Dataset Summary

Jialei Duan

Dataset Summary

Author

Affiliation

Jialei Duan

UT Southwestern Medical Center

Published

Sun Sep 10, 2023 03:32:04-05:00

Yu, L., Wei, Y., Duan, J., Schmitz, D.A., Sakurai, M., Wang, L., Wang, K., Zhao, S., Hon, G.C., and Wu, J. (2021). Blastocyst-like structures generated from human pluripotent stem cells. Nature 591, 620–626.
Petropoulos, S., Edsgärd, D., Reinius, B., Deng, Q., Panula, S.P., Codeluppi, S., Plaza Reyes, A., Linnarsson, S., Sandberg, R., and Lanner, F. (2016). Single-cell RNA-Seq reveals lineage and X chromosome dynamics in human preimplantation embryos. Cell 165, 1012–1026.
Zheng, Y., Xue, X., Shao, Y., Wang, S., Esfahani, S.N., Li, Z., Muncie, J.M., Lakins, J.N., Weaver, V.M., Gumucio, D.L., et al. (2019). Controlled modelling of human epiblast and amnion development using stem cells. Nature 573, 421–425.
Xiang, L., Yin, Y., Zheng, Y., Ma, Y., Li, Y., Zhao, Z., Guo, J., Ai, Z., Niu, Y., Duan, K., et al. (2020). A developmental landscape of 3D-cultured human pre-gastrulation embryos. Nature 577, 537–542.
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.
Tyser, R.C.V., Mahammadov, E., Nakanoh, S., Vallier, L., Scialdone, A., and Srinivas, S. (2021). Single-cell transcriptomic characterization of a gastrulating human embryo. Nature 600, 285–289.

Sys.time()

[1] "2023-09-10 03:31:48 CDT"

Sys.timezone()

[1] "America/Chicago"

Preparation

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

Functions

Load required packages.

library(tidyverse)
## ── Attaching core tidyverse packages ─────────────────── tidyverse 2.0.0.9000 ──
## ✔ dplyr     1.1.3          ✔ readr     2.1.4.9000
## ✔ forcats   1.0.0.9000     ✔ stringr   1.5.0.9000
## ✔ ggplot2   3.4.3.9000     ✔ tibble    3.2.1.9005
## ✔ lubridate 1.9.2.9000     ✔ tidyr     1.3.0.9000
## ✔ purrr     1.0.2.9000     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(Matrix)
## 
## Attaching package: 'Matrix'
## 
## The following objects are masked from 'package:tidyr':
## 
##     expand, pack, unpack
library(patchwork)
library(extrafont)
## Registering fonts with R
library(furrr)
## Loading required package: future

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

create_gt <- function(x) {
    x |>
        gt::gt() |>
        gt::data_color(
            columns = c(median_umis),
            fn = scales::col_numeric(
                palette = c(
                    "green", "orange", "red"
                ),
                domain = NULL
            )
        ) |>
        gt::fmt_number(
            columns = c(num_cells),
            sep_mark = ",",
            decimals = 0,
            suffixing = FALSE
        ) |>
        gt::fmt_number(
            columns = c(median_umis),
            sep_mark = ",",
            decimals = 1,
            suffixing = FALSE
        ) |>
        gt::grand_summary_rows(
            columns = c(num_cells),
            fns = c(
                Sum = ~ sum(.)
            ),
            fmt = ~ gt::fmt_number(., decimals = 0, use_seps = TRUE),
            suffixing = FALSE
        )
}

Python env

ad <- reticulate::import(module = "anndata", convert = TRUE)
print(ad$`__version__`)

[1] "0.8.0"

reticulate::py_config()

python:         /Users/jialei/.pyenv/shims/python
libpython:      /Users/jialei/.pyenv/versions/mambaforge-22.9.0-3/lib/libpython3.10.dylib
pythonhome:     /Users/jialei/.pyenv/versions/mambaforge-22.9.0-3:/Users/jialei/.pyenv/versions/mambaforge-22.9.0-3
version:        3.10.9 | packaged by conda-forge | (main, Feb  2 2023, 20:26:08) [Clang 14.0.6 ]
numpy:          /Users/jialei/.pyenv/versions/mambaforge-22.9.0-3/lib/python3.10/site-packages/numpy
numpy_version:  1.24.3
anndata:        /Users/jialei/.pyenv/versions/mambaforge-22.9.0-3/lib/python3.10/site-packages/anndata

