Single cell transcriptomes of human EP-structures generated by Sozen et al., 2021

Jialei Duan

2022-01-25

---

Sozen, B., Jorgensen, V., Weatherbee, B.A.T., Chen, S., Zhu, M., and Zernicka-Goetz, M. (2021). Reconstructing aspects of human embryogenesis with pluripotent stem cells. Nat. Commun. 12, 1–13.

• BioProject Accession: PRJNA738498
• GEO Accession: GSE178326

---

Load required packages.

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

Sys.time()

[1] "2022-01-25 01:39:15 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"

BACKED <- NULL
adata <- ad$read_h5ad(
    filename = file.path(
        PROJECT_DIR,
        "raw/public/PRJNA738498",
        "matrix",
        "adata.h5ad"
    ),
    backed = BACKED
)

matrix_readcount_use <- adata |> convert_adata()

dim(matrix_readcount_use)

[1] 33538  6231

Embedding

EMBEDDING_FILE <- "embedding_ncomponents18_ccc1_seed20210719.csv.gz"

embedding <- vroom::vroom(
    file = file.path(
        PROJECT_DIR,
        "raw/public/PRJNA738498/",
        "clustering/PRJNA738498/exploring/2021-11-12/Scanpy_Harmony",
        EMBEDDING_FILE
    )
)

Rows: 4840 Columns: 16
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (2): cell, batch
dbl (14): louvain, leiden, x_tsne, y_tsne, x_umap, y_umap, x_fitsne, y_fitsn...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

embedding |>
    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
GSM5387817_AAACCCAAGGTAAAGG	GSM5387817	1	0	22.552797	-76.99979	-1.8993253	12.636523	-15.494342	51.689038	-0.0211723	-0.0004932	-3.978256	11.463525	1.730775	-2.397553
GSM5387817_AAACCCAAGTTGAAGT	GSM5387817	1	0	19.913122	-73.46740	-2.1883996	11.400636	-11.716485	49.303629	-0.0206319	-0.0010031	-4.190414	10.756096	1.549940	-2.278008
GSM5387817_AAACCCACAAATCCCA	GSM5387817	4	1	6.317181	-37.71603	-3.8487771	8.504709	25.590685	36.332722	-0.0188673	-0.0016880	-5.449766	8.731751	1.097940	-1.886869
GSM5387817_AAACCCACAACCGTGC	GSM5387817	2	1	12.822221	-38.25879	-4.4371886	9.371312	21.998825	40.830924	-0.0201961	-0.0011995	-5.751806	8.929660	1.347752	-2.034147
GSM5387817_AAACCCACACAACATC	GSM5387817	2	1	17.348112	-38.47583	-3.6683993	10.983391	16.806704	48.373959	-0.0193466	-0.0013859	-5.615176	9.830839	1.549798	-2.219451
GSM5387817_AAACCCACATCGCTCT	GSM5387817	4	6	-31.408867	-59.78850	0.2084487	7.121182	5.117555	7.322763	-0.0210226	0.0004796	-2.895370	7.523944	0.502468	-2.048530

Metadata

cell_metadata <- vroom::vroom(
    file = file.path(
        PROJECT_DIR,
        "raw/public/PRJNA738498",
        "matrix",
        "cell_metadata.csv"
    )
) |>
    dplyr::mutate(
        group = stringr::str_remove(
            string = sample_id,
            pattern = "-.+$"
        ),
        group = case_when(
            group == "2D" ~ "hEPSCs in 2D",
            group == "D5" ~ "Day 5 hEP-structures",
            group == "D6" ~ "Day 6 hEP-structures",
            group == "unknown" ~ "hEPSCs, unknown",
            is.na(group) ~ "Natural human embryos"
        ),
        group = factor(
            group,
            levels = c(
                "Natural human embryos",
                "Day 5 hEP-structures",
                "Day 6 hEP-structures",
                "hEPSCs in 2D",
                "hEPSCs, unknown"
            )
        )
    ) |>
    dplyr::left_join(
        adata$obs |>
            tibble::rownames_to_column(var = "cell") |>
            dplyr::select(-batch),
        by = c("cell" = "cell")
    )

# cell_metadata$sample_id |> table(exclude = "")
# cell_metadata$developmental_stage |> table(exclude = "")
# cell_metadata |> dplyr::count(group)

cell_metadata |>
    dplyr::count(group, 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
    )

	group	num_cells
	Natural human embryos	542
	Day 5 hEP-structures	2013
	Day 6 hEP-structures	2057
	hEPSCs in 2D	228
	hEPSCs, unknown	1391
Sum	—	6,231

dim(cell_metadata)

[1] 6231    9

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.

