRJNA720968) |>
    dplyr::group_by(developmental_stage, origin) |>
    dplyr::summarise(
        num_cells = dplyr::n(),
        median_umis = median(num_umis),
        median_features = median(num_features),
        median_mt_percentage = median(mt_percentage)
    ) |>
    gt::gt() |>
    gt::tab_options(table.font.size = "median") |>
    gt::summary_rows(
        columns = c(num_cells),
        fns = list(
            Sum = ~ sum(.)
        ),
        decimals = 0
    ) |>
    gt::summary_rows(
        columns = c("median_umis", "median_features", "median_mt_percentage"),
        fns = list(
            Median = ~ median(.)
        ),
        decimals = 2
    )

	origin	num_cells	median_umis	median_features	median_mt_percentage
Day3
	Blastoid	159	7329571	9462.0	0.03540585
Day4
	Blastoid	108	7685250	9759.0	0.04054943
Day5
	Blastocyst	68	7692114	8909.0	0.07377675
Day6
	Blastocyst	80	6475820	8393.5	0.05267877
Day7
	Blastocyst	80	8645422	9454.0	0.06118594
Sum	—	495	—	—	—
Median	—	—	7,685,250.00	9,454.00	0.05

purrr::map(levels(cell_metadata_PRJNA720968$developmental_stage), \(x) {
    plot_embedding(
        embedding = embedding[, c(x_column, y_column)],
        color_values = embedding |>
            dplyr::left_join(cell_metadata_PRJNA720968) |>
            dplyr::mutate(
                value = dplyr::case_when(
                    developmental_stage == x ~ "1",
                    TRUE ~ "0"
                ),
                value = factor(value)
            ) |>
            dplyr::pull(value),
        label = glue::glue("{EMBEDDING_TITLE_PREFIX}; {x}"),
        label_position = NULL,
        show_color_value_labels = FALSE,
        show_color_legend = FALSE,
        geom_point_size = GEOM_POINT_SIZE,
        sort_values = TRUE,
        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 = theme(plot.margin = margin())
    )

Lineage

embedding |>
    dplyr::left_join(
        cell_metadata
    ) |>
    dplyr::left_join(cell_metadata_PRJNA720968) |>
    dplyr::group_by(annotated) |>
    dplyr::summarise(
        num_cells = dplyr::n(),
        median_umis = median(num_umis),
        median_features = median(num_features),
        median_mt_percentage = median(mt_percentage)
    ) |>
    dplyr::rename(lineage = annotated) |>
    gt::gt() |>
    gt::tab_options(table.font.size = "90%") |>
    gt::summary_rows(
        columns = c(num_cells),
        fns = list(
            Sum = ~ sum(.)
        ),
        decimals = 0
    ) |>
    gt::summary_rows(
        columns = c("median_umis", "median_features", "median_mt_percentage"),
        fns = list(
            Median = ~ median(.)
        ),
        decimals = 2
    )

	lineage	num_cells	median_umis	median_features	median_mt_percentage
	Blastocyst: Epiblast	31	8126430	9951.0	0.052335040
	Blastocyst: Hypoblast	14	9776420	9619.5	0.049162825
	Blastocyst: Inner Cell Mass	22	7335337	9136.5	0.071956141
	Blastocyst: Inner Cell Mass-Trophectoderm Transition	23	8187758	8853.0	0.079507155
	Blastocyst: Early Trophectoderm	18	8361592	8773.0	0.072238465
	Blastocyst: Trophectoderm	117	7541800	8597.0	0.060103082
	Blastocyst: Unknown	3	4890522	10178.0	0.060837473
	Blastoid: Epiblast	73	7916385	9707.0	0.034658941
	Blastoid: Hypoblast	13	8166701	9573.0	0.009467274
	Blastoid: Transitioning	7	4276121	8903.0	0.001258322
	Blastoid: Trophectoderm	174	7467628	9562.5	0.043218885
Sum	—	495	—	—	—
Median	—	—	7,916,385.00	9,562.50	0.05

