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

Jialei Duan

2022-01-25

---

Fan, Y., Min, Z., Alsolami, S., Ma, Z., Zhang, E., Chen, W., Zhong, K., Pei, W., Kang, X., Zhang, P., et al. (2021). Generation of human blastocyst-like structures from pluripotent stem cells. Cell Discovery 7, 1–14.

• BioProject Accession: PRJNA667174
• GEO Accession: GSE158971

---

Load required packages.

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

Sys.time()

[1] "2022-01-25 02:49:54 CST"

Data preparation

Functions loading

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

Data loading

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

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

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"

BACKED <- NULL
adata <- ad$read_h5ad(
    filename = file.path(
        PROJECT_DIR,
        "raw/public/PRJNA667174",
        "matrix",
        "adata.h5ad"
    ),
    backed = BACKED
)

matrix_readcount_use <- adata |> convert_adata()

dim(matrix_readcount_use)

[1] 33538 30439

Embedding

EMBEDDING_FILE <- "embedding_ncomponents10_ccc1_seed20210719.csv.gz"

embedding <- vroom::vroom(
    file = file.path(
        PROJECT_DIR,
        "raw/public/PRJNA667174",
        "clustering/PRJNA667174/exploring",
        "Scanpy_Harmony",
        EMBEDDING_FILE
    )
)

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

cell	batch	louvain	leiden	x_tsne	y_tsne	x_umap	y_umap	x_fitsne	y_fitsne	x_phate	y_phate	x_umap_min_dist=0.1	y_umap_min_dist=0.1	x_multicoretsne	y_multicoretsne
GSM4816780_AAACCCAAGTCACAGG	GSM4816780	0	0	-59.61914	-34.25100	-2.434389	8.971638	-68.549462	39.35378	-0.0098444	-0.0098271	-2.276734	9.453928	1.7424585	0.0409331
GSM4816780_AAACCCAAGTTACTCG	GSM4816780	3	10	72.60726	81.89310	12.410583	2.235562	2.113421	109.57434	0.0452768	0.0093461	12.907623	2.995861	0.1721521	2.7968170
GSM4816780_AAACCCACATAATCGC	GSM4816780	5	8	10.69915	11.80464	2.457198	12.121967	-18.971701	36.84989	0.0163275	-0.0094425	3.231614	10.969112	-0.0700925	0.1236116
GSM4816780_AAACCCACATCATCTT	GSM4816780	4	3	-57.99653	95.91599	12.462027	11.089141	90.450377	15.79619	0.0314861	-0.0030768	11.308686	10.344496	-2.5766431	1.8918896
GSM4816780_AAACCCACATTCCTAT	GSM4816780	1	4	-14.01938	-142.56613	-6.957992	10.141818	-96.927817	-53.61567	-0.0092716	-0.0123081	-5.528971	11.530880	2.6173480	-1.4231000
GSM4816780_AAACCCAGTGCCGTAC	GSM4816780	5	2	-10.25328	14.79214	3.759502	11.010690	-7.149860	32.01512	0.0171529	-0.0095562	4.935977	10.719850	-0.4245640	0.0006467

dim(embedding)

[1] 30439    16

Metadata

cell_metadata <- vroom::vroom(
    file = file.path(
        PROJECT_DIR,
        "raw/public/PRJNA667174",
        "matrix",
        "cell_metadata.csv"
    )
) |>
    dplyr::left_join(
        adata$obs |>
            tibble::rownames_to_column(var = "cell")
    )

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

cell	lineage	developmental_stage	batch	num_umis	num_features	mt_percentage
GSM4816780_AAACCCAAGTCACAGG	EPI	D6	GSM4816780	54160	6368	0.0201440
GSM4816780_AAACCCAAGTTACTCG	TE	D6	GSM4816780	1824	1041	0.0010965
GSM4816780_AAACCCACATAATCGC	TE	D6	GSM4816780	5850	2212	0.0025641
GSM4816780_AAACCCACATCATCTT	IM	D6	GSM4816780	898	571	0.0111359
GSM4816780_AAACCCACATTCCTAT	IM	D6	GSM4816780	36394	6010	0.0125295
GSM4816780_AAACCCAGTGCCGTAC	TE	D6	GSM4816780	4755	1953	0.0012618

Check memory usage.