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

134.8 MB
901 kB

Single-cell transcriptome analysis

embedding <- embedding |>
    dplyr::left_join(
        cell_metadata |>
            dplyr::select(
                cell, group:mt_percentage
            ),
        by = c("cell" = "cell")
    )

embedding |>
    dplyr::group_by(
        group
    ) |>
    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
    )

	group	num_cells	median_umis	median_features	median_mt_percentage
	Natural human embryos	542	1525.0	788	0.07816558
	Day 5 hEP-structures	2013	3916.0	1542	0.05452046
	Day 6 hEP-structures	2057	5634.0	2146	0.04631579
	hEPSCs in 2D	228	4658.5	1740	0.07582252
Sum	—	4,840	—	—	—
Mean	—	—	3,933.38	1,554.00	0.06

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")

leiden	num_cells	median_umis	median_features	median_mt_percentage
0	650	7229.0	2548.0	0.044980507
1	575	5098.0	2001.0	0.047900969
2	519	4596.0	1750.0	0.055059253
3	486	3592.5	1367.5	0.053230059
4	467	1615.0	828.0	0.076511094
5	381	5207.0	1995.0	0.045398773
6	331	5504.0	2094.0	0.044977861
7	329	5760.0	2061.0	0.053837597
8	259	563.0	293.0	0.286012526
9	252	2873.5	1286.0	0.051105461
10	212	4263.0	1723.5	0.087807433
11	172	3567.5	1502.5	0.055306764
12	96	4089.0	1676.5	0.043022000
13	83	1504.0	798.0	0.163967611
14	17	5581.0	1828.0	0.062293144
15	11	825.0	347.0	0.001129944

Embedding visualization

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

GEOM_POINT_SIZE <- 0.4
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,
    rasterise = RASTERISED
) +
    theme_customized()

CB_POSITION <- c(0.8, 0.325)
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 * 1.5,
    sort_values = TRUE,
    shuffle_values = FALSE,
    rasterise = RASTERISED,
    legend_size = 2
) +
    theme_customized(
        y = CB_POSITION[2],
        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 * 1.5,
    sort_values = TRUE,
    shuffle_values = FALSE,
    rasterise = RASTERISED,
    legend_size = 2
) +
    theme_customized(
        y = CB_POSITION[2],
        legend_key_size = 2,
        legend_text_size = 5
    )

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

purrr::map(c(0.4, 0.6, 0.8), \(x) {
    values <- embedding |>
        dplyr::mutate(
            value = case_when(
                mt_percentage >= x ~ "1",
                TRUE ~ "0"
            )
        ) |>
        dplyr::pull(value)

    plot_embedding(
        embedding = embedding[, c(x_column, y_column)],
        color_values = values |> as.factor(),
        label = glue::glue(
            "{EMBEDDING_TITLE_PREFIX}; MT% >= {scales::percent(x)}; ",
            "Cells: {sum(as.numeric(values))}"
        ),
        label_position = NULL,
        show_color_value_labels = FALSE,
        show_color_legend = FALSE,
        geom_point_size = GEOM_POINT_SIZE * 1.5,
        sort_values = TRUE,
        shuffle_values = FALSE,
        rasterise = RASTERISED,
        legend_size = 2
    ) +
        theme_customized(
            legend_key_size = 2,
            legend_text_size = 5
        ) +
        scale_color_manual(
            values = c("grey70", "salmon")
        )
}) |>
    purrr::reduce(
        `+`
    ) +
    patchwork::plot_layout(ncol = 3) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

Batch & group

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(
        legend_key_size = 2,
        legend_text_size = 5
    )

p_embedding_group <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding$group |> as.factor(),
    label = paste(EMBEDDING_TITLE_PREFIX, "Group", sep = "; "),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    rasterise = RASTERISED,
    legend_size = 2
) +
    theme_customized(
        legend_key_size = 2,
        legend_text_size = 5
    )

values <- embedding |>
    dplyr::mutate(
        value = case_when(
            num_features < 200 ~ "1",
            TRUE ~ "0"
        )
    ) |>
    dplyr::pull(value)

p_embedding_low_complexity <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = values |> as.factor(),
    label = paste(EMBEDDING_TITLE_PREFIX, "Genes < 200", sep = "; "),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = FALSE,
    geom_point_size = GEOM_POINT_SIZE * 1.5,
    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")
    )

This is a re-creation of Fig. 5AB.

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

Composition

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

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

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

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, \(x) {
    selected_feature <- x

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

    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 * 1.5,
        sort_values = TRUE,
        shuffle_values = FALSE,
        rasterise = RASTERISED,
        legend_size = 2
    ) +
        scale_color_viridis_c(
            na.value = "grey80"
        ) +
        theme_customized(
            y = CB_POSITION[2],
            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())
    )

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)
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 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)
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 haven           2.4.3       2021-08-04 [1] CRAN (R 4.1.1)
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 memoise         2.0.1       2021-11-26 [1] CRAN (R 4.1.2)
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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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