purrr::map(levels(cell_metadata_PRJNA720968$annotated), \(x) {
    plot_embedding(
        embedding = embedding[, c(x_column, y_column)],
        color_values = embedding |>
            dplyr::left_join(cell_metadata_PRJNA720968) |>
            dplyr::mutate(
                value = dplyr::case_when(
                    annotated == x ~ "1",
                    TRUE ~ "0"
                ),
                value = factor(value)
            ) |>
            dplyr::pull(value),
        label = glue::glue("{EMBEDDING_TITLE_PREFIX}; {x}"),
        label_position = NULL,
        show_color_value_labels = FALSE,
        show_color_legend = FALSE,
        geom_point_size = GEOM_POINT_SIZE,
        sort_values = TRUE,
        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 = theme(plot.margin = margin())
    )

Composition

p_barplot_composition_batch <- calc_group_composition(
    data = embedding |>
        dplyr::left_join(
            cell_metadata_PRJNA720968
        ),
    x = "leiden",
    group = "origin"
) |>
    dplyr::mutate(
        leiden = factor(leiden)
    ) |>
    plot_barplot(
        x = "leiden",
        y = "percentage",
        z = "origin",
        legend_ncol = 1
    )

p_barplot_composition_annotated <- calc_group_composition(
    data = embedding |>
        dplyr::left_join(
            cell_metadata_PRJNA720968
        ),
    x = "leiden",
    group = "annotated"
) |>
    dplyr::mutate(
        leiden = factor(leiden)
    ) |>
    plot_barplot(
        x = "leiden",
        y = "percentage",
        z = "annotated",
        legend_ncol = 1
    )


p_barplot_composition_lineage <- calc_group_composition(
    data = embedding |>
        dplyr::left_join(
            cell_metadata_PRJNA720968
        ),
    x = "leiden",
    group = "lineage"
) |>
    dplyr::mutate(
        leiden = factor(leiden)
    ) |>
    plot_barplot(
        x = "leiden",
        y = "percentage",
        z = "lineage",
        legend_ncol = 1
    )

p_barplot_composition_developmental_stage <- calc_group_composition(
    data = embedding |>
        dplyr::left_join(
            cell_metadata_PRJNA720968
        ),
    x = "leiden",
    group = "developmental_stage"
) |>
    dplyr::mutate(
        leiden = factor(leiden)
    ) |>
    plot_barplot(
        x = "leiden",
        y = "percentage",
        z = "developmental_stage",
        legend_ncol = 1
    )

list(
    p_barplot_composition_batch,
    p_barplot_composition_annotated,
    p_barplot_composition_lineage,
    p_barplot_composition_developmental_stage
) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 1, guides = "collect") +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

Expression

Genes used in Fig. 2C.

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"
)

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

    cat(selected_feature, "\n")
    values <- log10(calc_cpm(matrix_readcount_use[, embedding$cell])[selected_feature, ] + 1)
    values[embedding |>
        dplyr::left_join(cell_metadata_PRJNA720968) |>
        dplyr::pull(origin) == "Blastocyst"] <- NA

    p1 <- plot_embedding(
        embedding = embedding[, c(x_column, y_column)],
        color_values = values,
        label = paste(
            EMBEDDING_TITLE_PREFIX,
            "Blastoid",
            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
        )

    values <- log10(calc_cpm(matrix_readcount_use[, embedding$cell])[selected_feature, ] + 1)
    values[embedding |>
        dplyr::left_join(cell_metadata_PRJNA720968) |>
        dplyr::pull(origin) == "Blastoid"] <- NA

    p2 <- plot_embedding(
        embedding = embedding[, c(x_column, y_column)],
        color_values = values,
        label = paste(
            EMBEDDING_TITLE_PREFIX,
            "Blastocyst",
            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
        )
    return(list(p1, p2))
}) |>
    unlist(recursive = FALSE) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 4) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

