Cells of the adult human heart

---

Litviňuková, M., Talavera-López, C., Maatz, H., Reichart, D., Worth, C.L., Lindberg, E.L., Kanda, M., Polanski, K., Heinig, M., Lee, M., et al. (2020). Cells of the adult human heart. Nature 588, 466–472.

• BioProject Accession: PRJEB39602
• GEO Accession: ERP123138

---

Load required packages.

library(tidyverse)
library(magrittr)
library(Matrix)
library(patchwork)
library(extrafont)
library(reticulate)

Sys.time()

## [1] "2021-03-12 01:23:37 CST"

Data preparation

Functions

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

PROJECT_DIR <- "/Users/jialei/Dropbox/Data/Projects/UTSW/HCM/"

Anndata

Load re-mapped UMI count matrix stored in the h5ad file.

ad <- reticulate::import(module = "anndata", convert = TRUE)

## Python version used by reticulate is different to the current python runtime

## current: /Users/jialei/.pyenv/versions/3.8.2/bin/python

## reticulate: /Users/jialei/.pyenv/shims/python

## Switch radian to the target python environment?

## radian: force reticulate to use /Users/jialei/.pyenv/versions/3.8.2/bin/python

print(ad$`__version__`)

## [1] "0.7.5"

adata <- ad$read_h5ad(
    filename = file.path(
        PROJECT_DIR,
        "raw/public/PRJEB39602/matrices/predefined",
        "adata.h5ad"
    ),
    backed = "r"
)

Metadata

Load metadata released along with the publication (extracted from h5ad file hosted at the HCA portal).

embedding_predefined <- read_csv(
    file = file.path(
        PROJECT_DIR,
        "raw/public/PRJEB39602/ad98d3cd-26fb-4ee3-99c9-8a2ab085e737.hca_heart_global_ctl200723_freeze",
        "embedding_global_raw.gz"
    )
) %>%
    dplyr::rename(cell = X1)

## Warning: Missing column names filled in: 'X1' [1]

## 
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
##   .default = col_character(),
##   n_counts = col_double(),
##   n_genes = col_double(),
##   percent_mito = col_double(),
##   percent_ribo = col_double(),
##   scrublet_score = col_double(),
##   x_umap = col_double(),
##   y_umap = col_double(),
##   x_pca = col_double(),
##   y_pca = col_double()
## )
## ℹ Use `spec()` for the full column specifications.

colnames(embedding_predefined) <- tolower(colnames(embedding_predefined))

embedding_predefined %<>%
    dplyr::left_join(
        adata$obs %>% tibble::rownames_to_column(var = "cell"),
        by = c("cell" = "cell")
    )

embedding_predefined %>% head()

Summary

Orignial publication

Cell types

embedding_predefined %>%
    dplyr::group_by(cell_type) %>%
    dplyr::summarize(
        num_cells = n(),
        median_umi = median(n_counts),
        median_features = median(n_genes)
    ) %>%
    gt::gt() %>%
    # gt::cols_label(n = "num_cells") %>%
    gt::tab_options(table.font.size = "median")

cell_type	num_cells	median_umi	median_features
Adipocytes	3799	3875.0	1707.0
Atrial_Cardiomyocyte	23483	2868.0	1399.0
doublets	623	688.0	484.0
Endothelial	100579	1585.0	909.0
Fibroblast	59341	1195.0	757.0
Lymphoid	17217	1552.0	831.0
Mesothelial	718	1967.0	1082.5
Myeloid	23028	1655.5	896.0
Neuronal	3961	1039.0	716.0
NotAssigned	33998	2186.0	1230.0
Pericytes	77856	1321.0	790.0
Smooth_muscle_cells	16242	1831.0	988.0
Ventricular_Cardiomyocyte	125289	3621.0	1545.0

Re-mapped

Samples not included after re-mapping.

embedding_predefined %>%
    dplyr::filter(
        is.na(num_umis)
    ) %>%
    dplyr::count(sample.x, region, name = "num_cells")

Cell types for these two samples.

embedding_predefined %>%
    dplyr::filter(
        is.na(num_umis)
    ) %>%
    dplyr::count(cell_type, name = "num_cells")

Cell types

embedding_predefined %>%
    tidyr::drop_na() %>%
    dplyr::group_by(cell_type) %>%
    dplyr::summarize(
        num_cells = n(),
        median_umi = median(n_counts),
        median_features = median(n_genes),
        median_umi_remapped = median(num_umis),
        median_features_remapped = median(num_features)
    ) %>%
    gt::gt() %>%
    # gt::cols_label(n = "num_cells") %>%
    gt::tab_options(table.font.size = "median")

cell_type	num_cells	median_umi	median_features	median_umi_remapped	median_features_remapped
Adipocytes	3703	3824	1686.0	3892	1743
Atrial_Cardiomyocyte	22883	2819	1381.0	2855	1380
doublets	615	684	483.0	717	494
Endothelial	100066	1583	908.0	1700	963
Fibroblast	58631	1187	753.0	1206	763
Lymphoid	16785	1546	823.0	1597	858
Mesothelial	718	1967	1082.5	2023	1082
Myeloid	22705	1633	889.0	1699	924
Neuronal	3785	1007	701.0	1038	721
NotAssigned	33767	2170	1224.0	2234	1259
Pericytes	76843	1309	784.0	1352	814
Smooth_muscle_cells	15793	1795	975.0	1846	1008
Ventricular_Cardiomyocyte	125289	3621	1545.0	3706	1588

Anatomical region

embedding_predefined %>%
    tidyr::drop_na() %>%
    dplyr::group_by(region) %>%
    dplyr::summarize(
        num_cells = n(),
        median_umi = median(n_counts),
        median_features = median(n_genes),
        median_umi_remapped = median(num_umis),
        median_features_remapped = median(num_features)
    ) %>%
    gt::gt() %>%
    # gt::cols_label(n = "num_cells") %>%
    gt::tab_options(table.font.size = "median")

region	num_cells	median_umi	median_features	median_umi_remapped	median_features_remapped
AX	98251	1894	1007	1973	1047
LA	70321	1793	984	1840	1006
LV	107261	2139	1109	2256	1166
RA	39365	1509	901	1537	911
RV	84519	2173	1126	2308	1186
SP	81866	1948	1022	2015	1058