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

1.2 GB
3.9 MB

Single-cell transcriptome analysis

embedding <- embedding |>
    dplyr::left_join(
        cell_metadata |>
            dplyr::select(-batch),
        by = c("cell" = "cell")
    ) |>
    dplyr::mutate(
        developmental_stage = factor(
            developmental_stage,
            levels = c("D6", "D8", "D10")
        ),
        lineage = factor(
            lineage,
            levels = c(
                c("EPI", "PE", "TE", "IM", "CTB", "STB")
            )
        )
    )

embedding |>
    dplyr::group_by(
        batch
    ) |>
    dplyr::summarise(
        num_cells = 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_mt_percentage),
        fns = list(
            Mean = ~ mean(.)
        ),
        decimals = c(2)
    )

	batch	num_cells	median_umis	median_features	median_mt_percentage
	GSM4816780	10933	6203.0	2240.0	0.007092199
	GSM4816781	11634	12780.5	3357.0	0.052984867
	GSM4816782	7872	12238.0	3778.5	0.065663483
Sum	—	30,439	—	—	—
Mean	—	—	10,407.17	3,125.17	0.04

embedding |>
    dplyr::group_by(
        leiden
    ) |>
    dplyr::summarise(
        num_cells = 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_mt_percentage),
        fns = list(
            Mean = ~ mean(.)
        ),
        decimals = 2
    )

	leiden	num_cells	median_umis	median_features	median_mt_percentage
	0	3766	30355.5	5476.0	0.055449145
	1	3391	24105.0	4961.0	0.057462115
	2	3028	2982.0	1323.0	0.013956873
	3	2957	841.0	534.0	0.030703625
	4	2655	24943.0	5121.0	0.055765976
	5	2272	23833.0	4629.5	0.043561692
	6	2050	1134.0	666.0	0.013907496
	7	1990	1399.0	778.0	0.020248509
	8	1304	8219.5	2591.5	0.006524399
	9	1207	2036.0	1107.0	0.054143646
	10	1160	3418.0	1493.0	0.003813516
	11	1029	24929.0	4990.0	0.044210168
	12	904	16867.0	4129.0	0.056618586
	13	717	5813.0	2375.0	0.093905032
	14	592	31812.0	5605.0	0.053662762
	15	410	17966.5	4359.0	0.070959755
	16	382	3411.5	1537.0	0.035595393
	17	367	24991.0	5047.0	0.053125720
	18	258	1479.0	758.5	0.039723450
Sum	—	30,439	—	—	—
Mean	—	—	13,186.05	3,025.29	0.04

Embedding visualization

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

# GEOM_POINT_SIZE <- 1.25
GEOM_POINT_SIZE <- 0.3
EMBEDDING_TITLE_PREFIX <- "UMAP"
RASTERISED <- TRUE

Clustering & UMI & MT

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,
    shuffle_values = TRUE,
    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 = log10(embedding$num_umis),
    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 * 1.25,
    sort_values = TRUE,
    shuffle_values = FALSE,
    rasterise = RASTERISED,
    legend_size = 2
) +
    theme_customized(
        x = CB_POSITION[1],
        legend_key_size = 2,
        legend_text_size = 5
    )

p_embedding_MT <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding$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 * 2,
    sort_values = TRUE,
    shuffle_values = FALSE,
    rasterise = RASTERISED,
    legend_size = 2
) +
    theme_customized(
        x = CB_POSITION[1],
        legend_key_size = 2,
        legend_text_size = 5
    )

Clustering of 30,439 cells (10,933 cells in Fig. 3a, 11,634 and 7,872 cells in Fig. 4cd).

purrr::reduce(
    list(
        p_embedding_leiden,
        p_embedding_UMI,
        p_embedding_MT
    ),
    `+`
) +
    patchwork::plot_layout(ncol = 3) +
    patchwork::plot_annotation(
        theme = ggplot2::theme(plot.margin = ggplot2::margin())
    )

Lineage, origin & developmental stage

p_embedding_lineage <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding$lineage,
    label = paste(EMBEDDING_TITLE_PREFIX, "Lineage", sep = "; "),
    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(
        x = CB_POSITION[1],
        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 = paste(EMBEDDING_TITLE_PREFIX, "Batch", sep = "; "),
    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(
        x = CB_POSITION[1] - 0.1,
        legend_key_size = 2,
        legend_text_size = 5
    )

p_embedding_developmental_stage <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding$developmental_stage |> as.factor(),
    label = paste(EMBEDDING_TITLE_PREFIX, "Developmental stage", sep = "; "),
    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(
        x = CB_POSITION[1],
        legend_key_size = 2,
        legend_text_size = 5
    )

purrr::reduce(
    list(
        p_embedding_lineage,
        p_embedding_batch,
        p_embedding_developmental_stage
    ),
    `+`
) +
    patchwork::plot_layout(ncol = 3) +
    patchwork::plot_annotation(
        theme = ggplot2::theme(plot.margin = ggplot2::margin())
    )