ENSG00000204531_POU5F1 
ENSG00000111704_NANOG 
ENSG00000171872_KLF17 
ENSG00000186103_ARGFX 
ENSG00000164736_SOX17 
ENSG00000125798_FOXA2 
ENSG00000136574_GATA4 
ENSG00000134853_PDGFRA 
ENSG00000179348_GATA2 
ENSG00000070915_SLC12A3 
ENSG00000165556_CDX2 
ENSG00000007866_TEAD3 

R session info

devtools::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 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     2022-01-24
 pandoc   2.17.0.1 @ /opt/homebrew/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package       * version     date (UTC) lib source
 assertthat      0.2.1       2019-03-21 [1] CRAN (R 4.1.1)
 backports       1.4.1       2021-12-13 [1] CRAN (R 4.1.2)
 beeswarm        0.4.0       2021-06-01 [1] CRAN (R 4.1.2)
 bit             4.0.4       2020-08-04 [1] CRAN (R 4.1.1)
 bit64           4.0.5       2020-08-30 [1] CRAN (R 4.1.1)
 brio            1.1.3       2021-11-30 [1] CRAN (R 4.1.2)
 broom           0.7.11      2022-01-03 [1] CRAN (R 4.1.2)
 cachem          1.0.6       2021-08-19 [1] CRAN (R 4.1.1)
 callr           3.7.0       2021-04-20 [1] CRAN (R 4.1.1)
 cellranger      1.1.0       2016-07-27 [1] CRAN (R 4.1.1)
 checkmate       2.0.0       2020-02-06 [1] CRAN (R 4.1.1)
 cli             3.1.1       2022-01-20 [1] CRAN (R 4.1.2)
 colorspace      2.0-2       2021-06-24 [1] CRAN (R 4.1.1)
 crayon          1.4.2       2021-10-29 [1] CRAN (R 4.1.1)
 data.table      1.14.2      2021-09-27 [1] CRAN (R 4.1.1)
 DBI             1.1.2       2021-12-20 [1] CRAN (R 4.1.2)
 dbplyr          2.1.1       2021-04-06 [1] CRAN (R 4.1.1)
 desc            1.4.0       2021-09-28 [1] CRAN (R 4.1.1)
 devtools        2.4.3.9000  2022-01-22 [1] Github (r-lib/devtools@41280ac)
 digest          0.6.29      2021-12-01 [1] CRAN (R 4.1.2)
 dplyr         * 1.0.7.9000  2022-01-12 [1] Github (tidyverse/dplyr@0501335)
 dtplyr          1.2.1       2022-01-19 [1] CRAN (R 4.1.2)
 ellipsis        0.3.2       2021-04-29 [1] CRAN (R 4.1.1)
 evaluate        0.14        2019-05-28 [1] CRAN (R 4.1.1)
 extrafont     * 0.17        2014-12-08 [1] CRAN (R 4.1.1)
 extrafontdb     1.0         2012-06-11 [1] CRAN (R 4.1.1)
 fansi           1.0.2       2022-01-14 [1] CRAN (R 4.1.2)
 farver          2.1.0       2021-02-28 [1] CRAN (R 4.1.1)
 fastmap         1.1.0       2021-01-25 [1] CRAN (R 4.1.1)
 forcats       * 0.5.1.9000  2021-11-29 [1] Github (tidyverse/forcats@b4dade0)
 fs              1.5.2.9000  2021-12-09 [1] Github (r-lib/fs@6d1182f)
 gargle          1.2.0       2021-07-02 [1] CRAN (R 4.1.1)
 generics        0.1.1       2021-10-25 [1] CRAN (R 4.1.1)
 ggbeeswarm      0.6.0       2017-08-07 [1] CRAN (R 4.1.2)
 ggplot2       * 3.3.5       2021-06-25 [1] CRAN (R 4.1.1)
 ggrastr         1.0.1       2021-12-08 [1] Github (VPetukhov/ggrastr@7aed9af)