Chromium Single Cell 3’ Reagent Kit version

embedding_predefined %>%
    tidyr::drop_na() %>%
    dplyr::group_by(version) %>%
    dplyr::summarise(
        num_cells = n(),
        median_umi = median(n_counts),
        median_features = median(n_genes),
        median_umi_remapped = median(num_umis),
        median_features_remapped = median(num_features)
    ) %>%
    gt::gt() %>%
    gt::tab_options(table.font.size = "median")

version	num_cells	median_umi	median_features	median_umi_remapped	median_features_remapped
V2	238154	1511	837	1536	850
V3	243429	2358	1248	2522	1330

Cell source

embedding_predefined %>%
    tidyr::drop_na() %>%
    dplyr::group_by(cell_source) %>%
    dplyr::summarise(
        num_cells = n(),
        median_umi = median(n_counts),
        median_features = median(n_genes),
        median_umi_remapped = median(num_umis),
        median_features_remapped = median(num_features)
    ) %>%
    gt::gt() %>%
    gt::tab_options(table.font.size = "median")

cell_source	num_cells	median_umi	median_features	median_umi_remapped	median_features_remapped
Harvard-Nuclei	173610	2184	1211	2266	1264
Sanger-CD45	80597	2009	965	2079	1004
Sanger-Cells	45885	2884	1368	3474	1591
Sanger-Nuclei	181491	1423	837	1442	845

Source

embedding_predefined %>%
    tidyr::drop_na() %>%
    dplyr::group_by(source) %>%
    dplyr::summarise(
        num_cells = n(),
        median_umi = median(n_counts),
        median_features = median(n_genes),
        median_umi_remapped = median(num_umis),
        median_features_remapped = median(num_features)
    ) %>%
    gt::gt() %>%
    gt::tab_options(table.font.size = "median")

source	num_cells	median_umi	median_features	median_umi_remapped	median_features_remapped
CD45+	80597	2009	965	2079	1004
Cells	45885	2884	1368	3474	1591
Nuclei	355101	1811	1018	1861	1046

Age group

embedding_predefined %>%
    tidyr::drop_na() %>%
    dplyr::group_by(age_group) %>%
    dplyr::summarise(
        num_cells = n(),
        median_umi = median(n_counts),
        median_features = median(n_genes),
        median_umi_remapped = median(num_umis),
        median_features_remapped = median(num_features)
    ) %>%
    gt::gt() %>%
    gt::tab_options(table.font.size = "median")

age_group	num_cells	median_umi	median_features	median_umi_remapped	median_features_remapped
40-45	26934	2953	1481	3050	1544
45-50	32336	2100	1177	2192	1232
50-55	108472	2091	1148	2163	1196
55-60	48287	1571	914	1607	936
60-65	128207	2066	1043	2136	1079
65-70	108449	1803	937	1931	983
70-75	28898	1166	708	1162	702

Donor

embedding_predefined %>%
    tidyr::drop_na() %>%
    dplyr::group_by(donor) %>%
    dplyr::summarise(
        num_cell = n(),
        median_umi = median(n_counts),
        median_features = median(n_genes),
        median_umi_remapped = median(num_umis),
        median_features_remapped = median(num_features)
    ) %>%
    gt::gt() %>%
    gt::tab_options(table.font.size = "median")

donor	num_cell	median_umi	median_features	median_umi_remapped	median_features_remapped
D1	15397	1982.0	883	1997.0	895
D11	48930	2782.0	1263	2909.5	1325
D2	43143	1393.0	829	1414.0	845
D3	27022	1210.0	754	1217.0	757
D4	28898	1166.0	708	1162.0	702
D5	28799	1396.0	751	1416.0	759
D6	79650	1921.0	1003	2098.0	1069
D7	36134	2123.5	1050	2182.5	1074
H2	22216	1780.0	1065	1841.0	1107
H3	32458	2045.0	1176	2118.0	1227
H4	21265	1924.0	1076	1976.0	1112
H5	38401	2385.0	1273	2482.0	1337
H6	26934	2953.0	1481	3050.0	1544
H7	32336	2100.0	1177	2192.0	1232

Type: Donation after Circulatory Death (DCD), Donation after Brain Death (DCD)

embedding_predefined %>%
    tidyr::drop_na() %>%
    dplyr::group_by(type) %>%
    dplyr::summarise(
        num_cell = n(),
        median_umi = median(n_counts),
        median_features = median(n_genes),
        median_umi_remapped = median(num_umis),
        median_features_remapped = median(num_features)
    ) %>%
    gt::gt() %>%
    gt::tab_options(table.font.size = "median")

type	num_cell	median_umi	median_features	median_umi_remapped	median_features_remapped
DBD	216029	2058	1133	2126	1178
DCD	265554	1853	965	1943	1000

Adult human heart’s celluar landscape

Pre-defined

Cell embedding and annotation are extracted from h5ad file provided on the HCA portal.