NOTE: Python version was forced by RETICULATE_PYTHON

Matrix

# Petropoulos et al. 2016; PRJEB11202
# Zhou et al. 2019; PRJNA431392
# Xiang et al. 2020; PRJNA562548
# Tyser et al. 2021; PRJEB40781
# Zheng et al. 2019; PRJNA555602
# Yanagida et al. 2021; PRJNA720968
adata_files <- purrr::map(
    c(
        "PRJEB11202", "PRJNA431392", "PRJNA562548",
        "PRJEB40781", "PRJNA555602", "PRJNA720968"
    ), \(x) {
        file.path(
            PROJECT_DIR,
            "raw",
            "public",
            x,
            "matrix",
            "adata.h5ad"
        )
    }
)

adata_files <- unique(adata_files)
purrr::map_lgl(adata_files, file.exists)

[1] TRUE TRUE TRUE TRUE TRUE TRUE

BACKED <- NULL
matrix_readcount_use <- purrr::map(adata_files, function(x) {
    cat(x, "\n")

    ad$read_h5ad(
        filename = x, backed = BACKED
    ) |>
        extract_matrix_from_adata()
}) |>
    purrr::reduce(cbind)

/Users/jialei/Dropbox/Data/Projects/UTSW/Peri-implantation/raw/public/PRJEB11202/matrix/adata.h5ad 
/Users/jialei/Dropbox/Data/Projects/UTSW/Peri-implantation/raw/public/PRJNA431392/matrix/adata.h5ad 
/Users/jialei/Dropbox/Data/Projects/UTSW/Peri-implantation/raw/public/PRJNA562548/matrix/adata.h5ad 
/Users/jialei/Dropbox/Data/Projects/UTSW/Peri-implantation/raw/public/PRJEB40781/matrix/adata.h5ad 
/Users/jialei/Dropbox/Data/Projects/UTSW/Peri-implantation/raw/public/PRJNA555602/matrix/adata.h5ad 
/Users/jialei/Dropbox/Data/Projects/UTSW/Peri-implantation/raw/public/PRJNA720968/matrix/adata.h5ad

matrix_readcount_use <- matrix_readcount_use[
    , sort(colnames(matrix_readcount_use))
]
dim(matrix_readcount_use)

[1] 33538 19651

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, dplyr::everything())
}) |>
    dplyr::bind_rows()

cell_metadata |> head()

        cell      batch num_umis num_features mt_percentage
1 ERS1079290 PRJEB11202  1884175        10506   0.006999350
2 ERS1079291 PRJEB11202  1290557         8956   0.009932920
3 ERS1079292 PRJEB11202  1686855        10893   0.007484935
4 ERS1079293 PRJEB11202  1181562         9858   0.010599528
5 ERS1079294 PRJEB11202  1868361        10619   0.007091777
6 ERS1079295 PRJEB11202  1331354         8659   0.009590988

Summary

Petropoulos et al. 2016; PRJEB11202

cell_metadata_PRJEB11202 <- vroom::vroom(
    file = file.path(
        PROJECT_DIR,
        "raw",
        "public",
        "PRJEB11202",
        "matrix/cell_metadata.csv"
    ),
    show_col_types = FALSE
) |>
    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
    )

Lineage
Developmental stage

Code

cell_metadata_PRJEB11202 |>
    dplyr::left_join(
        cell_metadata,
        by = "cell"
    ) |>
    dplyr::group_by(lineage) |>
    dplyr::summarise(
        num_cells = n(),
        median_umis = median(num_umis)
    ) |>
    create_gt()

	lineage	num_cells	median_umis
	epiblast	127	1,501,176.0
	primitive_endoderm	108	1,626,576.5
	trophectoderm	861	1,528,414.0
	not_applicable	433	1,574,032.0
Sum	—	1,529	—

Code

cell_metadata_PRJEB11202 |>
    dplyr::left_join(
        cell_metadata,
        by = "cell"
    ) |>
    dplyr::group_by(developmental_stage) |>
    dplyr::summarise(
        num_cells = n(),
        median_umis = median(num_umis)
    ) |>
    create_gt()

	developmental_stage	num_cells	median_umis
	E3	81	1,560,759.0
	E4	190	1,737,179.5
	E5	377	1,320,641.0
	E6	415	1,303,893.0
	E7	466	1,826,628.0
Sum	—	1,529	—