 glue            1.6.1.9000  2022-01-23 [1] Github (tidyverse/glue@3da70df)
 googledrive     2.0.0       2021-07-08 [1] CRAN (R 4.1.1)
 googlesheets4   1.0.0       2021-07-21 [1] CRAN (R 4.1.1)
 gt              0.3.1.9000  2022-01-17 [1] Github (rstudio/gt@fcabb41)
 gtable          0.3.0.9000  2021-10-28 [1] Github (r-lib/gtable@a0bd272)
 haven           2.4.3       2021-08-04 [1] CRAN (R 4.1.1)
 highr           0.9         2021-04-16 [1] CRAN (R 4.1.1)
 hms             1.1.1       2021-09-26 [1] CRAN (R 4.1.1)
 htmltools       0.5.2       2021-08-25 [1] CRAN (R 4.1.1)
 htmlwidgets     1.5.4       2021-09-08 [1] CRAN (R 4.1.1)
 httr            1.4.2       2020-07-20 [1] CRAN (R 4.1.1)
 jsonlite        1.7.3       2022-01-17 [1] CRAN (R 4.1.2)
 knitr           1.37.1      2021-12-21 [1] https://yihui.r-universe.dev (R 4.1.2)
 labeling        0.4.2       2020-10-20 [1] CRAN (R 4.1.1)
 lattice         0.20-45     2021-09-22 [2] CRAN (R 4.1.2)
 lifecycle       1.0.1       2021-09-24 [1] CRAN (R 4.1.1)
 lubridate       1.8.0       2022-01-20 [1] Github (tidyverse/lubridate@566590f)
 magrittr        2.0.1       2020-11-17 [1] CRAN (R 4.1.1)
 Matrix        * 1.4-0       2021-12-08 [2] CRAN (R 4.1.2)
 memoise         2.0.1       2021-11-26 [1] CRAN (R 4.1.2)
 modelr          0.1.8.9000  2021-10-27 [1] Github (tidyverse/modelr@16168e0)
 munsell         0.5.0       2018-06-12 [1] CRAN (R 4.1.1)
 patchwork     * 1.1.0.9000  2021-10-27 [1] Github (thomasp85/patchwork@79223d3)
 pillar          1.6.4       2021-10-18 [1] CRAN (R 4.1.1)
 pkgbuild        1.3.1       2021-12-20 [1] CRAN (R 4.1.2)
 pkgconfig       2.0.3       2019-09-22 [1] CRAN (R 4.1.1)
 pkgload         1.2.4       2021-11-30 [1] CRAN (R 4.1.2)
 png             0.1-7       2013-12-03 [1] CRAN (R 4.1.1)
 prettyunits     1.1.1       2020-01-24 [1] CRAN (R 4.1.1)
 processx        3.5.2       2021-04-30 [1] CRAN (R 4.1.1)
 ps              1.6.0       2021-02-28 [1] CRAN (R 4.1.1)
 purrr         * 0.3.4       2020-04-17 [1] CRAN (R 4.1.1)
 R.cache         0.15.0      2021-04-30 [1] CRAN (R 4.1.1)
 R.methodsS3     1.8.1       2020-08-26 [1] CRAN (R 4.1.1)
 R.oo            1.24.0      2020-08-26 [1] CRAN (R 4.1.1)
 R.utils         2.11.0      2021-09-26 [1] CRAN (R 4.1.1)
 R6              2.5.1.9000  2021-12-09 [1] Github (r-lib/R6@1b05b89)
 ragg            1.2.1.9000  2021-12-08 [1] Github (r-lib/ragg@c68c666)
 Rcpp            1.0.8       2022-01-13 [1] CRAN (R 4.1.2)
 readr         * 2.1.1       2021-11-30 [1] CRAN (R 4.1.2)
 readxl          1.3.1.9000  2022-01-20 [1] Github (tidyverse/readxl@2ccb82c)
 remotes         2.4.2       2022-01-24 [1] Github (r-lib/remotes@7b0ee01)
 reprex          2.0.1       2021-08-05 [1] CRAN (R 4.1.1)
 reticulate      1.23        2022-01-14 [1] CRAN (R 4.1.2)