EMBEDDING_TITLE_PREFIX <- "UMAP"
embedding_type <- "predefined"

Cell type & region

GEOM_POINT_SIZE <- 0.1
p_embedding_predefined_cell_type <- plot_embedding(
    embedding = embedding_predefined[, c("x_umap", "y_umap")],
    color_values = embedding_predefined$cell_type %>% as.factor(),
    label = paste0(EMBEDDING_TITLE_PREFIX, "; Cell type"),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    rasterise = FALSE,
    legend_size = 2
) +
    theme_customized(legend_key_size = 2, legend_text_size = 5)

p_embedding_predefined_region <- plot_embedding(
    embedding = embedding_predefined[, c("x_umap", "y_umap")],
    color_values = embedding_predefined$region %>% as.factor(),
    label = paste0(EMBEDDING_TITLE_PREFIX, "; Region"),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    rasterise = TRUE,
    legend_size = 2,
    legend_ncol = 2
) +
    scale_color_manual(
        values = scales::hue_pal()(
            embedding_predefined$region %>% unique() %>% length()
        )
    ) +
    theme_customized(legend_key_size = 2, legend_text_size = 5)

p_embedding_predefined_donor <- plot_embedding(
    embedding = embedding_predefined[, c("x_umap", "y_umap")],
    color_values = embedding_predefined$donor %>% as.factor(),
    label = paste0(EMBEDDING_TITLE_PREFIX, "; Donor"),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    rasterise = TRUE,
    legend_size = 2,
    legend_ncol = 2
) +
    scale_color_manual(
        values = scales::hue_pal()(
            embedding_predefined$donor %>% unique() %>% length()
        )
    ) +
    theme_customized(legend_key_size = 2, legend_text_size = 5)

p_embedding_predefined_version <- plot_embedding(
    embedding = embedding_predefined[, c("x_umap", "y_umap")],
    color_values = embedding_predefined$version %>% as.factor(),
    label = paste0(EMBEDDING_TITLE_PREFIX, "; Version"),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    rasterise = TRUE,
    legend_size = 2
) +
    scale_color_manual(
        values = scales::hue_pal()(
            embedding_predefined$version %>% unique() %>% length()
        )
    ) +
    theme_customized(legend_key_size = 2, legend_text_size = 5)

p_embedding_predefined_source <- plot_embedding(
    embedding = embedding_predefined[, c("x_umap", "y_umap")],
    color_values = embedding_predefined$source %>% as.factor(),
    label = paste0(EMBEDDING_TITLE_PREFIX, "; Source"),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    rasterise = TRUE,
    legend_size = 2
) +
    scale_color_manual(
        values = scales::hue_pal()(
            embedding_predefined$source %>% unique() %>% length()
        )
    ) +
    theme_customized(legend_key_size = 2, legend_text_size = 5)

p_embedding_predefined_type <- plot_embedding(
    embedding = embedding_predefined[, c("x_umap", "y_umap")],
    color_values = embedding_predefined$type %>% as.factor(),
    label = paste0(EMBEDDING_TITLE_PREFIX, "; Type"),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    rasterise = TRUE,
    legend_size = 2
) +
    scale_color_manual(
        values = scales::hue_pal()(
            embedding_predefined$type %>% unique() %>% length()
        )
    ) +
    theme_customized(legend_key_size = 2, legend_text_size = 5)

list(
    p_embedding_predefined_cell_type,
    p_embedding_predefined_region,
    p_embedding_predefined_donor,
    p_embedding_predefined_version,
    p_embedding_predefined_source,
    p_embedding_predefined_type
) %>%
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 3) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

Mitochondria ratio distribution

embedding_predefined$percent_mito %>% range()

## [1] 0.0000000 0.1999672

p_embedding_predefined_MT <- plot_embedding(
    embedding = embedding_predefined[, c("x_umap", "y_umap")],
    color_values = embedding_predefined$percent_mito,
    label = paste0(EMBEDDING_TITLE_PREFIX, "; Mitochondria %"),
    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 = TRUE,
    dpi = 600,
    legend_size = 2,
    legend_ncol = 1
) +
    theme_customized(
        x = 0.035,
        y = 0.995,
        legend_key_size = 1.5,
        legend_text_size = 4,
        border_color = "#b3b3b3",
        strip_color = "#b3b3b3"
    )

MT_RATIO_THRESHOLD <- 0.15
p_embedding_predefined_MT2 <- plot_embedding(
    embedding = embedding_predefined[, c("x_umap", "y_umap")],
    color_values = as.numeric(
        embedding_predefined$percent_mito >= MT_RATIO_THRESHOLD
    ) %>% as.factor(),
    label = paste(
        "UMAP", MT_RATIO_THRESHOLD,
        sum(embedding_predefined$percent_mito >= MT_RATIO_THRESHOLD),
        sep = "; "
    ),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = FALSE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = TRUE,
    rasterise = TRUE,
    legend_size = 2
) +
    scale_color_manual(
        values = c("grey70", "salmon")
    ) +
    theme_customized(legend_key_size = 2, legend_text_size = 5)

The mitochondria ratios are extracted from h5ad file download from the HCA portal. There are 0 cells with mitochondria ratio higher than 0.2.

list(p_embedding_predefined_MT, p_embedding_predefined_MT2) %>%
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 2) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

embedding_predefined %>%
    dplyr::group_by(source) %>%
    dplyr::summarise(
        num_cells = n(),
        median_mt_ratio_reported = median(percent_mito)
    ) %>%
    gt::gt() %>%
    gt::tab_options(table.font.size = "median")

source	num_cells	median_mt_ratio_reported
CD45+	80597	0.046094750
Cells	45885	0.072591010
Nuclei	359652	0.001283697

Doublets and unassigned cells

purrr::map2(list("doublets", "NotAssigned"), c(0.15, 0.15), function(x, y) {
    plot_embedding(
        embedding = embedding_predefined[, c("x_umap", "y_umap")],
        color_values = as.numeric(embedding_predefined$cell_type == x) %>% as.factor(),
        label = paste(
            "UMAP", x, sum(embedding_predefined$cell_type == x),
            sep = "; "
        ),
        label_position = NULL,
        show_color_value_labels = FALSE,
        show_color_legend = FALSE,
        geom_point_size = y,
        sort_values = TRUE,
        rasterise = TRUE
    ) +
        scale_color_manual(
            values = c("grey70", "salmon")
        ) +
        theme_customized()
}) %>%
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 2) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

p_embedding_predefined_scrublet <- plot_embedding(
    embedding = embedding_predefined[, c("x_umap", "y_umap")],
    color_values = embedding_predefined$scrublet_score,
    label = paste0(EMBEDDING_TITLE_PREFIX, "; Scrublet_score"),
    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 = TRUE,
    dpi = 600,
    legend_size = 3,
    legend_ncol = 1
) +
    theme_customized(
        x = 0.035,
        y = 0.995,
        legend_key_size = 1.5,
        legend_text_size = 4,
        border_color = "#b3b3b3",
        strip_color = "#b3b3b3"
    )