Xiang et al. 2020; PRJNA562548

cell_metadata_PRJNA562548 <- vroom::vroom(
    file = file.path(
        PROJECT_DIR,
        "raw",
        "public",
        "PRJNA562548",
        "matrix/cell_metadata.csv"
    ),
    show_col_types = FALSE
) |>
    dplyr::select(
        cell = `Sample Name`,
        developmental_stage = Age,
        lineage = Cell_type
    ) |>
    dplyr::mutate(
        developmental_stage = stringr::str_replace(
            string = developmental_stage,
            pattern = "embryo invitro day ",
            replacement = "E"
        ),
        developmental_stage = factor(
            developmental_stage,
            levels = stringr::str_sort(
                x = unique(developmental_stage),
                numeric = TRUE
            )
        ),
        lineage = factor(
            lineage,
            levels = c(
                "ICM",
                "EPI",
                "PSA-EPI",
                "Hypoblast",
                "CTBs",
                "STBs",
                "EVTs"
            )
        )
    )

Lineage
Developmental stage

Code

cell_metadata_PRJNA562548 |>
    dplyr::left_join(
        cell_metadata,
        by = "cell"
    ) |>
    dplyr::group_by(lineage) |>
    dplyr::summarise(
        num_cells = n(),
        median_umis = median(num_umis)
    ) |>
    create_gt()

	lineage	num_cells	median_umis
	ICM	52	9,301,780.5
	EPI	126	8,140,892.0
	PSA-EPI	44	10,626,505.5
	Hypoblast	25	10,352,781.0
	CTBs	159	11,932,826.0
	STBs	109	10,947,518.0
	EVTs	40	7,776,425.0
Sum	—	555	—

Code

cell_metadata_PRJNA562548 |>
    dplyr::left_join(
        cell_metadata,
        by = "cell"
    ) |>
    dplyr::group_by(developmental_stage) |>
    dplyr::summarise(
        num_cells = n(),
        median_umis = median(num_umis)
    ) |>
    create_gt()

	developmental_stage	num_cells	median_umis
	E6	63	8,417,642.0
	E7	65	10,379,784.0
	E8	59	12,844,992.0
	E9	68	11,678,631.0
	E10	77	10,266,959.0
	E12	88	9,614,121.5
	E13.5	15	12,687,897.0
	E14	120	8,247,721.5
Sum	—	555	—

Tyser et al. 2021; PRJEB40781

cell_metadata_PRJEB40781 <- vroom::vroom(
    file = file.path(
        PROJECT_DIR,
        "raw",
        "public",
        "PRJEB40781",
        "matrix/cell_metadata.csv"
    ),
    show_col_types = FALSE
) |>
    dplyr::mutate(
        lineage = factor(lineage),
        lineage_ontology = factor(lineage_ontology),
        sampling_site = factor(sampling_site)
    )

Lineage
Developmental stage

Code

cell_metadata_PRJEB40781 |>
    dplyr::left_join(
        cell_metadata,
        by = "cell"
    ) |>
    dplyr::group_by(lineage) |>
    dplyr::summarise(
        num_cells = n(),
        median_umis = median(num_umis)
    ) |>
    create_gt()

	lineage	num_cells	median_umis
	advanced mesoderm	164	993,741.0
	axial mesoderm	23	853,681.0
	ectodermal cell	29	996,476.0
	emergent mesoderm	185	911,736.0
	endodermal cell	135	989,383.0
	epiblast cell	133	790,584.0
	erythrocyte	32	716,602.0
	hemogenic endothelial progenitor	111	925,287.0
	nascent mesoderm	98	787,231.5
	primitive streak	202	826,282.5
	yolk sac mesoderm	83	1,124,901.0
Sum	—	1,195	—

Code

cell_metadata_PRJEB40781 |>
    dplyr::left_join(
        cell_metadata,
        by = "cell"
    ) |>
    dplyr::group_by(developmental_stage) |>
    dplyr::summarise(
        num_cells = n(),
        median_umis = median(num_umis)
    ) |>
    create_gt()

	developmental_stage	num_cells	median_umis
	16 to 19	1,195	899,241.0
Sum	—	1,195	—