 rlang           1.0.0       2022-01-20 [1] Github (r-lib/rlang@f2fbaad)
 rmarkdown       2.11.12     2022-01-24 [1] Github (rstudio/rmarkdown@b53a7ce)
 rprojroot       2.0.2       2020-11-15 [1] CRAN (R 4.1.1)
 rstudioapi      0.13.0-9000 2022-01-15 [1] Github (rstudio/rstudioapi@5d0f087)
 Rttf2pt1        1.3.9       2021-07-22 [1] CRAN (R 4.1.1)
 rvest           1.0.2       2021-10-16 [1] CRAN (R 4.1.1)
 sass            0.4.0       2021-05-12 [1] CRAN (R 4.1.1)
 scales          1.1.1       2020-05-11 [1] CRAN (R 4.1.1)
 sessioninfo     1.2.2       2021-12-06 [1] CRAN (R 4.1.2)
 stringi         1.7.6       2021-11-29 [1] CRAN (R 4.1.2)
 stringr       * 1.4.0.9000  2022-01-24 [1] Github (tidyverse/stringr@85f6140)
 styler        * 1.6.2.9000  2022-01-17 [1] Github (r-lib/styler@9274aed)
 systemfonts     1.0.3.9000  2021-12-07 [1] Github (r-lib/systemfonts@414114e)
 testthat        3.1.2.9000  2022-01-21 [1] Github (r-lib/testthat@54b9db2)
 textshaping     0.3.6       2021-10-13 [1] CRAN (R 4.1.1)
 tibble        * 3.1.6.9000  2022-01-18 [1] Github (tidyverse/tibble@7aa54e6)
 tidyr         * 1.1.4       2021-09-27 [1] CRAN (R 4.1.1)
 tidyselect      1.1.1       2021-04-30 [1] CRAN (R 4.1.1)
 tidyverse     * 1.3.1.9000  2021-12-08 [1] Github (tidyverse/tidyverse@6186fbf)
 tzdb            0.2.0       2021-10-27 [1] CRAN (R 4.1.1)
 usethis         2.1.5.9000  2022-01-20 [1] Github (r-lib/usethis@57b109a)
 utf8            1.2.2       2021-07-24 [1] CRAN (R 4.1.1)
 vctrs           0.3.8       2021-04-29 [1] CRAN (R 4.1.1)
 vipor           0.4.5       2017-03-22 [1] CRAN (R 4.1.2)
 viridisLite     0.4.0       2021-04-13 [1] CRAN (R 4.1.1)
 vroom           1.5.7       2021-11-30 [1] CRAN (R 4.1.2)
 withr           2.4.3       2021-11-30 [1] CRAN (R 4.1.2)
 xfun            0.29        2021-12-14 [1] CRAN (R 4.1.2)
 xml2            1.3.3       2021-11-30 [1] CRAN (R 4.1.2)
 yaml            2.2.1       2020-02-01 [1] CRAN (R 4.1.1)

 [1] /opt/homebrew/lib/R/4.1/site-library
 [2] /opt/homebrew/Cellar/r/4.1.2/lib/R/library

─ Python configuration ───────────────────────────────────────────────────────
 python:         /Users/jialei/.pyenv/shims/python
 libpython:      /Users/jialei/.pyenv/versions/miniforge3-4.10.1-5/lib/libpython3.9.dylib
 pythonhome:     /Users/jialei/.pyenv/versions/miniforge3-4.10.1-5:/Users/jialei/.pyenv/versions/miniforge3-4.10.1-5
 version:        3.9.5 | packaged by conda-forge | (default, Jun 19 2021, 00:24:55)  [Clang 11.1.0 ]
 numpy:          /Users/jialei/.pyenv/versions/miniforge3-4.10.1-5/lib/python3.9/site-packages/numpy
 numpy_version:  1.20.3
 anndata:        /Users/jialei/.pyenv/versions/miniforge3-4.10.1-5/lib/python3.9/site-packages/anndata
 
 NOTE: Python version was forced by RETICULATE_PYTHON

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