SCRUBLET_SCORE_THRESHOLD <- 0.3
p_embedding_predefined_scrublet2 <- plot_embedding(
    embedding = embedding_predefined[, c("x_umap", "y_umap")],
    color_values = as.numeric(
        embedding_predefined$scrublet_score >= SCRUBLET_SCORE_THRESHOLD
    ) %>% as.factor(),
    label = paste(
        "UMAP", SCRUBLET_SCORE_THRESHOLD,
        sum(embedding_predefined$scrublet_score >= SCRUBLET_SCORE_THRESHOLD),
        sep = "; "
    ),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = FALSE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = TRUE,
    rasterise = TRUE
) +
    scale_color_manual(
        values = c("grey70", "salmon")
    ) +
    theme_customized()

The scrublet scores are extracted from h5ad file download from the HCA portal. There are 0 cells with scrublet scores higher than 0.3.

list(p_embedding_predefined_scrublet, p_embedding_predefined_scrublet2) %>%
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 2) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

The distribution of scrublet scores for cells annotated as “doublets” and “NotAssigned” in the publication.

embedding_predefined %>%
    dplyr::filter(cell_type %in% c("doublets", "NotAssigned")) %>%
    dplyr::group_by(cell_type) %>%
    dplyr::summarise(
        num_cells = n(),
        median_scrublet_score_reported = median(scrublet_score),
        median_mt_ratio_reported = median(percent_mito)
    ) %>%
    gt::gt() %>%
    gt::tab_options(table.font.size = "median")

cell_type	num_cells	median_scrublet_score_reported	median_mt_ratio_reported
doublets	623	0.1254753	0.007220217
NotAssigned	33998	0.1471215	0.011605416

Inconsistent cells

Cells not included after re-mapping.

embedding_predefined %>%
    dplyr::filter(
        is.na(num_umis)
    ) %>%
    dplyr::summarise(
        num_cells = n(),
        median_umis_reported = median(n_counts),
        median_features_reported = median(n_genes)
    ) %>%
    gt::gt() %>%
    gt::tab_options(table.font.size = "median")

num_cells	median_umis_reported	median_features_reported
4551	2680	1489

plot_embedding(
    embedding = embedding_predefined[, c("x_umap", "y_umap")],
    color_values = as.numeric(embedding_predefined$num_umis %>% is.na()) %>% as.factor(),
    label = paste(
        "UMAP", sum(embedding_predefined$num_umis %>% is.na()),
        sep = "; "
    ),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = FALSE,
    geom_point_size = 0.3,
    sort_values = TRUE,
    rasterise = TRUE
) +
    scale_color_manual(values = c("grey70", "salmon")) +
    theme_customized()

Cell group

cells_selected_atrial <- c(
    "aCM1",
    "aCM2",
    "aCM3",
    "aCM4",
    "aCM5"
)

cells_selected_ventricular <- c(
    "vCM1",
    "vCM2",
    "vCM3",
    "vCM4",
    "vCM5"
)

cells_selected_vascular <- c(
    "EC1_cap",
    "EC10_CMC-like",
    "EC2_cap",
    "EC3_cap",
    "EC4_immune",
    "EC5_art",
    "EC6_ven",
    "EC7_atria",
    "EC8_ln",
    "EC9_FB-like",
    "Meso",
    "PC1_vent",
    "PC2_atria",
    "PC3_str",
    "PC4_CMC-like",
    "SMC1_basic",
    "SMC2_art"
)

cells_selected_fibroblast <- c(
    "FB1",
    "FB2",
    "FB3",
    "FB4",
    "FB5",
    "FB6",
    "FB7"
)

cells_selected_immune <- c(
    "B_cells",
    "CD14+Mo",
    "CD16+Mo",
    "CD4+T_cytox",
    "CD4+T_tem",
    "CD8+T_cytox",
    "CD8+T_tem",
    "DC",
    "DOCK4+MØ1",
    "DOCK4+MØ2",
    "doublets",
    "IL17RA+Mo",
    "LYVE1+MØ1",
    "LYVE1+MØ2",
    "LYVE1+MØ3",
    "Mast",
    "MØ_AgP",
    "MØ_mod",
    "Mo_pi",
    "NK",
    "NKT",
    "NØ"
)

cells_selected_adipocyte <- c(
    "Adip1",
    "Adip2",
    "Adip3",
    "Adip4"
)

cells_selected_neuronal <- c(
    "NC1",
    "NC2",
    "NC3",
    "NC4",
    "NC5",
    "NC6"
)

cells_selected <- list(
    atrial = cells_selected_atrial,
    ventricular = cells_selected_ventricular,
    vascular = cells_selected_vascular,
    fibroblast = cells_selected_fibroblast,
    immune = cells_selected_immune,
    adipocyte = cells_selected_adipocyte,
    neuronal = cells_selected_neuronal
)