Zheng et al. 2019; PRJNA555602

cell_metadata_PRJNA555602 <- vroom::vroom(
    file = file.path(
        PROJECT_DIR,
        "raw",
        "public",
        "PRJNA555602",
        "matrix/cell_metadata.csv"
    ),
    show_col_types = FALSE
) |>
    dplyr::mutate(
        batch = case_when(
            old_ident == "10X_Embryoid" ~ "GSM3956280",
            old_ident == "10X_H9_Amnion" ~ "GSM3956281"
        ),
        cell = stringr::str_remove(string = cell, pattern = "\\..+"),
        # cell = paste(batch, cell, sep = "_"),
        #
        cell_type = case_when(
            rna_snn_res_0.3 %in% 0 ~ "Transwell-AMLC",
            rna_snn_res_0.3 %in% 1 ~ "MeLC2",
            rna_snn_res_0.3 %in% 2 ~ "Human_ES_cell",
            rna_snn_res_0.3 %in% 3 ~ "MeLC1",
            rna_snn_res_0.3 %in% 4 ~ "hPGCLC",
            rna_snn_res_0.3 %in% 5 ~ "AMLC"
        ),
        cell_type = factor(
            cell_type,
            levels = c(
                "Human_ES_cell",
                # human PGC-like cells
                "hPGCLC",
                "Transwell-AMLC",
                # amniotic ectoderm-like cells
                "AMLC",
                # mesoderm-like cell
                "MeLC1",
                "MeLC2"
            )
        )
    )

Cell type
Identity

Code

cell_metadata_PRJNA555602 |>
    dplyr::left_join(
        cell_metadata,
        by = "cell"
    ) |>
    dplyr::group_by(cell_type) |>
    dplyr::summarise(
        num_cells = n(),
        median_umis = median(num_umis)
    ) |>
    create_gt()

	cell_type	num_cells	median_umis
	Human_ES_cell	1,677	28,735.0
	hPGCLC	1,224	21,419.0
	Transwell-AMLC	2,845	27,137.0
	AMLC	979	22,686.0
	MeLC1	1,405	23,269.0
	MeLC2	1,836	25,066.5
Sum	—	9,966	—

Code

cell_metadata_PRJNA555602 |>
    dplyr::left_join(
        cell_metadata,
        by = "cell"
    ) |>
    dplyr::group_by(old_ident) |>
    dplyr::summarise(
        num_cells = n(),
        median_umis = median(num_umis)
    ) |>
    create_gt()

	old_ident	num_cells	median_umis
	10X_Embryoid	5,454	23,322.0
	10X_H9_Amnion	4,512	27,887.0
Sum	—	9,966	—

Yanagida et al. 2021; PRJNA720968

cell_metadata_PRJNA720968 <- vroom::vroom(
    file = file.path(
        PROJECT_DIR,
        "raw/public",
        "PRJNA720968",
        "matrix",
        "cell_metadata.csv"
    ),
    show_col_types = FALSE
) |>
    dplyr::mutate(
        origin = factor(
            origin,
            levels = c("Blastocyst", "Blastoid")
        ),
        #
        developmental_stage = factor(
            developmental_stage,
            levels = stringr::str_sort(
                unique(developmental_stage),
                numeric = TRUE
            )
        ),
        #
        lineage = paste(origin, lineage, sep = ": "),
        lineage = factor(
            lineage,
            levels = c(
                "Blastocyst: Epiblast",
                "Blastocyst: Hypoblast",
                "Blastocyst: Inner Cell Mass",
                "Blastocyst: Inner Cell Mass-Trophectoderm Transition",
                "Blastocyst: Early Trophectoderm",
                "Blastocyst: Trophectoderm",
                #
                "Blastoid: Epiblast",
                "Blastoid: Hypoblast",
                "Blastoid: Transitioning",
                "Blastoid: Trophectoderm"
            )
        )
    )

Lineage
Developmental stage

Code

cell_metadata_PRJNA720968 |>
    dplyr::left_join(
        cell_metadata,
        by = "cell"
    ) |>
    dplyr::group_by(lineage) |>
    dplyr::summarise(
        num_cells = n(),
        median_umis = median(num_umis)
    ) |>
    create_gt()

	lineage	num_cells	median_umis
	Blastocyst: Epiblast	31	8,126,430.0
	Blastocyst: Hypoblast	14	9,776,420.5
	Blastocyst: Inner Cell Mass	22	7,335,337.0
	Blastocyst: Inner Cell Mass-Trophectoderm Transition	23	8,187,758.0
	Blastocyst: Early Trophectoderm	18	8,361,591.5
	Blastocyst: Trophectoderm	117	7,541,800.0
	Blastoid: Epiblast	73	7,916,385.0
	Blastoid: Hypoblast	13	8,166,701.0
	Blastoid: Transitioning	7	4,276,121.0
	Blastoid: Trophectoderm	174	7,467,628.5
	NA	3	4,890,522.0
Sum	—	495	—