Developmental stage

embedding |>
    dplyr::group_by(
        developmental_stage
    ) |>
    dplyr::summarise(
        num_cells = 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_mt_percentage),
        fns = list(
            Mean = ~ mean(.)
        ),
        decimals = c(2)
    )

	developmental_stage	num_cells	median_umis	median_features	median_mt_percentage
	D6	10933	6203.0	2240.0	0.007092199
	D8	11634	12780.5	3357.0	0.052984867
	D10	7872	12238.0	3778.5	0.065663483
Sum	—	30,439	—	—	—
Mean	—	—	10,407.17	3,125.17	0.04

purrr::map(levels(embedding$developmental_stage), function(x) {
    plot_embedding(
        embedding = embedding[, c(x_column, y_column)],
        color_values = as.numeric(
            embedding$developmental_stage == x
        ) |> as.factor(),
        label = paste(EMBEDDING_TITLE_PREFIX, x, sep = "; "),
        label_position = NULL,
        show_color_value_labels = FALSE,
        show_color_legend = FALSE,
        geom_point_size = GEOM_POINT_SIZE,
        sort_values = TRUE,
        rasterise = RASTERISED
    ) +
        theme_customized() +
        scale_color_manual(
            values = c("grey70", "salmon")
        )
}) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 3) +
    patchwork::plot_annotation(
        theme = ggplot2::theme(plot.margin = ggplot2::margin())
    )

Lineage

embedding |>
    dplyr::group_by(
        lineage
    ) |>
    dplyr::summarise(
        num_cells = n(),
        median_umis = median(num_umis),
        num_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_mt_percentage),
        fns = list(
            Mean = ~ mean(.)
        ),
        decimals = c(2)
    )

	lineage	num_cells	median_umis	num_features	median_mt_percentage
	EPI	1340	28910.0	5194.0	0.018937359
	PE	2083	23615.0	4849.0	0.020250376
	TE	2472	2253.5	1112.5	0.003417852
	IM	6478	4360.5	1841.0	0.008306207
	CTB	9438	22634.5	4912.0	0.066582772
	STB	8628	2255.5	1219.0	0.030517943
Sum	—	30,439	—	—	—
Mean	—	—	14,004.83	3,187.92	0.02

purrr::map(levels(embedding$lineage), function(x) {
    plot_embedding(
        embedding = embedding[, c(x_column, y_column)],
        color_values = as.numeric(embedding$lineage == x) |> as.factor(),
        label = paste(EMBEDDING_TITLE_PREFIX, x, sep = "; "),
        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(
            x = CB_POSITION[1] - 0.3,
            legend_key_size = 2,
            legend_text_size = 5
        ) +
        scale_color_manual(
            values = c("grey70", "salmon")
        )
}) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 3) +
    patchwork::plot_annotation(
        theme = ggplot2::theme(plot.margin = ggplot2::margin())
    )

Composition

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

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

p_barplot_combined <- list(
    p_barplot_composition_lineage,
    p_barplot_composition_developmental_stage
) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(nrow = 2, guides = "collect") +
    patchwork::plot_annotation(
        theme = ggplot2::theme(plot.margin = ggplot2::margin())
    )
p_barplot_combined

calc_group_composition(
    data = embedding,
    x = "lineage",
    group = "developmental_stage"
) |>
    plot_barplot(
        x = "lineage",
        y = "percentage",
        z = "developmental_stage",
        legend_ncol = 1
    )

calc_group_composition(
    data = embedding,
    x = "developmental_stage",
    group = "lineage"
) |>
    plot_barplot(
        x = "developmental_stage",
        y = "percentage",
        z = "lineage",
        legend_ncol = 1
    )

Expression

Embedding

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
    )

    p1 <- plot_embedding(
        embedding = embedding[, 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 * 2,
        sort_values = TRUE,
        shuffle_values = FALSE,
        rasterise = RASTERISED,
        legend_size = 2
    ) +
        scale_color_viridis_c(
            na.value = "grey80"
        ) +
        theme_customized(
            x = CB_POSITION[1] + 0.05,
            legend_key_size = 2,
            legend_text_size = 5
        )

    return(p1)
}) |>
    # unlist(recursive = FALSE) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 3, byrow = FALSE) +
    patchwork::plot_annotation(
        theme = ggplot2::theme(plot.margin = ggplot2::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     2022-01-25
 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)
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 [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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