GEOM_POINT_SIZE <- 0.1

purrr::map2(names(cells_selected), c(TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE), function(x, y) {
    cells_selected_in_group <- cells_selected[[x]]

    color_palette_cell_state <- setNames(
        object = c("grey70", scales::hue_pal()(length(cells_selected_in_group))),
        nm = c("Other", cells_selected_in_group)
    )

    cell_type_labels <- get_middle_points(
        embedding = embedding_predefined[, c("x_umap", "y_umap", "cell_states")],
        x = "x_umap",
        y = "y_umap",
        group = "cell_states"
    ) %>%
        filter(cell_states %in% cells_selected_in_group)

    plot_embedding(
        embedding = embedding_predefined[, c("x_umap", "y_umap", "cell_states")],
        color_values = embedding_predefined %>%
            mutate(
                cell_states = case_when(
                    cell_states %in% cells_selected_in_group ~ cell_states,
                    TRUE ~ "Other"
                )
            ) %>%
            pull(cell_states) %>%
            as.factor() %>%
            forcats::fct_relevel("Other", after = Inf),
        label = paste0(EMBEDDING_TITLE_PREFIX, "; ", x),
        label_position = NULL,
        show_color_value_labels = FALSE,
        show_color_legend = y,
        geom_point_size = GEOM_POINT_SIZE,
        sort_values = FALSE,
        rasterise = TRUE,
        legend_ncol = 1
    ) +
        scale_color_manual(
            values = color_palette_cell_state
        ) +
        # theme_customized() +
        theme_customized(legend_key_size = 2, legend_text_size = 5) +
        ggrepel::geom_text_repel(
            data = cell_type_labels,
            ggplot2::aes(
                x = .data[["x_umap"]],
                y = .data[["y_umap"]],
                label = .data[["cell_states"]],
            ),
            color = "black",
            size = 1.8,
            family = "Arial",
            #
            box.padding = 0.4,
            point.padding = 1e-06,
            min.segment.length = 0,
            arrow = ggplot2::arrow(length = unit(0.015, "npc")),
            max.overlaps = Inf,
            nudge_x = 0,
            nudge_y = 0,
            #
            segment.color = "grey35",
            # segment.size = 0.25,
            segment.size = 0.1,
            segment.alpha = 1,
            # segment.inflect = TRUE,
            seed = 20201121
        )
}) %>%
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 3) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

embedding_predefined %>%
    tidyr::drop_na() %>%
    dplyr::group_by(cell_states) %>%
    dplyr::summarize(
        num_cells = n(),
        median_umi = median(num_umis),
        median_features = median(num_features),
        median_mt_percentage = median(mt_percentage),
        median_scrublet_score_reported = median(scrublet_score)
    ) %>%
    gt::gt() %>%
    gt::tab_options(table.font.size = "median")

cell_states	num_cells	median_umi	median_features	median_mt_percentage	median_scrublet_score_reported
aCM1	12511	2620.0	1278.0	0.0007818608	0.09822866
aCM2	4613	3105.0	1552.0	0.0007087172	0.10806175
aCM3	4018	3042.0	1358.5	0.0007296163	0.09822866
aCM4	1554	3136.5	1491.5	0.0008284325	0.11347518
aCM5	187	3396.0	1619.0	0.0008121003	0.16473318
Adip1	2235	2922.0	1379.0	0.0005921247	0.09822866
Adip2	972	8539.5	3630.5	0.0009412824	0.18575064
Adip3	269	4170.0	1788.0	0.0006259781	0.11347518
Adip4	227	2357.0	1348.0	0.0015748031	0.16473318
B_cells	1162	1913.5	939.5	0.0228684060	0.13214990
CD14+Mo	1876	3460.5	1311.5	0.0632523414	0.09822866
CD16+Mo	3243	3874.0	1530.0	0.0424010847	0.13214990
CD4+T_cytox	3055	927.0	565.0	0.0258379888	0.09822866
CD4+T_tem	1028	2639.5	955.0	0.0310788449	0.10806175
CD8+T_cytox	2824	2251.0	1107.0	0.0209109813	0.09822866
CD8+T_tem	2870	1101.0	714.0	0.0077821012	0.08554572
DC	805	2450.0	1396.0	0.0060975610	0.11926606
DOCK4+MØ1	3231	902.0	641.0	0.0033305579	0.08952959
DOCK4+MØ2	1495	1351.0	896.0	0.0015302219	0.07161804
doublets	615	717.0	494.0	0.0069930070	0.12547529
EC1_cap	27325	1379.0	815.0	0.0371859296	0.08554572
EC10_CMC-like	2585	2464.0	1224.0	0.0381282496	0.09822866
EC2_cap	13419	1586.0	898.0	0.0331683168	0.08952959
EC3_cap	16843	2085.0	1093.0	0.0247015233	0.09822866
EC4_immune	5612	1551.0	889.0	0.0492429450	0.09375000
EC5_art	20218	2212.0	1185.0	0.0310671082	0.10299003
EC6_ven	8332	2181.0	1148.0	0.0249139179	0.10299003
EC7_atria	4478	1123.0	753.0	0.0020844195	0.13934426
EC8_ln	734	1605.5	1029.5	0.0073016482	0.12547529
EC9_FB-like	520	2345.0	1481.5	0.0035700141	0.16473318
FB1	26555	1110.0	701.0	0.0020181635	0.10299003
FB2	13484	1216.0	784.0	0.0013404826	0.11926606
FB3	7525	1253.0	779.0	0.0017391304	0.12547529
FB4	6057	1389.0	871.0	0.0024600246	0.11926606
FB5	2507	1298.0	822.0	0.0027422303	0.10806175
FB6	1593	2705.0	1621.0	0.0024742805	0.17475728
FB7	910	3101.0	1704.5	0.0034325998	0.17475728
IL17RA+Mo	32	4715.5	2431.0	0.0012185808	0.10823261
LYVE1+MØ1	2955	2666.0	1307.0	0.0149476831	0.07821229
LYVE1+MØ2	2085	815.0	556.0	0.0058823529	0.09375000
LYVE1+MØ3	1924	1795.0	927.5	0.0075810110	0.08952959
Mast	1524	965.5	690.5	0.0018691654	0.04661017
Meso	718	2023.0	1082.0	0.0076335878	0.13934426
MØ_AgP	1272	4160.5	1508.5	0.0510975988	0.13934426
MØ_mod	1307	3069.0	1257.0	0.0530805687	0.08952959
Mo_pi	1640	1121.0	625.0	0.0634648370	0.14712154
nan	33767	2234.0	1259.0	0.0113879004	0.14712154
NC1	2977	889.0	632.0	0.0028901734	0.07482993
NC2	434	2599.5	1576.0	0.0029268404	0.11926606
NC3	145	2188.0	1207.0	0.0103848503	0.12547529
NC4	125	4677.0	2143.0	0.0016353230	0.13934426
NC5	84	3008.0	1762.5	0.0014192650	0.04095904
NC6	20	1627.0	1093.0	0.0022270115	0.14712154
NK	3591	2037.0	984.0	0.0276782807	0.06565657
NKT	1450	1138.5	678.0	0.0319841083	0.09375000
NØ	121	1092.0	497.0	0.0654008439	0.13214990
PC1_vent	49729	1231.0	746.0	0.0026455026	0.07482993
PC2_atria	10700	1505.0	914.0	0.0017618559	0.07161804
PC3_str	14217	1709.0	999.0	0.0190373563	0.11347518
PC4_CMC-like	2197	2901.0	1553.0	0.0016515277	0.15555556
SMC1_basic	12781	1651.0	938.0	0.0033112583	0.08177905
SMC2_art	3012	3713.0	1552.5	0.0284008680	0.10299003
vCM1	71162	3593.0	1547.0	0.0006483332	0.10299003
vCM2	25658	4019.0	1734.0	0.0006184929	0.10806175
vCM3	20168	3514.0	1506.0	0.0005975203	0.10806175
vCM4	7651	4055.0	1657.0	0.0006600116	0.11347518
vCM5	650	3606.0	1683.5	0.0009720928	0.16473318