Code

cell_metadata_PRJNA720968 |>
    dplyr::left_join(
        cell_metadata,
        by = "cell"
    ) |>
    dplyr::group_by(developmental_stage) |>
    dplyr::summarise(
        num_cells = n(),
        median_umis = median(num_umis)
    ) |>
    create_gt()

	developmental_stage	num_cells	median_umis
	Day3	159	7,329,571.0
	Day4	108	7,685,250.0
	Day5	68	7,692,113.5
	Day6	80	6,475,820.5
	Day7	80	8,645,422.0
Sum	—	495	—

R session info

devtools::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.3.1 (2023-06-16)
 os       macOS Ventura 13.5.2
 system   aarch64, darwin22.4.0
 ui       unknown
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       America/Chicago
 date     2023-09-10
 pandoc   2.19.2 @ /Users/jialei/.pyenv/shims/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package     * version     date (UTC) lib source
 bit           4.0.5       2022-11-15 [1] CRAN (R 4.3.0)
 bit64         4.0.5       2020-08-30 [1] CRAN (R 4.3.0)
 cachem        1.0.8       2023-05-01 [1] CRAN (R 4.3.0)
 callr         3.7.3       2022-11-02 [1] CRAN (R 4.3.0)
 cli           3.6.1       2023-03-23 [1] CRAN (R 4.3.0)
 codetools     0.2-19      2023-02-01 [2] CRAN (R 4.3.1)
 colorspace    2.1-0       2023-01-23 [1] CRAN (R 4.3.0)
 crayon        1.5.2       2022-09-29 [1] CRAN (R 4.3.0)
 devtools      2.4.5.9000  2023-08-11 [1] Github (r-lib/devtools@163c3f2)
 digest        0.6.33      2023-07-07 [1] CRAN (R 4.3.1)
 dplyr       * 1.1.3       2023-09-03 [1] CRAN (R 4.3.1)
 ellipsis      0.3.2       2021-04-29 [1] CRAN (R 4.3.0)
 evaluate      0.21        2023-05-05 [1] CRAN (R 4.3.0)
 extrafont   * 0.19        2023-01-18 [1] CRAN (R 4.3.0)
 extrafontdb   1.0         2012-06-11 [1] CRAN (R 4.3.0)
 fansi         1.0.4       2023-01-22 [1] CRAN (R 4.3.0)
 farver        2.1.1       2022-07-06 [1] CRAN (R 4.3.0)
 fastmap       1.1.1       2023-02-24 [1] CRAN (R 4.3.0)
 forcats     * 1.0.0.9000  2023-04-23 [1] Github (tidyverse/forcats@4a8525a)
 fs            1.6.3       2023-07-20 [1] CRAN (R 4.3.1)
 furrr       * 0.3.1       2022-08-15 [1] CRAN (R 4.3.1)
 future      * 1.33.0      2023-07-01 [1] CRAN (R 4.3.1)
 generics      0.1.3       2022-07-05 [1] CRAN (R 4.3.0)
 ggplot2     * 3.4.3.9000  2023-09-05 [1] Github (tidyverse/ggplot2@d180248)
 globals       0.16.2      2022-11-21 [1] CRAN (R 4.3.0)
 glue          1.6.2.9000  2023-04-23 [1] Github (tidyverse/glue@cbac82a)
 gt            0.9.0.9000  2023-09-02 [1] Github (rstudio/gt@c73eece)
 gtable        0.3.4.9000  2023-08-22 [1] Github (r-lib/gtable@c410a54)
 hms           1.1.3       2023-03-21 [1] CRAN (R 4.3.0)
 htmltools     0.5.6       2023-08-10 [1] CRAN (R 4.3.1)
 htmlwidgets   1.6.2       2023-03-17 [1] CRAN (R 4.3.0)
 jsonlite      1.8.7       2023-06-29 [1] CRAN (R 4.3.1)
 knitr         1.43        2023-05-25 [1] CRAN (R 4.3.0)
 lattice       0.21-8      2023-04-05 [2] CRAN (R 4.3.1)
 lifecycle     1.0.3       2022-10-07 [1] CRAN (R 4.3.0)
 listenv       0.9.0       2022-12-16 [1] CRAN (R 4.3.0)
 lubridate   * 1.9.2.9000  2023-07-22 [1] Github (tidyverse/lubridate@cae67ea)
 magrittr      2.0.3       2022-03-30 [1] CRAN (R 4.3.0)
 Matrix      * 1.6-1       2023-08-14 [2] CRAN (R 4.3.1)
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─ Python configuration ───────────────────────────────────────────────────────
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 NOTE: Python version was forced by RETICULATE_PYTHON

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