Expression

Embedding

# start
FEATURES_SELECTED_A <- c(
    "ENSG00000092054_MYH7",
    "ENSG00000111245_MYL2",
    "ENSG00000115641_FHL2",
    "ENSG00000175206_NPPA",
    "ENSG00000106631_MYL7",
    "ENSG00000198336_MYL4",
    "ENSG00000133392_MYH11",
    "ENSG00000149591_TAGLN",
    "ENSG00000107796_ACTA2"
)

FEATURES_SELECTED_B <- c(
    "ENSG00000143248_RGS5",
    "ENSG00000069431_ABCC9",
    "ENSG00000121361_KCNJ8",
    "ENSG00000119927_GPAM",
    "ENSG00000169710_FASN",
    "ENSG00000174697_LEP",
    "ENSG00000011465_DCN",
    "ENSG00000148180_GSN",
    "ENSG00000134853_PDGFRA"
)

FEATURES_SELECTED_C <- c(
    "ENSG00000110799_VWF",
    "ENSG00000261371_PECAM1",
    "ENSG00000179776_CDH5",
    "ENSG00000123560_PLP1",
    "ENSG00000179915_NRXN1",
    "ENSG00000021645_NRXN3",
    "ENSG00000170458_CD14",
    "ENSG00000173372_C1QA",
    "ENSG00000129226_CD68",
    "ENSG00000153563_CD8A",
    "ENSG00000168685_IL7R",
    "ENSG00000102245_CD40LG",
    "ENSG00000102854_MSLN",
    "ENSG00000184937_WT1",
    "ENSG00000169594_BNC1"
)

FEATURES_SELECTED <- c(
    FEATURES_SELECTED_A,
    FEATURES_SELECTED_B,
    FEATURES_SELECTED_C
)

Grey: cells included in the publication but have low UMI counts after re-mapping.

GEOM_POINT_SIZE <- 0.15

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

    plot_embedding(
        embedding = embedding_predefined[, c("x_umap", "y_umap")],
        color_values = log10(
            calc_cpm_anndata(
                anndata = adata,
                k = SELECTED_FEATURE,
                cells = embedding_predefined$cell
            ) + 1
        ),
        label = paste0(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 = TRUE,
        dpi = 600,
        legend_size = 2,
        legend_ncol = 1
    ) +
        theme_customized(
            x = 0.035,
            y = 0.995,
            legend_key_size = 1.5,
            legend_text_size = 4,
            border_color = "#b3b3b3",
            strip_color = "#b3b3b3"
        )
}) %>%
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 3) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

Lollipop

vCM

FEATURES_SELECTED <- c(
    "ENSG00000115641_FHL2",
    "ENSG00000092054_MYH7",
    "ENSG00000111245_MYL2",
    "ENSG00000169851_PCDH7",
    "ENSG00000169760_NLGN1",
    #
    "ENSG00000140945_CDH13",
    "ENSG00000186314_PRELID2",
    "ENSG00000282917_AC107068.2",
    "ENSG00000249797_LINC02147",
    #
    "ENSG00000162614_NEXN",
    "ENSG00000148677_ANKRD1",
    "ENSG00000022267_FHL1",
    "ENSG00000163092_XIRP2",
    "ENSG00000100345_MYH9",
    "ENSG00000198842_DUSP27",
    "ENSG00000168334_XIRP1",
    "ENSG00000130176_CNN1",
    #
    "ENSG00000109846_CRYAB",
    "ENSG00000101608_MYL12A",
    "ENSG00000075624_ACTB",
    "ENSG00000189043_NDUFA4",
    "ENSG00000127184_COX7C",
    "ENSG00000135940_COX5B",
    "ENSG00000147123_NDUFB11",
    #
    "ENSG00000164741_DLC1",
    "ENSG00000164330_EBF1",
    "ENSG00000134532_SOX5",
    "ENSG00000221818_EBF2",
    "ENSG00000169282_KCNAB1"
)

cells_states_selected <- c(
    "vCM1", "vCM2", "vCM3", "vCM4", "vCM5"
)
cells_selected_lollipop <- purrr::map(cells_states_selected, function(x) {
    embedding_predefined %>%
        filter(cell_states == x) %>%
        pull(cell)
})
names(cells_selected_lollipop) <- cells_states_selected

matrix_cpm_subset <- purrr::map(FEATURES_SELECTED, function(x) {
    v <- calc_cpm_anndata(
        anndata = adata,
        k = x,
        cells = embedding_predefined$cell
    )
    v[is.na(v)] <- 0

    return(v)
}) %>%
    purrr::reduce(rbind)
rownames(matrix_cpm_subset) <- FEATURES_SELECTED

Extended Data Fig. 3a

plot_lollipop(
    cells = cells_selected_lollipop,
    features = FEATURES_SELECTED,
    matrix_cpm = matrix_cpm_subset,
    color_range_limits = NULL
)

aCM

# grep(pattern = "gsn", x = rownames(adata$var), ignore.case = TRUE, value = TRUE)
FEATURES_SELECTED <- c(
    "ENSG00000197616_MYH6",
    "ENSG00000175206_NPPA",
    "ENSG00000198336_MYL4",
    "ENSG00000181072_CHRM2",
    "ENSG00000117114_ADGRL2",
    #
    "ENSG00000115641_FHL2",
    "ENSG00000184347_SLIT3",
    "ENSG00000105697_HAMP",
    "ENSG00000128918_ALDH1A2",
    "ENSG00000204928_GRXCR2",
    "ENSG00000162670_BRINP3",
    #
    "ENSG00000138347_MYPN",
    "ENSG00000211455_STK38L",
    "ENSG00000154553_PDLIM3",
    "ENSG00000198842_DUSP27",
    "ENSG00000100345_MYH9",
    "ENSG00000130176_CNN1",
    #
    "ENSG00000104879_CKM",
    "ENSG00000189043_NDUFA4",
    "ENSG00000131143_COX4I1",
    "ENSG00000121769_FABP3",
    #
    "ENSG00000164741_DLC1",
    "ENSG00000127472_PLA2G5",
    "ENSG00000184384_MAML2",
    "ENSG00000249669_CARMN",
    "ENSG00000148180_GSN"
)

cells_states_selected <- c(
    "aCM1", "aCM2", "aCM3", "aCM4", "aCM5"
)
cells_selected_lollipop <- purrr::map(cells_states_selected, function(x) {
    embedding_predefined %>%
        filter(cell_states == x) %>%
        pull(cell)
})
names(cells_selected_lollipop) <- cells_states_selected

matrix_cpm_subset <- purrr::map(FEATURES_SELECTED, function(x) {
    v <- calc_cpm_anndata(
        anndata = adata,
        k = x,
        cells = embedding_predefined$cell
    )
    v[is.na(v)] <- 0

    return(v)
}) %>%
    purrr::reduce(rbind)
rownames(matrix_cpm_subset) <- FEATURES_SELECTED

Extended Data Fig. 3b

plot_lollipop(
    cells = cells_selected_lollipop,
    features = FEATURES_SELECTED,
    matrix_cpm = matrix_cpm_subset,
    color_range_limits = NULL
)

Celluar composition

prepare_cluster_composition(
    embedding = embedding_predefined,
    x = region,
    group = cell_type
) %>%
    plot_barplot(
        x = region,
        y = percentage,
        z = cell_type,
        legend_ncol = 2
    ) +
    theme_bw(base_size = 6) +
    theme(
        legend.key.size = grid::unit(3, "mm")
    )

R session info

devtools::session_info()$platform

##  setting  value                       
##  version  R version 4.0.4 (2021-02-15)
##  os       macOS Big Sur 10.16         
##  system   x86_64, darwin20.3.0        
##  ui       unknown                     
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       America/Chicago             
##  date     2021-03-12

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 4.0.0)
backports	1.2.1	2020-12-09	CRAN (R 4.0.3)
beeswarm	0.3.1	2021-03-07	CRAN (R 4.0.4)
broom	0.7.5.9000	2021-03-08	Github (tidymodels/broom@1d68975)
bslib	0.2.4.9002	2021-02-25	Github (rstudio/bslib@aa5a842)
cachem	1.0.4.9000	2021-02-25	Github (r-lib/cachem@5964320)
callr	3.5.1.9000	2021-02-12	Github (r-lib/callr@0ca92e1)
cellranger	1.1.0	2016-07-27	CRAN (R 4.0.0)
checkmate	2.0.0	2020-02-06	CRAN (R 4.0.0)
cli	2.3.1	2021-02-23	CRAN (R 4.0.4)
colorspace	2.0-0	2020-11-10	R-Forge (R 4.0.3)
crayon	1.4.1	2021-02-08	CRAN (R 4.0.3)
DBI	1.1.1	2021-01-15	CRAN (R 4.0.3)
dbplyr	2.1.0	2021-02-03	CRAN (R 4.0.3)
debugme	1.1.0.9001	2021-02-27	Github (r-lib/debugme@e7df798)
desc	1.3.0	2021-03-05	CRAN (R 4.0.4)
devtools	2.3.2.9000	2021-02-23	Github (r-lib/devtools@4a55464)
digest	0.6.27	2020-10-24	CRAN (R 4.0.3)
dplyr	1.0.5.9000	2021-03-10	Github (tidyverse/dplyr@17fb462)
ellipsis	0.3.1	2020-05-15	CRAN (R 4.0.3)
evaluate	0.14	2019-05-28	CRAN (R 4.0.0)
extrafont	0.17	2014-12-08	CRAN (R 4.0.3)
extrafontdb	1.0	2012-06-11	CRAN (R 4.0.0)
fansi	0.4.2	2021-01-15	CRAN (R 4.0.3)
farver	2.1.0	2021-02-28	CRAN (R 4.0.4)
fastmap	1.1.0	2021-01-25	CRAN (R 4.0.3)
forcats	0.5.1.9000	2021-02-17	Github (tidyverse/forcats@b5fce89)
fs	1.5.0	2020-07-31	CRAN (R 4.0.3)
generics	0.1.0	2020-10-31	CRAN (R 4.0.3)
ggbeeswarm	0.6.0	2017-08-07	CRAN (R 4.0.3)
ggplot2	3.3.3.9000	2021-03-05	Github (tidyverse/ggplot2@4555055)
ggrastr	0.2.3	2021-03-01	Github (VPetukhov/ggrastr@1ef0ff5)
glue	1.4.1.9000	2021-02-19	Github (tidyverse/glue@f0a7b2a)
gt	0.2.2	2021-03-08	Github (rstudio/gt@e619ecb)
gtable	0.3.0	2019-03-25	CRAN (R 4.0.0)
haven	2.3.1	2020-06-01	CRAN (R 4.0.0)
highr	0.8	2019-03-20	CRAN (R 4.0.0)
hms	1.0.0	2021-01-13	CRAN (R 4.0.3)
htmltools	0.5.1.9000	2021-02-25	Github (rstudio/htmltools@ac43afe)
httr	1.4.2	2020-07-20	CRAN (R 4.0.2)
jquerylib	0.1.3	2021-02-10	Github (rstudio/jquerylib@94b1a3b)
jsonlite	1.7.2	2020-12-09	CRAN (R 4.0.3)
knitr	1.31.5	2021-02-12	Github (yihui/knitr@ca09938)
labeling	0.4.2	2020-10-20	CRAN (R 4.0.3)
lattice	0.20-41	2020-04-02	CRAN (R 4.0.4)
lifecycle	1.0.0	2021-02-15	CRAN (R 4.0.3)
lubridate	1.7.10	2021-03-02	Github (tidyverse/lubridate@ba6d598)
magrittr	2.0.1.9000	2020-12-14	Github (tidyverse/magrittr@bb1c86a)
Matrix	1.3-2	2021-01-06	CRAN (R 4.0.3)
memoise	2.0.0	2021-01-26	CRAN (R 4.0.3)
modelr	0.1.8.9000	2021-02-17	Github (tidyverse/modelr@16168e0)
munsell	0.5.0	2018-06-12	CRAN (R 4.0.0)
patchwork	1.1.1	2020-12-17	CRAN (R 4.0.3)
pillar	1.5.1	2021-03-05	CRAN (R 4.0.4)
pkgbuild	1.2.0	2020-12-15	CRAN (R 4.0.3)
pkgconfig	2.0.3	2019-09-22	CRAN (R 4.0.0)
pkgload	1.2.0	2021-02-23	CRAN (R 4.0.4)
png	0.1-7	2013-12-03	CRAN (R 4.0.0)
prettyunits	1.1.1.9000	2020-11-23	Github (r-lib/prettyunits@b1cdad8)
processx	3.4.5	2020-11-30	CRAN (R 4.0.3)
ps	1.6.0	2021-02-28	CRAN (R 4.0.4)
purrr	0.3.4.9000	2021-02-04	Github (tidyverse/purrr@af06d45)
R6	2.5.0	2021-02-04	Github (r-lib/R6@6cf7d4e)
ragg	1.1.1.9000	2021-03-05	Github (r-lib/ragg@2e70155)
Rcpp	1.0.6	2021-01-15	CRAN (R 4.0.3)
readr	1.4.0.9000	2021-03-05	Github (tidyverse/readr@9fd1160)
readxl	1.3.1.9000	2021-02-17	Github (tidyverse/readxl@9f85fa5)
remotes	2.2.0.9000	2021-02-09	Github (r-lib/remotes@69a8bd1)
reprex	1.0.0	2021-01-27	CRAN (R 4.0.3)
reticulate	1.18	2020-10-25	CRAN (R 4.0.3)
rlang	0.4.10.9000	2021-03-08	Github (r-lib/rlang@e0a0761)
rmarkdown	2.7.3	2021-03-05	Github (rstudio/rmarkdown@61db7a9)
rprojroot	2.0.2	2020-11-15	CRAN (R 4.0.3)
rstudioapi	0.13.0-9000	2021-02-25	Github (rstudio/rstudioapi@8ed4d50)
Rttf2pt1	1.3.8	2020-01-10	CRAN (R 4.0.0)
rvest	1.0.0	2021-03-09	CRAN (R 4.0.4)
sass	0.3.1	2021-01-24	CRAN (R 4.0.3)
scales	1.1.1	2020-05-11	CRAN (R 4.0.3)
sessioninfo	1.1.1	2018-11-05	CRAN (R 4.0.3)
stringi	1.5.3	2020-09-09	CRAN (R 4.0.3)
stringr	1.4.0.9000	2021-02-17	Github (tidyverse/stringr@1f03eb0)
styler	1.3.2.9000	2021-03-05	Github (r-lib/styler@30b3c0a)
systemfonts	1.0.1.9000	2021-02-09	Github (r-lib/systemfonts@bc0b76a)
testthat	3.0.2.9000	2021-02-15	Github (r-lib/testthat@4793514)
textshaping	0.3.2.9000	2021-03-10	Github (r-lib/textshaping@a5b5e4c)
tibble	3.1.0.9000	2021-02-25	Github (tidyverse/tibble@01280ac)
tidyr	1.1.3.9000	2021-03-05	Github (tidyverse/tidyr@2fd80d5)
tidyselect	1.1.0	2020-05-11	CRAN (R 4.0.3)
tidyverse	1.3.0.9000	2021-03-10	Github (tidyverse/tidyverse@8a0bb99)
usethis	2.0.1.9000	2021-03-05	Github (r-lib/usethis@4819d19)
utf8	1.1.4	2018-05-24	CRAN (R 4.0.0)
vctrs	0.3.6	2020-12-17	CRAN (R 4.0.3)
vipor	0.4.5	2017-03-22	CRAN (R 4.0.0)
viridisLite	0.3.0	2018-02-01	CRAN (R 4.0.0)
withr	2.4.1	2021-01-26	CRAN (R 4.0.3)
xfun	0.21	2021-02-10	CRAN (R 4.0.3)
xml2	1.3.2	2020-04-23	CRAN (R 4.0.0)
yaml	2.2.1	2020-02-01	CRAN (R 4.0.0)